U.S. patent application number 14/043804 was filed with the patent office on 2014-04-03 for offering survey response opportunities for sale.
The applicant listed for this patent is Cadio, Inc.. Invention is credited to Dennis A. Ehrich, Andrew Fromm, Robert Luedeman, Eric H. Weiss.
Application Number | 20140095259 14/043804 |
Document ID | / |
Family ID | 50386076 |
Filed Date | 2014-04-03 |
United States Patent
Application |
20140095259 |
Kind Code |
A1 |
Weiss; Eric H. ; et
al. |
April 3, 2014 |
OFFERING SURVEY RESPONSE OPPORTUNITIES FOR SALE
Abstract
A system for offering survey response opportunities for sale to
purchasers in a survey market system that is associated with a
consumer analytics system. A survey response "opportunity" may be
an expectation of the system that, if a survey is distributed, a
response will be received and represents a chance for a potential
surveyor to request that a survey be distributed on its behalf.
Using the system, a purchaser may specify one or more conditions
for distribution of a survey and one or more questions to be
included in the survey, and request that a consumer analytics
system distribute the survey in response to detecting that the
conditions are met. For example, if a condition relates to consumer
behavior, a survey may be distributed in response to detecting,
from an analysis of location data indicating geographic locations
of the consumer, that the consumer engaged in the behavior.
Inventors: |
Weiss; Eric H.; (Acton,
MA) ; Luedeman; Robert; (Cambridge, MA) ;
Fromm; Andrew; (Mission Hills, KS) ; Ehrich; Dennis
A.; (Mission Hills, KS) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cadio, Inc. |
Boston |
MA |
US |
|
|
Family ID: |
50386076 |
Appl. No.: |
14/043804 |
Filed: |
October 1, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61708305 |
Oct 1, 2012 |
|
|
|
Current U.S.
Class: |
705/7.32 |
Current CPC
Class: |
G06Q 30/0225 20130101;
G06Q 30/0203 20130101; G06Q 30/0207 20130101; G06Q 30/0211
20130101 |
Class at
Publication: |
705/7.32 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. An apparatus comprising: at least one processor; and at least
one storage medium having encoded thereon executable instructions
that, when executed by the at least one processor, cause the at
least one processor to carry out a method comprising: determining,
for a setting, a first number of survey responses that a consumer
analytics system projects receiving, in a time period, in response
to surveys that are distributed to consumers following detecting
that one or more characteristics of the consumers satisfy at least
one condition for the surveys to be distributed; determining, based
on the first number, a second number of survey response
opportunities to make available for purchase via a survey purchase
system of the consumer analytics system; and following a purchase
of a third number of survey response opportunities by a purchaser,
storing information configuring the consumer analytics system to
distribute a fourth number of surveys to consumers in response to
detecting that one or more characteristics of the consumers satisfy
the at least one condition, the fourth number being equal to or
greater than the third number, the information comprising
information identifying a first survey, the first survey comprising
one or more questions identified by the purchaser.
2. The apparatus of claim 1, wherein determining the second number
of survey response opportunities to make available for purchase
comprises: determining a fifth number of survey responses that the
consumer analytics system is configured to collect in response to
distributing surveys to consumers in response to detecting that the
at least one condition is met; and determining the second number of
survey response opportunities based at least in part on the first
number and the fifth number.
3. The apparatus of claim 2, wherein determining the fifth number
of survey responses that the consumer analytics system is
configured to collect comprises determining a number of survey
responses that one or more other purchasers have previously
requested that the consumer analytics system collect in response to
distributing surveys to consumers having one or more
characteristics that satisfy the at least one condition.
4. The apparatus of claim 1, wherein: prior to purchase of the
third number of survey response opportunities by the purchaser, the
consumer analytics system is configured to produce a syndicated
report of consumer analytics based at least in part on responses by
consumers to surveys distributed to consumers having one or more
characteristics that satisfy the at least one condition; and
determining the second number of survey response opportunities to
make available for purchase comprises: determining a fifth number
of survey responses needed for the consumer analytics system to
generate results having a desired level of accuracy for an analysis
of the responses to surveys distributed to consumers having the one
or more characteristics that satisfy the at least one condition;
and determining the second number of survey response opportunities
based at least in part on a difference of the first number and the
fifth number.
5. The apparatus of claim 1, wherein the method further comprises:
distributing the first survey to a plurality of consumers, wherein
distributing the first survey to a consumer comprises transmitting
at least one first message to a mobile device operated by the
consumer in response to determining that the consumer has one or
more characteristics that satisfy the at least one condition, the
at least one first message comprising information soliciting the
consumer to provide input in response to the one or more
questions.
6. The apparatus of claim 5, wherein the method further comprising:
receiving responses from consumers in response to the distributing
of the first survey, wherein receiving the response from the
consumer comprises receiving at least one second message from the
mobile device operated by the consumer, the at least one second
message comprising one or more inputs provided by the consumer in
response to the one or more questions; and transmitting information
regarding the responses to at least one computing device associated
with the purchaser.
7. The apparatus of claim 5, wherein distributing the first survey
to the plurality of consumers comprises distributing the first
survey until a number of received responses to the first survey
equals or exceeds the third number.
8. The apparatus of claim 1, wherein: the at least one condition
comprises at least one behavior characteristic; and storing
information configuring the consumer analytics system to distribute
the fourth number of surveys to consumers in response to detecting
that one or more characteristics of the consumers satisfy the at
least one condition comprises storing information configuring the
consumer analytics system to distribute the fourth number of
surveys to a consumer in response to detecting, based on an
evaluation of location data for the consumer, that the consumer is
or was engaging in the at least one behavior.
9. The apparatus of claim 8, wherein: the at least one behavior
characteristic comprises visiting a business; and storing
information configuring the consumer analytics system to distribute
the fourth number of surveys to a consumer comprises storing
information configuring the consumer analytics system to distribute
the fourth number of surveys to a consumer in response to
detecting, based on an evaluation of location data for the
consumer, that the consumer is visiting or was visiting the
business.
10. The apparatus of claim 8, wherein: the at least one behavior
characteristic comprises visiting one of a plurality of businesses
in a market category; and storing information configuring the
consumer analytics system to distribute the fourth number of
surveys to a consumer comprises storing information configuring the
consumer analytics system to distribute the fourth number of
surveys to a consumer in response to detecting, based on an
evaluation of location data for the consumer, that the consumer is
visiting or was visiting one of the businesses in the market
category.
11. The apparatus of claim 1, wherein the method further comprises:
receiving purchase data from the purchaser via a web interface for
the consumer analytics system, the web interface comprising one or
more web pages, the purchase data information regarding the third
number, the one or more questions, and the at least one
condition.
12. The apparatus of claim 11, wherein the method further
comprises: displaying, via the web interface, a prompt for the
purchaser to specify one or more consumer characteristics that will
serve as the at least one condition for distribution of the first
survey.
13. The apparatus of claim 11, wherein the method further
comprises: receiving, via the web interface, a bid from the
purchaser in an electronic auction for survey response
opportunities.
14. The apparatus of claim 13, wherein receiving the bid from the
purchaser in the electronic auction comprises receiving the bid in
an electronic auction for at least some of the second number of
survey response opportunities for distributing surveys in response
to the at least one condition.
15. A method of surveying a consumer in response to detecting that
the consumer has engaged in one or more behaviors, the method
comprising: receiving, from a mobile device operated by the
consumer, a plurality of units of location data each indicating a
geographic location of the consumer at a time the unit of location
data was generated; analyzing the plurality of units of location
data to determine at least one behavior in which the plurality of
units of location data indicate the consumer engaged; and in
response to determining that the at least one behavior of the
consumer satisfies one or more conditions associated with a
plurality of surveys, selecting a first survey of the plurality of
surveys, and transmitting at least one first message to the mobile
device soliciting the consumer to provide answers to one or more
questions of the first survey.
16. The method of claim 15, wherein: each survey of the plurality
of surveys is associated with a desired number of survey responses
to be received by the consumer analytics in response to
distribution of the survey to consumers; and selecting the first
survey of the plurality of surveys comprises selecting the first
survey based at least in part on, for each survey of the plurality
of surveys, a number of responses received in response to
distribution of that survey to consumers in the time period.
17. The method of claim 16, wherein selecting the first survey
based at least in part on, for each survey of the plurality of
surveys, the number of responses received comprises selecting a
survey having a lowest ratio of number of responses received to
desired number of survey responses.
18. The method of claim 15, further comprising: receiving responses
from consumers in response to distribution of the first survey,
wherein receiving the response from the consumer comprises
receiving at least one second message from the mobile device
operated by the consumer, the at least one second message
comprising one or more inputs provided by the consumer in response
to the one or more questions; and transmitting information
regarding the responses to at least one computing device associated
with a party that provided the survey for distribution.
19. The method of claim 18, wherein: the party that provided the
survey for distribution is a purchaser of a first number of survey
response opportunities; and the method further comprises, following
a purchase of the first number of survey response opportunities by
the purchaser, storing information configuring a consumer analytics
system to distribute a second number of surveys to consumers in
response to detecting that at least one behavior of each consumer
satisfies the one or more conditions.
20. The method of claim 15, wherein: the at least one behavior
comprises visiting a business; and selecting the first survey in
response to determining that the at least one behavior of the
consumer satisfies the one or more conditions comprises selecting
the first survey and transmitting the at least one first message in
response to determining that the consumer is visiting or has
visited the business.
21. The method of claim 15, wherein: the one or more conditions
comprise a first condition relating to the at least one behavior
and a second condition relating to at least one demographic
characteristic; and selecting the first survey and transmitting the
at least one first message in response to determining that the at
least one behavior of the consumer satisfies the one or more
conditions comprises selecting the first survey and transmitting
the at least one first message in response to determining that the
consumer is engaging or has engaged in the behavior and that the
consumer has the at least one demographic characteristic.
22. At least one non-transitory computer-readable storage medium
having encoded thereon executable instructions that, when executed
by the at least one processor, cause the at least one processor to
carry out a method comprising: determining, for a setting, a first
number of survey responses that a consumer analytics system
projects receiving, in a time period, in response to surveys that
are distributed to consumers following detecting that at least one
behavior of the consumers satisfy at least one condition for the
surveys to be distributed; determining, based on the first number,
a second number of survey response opportunities to make available
for purchase via a survey purchase system of the consumer analytics
system; and following a purchase of a third number of survey
response opportunities by a purchaser, storing information
configuring the consumer analytics system to distribute a fourth
number of surveys to consumers in response to detecting that the
consumers are engaging or have engaged in at least one behavior
that satisfies the at least one condition, the fourth number being
equal to or greater than the third number, the information
comprising information identifying a first survey, the first survey
comprising one or more questions identified by the purchaser.
23. The at least one computer-readable storage medium of claim 22,
wherein determining the second number of survey response
opportunities to make available for purchase comprises: determining
a fifth number of survey responses that the consumer analytics
system is configured to collect in response to distributing surveys
to consumers in response to detecting that consumers are engaging
in or have engaged in the at least one behavior; and determining
the second number of survey response opportunities based at least
in part on the first number and the fifth number.
24. The at least one computer-readable storage medium of claim 23,
wherein determining the fifth number of survey responses that the
consumer analytics system is configured to collect comprises
determining a number of survey responses that one or more other
purchasers have previously requested that the consumer analytics
system collect in response to distributing surveys to consumers
that are engaging in or have engaged in the at least one
behavior.
25. The at least one computer-readable storage medium of claim 22,
wherein: prior to purchase of the third number of survey response
opportunities by the purchaser, the consumer analytics system is
configured to produce a syndicated report of consumer analytics
based at least in part on responses by consumers to surveys
distributed to consumers that are engaging in or have engaged in
the at least one behavior; and determining the second number of
survey response opportunities to make available for purchase
comprises: determining a fifth number of survey responses desired
for the consumer analytics system to conduct an analysis of
responses to the surveys distributed to consumers that are engaging
in or have engaged in the at least one behavior; and determining
the second number of survey response opportunities based at least
in part on a difference of the first number and the fifth
number.
26. The at least one computer-readable storage medium of claim 22,
wherein the method further comprises: determining at least one
behavior in which a consumer is engaging in or has engaged in at
least in part by reviewing a plurality of units of location data
provided by a mobile device operated by the consumer, each unit of
location data of the plurality indicating a geographic location of
the mobile device at a time the unit of location data was generated
by the mobile device.
27. The at least one computer-readable storage medium of claim 22,
wherein: the at least one behavior is visiting a first business;
and the one or more questions of the first survey comprise a
question requesting that the consumer provide input regarding the
consumer's intentions in visiting the first business, experiences
at the first business, and/or impressions of the first
business.
28. The at least one computer-readable storage medium of claim 22,
wherein: the at least one behavior is visiting a first business;
and the one or more questions of the first survey comprise a
question requesting that the consumer provide input regarding
actions taken by the consumer while visiting the first business.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C.
.sctn.119(e) to U.S. Provisional Application Ser. No. 61/708,305,
titled "Consumer analytics system that determines, offers, and
monitors use of rewards incentivizing consumers to perform tasks"
and filed on Oct. 1, 2012, the entirety of which is incorporated
herein by reference.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not Applicable
REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM
LISTING COMPACT DISK APPENDIX
[0003] Not Applicable
FIELD OF THE INVENTION
[0004] Some embodiments of the invention relate to systems for
electronically gathering and analyzing information on and/or from
consumers. More specifically, some embodiments of the invention
collect relevant and timely data from and about consumers to make
inferences and predictions about the consumers, including by
collecting electronically-captured location data for the consumers,
and provide incentives and/or rewards to the consumers for
performing tasks to provide information to the system.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] The accompanying drawings are not intended to be drawn to
scale. In the drawings, each identical or nearly identical
component that is illustrated in various figures is represented by
a like numeral. For purposes of clarity, not every component may be
labeled in every drawing. In the drawings:
[0006] FIG. 1 illustrates one exemplary environment in which
embodiments may operate;
[0007] FIG. 2 is a flowchart of one exemplary process for
triggering actions that gather information from and/or on
consumers.
[0008] FIG. 3 is a block diagram of one exemplary computing device
with which embodiments may operate;
[0009] FIG. 4 is a flowchart of an example of a process for
determining a setting visited by a consumer based on location data
obtained for the consumer;
[0010] FIG. 5 is an exemplary image of an interface for a consumer
to see which surveys have been triggered and available for her to
complete.
[0011] FIG. 6 is an exemplary image of one screen of an interface
for a consumer to complete a survey.
[0012] FIG. 6A is a flowchart of a process that may be implemented
in some embodiments for selecting rewards to offer a consumer as an
incentive for performing an action.
[0013] FIG. 6B is a flowchart of a process that may be implemented
in some embodiments for determining whether a consumer is or will
be near a location at which a reward may be redeemed and notifying
the consumer of such.
[0014] FIG. 7 is an exemplary image of an interface for allowing a
consumer to view which rewards she has earned from her
participation.
[0015] FIG. 8 is an example analytic chart of showing average
Return On Investment (in dollars) for four different reward
types.
[0016] FIG. 9 is a flowchart of a process that may be implemented
in some embodiments for compiling a list of reward redemptions at a
business for use in identifying fraudulent redemptions.
[0017] FIG. 10 is a flowchart of a process that may be implemented
in some embodiments for, during a purchase in which a reward is to
be redeemed, determining whether the redemption is legitimate or
fraudulent and notifying a business of such.
[0018] FIG. 11 is a flowchart of a process that may be implemented
in some embodiments for, during a purchase in which a reward is to
be redeemed, determining whether to permit the redemption based on
whether a notification received from a consumer analytics system
indicates that the redemption is legitimate.
[0019] FIG. 12 is a flowchart of a process that may be implemented
in some embodiments for determining a value to a business of a
reward that is offered to consumers via a consumer analytics
system.
[0020] FIG. 13 is a flowchart of a process that may be implemented
in some embodiments for determining a reward value that will entice
a consumer to visit a business rather than a competitor.
[0021] FIG. 14 is a flowchart of a process that may be implemented
in some embodiments for determining a reward value that will
prevent a customer from lapsing or regain a lapsed customer.
[0022] FIG. 15 is a flowchart of a process that may be implemented
in some embodiments for making survey response opportunities
available for purchase and distributing surveys in response to
purchases of opportunities.
[0023] FIG. 16 is a flowchart of a process that may be implemented
in some embodiments for receiving a purchase of a number of survey
response opportunities and configuring a consumer analytics system
to distribute a survey in response to a purchase.
[0024] FIG. 17 is a flowchart of a process that may be implemented
in some embodiments for determining a survey to distribute to a
consumer in response to determining that a behavior of the consumer
satisfies a condition.
[0025] FIG. 18 is a flowchart of a process that may be implemented
in some embodiments for monitoring a brand strength of one or more
businesses.
[0026] FIG. 19 is a flowchart of a process that may be implemented
in some embodiments for determining factors relating to brand
strength for a particular business in a market category.
[0027] FIG. 20 is a flowchart of a process that may be implemented
in some embodiments for monitoring a change in brand strength over
time relative to an event that may impact brand strength.
ILLUSTRATIVE CONTEXT
[0028] FIG. 1 illustrates an exemplary environment in which some
embodiments may operate to detect location data for one or more
consumers and, by analyzing that location data, determine
characteristics of those consumers. The example of FIG. 1 is
described in connection with one consumer, but embodiments may
operate with any number of consumers.
[0029] In the environment of FIG. 1, a consumer 102 changes
location while going to work, going home, going to school, running
errands, or moving from any other place to place. In the specific
example of FIG. 1, the consumer 102 visits a golf course 121, cafe
122, and grocery store 123 during a day. The consumer analytics
system 108 monitors movements of the consumer 102 and, by detecting
and analyzing locations the consumer 102 visits, produces
inferences and predictions regarding characteristics of the
consumer, which may include inferences and/or predictions of
behavior characteristics relating to behaviors of the consumer.
[0030] Embodiments may monitor movements of the consumer 102 in any
suitable manner. In some embodiments, location data for a consumer
may be collected for the consumer using techniques described in
U.S. patent application Ser. No. 12/910,280, filed on Oct. 22,
2010, and titled "Electronically capturing consumer location data
for analyzing consumer behavior" ("the '280 application"). The '280
application is incorporated herein by reference in its entirety for
all purposes and at least for its disclosure of collecting and
analyzing location data for consumers to predict and/or infer
characteristics of the consumers. As discussed in detail below, in
some embodiments, the consumer analytics system 108 may prompt a
consumer 102 to perform one or more tasks (e.g., answer survey
questions, or obtain media such as a photograph) in response to
determining one or more characteristics of the consumer based on
location data. For example, the consumer analytics system 108 may
prompt a consumer 102 to perform a task when the consumer analytics
system 108 identifies, from the location data for the consumer 102,
a behavior in which the consumer 102 is inferred to be engaging, is
inferred to have been engaging, or is predicted to engage. In some
embodiments the system 108 may be configured to take an action,
including prompting a consumer to perform a task, using techniques
described in U.S. patent application Ser. No. 13/535,108, filed on
Jun. 27, 2012, and titled "Triggering collection of consumer input
based on location data" ("the '108 application"). The '108
application is incorporated herein by reference in its entirety for
all purposes and at least for its disclosure of a system taking
actions such as collecting information from and/or about consumers
in response to predicting and/or inferring characteristics of the
consumers.
[0031] In some embodiments, the consumer 102 is associated with a
device 104 that can be used to obtain location information for the
consumer 102 as the consumer 102 moves. The consumer 102 may move
with the device 104, as the consumer 102 may carry the device 104
or the device 104 may be embedded in a car, piece of clothing, or
baggage carried by the consumer 102. In some cases, the device 104
may be useful only in determining a location of the consumer 102,
while in other cases the device 104 may have additional
functionality. For example, the device 104 may be a mobile
telephone with location-identifying capabilities, such as a
cellular telephone with a built-in Global Positioning System (GPS)
or Assisted GPS (AGPS) receiver that the cellular telephone can use
to determine its current location. The device 104 may be able to
communicate with a network 106, which may be any suitable
communication network, including a wireless wide-area network
(WWAN). In cases where the device 104 is a cellular telephone, the
network 106 may be or include a cellular network.
[0032] The consumer analytics system 108 may obtain location data
for a consumer 102 from the device 104. In some cases, the consumer
analytics system 108 may request the location information from the
network 106 and, in turn, the network 106 may obtain location data
from the device 104. In some embodiments, the consumer analytics
system 108 may request the location data at varying intervals based
on various factors, including the current location of the consumer
102.
[0033] The consumer analytics system 108 may analyze the location
data to identify settings visited by the consumer, including
settings of the set of settings 109, and predict and/or infer
characteristics of the consumer 102. Inferring and/or predicting
characteristics of the consumer 102 may include inferring and/or
predicting behaviors in which the consumer 102 is engaging, was
engaging, or will engage.
[0034] A behavior may indicate a context of a consumer's presence
at a point of interest. Examples of information indicating a
context of a presence may include information indicating a purpose
or goal of the consumer in visiting the point of interest, other
points of interest visited by the consumer during a trip that
includes the point of interest, other points of interest bypassed
by the consumer in traveling to or from the point of interest,
routes traveled by the consumer to reach the point of interest, an
ultimate destination of a consumer in a trip that includes the
point of interest, or any other information describing the
circumstances of the consumer's presence at the point of interest.
In some embodiments, when the system 108 infers and/or predicts one
or more characteristics of one or more consumers (including the
consumer 102), the characteristic(s) of the consumer(s) trigger the
system 108 to take one or more actions.
[0035] The system 108 may take any suitable action, as embodiments
are not limited to taking any particular action. In some
embodiments, the action taken by the system 108 may include
collecting information regarding commercial activity, including
commercial activity of consumers. Commercial activity of a consumer
may include information regarding visiting a commercial entity,
purchasing a product or a service, and/or preferences of the
consumer regarding commercial entities, products, and/or services.
Commercial entities, products, or services about which information
is obtained may be commercial entities, products, or services to
which an inferred or predicted characteristic of the consumer 102
relates. For example, an inferred characteristic may relate to
interactions of the consumer 102 with a commercial entity, such as
behaviors or preferences of the consumer 102 with respect to the
commercial entity. In such a case, the product or service about
which information is obtained may be a product or service of the
commercial entity. In other cases, the commercial entity, product,
or service may not be related to an inferred or predicted
characteristic, but may be a product or service for which market
research is being conducted. Market research may be conducted to
determine characteristics of consumers related to the commercial
entity, product, or service, and the market research may include
collecting information from or about consumers for which a
characteristic has been inferred. When the characteristic is
inferred for the consumer 102, then, the system 108 takes the
action to obtain information about the product or service.
[0036] Embodiments are not limited to taking any particular action
in response to inferring or predicting any particular
characteristic. As an example of an action that the system 108 may
take, in some embodiments, in response to inferring and/or
predicting behavior of the consumer 102, the system 108 may solicit
information from the consumer regarding commercial activity. To
solicit the information, the system 108 may send the consumer 102
an alert or message on the device 104. The message sent to the
device 104 may include a request for the consumer 102 to complete a
task. The task may include providing information to the system 108,
which may include information regarding commercial activity. In
some cases, the task included in the message may include answering
survey questions provided to the consumer 102. The consumer 102
may, in some embodiments, respond to survey questions using the
device 104. Examples of other messages and tasks that may be
provided to a consumer 102 by the system 108 are described in
greater detail below.
[0037] As another example of actions that may be taken by the
system 108 in response to inferring or predicting one or more
characteristics of one or more consumers, the system 108 may
acquire information from at least one data source external to the
system 108. The information acquired from the at least one data
source may be any suitable information, as embodiments are not
limited in this respect. In some cases, the information may include
information regarding the consumer 102, regarding an inferred
characteristic, and/or regarding a commercial entity or a product
or service offered by a commercial entity. For example, in response
to inferring a characteristic of the consumer 102, the system 108
may obtain social networking data provided by a consumer to a
social networking service or that relates to the consumer 102. The
social networking data may be evaluated to determine whether the
social networking data indicates information relating to the
characteristic and/or to a product or service. For example, the
social networking data may include a review of a product or service
indicating opinions of the consumer 102 regarding the product or
service. Examples of other types of external data sources from
which information may be obtained are described in greater detail
below.
[0038] In some embodiments, as described in detail below, the
consumer analytics system 108 may incentivize the consumer 102 to
perform a task in response to the action taken by the system 108,
such as responding to a survey distributed by the system 108 or
obtaining media (e.g., a photograph) requested by the system 108.
As another example of an action the system 108 may take in response
to inferring or predicting one or more characteristics of one or
more consumers 102, the system 108 may determine one or more
rewards to offer consumer 102 in exchange for performing a
designated task.
[0039] Determining the one or more rewards may include determining
one or more parameters of the reward. Determining parameters of the
reward may include selecting whether the consumer is to be offered
one reward or a list of multiple rewards from which the consumer
may pick a reward. Determining parameters of a reward may
additionally or alternatively include selecting an organization
(e.g., a commercial entity) with which an offered reward is to be
associated, selecting a type of reward, selecting a value of the
reward, and/or selecting one or more conditions that are to be
imposed on the availability of the reward to the consumer for
redemption by the consumer. For example, where multiple different
organizations offer rewards via the system 108, the system 108 may
select an organization for which a reward is likely to incent the
consumer 102 to perform a desired task. The organization may be an
organization that the system 108 infers or predicts the consumer
102 favors and/or may be an organization related to the desired
task. An organization that the consumer 102 favors may be a
commercial entity of which the consumer 102 is a known, inferred,
or predicted to be customer or that provides a product or service
that the system knows, infers, or predicts is of interest to the
consumer.
[0040] An organization related to the desired task may be a
commercial entity about which the consumer 102 is to be asked
questions in a survey. The type of reward and value of the reward
may define what the consumer is to be provided by the organization
with which the reward is redeemable. Examples of types of rewards
include a discount on goods and services or a gift card. Examples
of value include how much of a discount to offer on the
goods/services and what value of gift card to offer. Conditions
under which the reward may be redeemed may include any suitable
conditions, including conditions on actions a consumer must take
for the value of the reward to be made available or conditions on
times or places at which the value of the reward is available.
[0041] The system 108 may determine a reward, including by
determining any of the parameters mentioned above or other
parameters of a reward, based on any suitable information. In some
embodiments, the system 108 may determine a reward based on
characteristics of the consumer 102 to which the reward is to be
offered. For example, characteristics of the consumer 102 that the
system 108 has inferred and/or predicted based on location data for
the consumer may be used. Such characteristics may include behavior
characteristics, preference characteristics, and/or identity
characteristics that the consumer is inferred to have now or
inferred to have previously had, or is predicted to have. For
example, if a consumer 102 is detected by the system 108 to be a
customer of one business, the system 108 may determine to offer a
reward to the consumer that is a discount for goods and/or services
available from the business. As another example, if a consumer 102
is detected by the system 108 to be a dedicated customer of one
business, the system 108 may determine to offer a reward to the
consumer that is a high-value gift card for a competitor of the
business. By offering the consumer 102 a high-value reward for the
competitor, the system may be able to determine whether the
consumer 102 can be influenced to visit the competitor rather than
the business typically visited by the consumer 102. In some
embodiments, the system 108 may determine the type or value of a
reward for the purpose of influencing a consumer 102 from being a
customer of one business to being a customer of another, or so as
to otherwise change the behaviors or preferences of the consumer
102. In some such cases, the system 108 may select different
types/values of rewards to offer different consumers for the
purpose of determining what type and value of reward will change
the behaviors/preferences of customers of one business to be
customers of another business. The different rewards may be similar
in some parameters, such as by being of the same type but having
different value, or by having the same type/value but different
conditions, or similar in any other way.
[0042] In addition to or as an alternative to characteristics of
the consumer, the system 108 may determine the reward to offer the
consumer 102 based on one or more metrics regarding the consumer's
performance of one or more tasks requested by the system 108. Such
metrics may relate to a quality of the consumer's performance of
the task for which the reward is offered or tasks previously
performed. Examples of such metrics include a timeliness of the
performance or a usefulness of information provided to the system
108 by the consumer 102 as a part of performing the task. For
example, a usefulness of responses to open-response survey
questions provided by the consumer 102 may be judged and used to
determine a reward to provide to the consumer 102 for providing
those responses or to provide at a future time to incent
performance of another task. Though, the quantity (such as length
in words) of an open-response may alternatively or additionally be
used as an indication of quality.
[0043] Further, in addition to or as an alternative to performance
metrics, in some embodiments the system 108 may determine the
reward to offer the consumer 102 based on a value of the consumer
102 or information that may be provided by the consumer 102 to the
system 108. For example, if the characteristics that are
predicted/inferred for the consumer 102 by the system 108 indicate
that the consumer 102 does not have any particularly-desirable
characteristics or is not capable of providing any
particularly-desirable information, the system 108 may select a
lower-value reward for the consumer 102. However, if the
characteristics for the consumer 102 indicate that the consumer 102
is valuable in some way, such as by having uncommon characteristics
including by engaging in uncommon behaviors that are potentially
indicative of some relevant information, the system 108 may select
a higher-value reward for the consumer 102. Lastly, in some
embodiments, the system 108 may determine the reward to offer the
consumer 102 based at least in part on attributes of a task that
the consumer 102 is to be requested to perform, for example, the
system 108 may select the reward based on a difficulty of a task,
or select a reward to include a parameter related to an attribute
of the task. As an example of a related parameter, the system 108
may select a reward redeemable at a business to which questions the
consumer is to be asked in a survey relate.
[0044] As another example of an action that may be taken by the
system 108 in response to inferring or predicting a characteristic
of the consumer 102, the system 108 may determine whether to inform
the consumer 102 of the specific nature of the reward(s) that are
to be offered to the consumer 102 to incent performance of a task.
The system 108 may then provide information to the consumer 102
about the reward in accordance with that determination. For
example, the system 108 may, when it is determined that the
specific nature is not to be revealed, inform the consumer 102 that
some reward will be made available. As another example, the system
108 may, when it is determined that the specific nature is to be
revealed, inform the consumer 102 of the particular reward(s) that
will be offered to the consumer 102. The system 108 may provide the
consumer with information about the reward(s) at any suitable time.
For example, the system 108 may provide the information about the
reward(s) before the consumer 102 performs the task, when the
consumer 102 is to be incentivized to perform the task, or after
the consumer 102 performs the task.
[0045] As another example of an action that may be taken by the
system 108 in response to inferring or predicting a characteristic
of the consumer 102, the system 108 may determine whether a reward
is available for redemption by the consumer 102 and/or whether to
notify the consumer 102 that a reward may be redeemed at a location
that the consumer 102 is or is predicted to soon be near. In
embodiments in which the system 108 provides rewards to consumers
to incent the consumers to perform tasks, some of the rewards may
be redeemable by the consumer only after one or more conditions are
met. As mentioned above, such conditions may relate to limitations
on a time at which the reward may be redeemed or a place the
consumer must visit to redeem the reward, or may relate to
behaviors in which the consumer 102 must engage before the reward
may be redeemed. Accordingly, in some embodiments, when the system
108 processes location data for the consumer 102 to determine one
or more characteristics of the consumer 102, the system 108 may
determine whether one or more conditions for a reward have been
met. If the system 108 determines that conditions for one or more
rewards have been met, the system 108 may notify the consumer 102
that the reward/rewards is/are available for redemption.
[0046] As another example of an action that may be taken by the
system 108 in response to inferring or predicting a characteristic
of the consumer 102, the system 108 may compare characteristics of
the consumer 102 predicted/inferred following the offering by the
system 108 of and/or redemption by the consumer 102 of a reward to
characteristics of the consumer 102 predicted/inferred beforehand.
Conducting such a comparison may enable the system 108 to determine
an impact of the reward on characteristics of the consumer 102. For
example, the system 108 may determine whether, when the consumer
102 was offered a reward redeemable at a business, the consumer 102
became a more frequent customer of that business. This may enable
the system 108 to determine whether the business is receiving a
good return on the business's investment, in the case that the
business pays a fee to the system 108 for the reward to be offered
to consumers. Though, investment may alternatively or additionally
be computed in other ways, such as based on the cost to the
business of supplying rewards redeemed by consumers.
[0047] Comparing consumer behavior before and after being offered a
reward may also enable the system 108 to determine whether rewards
are skewing characteristics of consumers. While it may be desirable
for a reward to influence behaviors of consumers to a degree, if
the behavior of the consumers is swayed too much as a result of
rewards, the influenced behaviors of the consumers may be too
different from the consumers' normal behaviors for a study of the
behavior to provide valuable information. Thus, in some
embodiments, the consumer analytics system 108 may compare
previously-determined characteristics and newly-determined
characteristics for a consumer 102 in response to predicting or
inferring one or more characteristics of the consumer 102. This
comparison may be made for a consumer individually or for a group
of consumers that are similarly situated. In some such embodiments,
if the system 108 detects a difference in characteristics that may
be indicative of skewing, the system 108 may notify an
administrator or take other suitable action. The notification may
identify the consumers believed to have been skewed and, if known,
the rewards believed to have caused the skewing. In the case that
skewing is detected, in some embodiments, rewards may be changed to
rectify the skewing. For example, the system 108 may be configured
to select a different reward for a skewed consumer, of the reward
that caused skewing may be discontinued.
[0048] In some embodiments in which the system 108 offers a
consumer 102 a reward for performing a task, the system 108 may
include functionality to be informed when the consumer 102 redeems
a reward. The system 108 may be able to receive information from
the consumer 102, an organization with which the reward was
redeemed, or any other party (e.g., a source of credit card
information for a credit card the consumer 102 may have used in a
transaction in which the reward was redeemed) regarding the
redemption. The system 108 may be configured to take any suitable
action in response to the reward being redeemed.
[0049] For example, the system 108 may be configured to determine,
when information from an organization indicates a time and place at
which the reward was redeemed, whether the consumer 102 was at that
place at that time. By determining whether the consumer 102 was at
the place of reported redemption at the time of reported
redemption, the system 108 can determine whether the report of the
redemption is accurate or is a potential sign of fraud. Fraud can
arise, for example, when employees of a business falsely report a
reward redemption. The system 108 can analyze other indicators of
potential fraud, discussed in detail below, in response to
receiving information informing the system 108 that a reward was
redeemed.
[0050] As another example of an action the system 108 can take in
response to receiving information indicating redemption of a
reward, the system 108 may distribute information regarding the
redemption to one or more other data stores, such as by
distributing information to services outside of the consumer
analytics system 108. For example, the consumer analytics system
108 may publish information regarding the redemption of the reward
on one or more social media services. In some embodiments, the
system 108 may be configurable with account information for one or
more social media services that are used by a consumer 102. When
the system 108 is so configured, the system 108 may publish to a
consumer's account on a social media service that the consumer 102
has redeemed a reward. As another example of an action the system
108 can take in response to receiving information indicating
redemption of a reward, the system 108 may determine attributes of
the manner in which the reward was redeemed. For example, a time
attribute for the redemption of the reward, such as a length of
time between earning and redeeming the reward, may be determined
for the consumer 102 and/or for a set of consumers who have
redeemed the reward. As another example, a behavior attribute,
indicating a behavior of the consumer at a time the reward was
redeemed, may be determined for the consumer 102 and/or for a set
of consumers who have redeemed the award. As another example, by
comparing information regarding redemption of rewards by multiple
consumers, differences in how different types or values of rewards
may be identified or how consumers with different characteristics
redeem the same rewards in different ways may be identified. When
the system 108 determines information regarding a redemption of a
reward, the system 108 may record this information in storage,
present the information to an administrator of the system 108, a
customer of the system 108 (e.g., an organization at which a reward
may be redeemed), a consumer 102, or any other suitable party.
[0051] While in the examples given above the system 108 is
described as taking the actions in response to predicting or
inferring characteristics, it should be appreciated that the system
108 may be triggered to take the action(s) at any suitable time. In
some embodiments, the system 108 may take the action
contemporaneous with making the prediction/inference, such that the
consumer 102 is prompted to perform a task and incented with a
reward while the consumer 102 is at the location from which the
characteristic was predicted/inferred. In other embodiments,
however, the system 108 may prompt a consumer to perform a task
and/or determine a reward at any time following the
prediction/inference as embodiments are not limited in this
respect. Examples of ways in which a consumer analytics system may
process location data for multiple consumers, determine
characteristics of consumers, and take actions based on determined
characteristics are described in greater detail below. It should be
appreciated that some of the examples below may not be described in
connection with the illustrative environment described above in
connection with FIG. 1. Embodiments are not limited to operating in
any particular environment, including the environment of FIG. 1.
Further, it should be appreciated that embodiments are not limited
to acting in accordance with any of the examples below. In some of
the examples below, tasks that a consumer may be requested to
perform are answering survey questions and obtaining media (e.g., a
photograph or video), but embodiments are not limited to requesting
that a consumer perform any particular task. Additionally, in some
examples below, an organization to which a task relates or with
which a reward may be redeemed is referred to as a business or,
specifically, an independent or chain retail chain/restaurant. It
should be appreciated, however, that embodiments are not limited to
operating with any particular type of organization, and that these
are merely examples of commercial entities that may be
organizations in some embodiments. Embodiments may operate in any
suitable manner to process location data for consumers related to
movements of the consumers in any suitable environment.
Illustrative Techniques
[0052] FIG. 2 illustrates one example of an overall process for
collecting relevant and timely data from and about consumers to
make inferences and predictions by using electronically-captured
location data. The process of FIG. 2 begins in block 201, in which
a set of actions to-be-triggered are input. The actions may be
specified by any suitable one or more parties, as embodiments are
not limited in this respect. In some embodiments, the actions may
be specified by an administrator of a consumer analytics system. In
other embodiments, the actions may additionally or alternatively be
specified by one or more market researchers as part of defining a
market research study. In embodiments in which the actions are
specified as part of defining a study, the actions specified in
block 201 may include actions to be taken by the consumer analytics
system to collect information to be analyzed as part of the study.
Actions to collect information may include actions to solicit
information from one or more consumers and/or acquire information
from one or more external data sources. Any suitable party may act
as a market researcher in these embodiments, including professional
market researchers or laymen doing market research. Additionally,
the study may relate to any suitable topic. For example, a market
research study may be carried out to determine characteristics of
consumers that relate to a setting, of the set of setting 109 of
the environment of FIG. 1, based on information about consumers of
interest. The setting of the set 109 may be a commercial entity,
such as a retail business.
[0053] Any suitable information regarding actions to be taken may
be specified in block 201. In some embodiments, information
describing the action to be taken may be specified. For example,
where the action includes requesting that a consumer perform a
task, the task may be described. Any suitable task to be performed
by a consumer may be included in an action, as embodiments are not
limited in this respect. In some cases, a task may include
prompting a consumer to answer survey questions, in which case the
survey questions and, optionally, acceptable answers to the
questions may be specified in block 201. In embodiments that
operate with surveys, any suitable surveys asking any suitable
questions may be provided as input to a system and distributed to
consumers, as embodiments are not limited in this respect. In some
embodiments, a survey may ask consumers about the consumer's
impressions of a setting visited by the consumer, the consumer's
experiences at a setting, or the consumer's intentions in visiting
a setting, or otherwise ask for subjective information held by the
consumer that may relate to the consumer's opinions. A survey may
additionally or alternatively ask a consumer about actions taken by
the consumer at a setting, such as about the consumer's commercial
interactions at a setting. For example, a survey may ask a consumer
about purchases made at a setting, such as goods or services
purchased or amounts spent. In other cases, a task may include
prompting a consumer to obtain media or scan a Universal Product
Code (UPC) barcode or Near Field Communication (NFC) tag, in which
case the subject of the desired media or the object desired to be
scanned may be specified in block 201. In still other cases, a task
may include requesting that a consumer visit a setting and provide
information or opinions about the setting, such as providing
opinions regarding an arrangement of items in a setting, and the
setting and topic of the desired opinion may be specified in block
201.
[0054] Additionally, specifying the action in block 201 may include
specifying one or more conditions that, when satisfied, will result
in the consumer analytics system taking the action. Any suitable
conditions may be specified, including conditions related to one or
more characteristics of one or more consumers determined from
location data. For example, a condition may be satisfied when the
consumer analytics system determines, from location data for a
consumer, a characteristic of a consumer. A characteristic of a
consumer may be a behavior characteristic of a consumer relating to
a behavior in which the consumer was engaging when the location
data was derived. Such a characteristic may be, for example, that
the consumer is a customer of a commercial entity. As another
example of a condition, a condition may be satisfied when the
consumer analytics system determines a characteristic of a group of
consumers. A characteristic of a group of consumers may be a
characteristic of the group and not of individual consumers of the
group (e.g., an average characteristic for the group) or a
characteristic shared by consumers of the group. As another example
of a condition, a condition may be satisfied based on an evaluation
of a characteristic that describes a behavior. For example, a
behavior characteristic may relate to a frequency with which a
consumer performs a behavior, such as a frequency with which the
consumer visits a retail business. An example of a condition that
may be associated with an action is a condition that a behavior
characteristic indicates that a frequency of a consumer's visits to
a retail business is greater than two visits per month.
[0055] In one illustrative example of an action and a condition, an
action includes requesting that a consumer respond to survey
questions regarding a commercial entity for which market research
is being conducted, and a condition for the action is that an
analysis of location data for a consumer produces an inference that
the consumer is a customer of the commercial entity. This action
and condition may be specified in block 201. Subsequently (as
discussed below), when location data for a consumer is analyzed and
a characteristic indicating that a consumer is a customer of the
commercial entity is inferred, the consumer analytics system may
prompt that consumer to provide responses to the survey questions.
The action taken by the consumer analytics system to prompt the
consumer may be taken by the system contemporaneously with the
consumer's presence at a location from which the characteristics
satisfying the conditions were inferred. As another example, a
system may infer from location data that consumers of a group of
consumers who frequently shop at one store (or type of store) are
visiting a competitor store not frequently visited by consumers of
the group. In response to drawing the inference, the system may
survey individual consumers who are members of the group to
determine a purpose of the consumers' visits to the competitor
store. The surveying may be conducted electronically, by
transmitting messages to the consumers, and may be performed
contemporaneously with the consumer's visit to the competitor
store.
[0056] In block 202, location data is obtained for multiple
consumers. Any suitable location data may be obtained, as
embodiments are not limited in this respect. Location data may, in
some embodiments, include geographic location data identifying a
geographic location that results from a location measurement
performed by a computing device using a location identification
system like the Global Positioning System (GPS). A geographic
location of a consumer may be defined according to a latitude,
longitude, altitude, and/or margin of error that identifies the
precision of the latitude, longitude, and altitude. Location data
may also include time data indicating a time at which the location
data for the consumer was obtained. Illustrative examples of
location data are discussed below.
[0057] The location data may be obtained in any suitable manner.
Examples of location data that may be obtained and ways in which
location data may be obtained are discussed in detail below and in
the '280 application that is incorporated herein by reference. In
some embodiments, the location data for a consumer may be obtained
in part using an electronic device associated with a consumer. The
electronic device may be any suitable portable device that may move
along with the consumer. The device may be carried by the consumer
or may be integrated into an item associated with the consumer
(e.g., integrated into a car, baggage, or clothing). The electronic
device may obtain location data or be used in obtaining location
data. Location data obtained by the electronic device may be
transmitted to a consumer analytics system at any suitable time and
in any suitable manner. In some embodiments, the electronic device
may continuously or occasionally transmit location data for the
consumer to a consumer analytics system without receiving a request
for the location data from the system. In other embodiments, the
consumer analytics system may occasionally request location data
from the electronic device and the electronic device may transmit
the location data upon receipt of the request. In still other
embodiments, the electronic device may transmit location data
without request at some times and the consumer analytics system may
request location data at other times.
[0058] In block 203, the location data for each consumer of the
multiple consumers is processed to determine characteristics for
the consumers. As described in the '280 application that is
incorporated herein by reference, the characteristics for a
consumer that may be determined from location data include behavior
characteristics, preference characteristics, and identity
characteristics. In block 203, determining the characteristics of a
consumer includes predicting and/or inferring behavior
characteristics of the consumer. The behaviors of a consumer that
may be indicated by characteristics may include visiting a
particular setting (e.g., a particular store), doing a specific
activity such as playing golf, or traveling via a specific mode of
transportation. The processing of location data of block 203 may be
performed by the consumer analytics system contemporaneously with
the consumer's movements, as the location data is obtained for the
consumer, such as while the consumer is visiting a setting or
moving to one or more settings on a path.
[0059] As part of the processing of location data for the
consumers, the consumer analytics system may determine whether to
take an action, including whether to request that the consumer
perform a task. To determine whether to take an action,
characteristics of consumers inferred and/or predicted during the
processing of block 203 are compared to conditions for actions
specified in block 201. When conditions for an action are
satisfied, the consumer analytics system may take the action.
Accordingly, in block 204, based on the characteristics of the
consumer inferred or predicted in block 203, an action is triggered
when the characteristics satisfy one or more conditions. As
discussed above, any suitable actions may have been specified in
block 201 and may be taken in block 204. Actions may include
prompting a consumer to perform a task, such as by sending a
consumer one or more survey questions to respond to. The actions
may additionally or alternatively include obtaining additional data
from an external data source, such as data related to the consumer.
Data related to the consumer may include sales transaction data,
information entered into social networking or other system, or any
other information. The actions may additionally or alternatively
include determining a reward to incent a consumer to perform a
task, notifying a consumer about a reward, determining whether a
reward is available for redemption, determining an effect of a
reward on a consumer's characteristics (including behaviors),
and/or monitoring attributes of a manner in which a reward is
redeemed and/or monitoring for potential fraud in redemption. As
another example, actions may include adjusting one or more
parameters of a visit detection process. The action taken by the
consumer analytics system may be taken at any suitable time,
including contemporaneously with the consumer's movements.
Detailed Description of Some Embodiments
System Overview
[0060] Some embodiments include a consumer analytics system,
implemented on a computing device, with a configured set of
actions. The consumer analytics system may include a facility for
processing location data, a set of points of interest, and a set of
actions which can be performed. The facility may be executed by the
computing device.
[0061] Techniques operating according to principles described
herein may be implemented in any suitable manner. For example, the
methods and systems described herein may be deployed in part or in
whole through a machine that executes computer software, program
codes, and/or instructions on a processor. The processor may be
part of a server, client, network infrastructure, mobile computing
platform, stationary computing platform, or other computing
platform. A processor may be any kind of computational or
processing device capable of executing program instructions, codes,
binary instructions and the like. The processor may be or include a
signal processor, digital processor, embedded processor,
microprocessor or any variant such as a co-processor (math
co-processor, graphic co-processor, communication co-processor and
the like) and the like that may directly or indirectly facilitate
execution of program code or program instructions stored thereon.
In addition, the processor may enable execution of multiple
programs, threads, and codes. The threads may be executed
simultaneously to enhance the performance of the processor and to
facilitate simultaneous operations of the application. By way of
example, methods, program codes, program instructions and the like
described herein may be implemented in one or more threads. The
threads may spawn other threads that may have assigned priorities
associated with them; the processor may execute these threads based
on priority or any other order based on instructions provided in
the program code. The processor may include memory that stores
methods, codes, instructions and programs as described herein and
elsewhere. The processor may access a storage medium through an
interface that may store methods, codes, and instructions as
described herein and elsewhere. The storage medium associated with
the processor for storing methods, programs, codes, program
instructions or other type of instructions capable of being
executed by the computing or processing device may include but may
not be limited to one or more of a CD-ROM, DVD, memory, hard disk,
flash drive, RAM, ROM, cache and the like. "Storage medium," as
used herein, refers to tangible storage media. Tangible storage
media are non-transitory and have at least one physical, structural
component. In a storage medium, at least one physical, structural
component has at least one physical property that may be altered in
some way during a process of creating the medium with embedded
information, a process of recording information thereon, or any
other process of encoding the medium with information. For example,
a magnetization state of a portion of a physical structure of a
computer-readable medium may be altered during a recording
process.
[0062] A processor may include one or more cores that may enhance
speed and performance of a multiprocessor. In embodiments, the
process may be a dual core processor, quad core processors, other
chip-level multiprocessor and the like that combine two or more
independent cores (called a die).
[0063] The methods and systems described herein may be deployed in
part or in whole through a machine that executes computer software
on a server, client, firewall, gateway, hub, router, or other such
computer and/or networking hardware. A software program may be
associated with a server that may include a file server, print
server, domain server, internet server, intranet server and other
variants such as secondary server, host server, distributed server
and the like. The server may include one or more memories,
processors, storage media, ports (physical and virtual),
communication devices, and/or interfaces capable of accessing other
servers, clients, machines, and devices through a wired or a
wireless medium, and the like. The methods, programs or codes as
described herein and elsewhere may be executed by the server. In
addition, other devices required for execution of methods as
described in this application may be considered as a part of the
infrastructure associated with the server.
[0064] The server may provide an interface to other devices
including, without limitation, clients, other servers, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
programs across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope of the
invention. In addition, any of the devices attached to the server
through an interface may include at least one storage medium
capable of storing methods, programs, code and/or instructions. A
central repository may provide program instructions to be executed
on different devices. In this implementation, the remote repository
may act as a storage medium for program code, instructions, and
programs.
[0065] A software program may be associated with a client that may
include a file client, print client, domain client, internet
client, intranet client and other variants such as secondary
client, host client, distributed client and the like. The client
may include one or more memories, processors, storage media, ports
(physical and virtual), communication devices, and interfaces
capable of accessing other clients, servers, machines, and devices
through a wired or a wireless medium, and the like. The methods,
programs or codes as described herein and elsewhere may be executed
by the client. In addition, other devices required for execution of
methods as described in this application may be considered as a
part of the infrastructure associated with the client.
[0066] The client may provide an interface to other devices
including, without limitation, servers, other clients, printers,
database servers, print servers, file servers, communication
servers, distributed servers and the like. Additionally, this
coupling and/or connection may facilitate remote execution of
programs across the network. The networking of some or all of these
devices may facilitate parallel processing of a program or method
at one or more location without deviating from the scope of the
invention. In addition, any of the devices attached to the client
through an interface may include at least one storage medium
capable of storing methods, programs, applications, code and/or
instructions. A central repository may provide program instructions
to be executed on different devices. In this implementation, the
remote repository may act as a storage medium for program code,
instructions, and programs.
[0067] The methods and systems described herein may be deployed in
part or in whole through network infrastructures. The network
infrastructure may include elements such as computing devices,
servers, routers, hubs, firewalls, clients, personal computers,
communication devices, routing devices and other active and passive
devices, modules and/or components as known in the art. The
computing and/or non-computing device(s) associated with the
network infrastructure may include, apart from other components, a
storage medium such as flash memory, buffer, stack, RAM, ROM and
the like. The processes, methods, program codes, and instructions
described herein may be executed by one or more of the network
infrastructural elements.
[0068] The methods, program codes, and instructions described
herein and elsewhere may be implemented on a cellular network
having multiple cells. The cellular network may either be frequency
division multiple access (FDMA) network, a time division multiple
access (TDMA) network, and/or a code division multiple access
(CDMA) network, or any other suitable form of network implementing
any suitable communication protocol and any suitable medium access
control protocol. The cellular network may include mobile devices,
cell sites, base stations, repeaters, antennas, towers, and the
like. The cell network may be a network carrying out a protocol for
Global System for Mobile Communications (GSM), General Packet Radio
Service (GPRS), any third-generation (3G) network, Evolution-Data
Optimized (EVDO), ad hoc mesh, Long-Term Evolution (LTE), Worldwide
Interoperability for Microwave Access (WiMAX), or other network
types.
[0069] The methods, programs codes, and instructions described
herein and elsewhere may be implemented on or through mobile
devices. The mobile devices may include navigation devices, cell
phones, mobile phones, mobile personal digital assistants, laptops,
palmtops, netbooks, pagers, electronic books readers, music players
and the like. These devices may include, apart from other
components, a storage medium such as a flash memory, buffer, RAM,
ROM and one or more computing devices. The computing devices
associated with mobile devices may be enabled to execute program
codes, methods, and instructions stored thereon. Alternatively, the
mobile devices may be configured to execute instructions in
collaboration with other devices. The mobile devices may
communicate with base stations interfaced with servers and
configured to execute program codes. The mobile devices may
communicate on a peer to peer network, mesh network, or other
communications network. The program code may be stored on the
storage medium associated with the server and executed by a
computing device embedded within the server. The base station may
include a computing device and a storage medium. The storage device
may store program codes and instructions executed by the computing
devices associated with the base station.
[0070] Computer software, program codes, and/or instructions may be
stored and/or accessed on machine readable storage media that may
include: computer components, devices, and recording media that
retain digital data used for computing for some interval of time;
semiconductor storage known as random access memory (RAM); mass
storage typically for more permanent storage, such as optical
discs, forms of magnetic storage like hard disks, tapes, drums,
cards and other types; processor registers, cache memory, volatile
memory, non-volatile memory; optical storage such as CD, DVD;
removable media such as flash memory (e.g. USB sticks or keys),
floppy disks, magnetic tape, paper tape, punch cards, standalone
RAM disks, Zip drives, removable mass storage, off-line, and the
like; or other computer memory such as dynamic memory, static
memory, read/write storage, mutable storage, read only, random
access, sequential access, location addressable, file addressable,
content addressable, network attached storage, storage area
network, bar codes, magnetic ink, and the like.
[0071] The methods and systems described herein may transform
physical and/or or intangible items from one state to another. The
methods and systems described herein may also transform data
representing physical and/or intangible items from one state to
another.
[0072] The elements described and depicted herein, including in
flow charts and block diagrams throughout the figures, imply
logical boundaries between the elements. However, according to
software or hardware engineering practices, the depicted elements
and the functions thereof may be implemented on machines through
computer executable media having a processor capable of executing
program instructions stored thereon as a monolithic software
structure, as standalone software modules, or as modules that
employ external routines, code, services, and so forth, or any
combination of these, and all such implementations may be within
the scope of the present disclosure. Examples of such machines may
include, but may not be limited to, personal digital assistants,
laptops, personal computers, mobile phones, other handheld
computing devices, medical equipment, wired or wireless
communication devices, transducers, chips, calculators, satellites,
tablet PCs, electronic books, gadgets, electronic devices, devices
having artificial intelligence, computing devices, networking
equipment, servers, routers and the like. Furthermore, the elements
depicted in the flow chart and block diagrams or any other logical
component may be implemented on a machine capable of executing
program instructions. Thus, while the drawings and descriptions
herein set forth functional aspects of the disclosed systems, no
particular arrangement of software for implementing these
functional aspects should be inferred from these descriptions
unless explicitly stated or otherwise clear from the context.
Similarly, it will be appreciated that the various steps identified
and described herein may be varied, and that the order of steps may
be adapted to particular applications of the techniques disclosed
herein. All such variations and modifications are intended to fall
within the scope of this disclosure. As such, the depiction and/or
description of an order for various steps should not be understood
to require a particular order of execution for those steps, unless
required by a particular application, or explicitly stated or
otherwise clear from the context.
[0073] The methods and/or processes described herein, and steps
thereof, may be realized in hardware, software or any combination
of hardware and software suitable for a particular application. The
hardware may include a general purpose computer and/or dedicated
computing device or specific computing device or particular aspect
or component of a specific computing device. The processes may be
realized in one or more microprocessors, microcontrollers, embedded
microcontrollers, programmable digital signal processors or other
programmable device, along with internal and/or external memory.
The processes may also, or instead, be embodied in an application
specific integrated circuit, a programmable gate array,
programmable array logic, or any other device or combination of
devices that may be configured to process electronic signals. It
will further be appreciated that one or more of the processes may
be realized as a computer executable code capable of being stored
on a machine readable medium.
[0074] Computer executable code may be created using a structured
programming language such as C, an object oriented programming
language such as C++, or any other high-level or low-level
programming language (including assembly languages, hardware
description languages, and database programming languages and
technologies) that may be stored, compiled or interpreted to run on
one of the above devices, as well as heterogeneous combinations of
processors, processor architectures, or combinations of different
hardware and software, or any other machine capable of executing
program instructions.
[0075] Thus, in one aspect, each method described herein and
combinations thereof may be embodied in computer executable code
that, when executing on one or more computing devices, performs the
steps thereof. In another aspect, the methods may be embodied in
systems that perform the steps thereof, and may be distributed
across devices in a number of ways, or all of the functionality may
be integrated into a dedicated, standalone device or other
hardware. In another aspect, the means for performing the steps
associated with the processes described herein may include any of
the hardware and/or software described herein. All such
permutations and combinations are intended to fall within the scope
of the present disclosure.
[0076] FIG. 3 illustrates one exemplary implementation of a
computing device in the form of a computing device 300 that may be
used in a system implementing the techniques described herein,
although others are possible. It should be appreciated that FIG. 3
is intended neither to be a depiction of necessary components for a
computing device to operate in accordance with the principles
described herein, nor a comprehensive depiction.
[0077] Computing device 300 may comprise at least one processor
302, a network adapter 304, and computer-readable storage media
306. Computing device 300 may be, for example, a desktop or laptop
personal computer, a server, a collection of personal computers or
servers that operate together, or any other suitable computing
device. Network adapter 304 may be any suitable hardware and/or
software to enable the computing device 300 to communicate wired
and/or wirelessly with any other suitable computing device over any
suitable computing network. The computing network may include
wireless access points, switches, routers, gateways, and/or other
networking equipment as well as any suitable wired and/or wireless
communication medium or media for exchanging data between two or
more computers, including the Internet. Computer-readable media 306
may be adapted to store data to be processed and/or instructions to
be executed by processor 302. Processor 302 enables processing of
data and execution of instructions. The data and instructions may
be stored on the computer-readable storage media 306 and may, for
example, enable communication between components of the computing
device 300.
[0078] The data and instructions stored on computer-readable
storage media 306 may comprise computer-executable instructions
implementing techniques that operate according to the principles
described herein. In the example of FIG. 3, computer-readable
storage media 306 stores computer-executable instructions
implementing various facilities and storing various information as
described herein. Computer-readable storage media 306 may store a
consumer analytics facility 308 for obtaining location data for
consumers via network adapter 304 and determining characteristics,
including behaviors, of the consumers. The consumer analytics
facility 308 may perform any of the exemplary techniques described
herein, and may include any of the exemplary facilities described
herein. In some embodiments, the consumer analytics facility 308
may include functionality relating to a survey market system,
including functionality discussed below. Computer-readable storage
media 306 may also include data sets to be used by the consumer
analytics facility 308, including a data set 312 of actions that
the facility 308 can be configured to prompt a consumer to take and
their associated triggering values, a data set 310 of points of
interest, which may include information about locations and types
of points of interest, and a data set 314 of rewards, including
information on parameters of rewards eligible to be offered to
consumers.
[0079] While not illustrated in FIG. 3, a computing device may
additionally have one or more components and peripherals, including
input and output devices. These devices can be used, among other
things, to present a user interface. Examples of output devices
that can be used to provide a user interface include printers or
display screens for visual presentation of output and speakers or
other sound generating devices for audible presentation of output.
Examples of input devices that can be used for a user interface
include keyboards, and pointing devices, such as mice, touch pads,
and digitizing tablets. As another example, a computing device may
receive input information through speech recognition or in other
audible format.
[0080] While the invention has been disclosed in connection with
the preferred embodiments shown and described in detail, various
modifications and improvements thereon will become readily apparent
to those skilled in the art. Accordingly, the spirit and scope of
the present invention is not to be limited by the examples herein,
but is to be understood in the broadest sense allowable by law.
Examples of Techniques for Obtaining Location Data
[0081] As mentioned above, embodiments are not limited to
implementing any particular technique for obtaining location data.
In some embodiments, techniques for obtaining location data
described in the '280 application incorporated herein by reference
may be implemented.
[0082] In some embodiments, a system may use one or more of many
different methods for gathering consumer location data based on a
personal device (such as a mobile phone, tablet, or laptop
computer). Location data may include information identifying a
geographic location. Information identifying a geographic location
may include latitude, longitude, altitude, and an error measure.
Location data may also include a timestamp. In some embodiments, an
electronic device associated with and/or operated by a consumer may
determine the location data alone and transmit the determined
location data to a consumer analytics system. In others, one or
more other devices, such as components of a network to which the
electronic device is connected and/or able to communicate, may
cooperate with the electronic device to determine the location
data.
[0083] Techniques for obtaining location data that may be used in
embodiments include techniques for measuring a physical location of
a consumer. Techniques for measuring a location including cell
tower identification, enhanced cell identification, Uplink-Time
difference of arrival, Time of arrival, Angle of arrival, enhanced
observed time difference (E-OTD), GPS, Assisted-GPS, hybrid
positioning systems, Global Navigation Satellite System (GLONASS),
the Galileo navigation system, location-determination services
using access points for wireless local area networks (WLANs), and
the like.
[0084] In some embodiments, location data comprising measurements
of physical location may additionally or alternatively be obtained
using paging, triangulation, and the like. A common method is to
triangulate a location of the device based on nearby towers that
provide wireless phone/data service. In the case of mobile phones,
the phones may emit a roaming signal to contact the next nearby
antenna tower. The phone's position can be figured out by
multilateration based on the signal strength of nearby antennas. A
similar method is to do a similar triangulation but instead of
using towers used to provide wireless service, use Wi-Fi or other
similar systems. This may be particularly useful in cases in which
mobile tower signal is poor (in remote areas, for example) or not
available on the device.
[0085] In some embodiments, in addition to or as an alternative to
obtaining location data that includes measurements of physical
location using satellite-based systems and/or triangulation,
location data may be determined from information stored by data
sources that are linked to the user and/or device. Such data may
include identifications by a user of setting visited by the
consumer or that the consumer is visiting. For example, if a
consumer provides information to a data source indicating a
location of the consumer, that information may be used in
identifying a location of the consumer. Such information may
include a message posted to a social networking service saying "I
just arrived in Boston." From the user's statement of his or her
location, a consumer analytics system with access to the
information can infer that the consumer is in the vicinity of
Boston. Additionally or alternatively, predictions of location may
be used. Predictions may be obtained in any suitable manner. For
example, by using an accelerometer built into an electronic device
that is carried by a consumer (e.g., an accelerometer of a mobile
phone), a speed the consumer is traveling may be estimated and used
along with a last known location for the consumer to estimate a
current location of the consumer. In some embodiments, multiple
different kinds of data indicative of location may be analyzed
together in determining locations visited by consumers, which may
increase the amount and quality of location data.
[0086] In some embodiments, different data sources may also be used
to increase the quality of the data collected by changing which
data sources are used and how often the data sources are polled.
For example, if location data indicates a consumer is moving, it
may be useful to increase the rate at which data is gathered.
[0087] Location data for consumers may be obtained by a consumer
analytics system in any suitable manner. In some embodiments,
location data can be pulled by the system. To pull the location
data, the consumer analytics system may query a communication
network, such as a communication network to which an electronic
device associated with a consumer is connected.
[0088] The network may locate the device in response to the query
and produce location data and/or request that the device provide
location data. In other embodiments, the consumer analytics system
may obtain location data for a consumer by having an electronic
device associated with the consumer push location data to the
consumer analytics system periodically. In some embodiments in
which a device pushes location data periodically, it may be
desirable that the device obtains location data and sends the
location data to the system automatically and transparently to a
consumer associated with the device, without receiving input from
the user.
Examples of Processing Location Data to Build a Consumer
Profile
[0089] The consumer analytics system may receive multiple different
units of location data for any given consumer over time. The
location data for a consumer may be in the form of a set of data
points that each identifies a location through which the consumer
passed.
[0090] From analyzing this location data, a consumer analytics
system may generate a unique list of settings visited by each
consumer. The list may be "unique" in that the list does not
include multiple entries corresponding to a single visit by a
consumer to a setting, or because the list does not include
multiple listings for a setting. To generate the unique list, the
consumer analytics system may identify "anchors" from locations
that are similar in time and space. The consumer analytics system
may also identify settings corresponding to the anchors and may
produce information about a consumer based on the settings visited
by a consumer. Additionally, by analyzing the unique list of
physical locations and/or settings visited by a consumer, patterns
can be identified in the settings that may be used by the consumer
analytics system to determine characteristics of a consumer. For
example, an identity, behaviors, and preferences of the consumer
can be identified through analysis of location data. The location
data that is analyzed may include an identification of locations at
which the consumer was present and/or settings visited by the
consumer. Additionally, personally-relevant locations for the
consumer, such as the place of residence and place of employment of
the consumer, can be determined through analysis.
[0091] The consumer analytics system may also examine sets of
location points corresponding to movement, rather than only
location points corresponding to stops the consumer made at
particular locations, to determine characteristics of a consumer.
Location data corresponding to movement may provide information
about paths traveled by a consumer. For example, by using the
distance and time between points, the consumer's speed can be
computed. The consumer's speed, along with whether or not the
points are over roads, rail lines, etc. may be used to determine if
a consumer is traveling by car, rail, plane, etc. In addition, the
distance from the consumer's home of a location visited by a
consumer can be computed using information about a path.
[0092] The '280 application that is incorporated herein by
reference describes in detail techniques that may be implemented in
some embodiments for determining anchors, paths, and settings from
location data for a consumer. The '280 application also describes
in detail techniques that may be implemented in some embodiments
for analyzing location data, anchors, paths, and settings to
determine characteristics of consumer.
Visit Detection
[0093] In some embodiments, when a consumer analytics system
receives location data for a consumer, the consumer analytics
system may perform a visit detection process on the location data
to identify settings visited by consumers. A setting may be a place
to which a location corresponds, such as a commercial or
non-commercial place (e.g., business or park). A position of a
setting may correspond to a set of physical location falling within
defined location boundaries of the setting, as discussed below.
When a consumer is detected to have been present at a location
falling within the location boundaries of a setting, through a
visit detection process the consumer can be detected to have
visited the setting.
[0094] A visit detection process may be performed by a consumer
analytics system in any suitable manner, as embodiments are not
limited to identifying settings visited by consumers in any
particular way. Examples of ways in which a visit detection process
may be carried out are described below and in the '280 application
that is incorporated by reference herein.
[0095] A visit detection process may be carried out because, in
some embodiments, one element of detecting consumer
characteristics, including behavior characteristics, from location
data is to determine what stores, restaurants, sports venues, and
other settings a consumer visits. The process 400 of FIG. 4 is an
example of a visit detection process that may be carried out in
some embodiments.
[0096] The process 400 of FIG. 4 begins in block 402, in which a
set of location data points for a consumer is obtained. The
location data points may be obtained in any suitable manner,
examples of which are described above. In block 404, the location
data may be analyzed to remove "noise" from the location data
points. Noise in the location data points may include location data
points that are not valid. Invalid location data points may include
points indicating locations that are not physically possible or
very unlikely. Impossible or unlikely location data points may
include data points such as: [0097] Points that indicate the
consumer is traveling faster than the speed of sound; and [0098] A
trail of connected points roughly following a line with one outlier
that is clearly disconnected.
[0099] In order to remove the noise in block 404, the consumer
analytics system can traverse the location data points for a
consumer one-by-one and discard any location data points that do
not meet one or more criteria for not being noise or satisfy one or
more criteria for being noise. Criteria for being noise may include
detecting that a location data point is either physically
impossible or very unlikely, or any other suitable criteria.
[0100] Once noise is removed in block 404, as part of the
processing of location data, in block 406 the consumer analytics
system may enhance the data by adjusting locations indicated by
location data. For example, location data points may be pushed from
unlikely places to likely places. As an example, if the time and
distance between points and altitude indicate the consumer is
likely traveling in a car, the points obtained during this time
could be cross-referenced with the known location of roads. The
points could be moved to correspond to a road, which is most likely
where the point is given that the car would likely be driving on
roads. Adjusting the location data points in this way may
compensate for errors in the locations identified by location data
points, such as errors that may result from imprecise processes for
obtaining location data.
[0101] Once a good set of location data points for a consumer have
been obtained through processing of block 402-406, the location
data points can be analyzed to identify travel paths ("paths") and
stationary locations ("anchors"). Paths and anchors may be
identified by the consumer analytics system in block 408 by looking
at the time and distance between points and by applying a
clustering algorithm. For example, such a clustering of the
sequential location points may be carried out using Euclidian
distance clustering. In one example of a Euclidean distance
clustering, locations within 200 meters of one another may be
identified as being related to a same potential anchor. In some
embodiments, each location identified by location data processed by
the consumer analytics system may include an uncertainty radius.
The uncertainty radius around each location may be used to more
accurately cluster nearby location points using statistical
methods. When a location indicated by a location data point is
similar to a location indicated by another location data point and
is within the uncertainty radius of the other location data point,
the consumer analytics system may conclude that the location data
points both relate to one location visited by a consumer. An anchor
may be identified at least in part as a cluster of locations
corresponding to multiple different location data points.
Additionally, by comparing time differences between location points
related to the same potential anchor, a duration of time spent by
consumer at the potential anchor can be determined. Each cluster of
locations associated with a duration above a threshold, such as
duration of greater than five minutes, may be identified by the
consumer analytics system as an anchor. In some embodiments, the
calculated location for an anchor may be a geometric mean of the
individual location data points associated with the anchor.
[0102] In block 410, the consumer analytics system may use the
anchors to identify settings visited by a consumer. The consumer
analytics system may utilize a data set of settings, including
Points of Interest (POIs), to identify settings, including
identifying locations corresponding to POIs defined in the data
set. The data set may include a collection of places of one or more
kinds (e.g., stores, restaurants, sports venues, transportation
terminals, office buildings, etc.) that a consumer may visit. Each
setting in the data set may be defined at least in part as a
polygon that defines a location of the point of interest. Examples
of ways in which the polygon may be defined are described in detail
below. Additionally, in some embodiments, information regarding a
setting may include a set of operational information (e.g., the
hours of operation, the operational type, e.g., a terminal for
plane/boat/rail travel, etc.) and a set of categorical information
about the setting (e.g., a retail location, restaurant, or
stadium).
[0103] The consumer analytics system may identify the settings
visited by the consumer by examining each anchor and determining a
likelihood that the consumer visited the given POI. A consumer
analytics system may determine the likelihood in any suitable
manner, as embodiments are not limited in this respect. In some
embodiments, the likelihood may be calculated by the consumer
analytics system based on a number of factors, including: [0104]
the likelihood that a cluster of location points representing the
anchor corresponds to a location within the bounds of the POI;
[0105] whether the time range of the anchor falls within the
operational hours of the POI; [0106] whether the anchor duration
falls with the expected visit duration to the given POI (e.g.,
consumers typically spend 1.5-3 hours at movie theatre; a visit of
30 minutes is unlikely); [0107] whether the already-computed
behavior of the consumer indicates that she is likely to visit the
POI or visit the POI at the time-of-day, day-of-week, time-of-year,
etc. at which location data for the anchor was collected; [0108]
and any other suitable factors.
[0109] When a likelihood of an anchor matching a setting is
calculated by the consumer analytics system, the likelihood may be
compared to a threshold. If the likelihood exceeds the threshold,
the anchor may be determined to correspond to the setting and the
consumer may be determined to have visited the setting. Any
suitable threshold having any suitable value may be used, as
embodiments are not limited in this respect. Additionally, the
threshold may be used for any suitable number of settings. In some
embodiments, the same threshold may be used by the consumer
analytics system for all settings, such that each time the consumer
analytics system calculates a likelihood of an anchor corresponding
to a setting, the likelihood may be compared to the threshold. In
other embodiments, different thresholds may be used for different
settings. In some embodiments that use multiple different
thresholds, each setting in the set of settings that can be
identified through the visit detection process may be associated
with an individual threshold corresponding to that setting. When a
likelihood of a consumer visiting the setting is calculated, the
likelihood may be compared to the threshold for that setting. In
other embodiments that use multiple different thresholds, a group
of multiple settings may share a threshold. Any suitable group of
settings may be defined, as embodiments are not limited in this
respect. Settings having a similar location or being of a similar
type may be grouped in some embodiments.
[0110] In block 412, once the consumer analytics system has matched
location data for consumers to settings visited by the consumers in
block 410, the consumer analytics system may store information
resulting from the determination of block 410. The stored
information may include information identifying that a consumer has
visited a setting, when an anchor for a consumer was determined to
match a setting. The stored information may also include
information identifying that an anchor of a consumer was not
matched to any settings, if the consumer analytics system could not
match an anchor to settings. Once the information is stored in
block 412, the process 400 ends.
[0111] Following the process 400, the information stored by the
consumer analytics system may be used in any suitable manner. For
example, as discussed herein and in the '280 application
incorporated herein by reference, settings visited by consumers may
be analyzed to determine characteristics of consumers and/or to
conduct market research. Characteristics of consumers determined
from the settings may also be compared to conditions for actions,
and a consumer analytics system may take an action in response to
determining that one or more characteristics of one or more
consumers satisfy conditions for an action. As another example,
information identifying that an anchor for a consumer does not
match any settings for which the consumer analytics system has
information may prompt adjustments to the visit detection process,
including adjustments to definitions of settings. As discussed in
detail below, in some cases in which the consumer analytics system
cannot match an anchor for a consumer to a setting, the consumer
and/or an administrator of the consumer analytics system may be
prompted to provide information about the location visited by the
consumer and this information may be used to define a setting. Once
the setting is defined, the consumer analytics system may be able
to match anchors to that setting.
[0112] The exemplary visit detection process described above in
connection with FIG. 4 was described as being carried out by a
consumer analytics system in response to receiving location data
from a source of location data, such as a device associated with a
consumer. It should be appreciated, however, that embodiments are
not limited to implementing the visit detection process on a server
or any other computing device that receives location data from
another device. In some embodiments, a device that measures a
physical location of a consumer may perform a visit detection
process. In such cases, the device may measure the physical
location of the consumer over time and apply a visit detection
process as above by comparing locations of the consumer to
definitions of settings. The set of settings may be stored on the
device that measures the location and performs the visit detection
process or may be stored elsewhere accessible to the device, such
as on a server that the device may communicate with over a network
(e.g., a local network or a wide-area network such as the
Internet).
Triggering Data Collection Actions Based on Determined Consumer
Characteristics
[0113] In some embodiments, location data, as well as settings
visited by a consumer and/or paths or trips taken by consumers, may
be analyzed by the consumer analytics system to infer and/or
predict characteristics of consumers or groups of consumers. The
characteristics of consumers may be used to build profile about
consumers, and these profiles may be used to perform market
research. In addition, in some embodiments, location data can be
used to discover when a consumer exhibits characteristics of
interest, including performing a behavior of interest.
[0114] A characteristic of interest, including a behavior of
interest, may be any suitable characteristic (including a behavior
characteristic) of a consumer that may be determined from location
data and in which a market researcher may be interested.
Characteristics of interest, as mentioned above, may be related to
conditions of an action that may be taken by a consumer analytics
system. The characteristics may relate to commercial activities of
consumers. For example, a market researcher may be interested in
better understanding how consumers choose which kind of peanut
butter to buy. By processing consumers' location data and
identifying, using a visit detection process, stores visited by
consumers, the consumer analytics system may be able to detect when
a consumer has arrived at or was present at a store that sells
peanut butter. In response to inferring a behavior characteristic
for a consumer indicating that the consumer has visited the store,
the consumer analytics system may take an action that includes
sending the consumer a message prompting the consumer to answer
survey questions. The survey questions may ask whether the consumer
bought peanut butter, which, if any, kinds of peanut butter the
consumer bought, and why, and/or kinds of peanut butter the
consumer did not buy and why not. The consumer's responses to these
survey questions may aid the market researcher in understanding the
mindset that went into the consumer's decision to purchase peanut
butter.
[0115] As mentioned above, characteristics of a consumer that may
be determined from location data include behavior characteristics
of consumers that relate to behaviors of the consumers. Behaviors
of consumers may include behaviors that extend for a period of
time. For example, a consumer's visit to a setting or a consumer's
shopping trip that includes visiting one setting and driving past
another setting may be behaviors that extend for a period of time
(e.g., the period of time the consumer was at a setting). When a
behavior extends for a period of time, in some embodiments a
consumer analytics system may obtain location data for the
consumer, determine characteristics for the consumer, and carry out
an action while the behavior is ongoing. In some such embodiments,
the consumer analytics system may determine characteristics of
consumers and take action contemporaneously with a consumer's
behavior by determining the characteristics and taking action when
the consumer is predicted to be about to engage in a behavior, when
the consumer is determined to be engaging in the behavior, when the
consumer is determined to have recently ended a behavior, and/or
when the consumer is detected to be about to end a behavior. A
consumer analytics system may take an action contemporaneously with
a consumer's behavior when the consumer has not yet engaged in
another behavior or moved in a manner from which the consumer
analytics system has determined another behavior of the
consumer.
[0116] As discussed above in connection with FIG. 2, a consumer
analytics system may receive input defining any suitable action to
be taken in response to any suitable condition(s). The condition(s)
may relate to any suitable one or more characteristics of one or
more consumers determined by a consumer analytics system from
location data for one or more consumers. The characteristic(s) that
may be determined by the consumer analytics system and that may
satisfy conditions for an action may include one or more
characteristics of a single consumer inferred or predicted by the
consumer analytics system. Additionally or alternatively, the
characteristics may include one or more characteristics that are
shared by consumers of a group of consumers and that are inferred
or predicted by the consumer analytics system, or one or more
characteristics of a group that are not associated with any
particular consumer (e.g., an average characteristic for a group).
The characteristics that may be determined for one or more
consumers may be characteristics that relate to commercial activity
of one or more consumers.
[0117] As mentioned above and as described in detail in the '280
application incorporated herein by reference, characteristics for
one or more consumers that may be inferred or predicted by a
consumer analytics system may include behavior characteristics,
identity characteristics, or preference characteristics.
[0118] Behavior characteristics may include any suitable
information regarding behaviors of a consumer. Characteristics of
behaviors may include information about activities in which a
consumer does or does not participate or a manner in which the
consumer participates in an activity. Information on a manner in
which the consumer participates in an activity may include
information on a frequency or periodicity of the consumer's
participation in the activity. Additionally, predictions of whether
a consumer is likely to participate in an activity may be inferred
or predicted as behavior characteristics. Behaviors of a consumer
may include retail-relevant behaviors and lifestyle-relevant
behaviors. Retail-relevant behaviors may include behaviors relating
to commercial activities engaged in by a consumer. Commercial
activities may include activities in which a monetary transaction
takes place or could take place, including visits to any location
at which consumers could purchase products or services.
Lifestyle-relevant behaviors may include information about
consumers' work life, home life, and regular routine, including
their recreational behaviors. Lifestyle activities include visits
to and time spent at a consumer's residence and place of
employment; travel patterns and habits, including commuting
patterns and air travel; and visits to outdoor recreation
destinations, nightlife locations, sports and entertainment venues,
museums, amusement parks, tourist destinations, or other
recreational destinations.
[0119] Identity characteristics may include demographic and
socioeconomic attributes of a consumer. Demographic and
socioeconomic attributes of a consumer may include where a consumer
lives, information about a consumer's family, where a consumer
works, and what a consumer does for work.
[0120] Preference characteristics may include information on
preferences of a consumer regarding commercial activities and/or
lifestyle-relevant activities in which the consumer engages or
desires to engage. Preference characteristics regarding commercial
activities of a consumer may include preferences of the consumer
for particular types of products or services or particular products
or services. Brand loyalties of a consumer may be included in
preference characteristics for the consumer.
[0121] For characteristics that a consumer analytics system is
configured to infer or predict based on location data, the consumer
analytics system may also infer or predict a strength of the
characteristic or a likelihood that the characteristic has been
correctly inferred/predicted.
[0122] Any characteristic of a consumer or group of consumers that
is inferred/predicted by the consumer analytics system for the
individual consumer or for a group of consumers in which the
consumer is included may be a condition of an action or may be
evaluated to determine whether one or more conditions have been
satisfied. In examples described below, characteristics of a
consumer that may trigger a consumer analytics system to take an
action include behavior characteristics that relate to commercial
activity, including that relate to a commercial activity in which
the consumer is engaging at the time the behavior characteristics
are identified. In some embodiments, identity and/or preference
characteristics may additionally or alternatively satisfy
conditions that, when met, trigger the system to take an action.
Further, while examples of behavior characteristics that may
trigger an action are described herein, it should be appreciated
that characteristics of a consumer related to any suitable
behaviors may be used as conditions of an action or evaluated to
determine whether one or more conditions have been met. Examples of
behaviors that, in embodiments, could trigger a consumer analytics
system to take actions when the system infers/predicts
characteristics of a consumer related to the behavior include (but
are not limited to): [0123] Outdoor recreational (hiking, biking,
swimming, sailing, beach, etc.); [0124] Viewing or playing sports
(baseball, football, golf . . . ); [0125] Watching a movie in a
movie theatre; [0126] Visiting a known location (like one's
place-of-work or home); [0127] Going inside a retail store,
restaurant, convention center, or other point of interest; [0128]
Driving past a retail, store, restaurant, convention center, or
other point of interest; [0129] Traveling on a path that includes
visits to particular stores, such as a first store or store of a
first type (e.g., a grocery store) and a second store or store of a
second type (e.g., a department store that includes a grocery
department); [0130] Deviating from a behavioral pattern, such as by
visiting a setting or type of setting the consumer does not
typically visit; [0131] Traveling toward a setting; [0132] Making a
purchase at a setting; [0133] Moving in a trip that includes a
visit to one setting or type of setting and does not a visit to
another setting or another type of setting; [0134] Driving past a
billboard or other "Out of Home" (OOH) advertisement; [0135] Taking
a trip by air, rail, car, bus, or boat; and [0136] Any combination
of the foregoing.
[0137] As consumer characteristics are predicted and/or inferred by
a consumer analytics system of a consumer analytics system, the
consumer analytics system may take one or more actions when
conditions for taking the actions are satisfied by the
characteristics. Any suitable action may be taken. In some
embodiments, information collection actions may be triggered by
consumer characteristics meeting conditions for the actions. In
some embodiments, information storage actions may be triggered by
consumer characteristics meeting conditions for the actions.
[0138] An information collection action that may be taken by a
consumer analytics system may include collecting any suitable
information from any suitable source. In some cases, a consumer
analytics system may collect information from a consumer by
soliciting information from the consumers. Information may be
solicited in any way, including by sending messages to a consumer
requesting that the consumer perform a task. In other cases, a
consumer analytics system may collect information from a data
source external to the consumer analytics system. information that
may be collected by a consumer analytics system may include any
suitable information, including information related to one or more
commercial entities, products, and/or services. In some
embodiments, a consumer analytics system may collect information
relating to commercial activity. Information regarding commercial
activity may relate to commercial activity of a consumer and/or of
a commercial entity. Information regarding a commercial activity
may relate to a consumer, a commercial entity, and/or interactions
between a consumer and a commercial entity. The information that is
collected may be information that the consumer analytics system may
evaluate to determine characteristics of a consumer and/or
characteristics of a group of consumers related to commercial
activity, such as behavior, identity, or preference characteristics
of a consumer or behavior, identity, or preference characteristics
shared by consumers of a group of consumers.
Rewards Types & Granting of Rewards
[0139] In some embodiments, the consumer analytics system may
incent consumers to opt-in to sharing electronically-captured
location data with the consumer analytics system and/or to perform
a task (e.g., provide information as part of a task, such as by
completing a survey) requested by such a system as a result of
location-triggered actions, in return for rewards. Such rewards may
include, but are not limited to, food or beverage coupons or
vouchers, merchandise discounts, discounts on their purchases
(e.g., 15% off), entries into prize drawings, material goods, or
cash. As illustrated in FIG. 5, for example, the consumer analytics
system may render an interface, which may be displayed to a
consumer via a mobile device, by which a consumer may view tasks
the system 108 desires the consumer to perform (e.g., answering
survey questions) and rewards that are offered to the consumer for
performing the tasks. FIG. 6 illustrates an example of another
interface that may be displayed to a consumer via a mobile device
by which a consumer may perform a task that includes answering
survey questions.
[0140] In some embodiments, consumers may be incentivized to
complete a location-triggered task by being offered a reward that
they will receive upon completion of a task. In some embodiments,
the consumer analytics system may inform consumers in advance of
performing the task of a specific reward the consumers will
receive. In other embodiments, consumers will not be informed of
their reward until after they have completed the task. In still
other embodiments, the system may determine dynamically whether or
not to inform a consumer of the reward in advance, and this element
of variability may be used, in part, to maintain an element of
surprise so as to increase consumer engagement.
[0141] In some embodiments, consumers may earn rewards through the
accrual of reward points that are granted for performance of tasks.
Such rewards points may be redeemable for discounts on purchases at
business, gift cards, cash, any good or service, or any other
suitable incentive. Such rewards points may, in some embodiments,
be redeemable for incentives with the consumer analytics system or
with a business, different from the consumer analytics system, that
the consumer may visit. Accordingly, in some embodiments, the
consumer analytics system may maintain accounts or other
information relating to consumers in which information about
rewards is stored and updated. In some such embodiments, the
consumer analytics system may grant rewards points based on any
suitable factors, such as in proportion to the number of days of
location data-sharing by the consumer, the number of
location-triggered tasks completed by the consumer, and/or any
other metric by which a consumer's performance of tasks may be
measured. Some other metrics that may be used may relate to the
manner in which consumers perform tasks. For example, the brevity
of the turnaround time with which a consumer fills out a
location-triggered survey, obtains media (e.g., a photograph), or
otherwise completes a task, or the quality of their open-response
survey response text, the quality of their media, or the quality of
any other response requested by a task, or any other standard by
which the quality of a survey response or response requested by a
task, may be used by the system to determine a reward (including a
type of reward and/or a value of a reward) to offer.
[0142] In some embodiments in which reward points are offered by
the consumer analytics system, the system may also offer rewards
that may be redeemed at organizations visited by consumers, Such
organizations visited by consumers may be organizations that are
related to a condition for a survey to be distributed to a
consumer. For example, when a survey is distributed in response to
a consumer's characteristics relating to a business (e.g., a manner
in which the consumer visits or does not visit the business), the
consumer analytics system may offer one or more rewards that are
redeemable with the business. The consumer analytics system may
offer rewards redeemable at a business to consumers that received
surveys based on the characteristics of the consumer's interactions
with the business. In some such embodiments, the consumer analytics
system may determine either to provide consumers with rewards that
are redeemable at organizations or with reward points. The system
may determine which to provide based on commercial relationships
the consumer analytics system has (or an operator of such a system
has) with organizations, such as contracts the system has with
businesses. For example, if characteristics of a consumer's
interactions with a business triggered distribution of a survey,
the system may select whether to provide the consumer with a reward
redeemable at the business or reward points based on an identity of
that business. The system may evaluate the identity of the business
to determine whether the business is one with which the system has
a commercial relationship, such as whether the business purchases
consumer analytics data or survey opportunities from the consumer
analytics system. If the business has a commercial relationship
with the system, the system may offer consumers a reward that is
redeemable at the business, which may encourage consumers to later
return to the business and patronize the business again. If,
however, the business does not have a commercial relationship with
the system, the system may offer consumers reward points.
[0143] In some embodiments, the reward that the consumer analytics
system may offer to a consumer may be an instant reward or may be a
conditional reward.
[0144] An instant reward may be one that provides a value to the
consumer when the consumer is offered the reward. An instant reward
may not have a condition attached to the reward and/or may not
require that the consumer perform any other task or take any other
action (e.g., engage in any other behavior) to earn the value of
the reward. Instant rewards may be granted following completion of
specific location-triggered tasks by the consumer, or may be
granted randomly or pseudo-randomly following the completion of
such tasks. In some embodiments, at least some instant rewards may
be of high value, which may increase consumer engagement by
encouraging customers to desire rewards. In some such embodiments,
high-value rewards may be given out with a lower frequency than
lower value rewards. This may provide consumers with a game or
lottery element for the rewards, where the consumers have a chance
of obtaining a high-value reward, but where there may be a low
probability of any particular consumer receiving the high-value
reward in response to the consumer performing any particular
task.
[0145] A conditional reward may be a reward for which a consumer
may only redeem the value once the consumer has met one or more
conditions, such as by performing some other task or engaging in
some behavior. In some embodiments, the identity of a conditional
reward may be hidden from the consumers until the one or more
conditions are met, while in other embodiments the consumer may be
notified of the reward and the conditions that are to be fulfilled
for the value of the reward to be made available to the consumer.
Any suitable set of conditions may be associated with a reward, as
embodiments are not limited in this respect. A condition may be
based on information that may be detected from a consumer's
location, such as based on characteristics of a consumer (including
behaviors of the consumer) that may be determined from the
consumer's location. For example, the value of a food coupon to a
particular restaurant may only be made available to the consumer if
the consumer is within X miles of the restaurant at a particular
time of day and/or day of week.
[0146] The system may also be programmed to select the rewards that
may be offered to a consumer for completion of a task based upon
any suitable factors, including any number of consumer location
characteristics. For example: [0147] A reward may be granted
following the Nth visit to a particular retail/restaurant location
or chain, or the Nth survey response for surveys that relate to a
particular retail/restaurant location or chain [0148] A reward may
be granted after the consumer drives by a competitor before filling
out a location-triggered survey at a specific retailer [0149] A
reward may be granted after a consumer fills out a
location-triggered survey at a location which they do not typically
visit as part of their normal routine
[0150] In some embodiments, the system may additionally or
alternatively customize a size of a reward's value to a particular
consumer or for a particular task to provide larger incentives for
the consumer or consumers to complete a specific task of interest.
In some cases, a task that a consumer may be prompted to perform
may be uncommon, and in these cases the value of collecting
consumer information when these tasks are performed is great, so
rewards for these tasks may have a higher value than other rewards.
Consumers may thus be incentivized with larger rewards to maximize
the rate of information collection from these tasks. As another
example, a behavior of interest that may trigger the system to
request a consumer to perform a task (e.g., complete a survey) may
be rare, and the value of information that may be provided by a
consumer engaging in the behavior may therefore be great. For
example, a consumer may first visit Retailer 1 and subsequently
drive directly to Retailer 2, a competitor. Because that consumer
may be able to provide valuable information regarding consumers who
are customers of both retailers, or about something lacking about
Retailer 1 or something offered by Retailer 2 that led the consumer
to visit both retailers, the consumer may be requested to complete
a location-triggered survey and may be incentivized, with a larger
reward, to do so.
Reward Selection
[0151] Rewards may be offered to consumers in any suitable manner.
In some embodiments, by completing tasks, the consumer may earn
reward points that the consumer may redeem for rewards. Such
redemption of reward points may be with the consumer analytics
system or with a business or other organization. The
business/organization may be one that has a commercial relationship
with the system or that the consumer has been previously detected
to visit, or any other suitable business/organization, as
embodiments are not limited in this respect. In other embodiments,
a particular reward that may be redeemable at a business or
organization the consumer may visit may be offered to a consumer in
exchange for completing a task. In other embodiments, a consumer
may be presented with a list of one, two, or more rewards from
which the consumer may select the reward with which the consumer
will be presented. In some embodiments in which consumers earn
reward points, the consumer may be presented with such a list that
includes point values assigned to each reward, and the consumer may
be able to select any reward that points has a worth in rewards of
equal or lesser point value than a total rewards points accrued by
the consumer and/or offered to incent performance of the specific
task.
[0152] In some embodiments, the consumer analytics system may
customize a particular reward offered to a consumer, a list of
particular rewards and/or a point value of each reward, and/or a
number of reward points provided to a consumer for the consumer
based on the specific location behaviors of the consumer or other
information known about the consumer. For example, in some
embodiments, consumer location/visit history may be used by the
system to determine a set of retailers and restaurants that have
been visited by the consumer, and the system may limit the list of
rewards to offer the consumer to rewards redeemable at these
retailers and restaurants. As another example, a list of available
rewards may be limited to include those redeemable at
establishments on known travel routes for a consumer, such as a
specific cafe on the consumer's known commute route or another
travel path. As another example, the system may select a reward or
list of reward options to offer that includes only rewards that are
redeemable within a threshold distance of a personally-relevant
location for the consumer. For example, the system may only offer a
reward to a consumer if the reward is redeemable at a location
within 50 miles of the consumer's home. As another example, the
system may select rewards that are redeemable within a threshold
distance of a current location of the consumer at a time the
consumer completes a survey or other task, or at a time the rewards
are selected by the system. A further example of tailoring reward
selection based on the consumer is that the system may detect a
workplace for the consumer and refrain from presenting the consumer
rewards or reward options that are redeemable at the consumer's
workplace. Refraining from offering rewards redeemable at a
consumer's workplace may be advantageous first because the reward
may be more enticing for a consumer when it is redeemable at
somewhere other than the consumer's workplace. Refraining from
offering rewards redeemable at a consumer's workplace may be
further advantageous because it may aid in fraud detection or
prevention, as the system can identify that any attempt at reward
redemption by an employee of a location at which the reward is
being redeemed is illegitimate. Additionally or alternatively, in
some embodiments the system may select rewards or values of rewards
based on information provided by a consumer in response to a
survey. For example, if a consumer responds to a survey question in
a way that indicates that the consumer had a dissatisfactory
experience at a business, the system may offer the consumer a
reward redeemable at the business or increase a value of a reward,
redeemable at the business, that is offered to the consumer. The
system may recognize an expression of dissatisfaction by a consumer
in any suitable manner, as embodiments are not limited in this
respect. For example, the system may determine whether the consumer
answered "no" to a question regarding satisfaction or similarly
expressed dissatisfaction in a binary "yes/no" manner. As another
example, the system may determine whether the consumer indicated a
degree of satisfaction/dissatisfaction that suggests the consumer
was dissatisfied, such as by providing a low rating on a scale
relating to satisfaction. As a further example, the system may
determine whether the consumer provided input suggestive of
dissatisfaction, such as by providing text in a response that a
semantic interpretation engine determines has a high likelihood of
indicating dissatisfaction. Embodiments are not limited to
determining dissatisfaction of a consumer in any particular
manner.
[0153] In some embodiments, the system may determine reward types
based at least in part on specific information received from a
consumer when the consumer performs a task (e.g., specific
responses within a survey) or on other information collected from
or about the consumer. For example, a consumer might be given a
reward that best reflects the consumer's self-reported preferences.
Such preference information may be received from a consumer in any
suitable form. For example, the information may be collected from a
consumer by the system in response to a question by the system
regarding the consumer's preferences for rewards. As another
example, the system may determine the preference from information
provided by a consumer as part of performing a task, such as where
a consumer expresses in a survey response a preference for
healthful food options. As an example of such reward-tailoring, in
the case of a consumer who prefers healthful food options, the
system may identify that the consumer is to be presented with a
reward that is a coupon for carrot sticks rather than a coupon for
a soda at a quick service restaurant. As another example, a
consumer might express a preference for salads rather than
sandwiches at a particular restaurant chain, and might receive
therefore receive tailored rewards for salads at that particular
restaurant chain.
[0154] In some embodiments in which a list of reward options is
presented to a consumer following performance of a task, for the
consumer to select one of the options, the consumer analytics
system may select the rewards to include as options in the list
such that rewards of different types may be offered. Offering
different types of rewards may be advantageous because it gives the
consumer selecting between the options a wider array of choices and
may make it easier for the consumer to differentiate between the
options and make a selection. When different types of rewards are
offered, the rewards may be differentiated into types in any
suitable manner. For example, in some embodiments the type of a
reward may relate to a business at which the reward is redeemable,
and rewards that are redeemable at different businesses may be
considered to be of different types. In other embodiments, a type
of a reward may relate to a market category of a business at which
the reward is redeemable and rewards that are redeemable at
businesses of different market categories may be considered to be
of different types. In this context, a market category may relate
to the products or services offered by the business. Businesses
that offer different products or services, or products/services of
different quality or price, may be considered to be businesses in
different market categories.
[0155] Additionally, in some embodiments in which a list of reward
options is presented to a consumer, the consumer analytics system
may track reward options that are selected or not selected by the
consumer from the list and use that information in selecting reward
options to offer in future lists. The system may do so to avoid
presenting reward options that a consumer consistently declines to
select and/or to present reward options that the consumer has
previously selected or are similar to reward options the consumer
previously selected and therefore may be enticing to that consumer.
In some such embodiments, the consumer analytics system may
maintain information regarding specific rewards or types of rewards
that were offered by the system to each consumer and, for each
consumer, information regarding which rewards or types were
selected or not selected by the consumer. Using that information,
the system may identify a specific reward or type of reward that
was offered to a consumer multiple times and was not selected by
the consumer multiple times, and refrain from offering that reward
to the consumer again.
[0156] In some embodiments, rewards and/or types of rewards offered
to a consumer (including particular rewards or rewards included in
a list of options) may additionally or alternatively be customized
by the system for a consumer based on known consumer demographic
information. Consumer demographic information that may be used by
the system may potentially include, but is not limited to, age,
gender, number and age of children, household income, and/or
whether the consumer has pets at home.
[0157] In some embodiments, the system may additionally or
alternatively select rewards, types of rewards, values of rewards
and/or other parameters of rewards to fulfill a goal of influencing
a consumer to visit a particular business, business chain, or
individual location of a business chain. For example, when the
consumer analytics system requests that a consumer complete a
survey in response to detecting that the consumer has visited a
STARBUCKS.RTM. or is a regular customer of STARBUCKS.RTM., the
system may present a reward to determine whether the consumer can
be encouraged to visit a DUNKIN DONUTS.RTM.. For example, the
reward may be a coupon for a free coffee at DUNKIN DONUTS.RTM.,
regardless of whether the consumer has previously visited a DUNKIN
DONUTS.RTM., or the reward may be a coupon redeemable at a specific
DUNKIN DONUTS.RTM. location known to be on the consumer's commute
route.
[0158] The system may additionally or alternatively select a value
or other parameters of the reward to test the strength of customer
affinity to a business or willingness to visit a new business. For
example, rewards of different values may be presented to different
consumers to determine a value that will influence consumers to
visit a business the consumer does not routinely visit or has not
previously visited. For example, consumers who the system detects
to be dedicated customers of STARBUCKS.RTM. may be offered rewards
for DUNKIN DONUTS.RTM. of differing value (e.g., coupons with
varying discounts, or coupons offering free items for different
lengths of time) to determine how much of a reward will drive a
regular STARBUCKS.RTM. customer to visit a DUNKIN DONUTS.RTM.. The
reward value may additionally or alternatively be selected based on
a detected strength of characteristics of a consumer. For example,
a consumer who visits a STARBUCKS.RTM. often may be offered a
DUNKIN DONUTS.RTM. coupon of a greater value than a consumer who
visits a STARBUCKS.RTM. only occasionally.
[0159] FIG. 6A illustrates an example of a process that a consumer
analytics system may implement in some embodiments to carry out
some of the techniques described in this section. It should be
appreciated, however, that embodiments are not limited to
implementing the illustrative technique of FIG. 6A nor any other
particular technique.
[0160] Prior to the start of the process 600 of FIG. 6A, a consumer
analytics system may register multiple consumers and be configured
with one or more surveys or other tasks that may be distributed to
the consumers when characteristics of the consumers satisfy
conditions associated with the surveys or other tasks. The consumer
analytics system may register consumers and be configured with
tasks in any suitable manner, including according to techniques
discussed above in connection with FIGS. 1-2. The process 600 may
be carried out by the consumer analytics system to distribute a
survey to a particular consumer in response to inferring behaviors
in which the consumer is engaged and offering a reward to the
consumer in exchange for providing a response to the survey.
[0161] The process 600 begins in block 602, in which the consumer
analytics system analyzes location data for the consumer to detect
a behavior in which the consumer is currently engaging or has
engaged. The behavior may be inferred from the consumer's location
data in any suitable manner, including according to techniques
discussed above. The behavior that is detected may be any suitable
behavior, including any of the illustrative behaviors discussed
above, as embodiments are not limited in this respect.
[0162] After determining a behavior in which the consumer is or was
engaged, the consumer analytics system compares the behavior to the
conditions associated with each of the surveys (or other tasks)
that may be distributed to consumers by the system to determine
whether the behavior satisfies any of the conditions. In block 604,
in response to determining that a behavior satisfies a condition
for a survey, the system distributes the survey to the consumer.
The system may distribute the survey to the consumer in any
suitable manner. In some embodiments, the system may transmit at
least one message to a mobile device operated by the consumer,
soliciting the consumer to provide a response to the survey. In
such a case, the message may include any suitable content regarding
the survey, including questions included in the survey or a link to
a location at which the mobile device may retrieve the questions of
the survey.
[0163] In block 606, the system receives at least one message from
a computing device (e.g., a mobile device) operated by the consumer
that includes a response to the survey and, in block 608, selects
two or more rewards to present as options to a consumer, such that
the consumer may select one of the reward options to receive as
compensation for providing the response to the survey. The rewards
may be selected in any suitable manner, including according to
techniques discussed above. For example, in some embodiments, the
rewards may be selected based on a consumer profile or based on the
survey responses received in block 606. The information in the
consumer profile may include information about the consumer,
including information regarding locations previously visited by the
consumer, information inferred from location data like consumer
behaviors or preferences, or previous survey responses. The profile
information may include information on an identity (e.g.,
demographics) of the consumer or preferences of a consumer. Profile
information may additionally or alternatively include information
on behaviors of a consumer, such as businesses the consumer has
visited or has visited often, or personally-relevant locations for
the consumer.
[0164] Once the system selects the reward options in block 608, in
block 610 the system transmits the options to a device (e.g., a
mobile device) operated by the consumer, such as by transmitting at
least one message that includes information on the reward options.
In block 612, the system receives a notification from the device
that the consumer has selected one of the options, and the system
stores an identification of which reward option the consumer
selected. After the system stores the selection, the process 600
ends.
[0165] As a result of the process 600, the consumer analytics
system stores information regarding a reward that may be redeemed
by the consumer and the consumer is aware of the reward that was
provided to the consumer. Information regarding the reward may be
stored in one or more non-volatile memories of a mobile device of
the consumer (e.g., smartphone) and/or in one or more non-volatile
memories of a server of the consumer analytics system. The consumer
may be able to view rewards that are available to the consumer by
requesting that the mobile device display the rewards, after which
the mobile device may compile information on rewards from the
non-volatile memory/memories of the mobile device and/or of the
server and display the rewards to the consumer on a display of the
mobile device. Additionally, following the process 600, the
consumer may redeem the reward or other rewards earned by the
consumer. For example, the consumer may visit a business at which
the reward may be redeemed and communicate to the business and to
the consumer analytics system that the consumer would like to
redeem the reward. The consumer may communicate about the
redemption to the consumer analytics system in any suitable manner,
including by operating a user interface of the mobile device to
transmit a message to a server of the system that the consumer
wants to redeem a reward.
[0166] In some embodiments, a consumer analytics system may assist
a consumer with redeeming rewards, including by notifying a
consumer of a previously-earned reward that the consumer has an
opportunity to redeem. For example, when the consumer analytics
system determines that a consumer is or is predicted to soon be
within a threshold distance of a location at which the reward may
be redeemed, the consumer analytics system may provide a
notification to the consumer, such as via a mobile device operated
by the consumer, that the consumer has an opportunity to redeem a
reward. The consumer analytics system may do this in any suitable
manner. FIG. 6B illustrates a process 620 that may be implemented
in some embodiments by a consumer analytics system to provide a
notification of a reward to a consumer. The process 620 may be
carried out by a mobile device and/or a server of the consumer
analytics system, or by a combination of a mobile device and a
server. For example, in some embodiments, the process 620 may be
carried out by a mobile device, such that the mobile device carries
out each of the acts illustrated in FIG. 6B, including the
monitoring, comparing, and notifying. In embodiments in which the
mobile phone performs the process 620, the mobile phone may have
stored locally, in a memory of the mobile phone, information on a
consumer, such as a profile of the consumer. The information may
include any suitable information regarding a consumer, such as
characteristics determined by a consumer analytics system for the
consumer. Such characteristics may include, for example, behavior
characteristics determined by the consumer analytics system using
techniques described herein. In some such embodiments, the
information for the consumer may be determined by a server of the
consumer analytics system and transmitted to the mobile device. It
should be appreciated, though, that embodiments are not limited to
implementing the process of FIG. 6B on a mobile device. In other
embodiments, for example, a server may carry out the monitoring and
comparing and may instruct the mobile device (such as by
transmitting one or more messages to the mobile device) to perform
the notifying. Embodiments are not limited to any particular
[0167] The process 620 begins in block 602, in which the consumer
analytics system monitors a current location of a consumer over
time by receiving multiple units of location data for the consumer,
each identifying a geographic location at which the consumer was
present when the location data was generated. In block 624, the
consumer analytics system evaluates the location data to infer a
path that the consumer is taking and predict locations at which the
consumer will be within a threshold amount of time. This may be
done, for example, by evaluating the current location and a profile
maintained by the consumer by the consumer analytics system. The
consumer profile may identify behaviors in which a consumer has
previously engaged, habits of the consumer, paths previously
traveled by the consumer, and other information regarding prior
movements of the consumer. In block 626, the consumer analytics
system compares the consumer's current location and the locations
at which the consumer is predicted to be to the location(s) at
which each of the rewards that the consumer has earned and may
redeem.
[0168] Thus, in block 628, the consumer analytics system determines
whether the consumer is within a threshold distance of a location
at which a reward may be redeemed or is on a path that the system
predicts will take the consumer within a threshold distance of the
location. If so, then in block 630 the system presents a
notification to the consumer that the reward may be redeemed at a
nearby location.
[0169] The system may present the notification in any suitable
manner. In embodiments in which the process 620 is implemented by
one or more servers of a consumer analytics system, the server may
transmit one or more messages to a mobile device operated by the
consumer requesting that the mobile device display a notification
to the consumer that the reward may be redeemed at a nearby
location. The mobile device may then present the notifications on
the display screen and/or produce an alert sound, or take any other
action to present a message to a consumer via any suitable audio,
visual, or tactile user interface. Once the notification is
presented or if the system determines in block 626 that the
consumer is not and/or will not be within a threshold distance of a
location at which a reward may be redeemed, the process 620 returns
to block 622 in which the system monitors location data for the
consumer.
[0170] In some embodiments, the consumer analytics system may limit
a number of times that notifications of nearby reward redemption
opportunities are presented to a consumer, so as to avoid
overwhelming a consumer with the number of notifications presented.
Accordingly, in some such embodiments, the consumer analytics
system may in some cases refrain from presenting some notifications
even when the consumer is within a threshold distance of a location
at which a reward may be redeemed. The consumer analytics system
may determine whether to present a notification in any suitable
manner based on an evaluation of any suitable factors, as
embodiments are not limited in this respect. In some embodiments,
the consumer analytics system may determine whether to distribute a
notification based at least in part on a process for determining a
number of notifications that may cause a consumer to change a
behavior. For example, as discussed in greater detail below in
connection with FIGS. 13-14, the consumer analytics system may
determine a number of notifications to present to a consumer that
may prevent the consumer from becoming a lapsed customer of a
business or that may entice a consumer to switch from being a
customer of one business to another.
[0171] While an embodiment has been described in which distance is
used to determine whether to notify a consumer of an opportunity to
redeem a previously-earned reward, embodiments are not limited to
making such a determination based only on distance. Any suitable
criterion may be evaluated to determine whether to notify a
consumer regarding a reward that may be redeemed. For example, in
some embodiments, a current time of day and times of past visits to
a setting by the consumer and/or other consumers may additionally
or alternatively be considered in determining whether to present a
notification for a reward. For example, if a reward is redeemable
for a meal at a restaurant and the system detects that most visits
to the restaurant are in the evening, the system may refrain from
presenting a notification of the reward to the consumer in the
early morning. The system may refrain from presenting the
notification of the reward in this case even if the consumer is at
a location within a threshold distance of a location at which the
reward may be redeemed. Though, it should be appreciated that
embodiments are not limited to considering distance in determining
whether to present a notification, and that distance may not be
considered in some embodiments or in all cases.
Reward Availability and Redemption
[0172] In some embodiments, a consumer analytics system that offers
rewards may maintain information about consumers, which may
implement a "rewards wallet" that includes a list of the rewards
that the system has offered to the consumer but that the consumers
have not yet redeemed. FIG. 7 illustrates an example of an
interface, which may be displayed to a consumer via a mobile
device, by which the system may display a rewards wallet to a
consumer. The rewards in the rewards wallet may include any
suitable type of reward, including instant rewards that the
consumers have earned the value of, conditional rewards that the
consumers have earned the value of, and/or conditional rewards that
the consumer has not yet earned the value of. Thus, at any specific
moment, any of the rewards in a consumer's rewards wallet may be
immediately available for redemption or may be unavailable, based
on a variety of conditions, including rules, and/or qualifiers such
as those outlined below. In some embodiments, rewards not available
for immediate redemption (due, for example, to unfulfilled
conditions) may be identified as such in the rewards wallet. The
unavailable rewards may be identified through being displayed
differently, such as by being grayed out and/or being displayed
together with a message explaining why the reward is not currently
available. A message explaining why the reward is not available
may, for example, list unfulfilled conditions for the reward. Such
a display may be generated based on data about the consumer and/or
terms of rewards that may be stored in any suitable way.
[0173] As mentioned above, a reward may be associated with any
suitable conditions that govern the availability of the reward for
redemption by a consumer, after the consumer has been offered the
reward in response to the consumer taking a task. In some cases,
the availability of a reward for redemption may be determined by
location behaviors of a consumer and/or any other suitable inputs.
Reward availability may be tied, for example, to: [0174] The
current time of day, or a specific time of day/day of week. For
example, a sandwich coupon may be available only during the weekday
lunch period. [0175] The area around the consumer's current
location, or other defined region, for example their home or work
locations. [0176] A combination of these or other location-gated
and time-gated metrics
[0177] In some embodiments that operate with conditional rewards,
the system may provide a notification, for example via a message
displayed in a user interface of the consumer analytics system, a
text message (such as a message in SMS format), email, or other
form of communication, to a consumer regarding the upcoming
availability of a reward. For example, the consumer analytics
system may provide a consumer with a message that "tomorrow at noon
a lunch food reward will be available" or "a food reward is now
available for a restaurant within one mile of your current
location." Such a message may be presented to a consumer in cases
in which the system has not yet revealed the nature of the reward
to the consumer and cases in which the system has revealed the
nature of the reward. In some embodiments, the system may conceal
the nature of such a reward from a consumer, including in cases in
which the reward is included in the consumer's rewards wallet. For
example, a consumer may not be informed of a reward's value until
the reward is available. As another example, in some embodiments,
the system may not inform a consumer of the identity of the
redemption location for a reward until the consumer is within very
close proximity of the location. The system, based on analyzing the
consumer's location, may in some such embodiments lead the consumer
to the location at which the reward may be redeemed by presenting
the consumer with directions on a map, with a specific compass
direction or with a set of instructions. In other embodiments,
however, the system may provide a consumer with a message that
identifies the reward as well as the circumstances under which the
reward will be available soon.
[0178] In some embodiments, the system may display a list of
redemption locations for available rewards graphically on a map, so
that the consumer can easily identify the nearby locations at which
available rewards may be used.
Rewards Redemption Verification and Fraud Prevention
[0179] In some embodiments, each time a consumer redeems a reward,
the consumer analytics system may record into storage the details
of the redemption. The record of the redemption may include any
suitable information, as embodiments are not limited in this
respect. For example, the record may include information such as:
[0180] Where--latitude, longitude as well as the name and/or other
identifier of the store [0181] When--a timestamp of when the reward
was redeemed [0182] Device details--If a consumer's device is used
to redeem the reward (e.g., via a rewards application and/or
consumer analytics application executing on a smart phone), the
type of phone running the application, as well as which operating
system version, etc. [0183] Point-of-Sale information--Any suitable
details from the point of sale system, such as the items purchased,
the ID of the employee servicing the consumer, etc.
[0184] In some embodiments, when this data is recorded, the data
may be compared with the store's computing systems to verify that
the store's systems show that a similar transaction has taken
place. This verification can be done either at the time of the
transaction or at a later date/time. The latter case may be easier
for stores whose systems make real time access difficult.
[0185] In some embodiments, the consumer analytics system may
periodically or occasionally process records of rewards redemption
to identify any potential fraud, such as by searching for patterns
that indicate fraud. Potential fraud may be identified based on
data indicating unlikely events, such as: [0186] A consumer
redeeming rewards far away from where location data for the
consumer indicates the consumer was at the time. [0187] A consumer
redeeming a significant and/or large number of rewards within a
small time period.
[0188] Potential fraud may also be identified based on
probabilistic indications of fraud, such as by identifying: [0189]
One employee who has significantly higher rates of rewards
redemption than other employees at a given store. [0190] One store
location that has significantly higher rates of rewards redemption
than comparable stores. [0191] One specific make, model, and OS
version of phone that has significantly higher numbers of rewards
than its market share would predict it should. [0192] Any
combination of similar metrics
[0193] When potential fraud is identified, the system may issue an
alert to the business at which the reward is redeemable (e.g., by
issuing an alert to a central office for a chain or to a particular
location of a chain) to notify the business of the potential fraud
and enable investigation. The alert may include any suitable
information, as embodiments are not limited in this respect. For
example, a report of the suspicious activity and related
information (store locations involved, log of redemptions, etc.)
may be generated automatically by the consumer analytics system and
sent to the store as part of the alert.
[0194] In embodiments that implement techniques for identifying
fraud or determining whether a redemption of a reward is
legitimate, any of the techniques discussed above or any
combination of techniques may be implemented, as embodiments are
not limited to implementing any particular process for detecting
fraud or redemption legitimacy. FIGS. 9-11 illustrate examples of
three techniques that may be used in some embodiments for
determining whether a reward redemption is fraudulent/illegitimate
or is legitimate.
[0195] FIG. 9 illustrates a process that may be used by a consumer
analytics system for evaluating legitimacy of rewards in a
non-real-time manner. Prior to the start of the process 900, the
consumer analytics system may be configured with conditions, tasks,
and rewards, and has presented rewards to consumers in exchange for
consumers completing a task (e.g., providing a response to a
survey). Such configuration may be carried out as discussed above
or in any other suitable manner. For example, the consumer
analytics system may be provided with and subsequently store
information specifying multiple tasks and, for each, one or more
conditions that trigger a request to a consumer to perform the task
and reward(s) that may be granted for performance of the task. Once
provided with and storing such information, the consumer analytics
system may process the information and transmit and receive
electronic messages in accordance with the information.
[0196] The process 900 begins in block 902, in which the consumer
analytics system receives notifications of reward redemptions from
mobile devices operated by consumers. The mobile device operated by
the consumer may have an interface that displays previously-earned
rewards to consumers and enables consumers to notify the system
that the consumer is redeeming the reward. The notification may
include any suitable information, including a unique identifier for
the reward being redeemed and/or a location at which the reward is
being redeemed. The unique identifier may be, for example, a serial
number or bar code number for the reward that identifies the
specific reward to the consumer analytics system and to the
business(es) at which the reward may be redeemed. In some
embodiments, as discussed above, the notification from the mobile
device may additionally include location data indicating a current
location of the mobile device at a time that the reward was
redeemed. The notification may additionally or alternatively
include any other suitable information regarding the redemption,
including responses from the consumer to questions such as an
amount spent at the business on the visit during which the reward
was redeemed, what items or services were purchased, the consumer's
subjective impressions of the business on that visit, or any other
suitable information.
[0197] After receiving the notifications from the mobile devices,
the consumer analytics system may aggregate redemptions for each
business at which rewards are redeemed. The redemptions that are
aggregated may be all redemptions for which notifications were
received in block 902 or, in some embodiments, redemptions that the
consumer analytics system has determined to be legitimate. In
embodiments in which the system determines whether a reward
redemption is legitimate, the system may determine whether a
redemption is legitimate in any suitable manner, including using
any of the illustrative techniques discussed above. In some
embodiments, for example, in response to receiving a notification
from a mobile device that a consumer is redeeming a reward, the
consumer analytics system may review a list of rewards that were
previously provided to that mobile device and remove a reward from
the list.
[0198] In block 904, the consumer analytics system compiles a list
of reward redemptions at a business. The list may include any
suitable information about a reward redemption, including the
unique identifier for a reward, a location of redemption, a time of
redemption, and/or another information that was included in a
notification about the redemption. In block 906, after the list is
compiled, the consumer analytics system sends the list to a
computing device associated with the business for which the list
was compiled. The consumer analytics system may send the list in
any suitable manner, including by transmitting one or more messages
including the list. After the list is sent, the process 900
ends.
[0199] As a result of the process 900, the computing device
associated with the business has a list of reward redemptions that
may be considered legitimate and may compare the list to
information regarding rewards that were previously redeemed at the
business to determine whether any of the previous redemptions were
or appear to be fraudulent. The businesses may determine
redemptions that appear to be fraudulent in any suitable manner.
For example, the system may review the list to determine whether
there is evidence of suspicious patterns in the list, such as a
higher number of redemptions at one branch than others or a higher
number of redemptions at some times than others. If the business
identifies fraudulent redemptions, the business may then take any
suitable action to attempt to undo or mitigate the fraud.
[0200] In some cases, an option a business has for later undoing or
mitigating fraudulent reward redemptions may be limited. It may
therefore be advantageous in some embodiments to determine during a
transaction in which a reward is to be redeemed whether the
redemption is legitimate or fraudulent. FIGS. 10-11 illustrate a
technique that may be used in some embodiments for a real-time
process for determining whether a reward redemption is
legitimate.
[0201] FIG. 10 illustrates a process that may be carried out in
some embodiments by a server of a consumer analytics system to
determine, in real time during a transaction in which a reward is
presented for redemption, whether the redemption is legitimate.
Prior to the start of the process 1000, the consumer analytics
system may be configured with conditions, tasks, and rewards, and
may have presented rewards to consumers in exchange for consumers
completing a task (e.g., providing a response to a survey).
[0202] The process 1000 begins in block 1002, in which the server
of the consumer analytics system receives a notification from a
mobile device operated by a consumer that the consumer would like
to redeem a reward. The notification received in block 1002 may
include any suitable information, including exemplary types of
information discussed above in connection with FIG. 9. In block
1004, after receiving the notification from the consumer (and, in
some embodiments, after confirming legitimacy of the redemption in
any suitable manner) such as using techniques described above, the
server of the consumer analytics system transmits a message to a
computing device associated with the business at which the consumer
is redeeming the reward. The message may include any suitable
information about the reward being redeemed. For example, the
message may include a unique identifier for the reward. Once the
message is transmitted in block 1004, the process 1000 ends.
[0203] FIG. 11 illustrates a process that may be carried out in
some embodiments by a point-of-sale (POS) terminal or other
computing device associated with a business at which a consumer
desires to redeem a reward. The process 1100 of FIG. 11 may be used
by the POS terminal to determine, in real time during a transaction
in which a reward is presented for redemption, whether the
redemption is legitimate. The process 1100 begins in block 1102, in
which the computing device receives input regarding a reward that a
consumer would like to redeem as part of a purchase or other
transaction with the business. The input that is received in block
1102 may include any suitable information about the reward
including, for example, a unique identifier for the reward like a
serial number or bar code. After the POS terminal receives the
input regarding the reward in block 1102, the POS terminal waits
for a confirmation message from the consumer analytics system that
indicates that the reward redemption is legitimate. This message
may be transmitted by the system in response to the consumer
notifying the system that the consumer is redeeming a reward, such
as following the process 1000 of FIG. 10. Accordingly, in block
1104, the POS terminal determines whether it has received a message
from a server of the consumer analytics system that identifies the
reward redemption as legitimate. The notification may, as discussed
above, include a unique identifier for the reward, which the POS
terminal may compare to the information received in block 1102 to
determine if there is a match and, thereby, determine whether the
reward is legitimate. In block 1106, if the message from the server
indicates that the reward is legitimate, the redemption is flagged
as legitimate and the POS terminal allows the purchase and the
reward redemption to continue. If, however, no message is received
from the server or the message from the server indicates that the
redemption may be illegitimate, then the POS terminal flags the
redemption as illegitimate and does not permit the redemption.
[0204] After the redemption is flagged as either legitimate or
illegitimate in blocks 1106, 1108, the process 1100 ends.
[0205] While the example of FIGS. 10-11 is described as including a
"push" notification from a server regarding a legitimate
redemption, in some embodiments the system may operate with a POS
terminal (or other computing device for a business) "pulling"
redemption verifications from the consumer analytics system. In
such a "pull" embodiment, when the POS terminal receives input
regarding a reward redemption, the POS terminal may query the
consumer analytics system for information on whether the redemption
is legitimate.
Rewards Redemption Analytics
[0206] In some embodiments, each time a reward is redeemed by a
consumer, in addition to or as an alternative to the information
described above for verification and fraud detection, the system
may record into storage details of the redemption that may be
useful for consumer analytics and business intelligence. The
details regarding the redemption may include any suitable
information. For example, the details may include information such
as: [0207] Demographic information and/or other details regarding
the consumer [0208] Amount spent at the business on the visit in
which the reward is redeemed at the business [0209] Items purchased
in that visit [0210] Location analytics of that visit, including
stores and other POIs driven past, at which personally-relevant
location (home, work, etc.) the consumer's trip began, etc.
[0211] The consumer analytics system may compute any suitable
consumer analytics based on this stored information. The consumer
analytics system may then present the information to any suitable
party, such as one or more businesses at which the reward was
redeemable. The system may compute and present, for example,
information that allows a retailer/restaurant (or any other
category of POI at which a reward is redeemable) to understand how
effective each reward is at encouraging consumers to visit a
business. Such information may include: [0212] Average and median
times between reward earned and reward redeemed by a consumer
[0213] Average and median amount of money and/or time spent at the
business on visits in which the reward is redeemed [0214] Average
and median different amount of money and/or time spent by a
consumer at the business in the N days after the reward is
earned.
[0215] Additionally or alternatively, the consumer analytics system
may determine a baseline average amount of time and/or money spent
at a business, collected via surveys or any other source, and use
this baseline to compute the additional revenue earned by the
business that may be attributed to the reward. The system may then
provide the number identifying the additional revenue earned by the
business to the business as a specific measure of the reward's
success.
[0216] One method the consumer analytics system may use, in some
embodiments, to compute a baseline average amount of money and/or
time spent is to compare the amount of time and/or money consumers
who redeemed rewards had spent at the given POI in the period
before the reward was earned. The consumer analytics system may
then compare the amount spent by the consumers before the reward
was earned to the amount spent after the reward was earned. Another
method that the consumer analytics system may use is, for the time
period in which the rewards were redeemed, comparing one or more
characteristics of consumers who redeemed a reward with those who
earned the reward and did not redeem the reward. Any identified
trends in differences in characteristics between consumers who
redeemed a reward and those who did not may also be used as a
measure of the additional revenue accounted for by the reward being
given.
[0217] In addition, in some embodiments, the system may calculate a
total gross profit on the consumers' visits. The system may, in
some such embodiments, compute this total gross profit by
subtracting the cost of the products purchased in each consumer's
visit from the amount of money spent by consumer in each visit. The
system may then use the total gross profit to compute a specific
Return On Investment (ROI) for the reward, such as by comparing the
cost of the reward with the total gross profit. The system may then
provide the ROI to a business at which the reward was redeemable to
provide the business with a measure of the effective profit the
rewards program.
[0218] FIG. 12 illustrates an example of a technique that may be
used to determine a value of a reward to a business, including a
return on the reward or an ROI of a reward. It should be
appreciated, however, that embodiments may determine a value of a
reward in any suitable manner and are not limited to implementing
the techniques described in connection with FIG. 12.
[0219] A "return" on a reward may be an expression of a value that
the business has received in exchange for accepting redemptions of
the reward. A return may be expressed in any suitable manner,
including in any suitable manner relating to consumers'
interactions with the business. For example, a return may be
expressed in terms of a number of consumers who are visiting the
business that can be attributed to the reward, an amount spent by
consumers that can be attributed to the reward, a change in
frequency of visits that can be attributed to the reward, or any
other suitable metric relating to consumers' interactions with a
business. In some cases, a return for a reward may be expressed as
a return on an investment by the business, or ROI. In this case,
the ROI may be a value received by the business that accounts for
an investment by the business in the reward. A business may invest
in a reward in any suitable manner. For example, a business may
spend money upfront to offer the reward, allocate personnel to
prepare a reward to be offered or process redemptions of a reward,
or receive a lower profit margin on goods as a result of discounts
or other types of rewards offered. A return on an investment by a
business may be calculated through accounting for an investment in
any suitable manner. For example, where a business has made a
monetary investment in a reward, and a return is valued in a dollar
amount, the ROI for the reward may be calculated by subtracting the
investment from the dollar amount of the return.
[0220] Prior to the start of the process 1200 of FIG. 12, the
consumer analytics system is configured with conditions, tasks, and
rewards, and has presented rewards to consumers in exchange for
consumers completing a task (e.g., providing a response to a
survey). The process 1200 begins in block 1202, in which the
consumer analytics system monitors consumers' interactions with a
business before a reward that is redeemable at the business is
offered to the consumers and/or before the reward is redeemed by
the consumers. The consumers' actions that are monitored in block
1202 may include any suitable characteristics relating to the
consumers' patronage or lack of patronage at a business, including
amounts spent at the business or behaviors like frequency of visits
to the business. In block 1204, the consumer analytics system
further monitors consumers' interactions with the business
following redemption of the reward. The monitoring of blocks 1202,
1204 may be performed by the consumer analytics system in any
suitable manner, including by evaluating location data for each of
the consumers and/or survey responses provided by each of the
consumers.
[0221] In block 1206, the consumer analytics system computes a
value of the reward to the business based at least in part on a
comparison of interactions with a business before and after
redemption of the reward by each of the consumers. The comparison
may be done in any suitable manner. For example, in some
embodiments, the system may determine whether a particular
characteristic (e.g., visit frequency) changed from one value
before redemption to another after redemption. As another example,
the system may determine whether a consumer has deviated from a
pattern of behavior in a manner that indicates that the reward may
have affected the consumer's behavior. For example, if a consumer
has a pattern of visiting one coffee shop in the morning for
coffee, but visits another coffee shop once or multiple times after
receiving a reward for that other coffee shop, the deviation may be
indicative of an effect of the reward on the consumer. In turn,
this may be indicative of a value of the reward to the second
coffee shop. After the value of the reward to the business is
computed in block 1206, the consumer analytics system stores the
value and the process 1200 ends. Following the process 1200, the
system may provide the computed value of the reward to the business
and the business may use this information in any suitable manner,
including, for example, determining whether to continue to offer
the reward or whether to change a value of a future reward that
will be offered.
[0222] In addition to determining a change in consumer behavior or
other characteristics following a reward, in some embodiments the
consumer analytics system may determine a "stickiness" or
persistence to the change in consumer behavior. A reward may, in
some cases, be more valuable to a business if the reward causes a
change in behavior that is long-lasting or permanent, rather than
fleeting. Accordingly, in some embodiments in which a change in
characteristics is determined by comparing characteristics of
individual consumers or groups of consumers before and after an
event or between a group that has redeemed a reward and a group
that has not (a "control group"), the system may perform multiple
comparisons. Each of the comparisons performed by the system may be
done using characteristics determined from location data, survey
responses, or other information received relating to consumers at
different points in time, such as one day after a reward is
redeemed, three days after, a week after, a month after, etc. By
comparing these later characteristics, a change over time may be
determined, which may enable the system to observe how a change
immediately following redemption of a reward changes over a period
of time following the reward. If, during or after the period of
time, the characteristics of the consumers have values that are
closer to the "before" or "control group" values than to the values
immediately following the reward, a reward may be considered not to
have a lasting effect on consumers. A result of a comparison may be
provided to businesses to inform them of the persistence of any
change created by a reward, which may include any suitable
information about the persistence. For example, in some embodiments
the consumer analytics system may determine an average span of time
until values of an individual consumer's changed characteristics
become more similar to values of the "before" or "control group"
characteristics than to the values of the consumer's
characteristics immediately following redemption of the reward.
[0223] In some embodiments, such techniques for determining
persistence of a change in behavior (or other characteristics)
relating to a reward may additionally or alternatively be used to
determine a value to offer in a reward to entice a customer of one
business to become a customer of another, or to determine whether a
reward was successful in regaining a lapsed customer of a business
or preventing a dissatisfied customer from becoming a lapsed
customer. FIGS. 13-14 illustrate examples of techniques that may be
used to entice a customer away from one business or prevent a
customer from lapsing.
[0224] The process 1300 of FIG. 13 may be used by a business to
entice consumers that are customers of a competitor into becoming
customers of the business. The consumers may be enticed to stop
visiting the competitor and begin visiting the business, or may be
enticed to visit the competitor less frequently and the business
more frequently. Prior to the start of the process 1300, a number
of consumers may register with the consumer analytics system and
the system may evaluate location data for the consumers to
determine one or more characteristics of the consumers. The process
1300 begins in block 1302, in which the system identifies two
consumers that are customers of at least one competitor business
that is a competitor of a business that desires to recruit new
customers through the use of one or more rewards. The two consumers
may visit the competitor business(es) in addition to visiting the
business, or may visit the competitor business(es) and not visit
the business. In some cases in which the consumers visit both the
competitor(s) and the business, the consumers may visit the
competitor(s) more frequently than the business.
[0225] In block 1304, the consumer analytics system selects two
rewards that are to be offered to the consumers identified in block
1302. The rewards may be selected such that one has a higher value
than another. For example, one reward may be for a $2 discount on a
purchase and another for a $5 discount, or one for a fixed value
discount and one for a free product/service, or any other
combination of reward values. By selecting a rewards that have
different values, the consumer analytics system can observe the
impact of the different values on the consumers and determine
whether what reward value reward will have an intended effect on
consumers. By doing so, the system can determine that a lower-value
reward will have an intended effect and thereby lower costs for a
business that may otherwise offer a higher-value reward, or
determine that a lower-value will not have an intended effect and a
higher-value reward is needed, thereby preventing the business from
investing in a reward that will not satisfy a goal.
[0226] In block 1306, the system offers the two rewards determined
in block 1304 to the consumers and determines characteristics of
consumers before and after redemption of the rewards. The
characteristics of the consumers before redemption may include
characteristics of the consumers before the rewards were offered.
Any suitable characteristics of the two consumers may be evaluated,
as embodiments are not limited in this respect. In the embodiment
of FIG. 13, the characteristics that are monitored may include
characteristics (e.g., behavior characteristics) that relate to the
consumers' interactions with the business. Characteristics of the
interactions with the business may include any suitable information
about interactions, such as frequency of visits or time between
visits, length of visits, amount spent during visits, patterns of
past visits and deviations from patterns, or other characteristics.
In block 1308, the consumers' behaviors (and/or other
characteristics) relating to the business before and after
redemption of the reward are compared to determine whether there
was any change before and after the reward was redeemed.
[0227] In block 1310, the system determines a reward value that
will entice a customer to visit the business based in part on the
result of the comparison in block 1308. The system may determine
the value in block 1310 based on a result of the comparing of the
effect of the two rewards on the two consumers. In some
embodiments, the two rewards may be offered to other consumers in
addition to the two that were offered the rewards in block 1306. In
these embodiments, the effect of the rewards on multiple different
consumers may be evaluated. In particular, in block 1310, the
system may compare effects of impacts of the two rewards on
multiple other consumers that receive one or both of the rewards.
The system may then aggregate the results of the comparisons for
the multiple consumers to determine a reward value that will entice
a customer. The reward determined in block 1310 may be a reward
having a value optimized to produce a desired effect on consumers,
which in the example of FIG. 13 may be to entice consumers to
become consumers of a business. The value may, in some cases, be a
value that has a lowest cost to the business or otherwise be the
lowest value that still produces the desired effect in consumers
that receive the reward. The reward determined in block 1310 may
have a value that is higher, lower, or between the values of the
rewards selected in block 1304. For example, if both the
lower-value reward and the higher-value reward selected in block
1304 are determined to have the desired effect on the consumers, a
value determined in block 1310 may have a lower value than the
lower-value reward. This may be done to determine a minimum cost
for rewards that would have the desired effect, which may increase
an ROI of a reward to a business. Similarly, to determine a value
of the reward in block 1310, if the lower-value reward selected in
block 1304 is determined not to have the desired effect on
consumers while the higher-value reward selected in block 1304 has
the desired effect, the value determined in block 1310 may be a
value between the two rewards. And if both the lower-value reward
and the higher-value reward are not determined to have the desired
effect on consumers, the value determined in block 1310 may be
higher than the values determined in block 1304.
[0228] In some embodiments, in block 1310, as part of determining a
reward value that will entice a consumer, the consumer analytics
system may determine one or more other rewards based on the
comparison of block 1308 and distribute these rewards to test
effects of these rewards on consumers. For example, the consumer
analytics system may, in block 1310, determine third and/or fourth
rewards and distribute the reward(s) to consumers. The values of
the third and fourth rewards may be set based on the comparison of
block 1308 and may be designed to further ascertain the optimized
value. Through repeatedly identifying reward values and determining
an effect of the reward values on consumers, the system may
identify the "optimized" value mentioned above or otherwise
determine a value that will produce a desired effect.
[0229] Once the value is determined in block 1310, the process 1300
ends. Following the process 1300, the consumer analytics system may
store in a non-volatile memory of the system a value of the third
reward determined in block 1310. In addition, following the process
1300, the system may provide information to the business that
includes the identified reward value. In some cases, the business
may then elect to offer a reward having the value. To do so, the
business may request that the system be configured to provide the
reward to consumers in exchange for completion of tasks by the
consumers. The system may be configured in any suitable manner,
including according to techniques described above. Once configured,
the system may also, in some embodiments, begin offering to
consumers a reward having that value, such as by offering that
reward to consumers in exchange for finishing tasks.
[0230] The process 1400 of FIG. 14 is similar in some ways to the
process 1300 of FIG. 13 and may be used by a business to prevent
customers from lapsing by determining a value of a reward to offer
consumers to prevent a lapse or recover a lapsed customer. A lapsed
customer of a business may be a customer that no longer visits a
business that the consumer previously visited or visits a business
with a lower frequency than the consumer previously visited the
business. In embodiments, a lapsed customer may be determined from
evaluating location data for that customer. As in examples above,
the location data may include data indicating geographic locations
at which a consumer was present, which may be provided by a mobile
device operated by the consumer. Thus, in some embodiments, a
lapsed customer may be determined through analyzing location data
provided by a mobile device operated by the lapsed customer.
[0231] The process 1400 begins in block 1402, in which the consumer
analytics system identifies two consumers who are customers of a
business and have lapsed or are predicted to lapse (and thereby may
be considered at risk of becoming lapsed). In some embodiments, the
system may determine that a consumer has lapsed or is predicted to
lapse by evaluating frequency with which each consumer visits a
business over time, and determine whether a consumer has stopped
visiting a business or is visiting a business less frequently. For
example, the consumer analytics system may determine from observing
locations visited by a consumer that there is more than a threshold
difference in the previous frequency of visit and the current
frequency of visit to a business. The consumer analytics system may
identify consumers who are at risk of becoming lapsed by evaluating
locations visited by the consumer or evaluating survey responses
provided by the consumer. For example, the system may identify a
trend or pattern in visits by a consumer that the system projects
will lead to the consumer lapsing in visits to a business. As
another example, the system may evaluate satisfaction of the
consumers in their interactions with the business. Information
regarding satisfaction of a consumer may be provided by the
consumers in any suitable manner, including in response to surveys
distributed by the consumer analytics system. Consumers that the
system determines are dissatisfied may be identified as consumers
that are predicted to lapse.
[0232] In response to determining that two consumers that are
lapsed customers, either by identifying customers that have already
lapsed or are predicted to lapse, in block 1404 the system selects
two rewards to offer to the consumers that are redeemable at the
business, one having a higher value than the other. In block 1406
the system sends each of the consumers one of the rewards and
determines characteristics of consumers before and after redemption
of the rewards. The characteristics of the consumers before
redemption may include characteristics of the consumers before the
rewards were offered. Any suitable characteristics of the two
consumers may be evaluated, as embodiments are not limited in this
respect. In the embodiment of FIG. 14, the characteristics that are
monitored may include characteristics (e.g., behavior
characteristics) that relate to the consumers' interactions with
the business.
[0233] In block 1408, the system compares characteristics of the
consumers' interactions with the business. In block 1410, based at
least in part on the comparison, the system determines a reward
value. The reward value that is determined may be a value that will
drive a customer to visit a business of which the consumer is a
lapsed customer. Additionally or alternatively, the value may be
one that will prevent a consumer from lapsing. In either case, the
desired effect that a reward is to have on a consumer's behavior
may be to increase a visit frequency of the consumer to a business.
The reward may be successful when a visit frequency increases more
than a threshold or threshold percent, or a visit frequency of a
consumer increases to a previous visit frequency for that consumer,
or according to any other suitable measure of an amount of increase
in a visit frequency. The system may perform the comparison and
determination of blocks 1408, 1410 in any suitable manner,
including according to techniques described above in connection
with blocks 1308, 1310 of FIG. 13.
[0234] The techniques of FIGS. 13-14 were discussed separately,
however, in some embodiments, a combined technique may be used to
determine a value of a reward that will entice a lapsed customer
(which may be a customer that has already lapsed or a customer that
is predicted to become a lapsed customer) of one business to visit
a competitor. The combined processor may be performed in a manner
that should be appreciated from the foregoing discussion.
[0235] It should further be appreciated that while, for ease of
description, the examples of FIGS. 13 and 14 are described in
connection with evaluating the effects of two rewards on consumers,
one having a higher value than the other, embodiments are not
limited to evaluating only two rewards. Embodiments may distribute
any number of rewards having different values to evaluate the
effect of different value rewards on consumers and determine a
reward value that produces a desired effect.
[0236] While the techniques of FIGS. 13-14 were described above in
connection with determining a value of a reward that will have a
desired effect on consumers, it should be appreciated that the
techniques may be used to determine other parameters of a reward.
For example, as discussed above, in some embodiments the consumer
analytics system may vary a number of notifications a consumer
receives that a reward may be redeemed nearby. In some such
embodiments, the system may vary a number of notifications that are
provided to consumers as part of enticing a competitor's customers
to visit a business or encouraging lapsed customers to visit a
business.
[0237] For example, the system may provide reward notifications to
some consumers with one frequency and reward notifications to other
consumers with a higher frequency, and determine a frequency of
notifications that has the desired effect.
Comparing Rewards
[0238] In embodiments in which the system calculates an ROI of the
type described above, the measure of ROI may be computed
individually for each type of reward given out. The ROI on each
reward may then be compared with the ROI of one or more other
rewards, such as multiple rewards offered by the same business, to
determine which reward provides the best ROI. Determining which
reward provides the best ROI may provide an indication of which
reward the system should offer more often in the future or
otherwise expand on.
[0239] In some embodiments, ROIs may also be used as a basis for
the system, an administrator of the system, or a business at which
the reward is redeemable to change the details of a reward. These
changes may be made by manual input or, in some embodiments, may be
performed automatically by the system based on criteria programmed
into the system. For example, if one reward happens to have a
significantly lower ROI than others, a cost of the reward may be
lowered by changing the reward to offer a lower discount or to be
limited to products for which the business has a higher margin.
FIG. 8 is an example analytic chart of showing average Return On
Investment (in dollars) for four different reward types. As shown
in FIG. 8, the "Hat Giveaway" and "Free Size Up" rewards have a
negative ROI and are costing the business each time these rewards
are redeemed. Therefore, when the ROI for these rewards programs is
evaluated, this may trigger the system and/or a person associated
with the consumer analytics system and/or a business at which the
reward may be redeemed to change the reward.
Avoiding Skew
[0240] In some embodiments, the consumer analytics system may also
determine how much, if any, skew is being introduced into the data
collected from consumers as a result of rewards influencing the
behavior of the consumers. While businesses may be interested in
how much a reward can or does influence consumer behavior, if a
consumer's behavior is influenced too much by a particular reward
or by multiple rewards offered to a consumer, information regarding
the consumer's behavior may not be valuable to the business. For
example, if a consumer's travel routes are influenced too much by
rewards, information regarding that consumer's travel routes may
not be valuable to businesses. In some embodiments in which the
consumer analytics system computes or estimates skew introduced by
rewards, the system may identify, for a set of consumers related in
some way (e.g., consumers who are all customers of a business, or
who share a demographic characteristic), characteristics (e.g.,
behaviors) of consumers who have been offered rewards and
characteristics of consumers who have not been offered rewards. The
system may then compare those characteristics to identify any
differences between the consumers of the set that may be indicative
of skew. Additionally or alternatively, in some embodiments that
compute or estimate skew, the system may compare characteristics
determined for a consumer before and after one or more rewards were
offered to determine whether the characteristics differ greatly,
which may be indicative of potential skew.
Linking Other Data
[0241] In some embodiments, each reward a consumer may earn may be
linked by the consumer analytics system to the consumer's loyalty
cards, credit or debit card transaction log data, social media
data, and any other data source for the consumer.
[0242] By connecting these data sources onto the rewards, the
consumer analytics system may compute additional metrics and
analytics for the consumer. For example, the system may be able to
obtain information about the consumer from these sources rather
than others (e.g., surveys) to provide information on how much and
what the consumer bought on each visit.
Published to Social Media
[0243] As part of establishing a consumer account with the consumer
analytics system, the consumer analytics system may prompt a
consumer to identify social media services, such as FACEBOOK.RTM.,
TWITTER.RTM., and FOURSQUARE.RTM., that the consumer uses. If the
consumer identifies social media services, and provides permission
to do so, the system may publish to these services information
about the rewards redeemed by the consumer. For example, the system
may publish information regarding rewards to the consumer's social
media feed at various stages, such as by publishing a post to a
FACEBOOK.RTM. account each time the consumer earns a reward,
telling the consumer's friends about the reward.
Survey Market System
[0244] The system described above may, in some embodiments, provide
incentives to consumers in exchange for participating in market
research. Retailers, restaurants, and other businesses or
organizations that may be points of interest in the consumer
analytics system can use analytics produced by the system and
therefore may pay for access to the system and/or the analytics. In
this way, the system may function in some embodiments as a two
sided market: the consumers on one side, providing information or
access to information in exchange for rewards, and the businesses
on the other, acting as the customer of the system to purchase
information on consumers.
[0245] One method for creating a business around this system is to
charge businesses for the ability to field survey questions and for
access to survey response and location analytics data. The fee
could be a set amount per survey, per survey response, or per
store, or in any other manner.
[0246] Another approach may be more effective in some environments
because, to keep the market functioning well in these environments,
the number of requests for survey responses may have to be kept in
balance. If there are too few tasks and rewards, consumers who
participate in the system may get frustrated and stop performing
tasks in exchange for rewards. If there are too many tasks and
rewards, either consumers may be feel they are being excessively
bothered, or consumers may complete only some tasks such that some
businesses will not receive as many responses as may be needed to
make informed decisions based on information generated from the
responses. Thus, in some embodiments that may be used in these
environments, stores are charged a fee and the system operator will
use part of the fee to cover the costs of the rewards for
consumers. In other embodiments, stores will provide (or defray the
cost of) rewards for their own stores. As described elsewhere, in
many cases, properly targeted and optimized rewards will actually
have a positive return on investment as the reward may bring the
consumer back into the store. To maintain a balance in the system,
in these embodiments, the businesses can be charged dynamic prices
based on a few different factors including: [0247] Which POIs are
involved [0248] How quickly responses are needed [0249] Which kinds
of consumers responses are needed from
[0250] In some embodiments, to facilitate such dynamic pricing, the
system may include an interface through which electronic
information may be provided by businesses to indicate what prices
they are willing to pay for a request to perform a task, in a
particular context, to be delivered to a consumer. This information
may be provided through the interface dynamically, as the system
detects opportunities to request a task of a consumer, or may be
provided in terms of criteria or an algorithm that may be evaluated
by the system to determine a price to request a task in a
particular context. Regardless of how this information is provided,
the system may be programmed to detect opportunities to request
tasks and evaluate information received from one or more businesses
to determine which task is requested in a particular context.
[0251] The system can balance the requests for survey responses or
other tasks with the predicted number of consumers that will visit
various stores and determine a price for each survey response
request. As the predicted likelihood of a deficit in survey
responses increases, the system may increase the price for those
kinds of responses Likewise, if the number of rewards which are
being supported by stores dwindles, the system may encourage
businesses to add more by reducing the price for participation by
the businesses.
[0252] Additionally or alternatively, as multiple different stores
use the system, there may naturally be competition for survey
responses. A business interested in data on its competitors may
want to gather analytics regarding its competitors. In some such
cases, one of the competitors may also be a customer of the system
and also want these analytics. In some embodiments, therefore,
businesses may be given the ability to block competitors from
obtaining information generated by consumers regarding the
business. The system may enable businesses to block access by
charging a fee for the business to block access by other businesses
in a specified market area or by other specified businesses. In
other embodiments, the system may administer an auction in which
businesses are allowed to buy all of the analytics generated based
on information regarding any subset of POIs, such as all businesses
in a certain market area. In some such other embodiments, a
business may thus be able to purchase all of the analytics
generated based on information regarding its own stores and
regarding its competitors, which would prevent competitors from
learning about consumer behavior across the whole market area. For
example, a burger restaurant may be able to purchase in such an
auction exclusive access to data from all hamburger restaurants for
which the system collects information from consumers and/or
generates information, which would prevent all other hamburger
restaurants (including the hamburger restaurant's competitors) from
obtaining this information. In embodiments in which the system
operates an auction, a set of information (e.g., survey responses)
on which a business may bid may be segmentable in any suitable
manner. For example, the system may segment, or the business may be
able to segment, the information based on characteristics of the
consumers, including by location behaviors exhibited by those
consumers. For example, a business may be able to bid on (and
receive exclusive access to) responses from consumers who had
driven past a Tofu Tiles restaurant and then visited a Shortstack
Pancake stand. As another example, a business may be able to bid on
(and receive exclusive access to) all consumers who have a driving
commute which goes past at least two hamburger restaurants.
[0253] In still other embodiments, the system may permit different
businesses to purchase copies of the same survey responses or
location analytics data collected and/or generated regarding the
businesses. In some such embodiment, for example, two separate
hamburger restaurant chains may be able to receive all of the
information collected and/or generated by the system regarding any
hamburger restaurants.
[0254] FIGS. 15-17 illustrate techniques that may be implemented in
some embodiments by a survey market system operating in accordance
with the foregoing concepts. Such a survey market system may be a
component of a consumer analytics system in some embodiments. The
survey market system may be implemented on one or more servers and
may electronically communicate via one or more networks (e.g., the
Internet) to receive from purchasers requests to electronically
distribute surveys to consumers using techniques discussed above
and to electronically distribute information resulting from the
surveys to the purchasers. It should be appreciated, however, that
embodiments are not limited to implementing a survey market system
according to any of these illustrative techniques, nor limited to
implementing a survey market system in any particular form.
[0255] FIG. 15 illustrates a process 1500 that may be used in some
embodiments for offering survey response opportunities for sale to
one or more purchasers in a survey market system that is associated
with a consumer analytics system that distributes surveys. A survey
response "opportunity" may be, as discussed in greater detail
below, an expectation of the system that, if a survey is
distributed, a response will be received and represents a chance
for a potential surveyor to request that a survey be distributed on
its behalf. Such an opportunity may reflect a number of responses
that the system anticipates receiving in a circumstance, rather
than anything regarding a specific consumer or specific survey.
Because, in some cases, a consumer may not respond to a survey
distributed to that consumer, a number of responses or response
opportunities may be smaller than a number of surveys distributed.
A purchaser who purchases a response opportunity may, in some
embodiments, purchase a corresponding number of survey
distributions or any other suitable number of survey distributions.
In other embodiments, a purchaser may purchase a number of
responses and the system may distribute any suitable number of
surveys to yield the number of responses that the purchaser
requested.
[0256] A consumer analytics system that offers survey response
opportunities for sale may operate in accordance with techniques
discussed above, including by receiving location data identifying
the locations of consumers over time, inferring characteristics
(including behavior characteristics) of the consumers based on the
locations, distributing a survey to a consumer in response to
determining that inferred characteristics for a consumer satisfy
one or more conditions for the survey to be distributed, and
receiving responses to the surveys.
[0257] For ease of description, the process 1500 will be described
below in terms of a single behavior that, when a consumer is
detected to be engaging in the behavior, causes the consumer
analytics system to distribute one of one or more surveys to the
consumer. It should be appreciated, however, that a consumer
analytics system as described herein may operate with any number of
different behaviors that serve as conditions for different surveys
to be distributed to consumers. Thus, while the process 1500 is
described in connection with a single behavior that serves as the
condition for a survey to be distributed, it should be appreciated
that the process 1500 may operate with a set of one or more
behaviors or may be repeated any number of times by a consumer
analytics system for different behaviors or sets of behaviors.
[0258] It should be further appreciated that the behavior that
serves as the condition in process 1500 may be any suitable
behavior, as embodiments are not limited in this respect. For
example, the condition for a survey may be that a consumer engage
in a behavior that is visiting a particular store, such as a
WAL-MART.RTM. (any WAL-MART.RTM. or a particular branch/store
location). Other behaviors, such as exemplary behaviors discussed
above, may additionally or alternatively serve as conditions for
surveys. It should also be appreciated that, in some embodiments,
other characteristics of consumers may be used as conditions for a
survey in addition to or instead of behavior characteristics,
including identity or preference characteristics for a consumer.
Thus, while for ease of description the process 1500 of FIG. 15
(and some additional examples below) is discussed in terms of a
condition that is a consumer engaging in a behavior, in some
embodiments the conditions may alternatively or additionally
include one or more other characteristics of a consumer.
[0259] Prior to the start of the process 1500, the consumer
analytics system may collect location data for multiple consumers
over time, including by monitoring consumers visiting various
settings and consumers who respond to surveys. This information on
consumers visiting settings and responding to surveys may be stored
in one or more data stores of the consumer analytics system.
[0260] The process 1500 begins in blocks 1502 and 1504, in which a
survey market system of the consumer analytics system determines,
prior to offering survey response opportunities for sale, whether
the system has survey response opportunities available for sale. In
some cases, the survey market system may not explicitly determine
whether there are opportunities available for sale, but may instead
have knowledge that there are opportunities available. In other
embodiments, however, the system may make an explicit
determination, for a particular condition or set of conditions
(e.g., behaviors) that are to trigger a survey, whether there are
available survey response opportunities and/or a number of
available response opportunities.
[0261] In the example of FIG. 15, in block 1502, the system
determines, for the particular behavior that serves as a condition
for distribution of surveys to a consumer (e.g., visiting a
WAL-MART.RTM. or a particular branch of WAL-MART.RTM., or visiting
any brand of big-box store), a number of survey responses the
system projects receiving in a time period. This number of survey
responses the system projects receiving may be a number of
responses that the system expects to receive in response to
distributing one of one or more surveys to consumers when the
system detects that the consumers are engaging in the behavior. The
consumer analytics system may determine this number by reviewing
past data collected on consumers and survey responses provided by
those consumers. For example, if a time period is a number of days
(e.g., a month), the consumer analytics period may review past data
collected over the past number of days or a corresponding set of
days from a previous year. The consumer analytics system may also
evaluate past data collected in the time period across multiple
previous iterations of the time period and perform an analysis to
produce an average, median, or other statistical value for the
multiple iterations.
[0262] In some cases, the consumer analytics system may have
previously distributed surveys when the behavior was detected. In
these cases, in block 1502, the consumer analytics system may
review past data to determine (1) a number of times the behavior
(or other characteristic) was previously detected in the time
period, and (2) out of a number of surveys distributed to consumers
in response to the behavior, a number of responses to those surveys
received by the system.
[0263] In other cases, the system may not have previously
distributed surveys in response to detecting the behavior. In some
such cases, the system may have previously received location data
indicating the behavior and have detected consumers engaging in the
behavior, and the system may review this data to determine the
number of times the behavior was previously detected in the time
period. In this case, in block 1502 the system may then determine a
number of responses the system projects receiving in response to
distributing any surveys, which may be an average or median
proportion of distributed surveys to which a response is received.
The average or median proportion may be determined by the system
based on any suitable responses previously received by the system.
For example, the system may determine the average or median
proportion based on responses received to all surveys distributed
by the system or any suitable portion of surveys distributed by the
system. As another example, the portion may, in some cases, include
responses received from consumers who the system considers
representative of the consumers to which surveys would be
distributed in response to detecting the behavior. For example, if
the system determines that consumers previously detected to have
engaged in the behavior have some characteristic, the system may
evaluate a proportion of responses received for surveys distributed
to consumers having the characteristic. By evaluating responses
from representative consumers, the system may try to estimate a
number of responses the system can expect to receive if the system
begins distributing surveys in response to detecting the
behavior.
[0264] In some cases, such as where a set of consumers to be
surveyed or asked to complete other tasks is limited by some
characteristic(s), the system may also project responses based in
part on consumers who have the characteristic(s). For example, if a
survey is to be distributed to consumers who live in a geographic
area, the system may evaluate a number of consumers that are
registered with the system and are known to live in that geographic
area, when projecting a number of responses that will be
received.
[0265] In addition, in some embodiments, the consumer analytics
system may evaluate a number of surveys that are to be distributed
when determining a number of responses that the system projects
receiving in a time period. As discussed above, in some embodiments
the system may regulate a number of surveys distributed to
consumers to avoid overwhelming consumers with too many surveys. It
may be the case that as more purchasers purchase more survey
opportunities and more surveys are available to be distributed to
consumers via the consumer analytics system, the system may opt to
reduce a frequency of distribution of surveys to avoid such
overwhelming. For example, the system may elect to distribute
surveys to individual consumers only after a delay, such as only
one per day or one every few days or other suitable timeframe.
Accordingly, in some embodiments, the system may consider a number
of surveys that are distributed to determine a number of responses
that will be received.
[0266] Once the consumer analytics system determines in block 1502
a number of survey response opportunities (i.e., a number of
responses the system projects receiving), the system can determine
how many response opportunities to offer for sale via the survey
market system. Accordingly, in block 1504, the consumer analytics
system determines a number of available survey response
opportunities to offer for sale to purchasers. The number of
available response opportunities may, in some cases, be equal to
the number of survey responses the system expects to receive. This
may be the case where the system has not been previously
distributing surveys in response to detecting consumers engaging in
the behavior and, thus, all surveys that are distributed in
response to the behavior may be distributed on behalf of one or
more new purchasers and all responses received may be provided to
the purchaser(s). In other cases, however, the number of available
response opportunities may be less than the number of survey
response opportunities the system determined in block 1502. This
may be the case where the system has previously been distributing
surveys in response to the condition. For example, when the system
distributes a default survey, for production of a syndicated data
feed, in response to the condition, the system may need to receive
a pre-established number of responses, such as a number of
responses that would produce statistically-significant results, or
at least meaningful or scientifically-relevant results or results
otherwise having a desired level of accuracy, for the syndicated
data feed. As another example, if one or more other purchasers have
previously purchased a number of responses for surveys provided by
the purchasers, the system may be configured to distribute surveys
to consumers in a manner that will generate a desired number of
survey responses for those purchasers. The desired number of survey
responses may be, for example, a number of survey responses that
has been purchased by a purchaser and that the consumer analytics
system must receive to fulfill the purchaser's request. The desired
number of survey responses may additionally or alternatively
include a number of survey responses to a particular survey (e.g.,
a purchaser's survey or a default survey that is distributed by the
system and is unrelated to a purchase from a purchaser) that will
generate statistically-significant results (or meaningful or
scientifically-relevant results) or results having a desired
statistical margin of error for questions in the survey.
[0267] Thus, in block 1504, the consumer analytics system may
determine whether the number of expected survey response
opportunities determined in block 1504 is greater than a desired
number of survey responses that will be used for
previously-generated surveys. If the expected number is not greater
than the desired number, then there are no available survey
response opportunities that can be offered for sale, as all of the
expected responses will be used by the system for receiving
responses to the surveys already being distributed by the consumer
analytics system. In this case, the process 1500 would end, as
there are no responses to sell to new purchasers via the survey
market system. However, if the desired number of survey responses
is less than the expected survey response opportunities, then in
block 1504 the system determines the difference to be a number of
available response opportunities. These response opportunities may
be considered to be the "excess" response opportunities beyond what
the system currently needs, and represent an opportunity to
distribute a new survey and receive responses to the new survey in
those "excess" responses.
[0268] Following determining a number of available response
opportunities in block 1504, the survey market system may make the
number of available response opportunities available for purchase.
The survey market system may do so in any suitable manner, as
embodiments are not limited in this respect. Examples of ways in
which the survey market system may make response opportunities
available for purchase are discussed below in connection with FIG.
16.
[0269] As a result of making the opportunities available for sale,
in block 1506 the survey market system receives a purchase from a
purchaser in block 1506. The purchase information may include
information identifying a minimum number of responses the purchaser
would like to receive, which may be equal to or less than the
number of available survey responses. The purchase information may
also include questions that the purchaser would like to ask in a
new survey distributed to consumers.
[0270] Following the receipt of the purchase in block 1506, in
block 1508 the consumer analytics system configures itself to
distribute the new purchaser's survey to consumers when the
behavior is detected. The consumer analytics system may configure
itself to distribute the survey a greater number of times than the
minimum number of responses that the purchaser has purchased, as
some consumers may not respond to the survey when the consumers
receive the survey. The consumer analytics system may distribute
the survey during the time period a number of times such that the
system expects to receive the minimum number of responses. Once the
system is configured, in block 1510 the consumer analytics system
distributes the survey to consumer detected to be engaging in the
behavior, receives responses to the survey, and aggregates the
responses in any suitable manner. In block 1512, the responses are
provided to the purchaser, and the process 1500 ends.
[0271] Following the process 1500, the consumer analytics system
may continue to distribute the survey in future time periods, if
the purchase by the purchaser requested that the survey be
distributed across multiple time periods. As a result of the
process 1500, the consumer analytics system is configured to
distribute a new survey a number of times so as to receive a
minimum number of survey responses, and stores responses to the
survey received from consumers.
[0272] FIG. 16 illustrates an example of a process that may be
carried out by a survey market system to receive a purchase of
survey response opportunities from a purchaser. The process may be
carried out by a survey market system executing on one or more
computing devices and receiving information via the Internet. For
example, in some embodiments, the survey market system may receive
information from purchasers via the web, and purchasers may provide
input to one or more web pages served by the survey market
system.
[0273] The process 1600 begins in block 1602, in which the survey
market system receives a specification of a behavior that a
purchaser would like to use to trigger distribution of surveys on
behalf of the purchaser. The behavior may be any suitable behavior
or set of multiple behaviors, including any of the examples given
above, as embodiments are not limited in this respect. The survey
market system may receive the specification from the purchaser in
any suitable manner. In some embodiments, the survey market system
may make limited options available to purchasers and may, in some
such cases, provide a multiple-choice list of behaviors (or other
characteristics) that purchasers may use to specify the behavior
they would like to trigger distribution of their survey. In other
embodiments, the survey market system may provide an interface that
allows purchasers to build a set of one or more behaviors or other
consumer characteristics through specifying each of the
characteristics to the system. In such embodiments, the system may
provide some template options, such as a "visit" to a store or
"passing by" (i.e., travelling within a threshold distance of and
not visiting) another store or location, and the purchaser may
select a template option and provide input regarding the option.
The input regarding an option may be any suitable input, such as
the name of a store in the preceding examples. Such input may be
received from a purchaser via one or more web pages.
[0274] After the system receives the specification of the
behavior(s) in block 1602, the system determines, in block 1604, a
number of available survey response opportunities for the
behavior(s). The system may determine the number in any suitable
manner, including using techniques discussed above in connection
with FIG. 15. Once the system determines the number of available
response opportunities, the system transmits the information to the
potential purchaser to inform the potential purchaser of what is
available for purchase.
[0275] In block 1606, the survey market system receives from the
purchaser the purchase price and a desired number of survey
responses that the purchaser is purchasing for the price. Receiving
the purchase price may involve receipt of funds or receipt of a
commitment to provide funds in an agreed-upon amount. The amount of
funds that are provided or will be provided by the purchaser may be
agreed upon by the purchaser and the survey market system in any
suitable manner.
[0276] In some embodiments, the survey market system may determine
a price for survey response opportunities, as discussed above. In
some such embodiments, the survey market system may evaluate a
commonness/scarcity of the behavior(s) that the purchaser wishes to
use to trigger surveys. The system may set a higher price for
behaviors that are more rare or difficult to detect, and a lower
price for behaviors that are more common. The system may
additionally or alternatively evaluate a generality of the
behavior(s) specified, which may include evaluating a breadth of a
market category (or categories) covered by the specified
behavior(s). The system may set a higher price for behaviors that
cover a wider section of the market, and a lower price for
behaviors that cover a narrower section of the market. The survey
market system may additionally or alternatively evaluate a number
of survey response opportunities that the purchaser would like to
purchase. In these cases, the system may charge a higher price for
each survey response or charge an additional fee when a purchaser
would like to purchase a large number of surveys. This may be
because the purchaser, by asking for a large number (which may be,
for example, all available surveys), is asking for a monopoly or
near monopoly on responses to surveys triggered by the behavior,
and the system may charge extra for that monopoly. The survey
market system may additionally consider a number of other surveys
that are being offered by the system in general or in response to
the behaviors specified in block 1602, and increase the price as a
number of surveys that are being distributed by the system
increases and decrease the price when the number of surveys
decreases. As discussed above, this may be done to prevent
consumers from being overloaded by surveys or to prevent consumers
from losing interest in the system (and ceasing to view or respond
to surveys) when the surveys are seldom distributed. In embodiments
in which the system sets a price, the system may evaluate any of
these or any other suitable option.
[0277] In other embodiments, an auction system may be used in which
purchasers bid for available survey response opportunities. In some
such embodiments, the survey market system may set a starting price
for an auction on one or more blocks of a certain number of
available survey response opportunities (e.g., 50 responses, 100
responses, 500 responses, etc.), and may set the starting price
using any suitable technique, including those discussed in the
preceding paragraph or otherwise discussed herein. After the
starting price is set by the survey market system or by an opening
bid from a potential purchaser, potential purchasers may bid on
each block of available survey response opportunities and the
system may track the bids and inform other purchasers of the bids.
After a time period has elapsed, the system may declare the highest
bidder on each block of available survey response opportunities and
receive funds from the purchaser of each block.
[0278] In block 1608, in addition to receiving from the purchaser
the purchase price and the number of desired survey responses, the
survey market system receives one or more questions to be included
in a survey distributed by the consumer analytics system on behalf
of the purchaser. As discussed above, the questions that may be
included in a survey may be any suitable questions, including
multiple choice or open answer, as embodiments are not limited in
this respect.
[0279] In block 1610, the consumer analytics system configures
itself with the information provided by the purchaser to the survey
market system. As discussed above in connection with block 1508 of
FIG. 15, the consumer analytics system may configure itself to
distribute the survey a greater number of times than the minimum
number of responses that the purchaser has purchased, as some
consumers may not respond to the survey when the consumers receive
the survey. The consumer analytics system may distribute the survey
during the time period a number of times such that the system
expects to receive the minimum number of responses. Once the system
is configured, the process 1600 ends.
[0280] As a result of the process 1600, the consumer analytics
system is configured to distribute the newly-received survey to
consumers in response to detecting the behavior specified in block
1602, to receive responses to the surveys, and to provide the
responses to the purchaser. Accordingly, following the process
1600, the consumer analytics system stores information regarding
the new survey and the behavior(s) that will trigger distribution
of the survey, as well as the number of survey responses desired in
a time period.
[0281] As a result of offering available survey response
opportunities for sale via a survey market system, it may be the
case that a number of different surveys are to be distributed to
consumers in response to detecting the same behavior in consumers.
For example, it may be the case that two different companies are
interested in the opinions of consumers who shop at WAL-MART.RTM.
and have each asked that surveys be distributed to consumers that
are detected to visit a WAL-MART.RTM. and have each asked that they
receive a minimum number of responses to their surveys. Thus, it
may be the case that the consumer analytics system may expect to
observe 5,000 consumers (who have previously registered with the
system) visit a WAL-MART.RTM. in a time period and to distribute
surveys to those consumers and may expect to receive 3,000
responses to those surveys, and each company has asked to receive
at least 1,000 surveys. The consumer analytics system should
therefore, when choosing which survey to distribute to a particular
consumer who has been detected to visit a WAL-MART.RTM. in the time
period, observe how many responses have been received for each
survey and distribute a survey to ensure that the system receives
at least 1,000 responses to each survey.
[0282] FIG. 17 illustrates an example of a process that may be
implemented by a consumer analytics system to select a survey to
distribute to consumers in response to observing locations visited
by the consumers. The process 1700 begins in block 1702, in which
the consumer analytics system receives from mobile devices operated
by consumers location data that indicates a geographic location of
each consumer at a time that the location data was generated. In
block 1704, the consumer analytics system loops through the
location data for each consumer, evaluating new location data
together with previously-received location data and/or profile data
for a consumer to predict or infer characteristics, including
behaviors, of each consumer. In block 1706, the system determines
whether the location data for the consumer currently being
evaluated is indicative of a behavior that satisfies one of the
conditions that are associated in the system with surveys, such
that satisfaction of the condition may result in a survey being
distributed to the consumer. If not, then the process 1700 returns
to block 1704 and the consumer analytics system evaluates location
data for a new consumer. If, however, the system determines that a
condition (of the various conditions that may be associated with
surveys and are evaluated by the system) is satisfied by the
consumer's behavior, the system may determine that a survey is to
be distributed and proceed to select a survey to distribute.
[0283] Accordingly, in block 1708 the system determines whether the
condition that was satisfied in associated with multiple surveys,
each of which is available to be distributed to the consumer in
response to the condition being satisfied. If not, then the process
1700 continues to block 1712. If, however, the condition is
associated with multiple surveys, then in block 1710 the system
selects a survey to distribute to the consumer. The selection may,
in some cases, be a random selection between the available surveys.
In other cases, the selection may be random, but the surveys may
have unequal probabilities of being randomly selected by the system
for distribution to the consumer. This may be the case where each
survey has a minimum number of responses that are to be received in
response to the survey in a time period, such as a minimum number
for the survey results to be statistically significant or a minimum
number for which a purchaser has paid. In this case, as responses
are received to each survey, the system may automatically adjust
the probabilities associated with each survey and the condition.
The probabilities for each survey may be equal at a start of the
time period, but may deviate if responses are received to each
survey unequally. For example, if a survey is receiving more
responses than others, then the probability associated with that
survey may be lowered, such that the survey is less likely to be
picked than one or more other surveys that are receiving fewer
responses. By doing so, one of the other surveys is more likely to
be picked by the system and sent to consumers, such that the system
is more likely to receive responses to the survey and the minimum
number of responses is more likely to be met. While a random,
probabilistic selection may be made in some cases, in some
embodiments the consumer analytics system may perform a simpler
process, such as by selecting a survey that has the smallest
fraction of responses received relative to the minimum number of
responses needed for that survey. Embodiments are not limited to
selecting a survey for distribution in any particular manner.
[0284] Once the survey is selected in block 1710, or if the
condition is determined in block 1708 to be associated with only
one survey, the survey is distributed to the consumer in block
1712. The survey may be distributed in any suitable manner,
including according to techniques described above. For example, at
least one message may be sent by the system to the mobile device
that transmitted the location data for the consumer that was
received in block 1702. The message that is sent to the mobile
device may solicit the consumer to complete the survey and may
include any suitable information about the survey. In some
embodiments the information about the survey may include one or
more questions included in the survey or a link to a web location
(e.g., a URL) from which the mobile device can retrieve the
questions of the survey for presentation to the consumer.
[0285] Once the survey is distributed in block 1712, the system
determines in block 1714 whether more consumers are to be
evaluated. If so, the process 1700 returns to block 1704 to
evaluate location data for another consumer. If not, the process
1700 ends.
[0286] As a result of the process 1700, surveys are distributed to
consumers in a manner that is designed to ensure that the minimum
number of responses for each survey is received. The consumer
analytics system will receive the responses to the surveys and
store the responses, and may further aggregate responses received
for each of the surveys and provide the responses to the party
(e.g., a purchaser) on whose behalf the consumer analytics system
distributed the surveys.
[0287] It should be appreciated that while the example of FIG. 17
was, for ease of description, described as operating with a single
condition for the surveys (e.g., a behavior that satisfies a
condition for one or more surveys to be distributed), embodiments
are not limited to operating with a single condition to distribute
surveys. As discussed above, any suitable combination of two or
more conditions may also be used, which may relate to any suitable
type of characteristic for a consumer. Accordingly, in some
embodiments, a process similar to the process 1700 may be carried
out in which multiple characteristics for a consumer are compared
to multiple conditions to determine if the conditions are met.
[0288] Additionally, it should be appreciated that the example of
FIG. 17 was, for ease of description, described in the context of
multiple surveys that are all associated with the same condition.
If one or more conditions are associated with each survey, two
surveys may have different, but at least partially overlapping,
conditions and characteristics for a consumer may satisfy both
conditions. For example, one survey may have as a condition that a
consumer visited a WAL-MART.RTM. and another survey may have as
conditions that a consumer visited a WAL-MART.RTM. in the middle of
a weekday on a path that started at the consumer's workplace. The
system may detect that a consumer who leaves work at noon on a
Tuesday to go to a WAL-MART.RTM. has characteristics that satisfy
conditions of both surveys, even though the surveys do not have
identical conditions. In that case, in some embodiments, the system
may carry out a process similar to the one described above in
connection with block 1710 to select one of the surveys to
distribute to the consumer.
[0289] Lastly, it should be appreciated that while each of the
examples of a marketing system described in connection with FIGS.
15-17 is described in the context of surveys, some embodiments may
additionally or alternatively operate with other tasks that may be
distributed to consumers, such as obtaining a photograph or other
media.
Survey Administration
[0290] In some embodiments, the consumer analytics system provides
a user interface for businesses to enter and manage the survey
questions the businesses would like to ask consumers. The interface
may also enable businesses to specify how much the businesses are
willing to pay for the survey responses and data generated by the
consumer analytics system based on consumer locations and/or the
survey responses. The system may also include an interface for
configuring where, when, and to whom the survey should be provided,
such as locations and demographic characteristics that should be
detected for a consumer to be prompted to answer the questions.
Survey Metering
[0291] As discussed previously, in some embodiments, the system may
function as a two-sided market. In some such embodiments, to
maintain the balance and not overly burden consumers, the system
may set prices dynamically. In addition or alternatively, the
system may meter requests for tasks (e.g., completion of surveys)
to consumers so as not to overburden the consumers. In some such
embodiments, the system may have a rules engine that allows an
administrator to control how many requests of which type are sent
to which kinds of consumers.
[0292] A potential problem that may arise in this system in some
environments is that the surveys themselves or the rewards can
introduce skew into the results. Skew, as discussed above, may be
undesirable. For example, simply asking a consumer in a survey if
she enjoys the french fries from a restaurant, Tofu Tiles, may
raise that consumer's awareness of the Tofu Tiles brand and the
french fries from there, potentially making it more likely the
consumer will visit a Tofu Tiles location in the future. In
addition, by providing a consumer a reward for a free product
redeemable at a given POI, the consumer may be more likely the
visit the given POI.
[0293] In some embodiments, to help manage this skew, the rules
engine of the system may meter which surveys and rewards go to
which consumers.
Brand Tracking and Advertising Awareness
[0294] In some embodiments, the consumer analytics system may be
used to measure the effectiveness of advertising and other types of
marketing campaigns. To do so, tasks requested by the system may
relate to the marketing campaigns, such as a task involving
answering survey questions. For example, as part of a survey,
questions may be asked to gauge: [0295] How aware of a store's
brand was the consumer [0296] Which brands of store did the
consumer consider for the visit [0297] Which brand(s) are the
consumer's favorite
[0298] By measuring the responses to these questions across
multiple consumers, POIs, and times, the consumer analytics system
may create measures of the overall awareness and sentiment among
consumers for each brand.
[0299] For advertising campaigns involving physical signage (such
as billboards, signs on buses, etc.), location analytics can be
used to measure exposure by consumers to the advertising campaign.
In some embodiments, for example, location analytics can be used to
divide consumers into two sets: consumers who have been detected to
be in the vicinity of an advertisement and can be inferred to have
been exposed to the advertising and consumers who have not been
detected to be in the vicinity of an advertisement and can be
inferred not to have been exposed. The system, by comparing the
brand awareness and number of visits to POIs for a given brand
between the two groups, may measure how effective the
advertisements of the campaign are at increasing brand awareness
and driving foot traffic to locations. In other embodiments,
locations of a group of consumers can be measured, and analytics
generated, for a period of time that includes some periods in which
the advertisements are active and some in which the advertisements
are not active. By comparing brand awareness and visit numbers for
the two periods for this set of consumers, the system may measure
how effective the advertisements are at increasing brand awareness
and driving foot traffic to locations.
[0300] FIGS. 18-20 illustrate examples of techniques that may be
used by a consumer analytics system to track consumers' knowledge
of brands and interactions with brands. A "brand" of a business is
a name by which the business is known to consumers. The brand may
appear on goods produced and/or sold by the business, appear in
advertisements for the business, or appear on storefronts for the
business. In some embodiments, a determination of brand strength
may be performed for businesses with one or more stores and, as
discussed below, the determination may be made in part based on
visits by consumers to those stores. For example, a brand strength
of a retailer or restaurant may be measured. It should be
appreciated, however, that embodiments are not limited to
implementing the techniques illustrated in any of FIGS. 18-20 and
that other techniques may be used.
[0301] Brand strength for a business may be measured in any
suitable manner. A brand strength may represent any suitable
measurement of a fraction or share of a market category that is
held by a business. Such a share of a market category may be
measured by a share of money spent by consumers at a business
relative to the market category, or a share of consumers' attention
that is received by a business relative to the market category as a
whole. As another example, a share of a market category may be
measured relative to an amount of time consumers spend in a
business' store relative to time spent in other stores in the
market category.
[0302] In the embodiments of FIGS. 18-20, a consumer analytics
system may determine brand strength for a business using five
factors that each represent a "share" of a market category held by
a business. The five factors that may be tracked by the consumer
analytics system in these embodiments may be brand awareness for a
business, brand accessibility for the business, brand
consideration, brand usage, and brand favorites. The system may
additionally track changes in these five factors over time. The
five factors that may be tracked may be factors that describe the
share of consumers that are customers or are potential customers of
the business. The five factors may each be related to a share of
consumers and may for many (but not necessarily all) businesses
have the same order when ordered from largest share to smallest
share.
[0303] Brand awareness may identify a share of consumers who are
aware of a business or are at least aware of the name of the
business. Brand accessibility represents a share of consumers who
have an opportunity to visit the business, which may in some
embodiments be defined by the share of consumers for whom
personally-relevant locations (e.g., home locations) are within a
threshold distance of the business or a branch of the business.
Brand consideration may represent a share of consumers that are not
only aware of the business, but actively consider visiting a
business to purchase goods or services. Brand usage may represent a
share of consumers that have visited the business at least once,
and brand favorites may represent a share of consumers for whom the
business is a "favorite" business to shop at. The consumer
analytics system may determine that a business is a favorite in any
suitable manner, including by determining whether a consumer visits
the business more often than any other business in a market
category (e.g., more often than competitors of the business) or
more than a threshold number of times in a time period (e.g., more
than 5 times a month). The consumer analytics system may
additionally or alternatively determine that a business is a
favorite by asking a consumer for his or her favorite businesses,
such as in a survey. These five factors may, in many cases,
represent a decreasing share of consumers for a business in this
order: awareness, accessibility, consideration, usage, and
favorite.
[0304] It should be appreciated that a share of consumers
determined using these techniques may be a share of any suitable
pool of consumers. In some embodiments, the pool of consumers may
be all consumers in a geographic area, such as all Americans or all
people within a state or region. In other embodiments, other pools
of consumers may be used. For example, in some embodiments a brand
strength for a business may be based on a pool of consumers who
visit one or more businesses in a market category of that business.
In other embodiments, a brand strength for a business may be
determined based on a particular demographic group, such as
consumers in a certain age group or income range. Any suitable
consumers defined in any suitable manner may be used to determine
brand strength for a business.
[0305] In embodiments, the five factors (awareness, accessibility,
consideration, usage, and favorite) may be determined from
analyzing location data for consumers and survey responses for
consumers. In particular, in some embodiments, brand accessibility,
usage, and favorite for a business may be determined based on
location data for consumers and brand awareness and consideration
may be determined based on survey responses.
[0306] FIG. 18 illustrates an example of a process that a consumer
analytics system may implement in some embodiments for determining
brand strength information for a business. Prior to the start of
the process 1800 of FIG. 18, the consumer analytics system may be
configured with conditions, tasks, and rewards, and has presented
rewards to consumers in exchange for consumers completing a task
(e.g., providing a response to a survey). The process 1800 begins
in block 1802, in which the consumer analytics system receive
location data for multiple consumers. In block 1804, from an
analysis of the location data for each consumer, the consumer
analytics system determines settings that each consumer visited and
personally-relevant locations for the consumers.
[0307] In block 1806, the system analyzes the location data and
distributes surveys to consumers in response to determining that
the consumers' behaviors (or other characteristics) satisfy
conditions for distribution of the surveys. The surveys that are
distributed in block 1806 include questions on brands of which the
consumers are aware as well as brands that the consumers considered
visiting for the shopping trip the consumers were on at the time
they received the survey. The questions on brand awareness and
consideration may be formatted in any suitable manner, as
embodiments are not limited in this respect. In some embodiments,
the questions may be presented as multiple choice, with the choice
options for the questions listing multiple brands, that ask the
consumer to select all of the brands of which the consumer is aware
or considered visiting. In other embodiments, the questions may be
presented as free response and the consumer may be asked to provide
the names of the brands in text.
[0308] In block 1808, for each of multiple businesses, the consumer
analytics system determines shares of consumers who are aware of
the business and who considered visiting the brand from the survey
responses. In addition, the consumer analytics system determines
which consumers visited the business and how often, from which the
consumer analytics system can determine shares of consumers who
"use" the business and from whom the business is a favorite.
Lastly, the consumer analytics system reviews the
personally-relevant locations for each consumer and the locations
of the business to determine a share of consumers for whom the
business is accessible.
[0309] In block 1810, the consumer analytics system uses the
awareness, accessibility, consideration, usage, and favorites data
determined for each business in block 1808 to generate brand
strength information for each business and provides that brand
strength information to the businesses. The brand strength
information that is generated in block 1810 may be any suitable
information in any suitable format, as embodiments are not limited
in this respect. In some embodiments, the information may include
percentages of consumers for each of the five factors discussed
above and a description of the pool of consumers of which the
percentages represent a part. After the brand strength information
is generated and provided, the process 1800 ends.
[0310] FIG. 18 described in general an overall process for
determining brand strength information from location data and
survey responses. FIG. 19 shows an example of a process 1900 that
may be used in some embodiments for determining brand strength
information for a particular business in a market category based on
location data and survey responses from consumers.
[0311] The process 1900 of FIG. 19 begins in block 1902, in which
the consumer analytics system reviews location data for consumers
to identify consumers who visited any business in the market
category (e.g., the business for which brand strength information
is to be generated and competitors of that business). The consumers
that are identified may be those who the system has ever detected
to have visited a business in the category, or those consumers who
visited a business in the category within a time period. In
embodiments in which a time period is used, the time period may be
selected relative to a market category of the business being
evaluated, as consumers may visit businesses in different
categories with different frequencies. For example, for a grocery
store, a relatively short time period (e.g., one or two weeks) may
be used, while for a home renovation store, a relatively long time
period (e.g., three or six months) may be used, as consumers
typically visit grocery stores more often than home renovation
stores.
[0312] By identifying all consumers who visited one of the
businesses in the category, the consumer analytics system may
thereby identify a pool of consumers who are customers of
businesses in the category. The system may then generate brand
strength information for a particular business that identifies a
share of consumers in this pool that are customers of the business
or potential customers of the business.
[0313] In block 1904, after identifying the pool of consumers in
block 1902, the system identifies personally-relevant locations for
each of the consumers and determines whether the particular
business (or a branch of the particular business) is within a
threshold distance of the personally-relevant locations for each
consumer. The threshold distance may be any suitable distance, such
as 10 miles or 50 miles, as embodiments are not limited in this
respect. In some embodiments, the threshold distance may be
different for different market categories, as consumers may be
willing to travel different distances for different types of
businesses. By identifying the consumers for whom the business is
within a threshold distance of personally-relevant locations, the
system may identify consumers for whom the particular business is
"accessible." The proportion of consumers (from the pool identified
in block 1902) who can access the particular business may be the
"accessibility" share for the business.
[0314] In some embodiments, accessibility for each consumer may be
a binary "yes/no" determination regarding proximity of the business
to a personally-relevant location for the consumer. In other
embodiments, however, the system may evaluate a number of branches
of the business to determine a density of the business within the
threshold distance of each consumer. For example, the system may
determine whether the business has a number of locations above a
threshold and, if so, identify that the business has "high"
accessibility for a consumer, and "low" accessibility
otherwise.
[0315] In block 1906, the consumer analytics system additionally
determines, from the pool of consumers identified in block 1902,
which consumers visited the particular business at least once. The
visits that are evaluated may be visits within a time period, such
as the time period used in block 1902. The visits may be identified
by the consumer analytics system from location data for the
consumers. The identified consumers are those who have "used" the
particular business, and the proportion of consumers from the pool
who have visited the particular business may be the "usage" share
for the business.
[0316] In block 1908, the consumer analytics system determines a
share of consumers (of the pool identified in block 1902) for whom
the particular business is a "favorite" in the market category. In
the example of FIG. 19, the consumer analytics system determines
the "favorite" share by determining, from location data for the
consumers, those consumers who visited the particular business more
often than any other business in the market category. In some
embodiments, the consumer analytics system may analyze the number
of visits to the various businesses of the category in a time
period, such as a time period that was used to identify the pool of
consumers in block 1902.
[0317] In block 1910, the consumer analytics system reviews survey
responses from consumers to whom the system distributed surveys in
response to detecting that the consumers visited other businesses
in the market category. The survey responses may be responses
received to surveys that were distributed in a time period, such as
the time period used in block 1902. The surveys that were
distributed may include questions relating to brand awareness and
consideration, such as the questions discussed above in connection
with block 1806. The system may only analyze responses triggered by
visits to other businesses because the system may infer that
consumers that were detected to visit the particular business are
both aware of the business and considered visiting the business
(because they did visit the business). From reviewing the
businesses identified by consumers in response to brand awareness
questions and brand consideration questions, the system may
identify a share of the pool of consumers who are aware of the
particular business and who considered visiting the particular
business. In some embodiments, the consumer analytics system may
also increase the "awareness" and "consideration" shares to account
for consumers who visited the business, such that the "awareness"
and "consideration" shares reflect the responses to the surveys and
the actual visits to the business.
[0318] In block 1912, the consumer analytics system may compile the
brand strength s identified in blocks 1906-1910 and generate
overall brand strength information for the particular business,
then provide the brand strength information to the business. The
brand strength information that is generated in block 1912 may be
any suitable information in any suitable format, as embodiments are
not limited in this respect. In some embodiments, the information
may include percentages of consumers for each of the five factors
discussed above and a description of the pool of consumers of which
the percentages represent a part. After the brand strength
information is generated and provided, the process 1900 ends.
[0319] In the examples of FIGS. 18 and 19 described above, for ease
of description, the consumer analytics system was described as
generating brand strength information once and providing the brand
strength information to businesses after generation. However, in
some embodiments, the consumer analytics system may continually
monitor brand strength information, including by monitoring a
change over time in any of the five factors mentioned above. For
example, the system may monitor how consumers' "favorite"
businesses change over time. The system may, for example, determine
how many "favorite" businesses in a market category consumers have
in a time period.
[0320] In some embodiments, the consumer analytics system may
monitor a change in brand strength over time relative to particular
events. For example, if a business begins a new advertising
campaign, the consumer analytics system may determine whether and
how the brand strength of the business (e.g., awareness,
consideration, and usage for the business) changes over time,
during and after the advertising campaign. As another example, if a
business opens a new branch at a new location, the consumer
analytics system may determine whether the new branch has an impact
on the brand strength of the business, such as whether the
increased accessibility increases consideration and usage. As a
further example, in some embodiments, the consumer analytics system
may analyze brand strength relative to rewards that a
business/brand has asked the consumer analytics system to
distribute. As discussed above in connection with FIGS. 13-14, in
some embodiments the consumer analytics system may determine a
value of a reward that will have a desired effect on
characteristics of a consumer's interactions with a business, such
as a desired effect on behaviors of consumers relative to a
business. For example, the reward value may be one that increases a
visit frequency of consumers. Increased visit frequency by
consumers may, in some cases, increase a "usage" brand strength
parameter for a business and may increase a "favorites" brand
strength parameter for the business. Accordingly, in some
embodiments, the system may track changes in brand strength of a
business relative to a reward that has been offered by the
business. In some such embodiments, such changes in brand strength
may be used as part of determining a value of a reward to offer,
such as in determining an optimal value of a reward. For example,
the system may determine a lowest value of a reward that still
drives a desired change in one or more brand strength parameters
(e.g., change in consideration or usage).
[0321] FIG. 20 illustrates an example of a process 2000 that a
consumer analytics system may use in some embodiments to track
brand strength, and changes in brand strength, over time. Prior to
the start of the process 2000, the consumer analytics system may
register multiple consumers and receive location data for those
consumers, distribute surveys to those consumers and receive
responses, and identify businesses for which brand strength
information is to be generated.
[0322] The process 2000 begins in blocks 2002, in which the system
determines brand strength information for a business based on
location data and survey responses. The system may determine the
brand strength information in any suitable manner, including
according to techniques described above in connection with FIGS.
18-19.
[0323] In block 2004, the consumer analytics system identifies an
event that may impact brand strength. The system may identify the
event in any suitable manner, including according to input by a
user. Such a user may be, for example, an administrator of the
system or a market researcher using the system to conduct research
on brand strength.
[0324] In block 2006, the consumer analytics system determines
brand strength information based on location data and survey
responses collected by the system subsequent to the event
identified in block 2004. As in block 2002, the system may identify
the brand strength information in any suitable manner, including
according to techniques described above in connection with FIGS.
18-19. After determining the brand strength information from after
the event, the system may in block 2006 compare the brand strength
determined in blocks 2002, 2006 to determine whether there are any
changes in brand strength. Such a change may be identified by
comparing percentages associated with each of the five brand
strength factors discussed above. In some embodiments, the consumer
analytics system may additionally segment a pool of consumers for
which brand strength information was generated into one or more
subpools and determine whether there is any difference in brand
strength for the subpools before and after the event. The subpools
may relate to any suitable segment of consumers, such as
demographic segments or segments based on other characteristics.
The consumer analytics system may generate brand strength
information for the subpools of consumers, as in blocks 2002, 2006,
and then perform a comparison of the brand strength information for
the subpools to determine whether there is any change.
[0325] In block 2010, in addition to determining whether there is a
change in brand strength information, the consumer analytics system
may determine a persistence of any change in brand strength. As
discussed above, a persistent change in brand strength may be more
valuable to a business than a fleeting change and, as such,
businesses may be more interested in determining whether there is a
persistent change than merely whether there is a change. The system
may determine a persistence of a change in block 2010 in any
suitable manner, including by determining brand strength
information for the business a number of additional times, in
successive time periods following the event. The system may then
analyze the brand strength information from the successive periods
and determine how the brand strength for the business changes
relative to the other periods and how the brand strength compares
to the brand strength for the business before the time period.
[0326] After the system determines the persistence of brand
strength change in block 2010, the process 2000 ends. Following the
process 2000, the system may provide brand strength information to
the business, including information on changes in brand strength
following the event and persistence of any such changes.
[0327] While the brand strength analysis techniques described above
in connection with FIGS. 18-20 were described relative to five
factors for brand strength, it should be appreciated that other
factors may be analyzed. For example, by reviewing survey responses
regarding amounts spent in stores, the system may determine a
"wallet" share for each business in a category. As another example,
by reviewing location data for consumers, the system may track
amounts of time spent in each store or a time fraction spent in
each store of trips taken by consumers to determine a "time" share.
As a further example, by reviewing survey responses regarding
purchases by consumers, the system may track, for each business, a
share of visits at which consumers did not purchase
products/services at a business or did not purchase everything the
consumers intended to purchase when visiting the store.
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