U.S. patent application number 17/522208 was filed with the patent office on 2022-05-05 for dynamic promotion analytics.
The applicant listed for this patent is Groupon, Inc.. Invention is credited to Amit Aggarwal, Kevin Chang, Paul Gauthier, David Thacker.
Application Number | 20220138794 17/522208 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-05 |
United States Patent
Application |
20220138794 |
Kind Code |
A1 |
Chang; Kevin ; et
al. |
May 5, 2022 |
DYNAMIC PROMOTION ANALYTICS
Abstract
A promotion program analytical system and method is disclosed.
The promotion program analytical system and method selects a
promotion program to offer to a consumer. Selection of the
promotion program to present to the consumer includes determining a
probability that the consumer will accept the promotion program.
The probability of acceptance may be determined based on past
performance data of similar promotion programs, and also past
performance data on the promotion program itself when it is
available.
Inventors: |
Chang; Kevin; (Mountain
View, CA) ; Thacker; David; (Burlingame, CA) ;
Gauthier; Paul; (San Francisco, CA) ; Aggarwal;
Amit; (Los Altos, CA) |
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Applicant: |
Name |
City |
State |
Country |
Type |
Groupon, Inc. |
Chicago |
IL |
US |
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Appl. No.: |
17/522208 |
Filed: |
November 9, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16427380 |
May 31, 2019 |
11263659 |
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17522208 |
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13839360 |
Mar 15, 2013 |
10346870 |
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16427380 |
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61644352 |
May 8, 2012 |
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International
Class: |
G06Q 30/02 20120101
G06Q030/02 |
Claims
1.-24. (canceled)
25. A method for determining whether to provide a particular offer,
from a promotion program, to a consumer device associated with a
particular consumer, the method comprising: deriving, via a
processor, from the promotion program, a first promotion program
attribute and a duration, wherein the duration is comprised of a
plurality of time periods; generating, using a historical
predictive model, a historical predicted probability, wherein the
historical predicted probability is based on performance data from
one or more different promotion programs and consumer attribute
data associated with the particular consumer; generating, using a
promotion program predictive model, a promotion program predicted
probability, wherein the promotion program predicted probability is
based on real performance data from the promotion program and the
consumer attribute data; determining sufficient of real performance
data based on whether an amount of the real performance data is
greater than each of a first threshold and a second threshold;
determining a final predicted probability that the particular
consumer will accept the particular offer based on the
determination of whether the amount of real performance data is
sufficient, wherein the final predicted probability is a function
of the historical predicted probability, the promotion program
predicted probability, or the historical predicted probability and
the promotion program predicted probability; dynamically and
iteratively updating the promotion program predictive model as
additional real performance data from the promotion program is
available, wherein the additional real performance data includes
performance data from a previous time period; and as a result of
the updating of the promotion program predictive model,
re-determining sufficiency of the real performance data and
re-determining the final predicted probability that the particular
consumer will accept the particular offer, resulting in gradual
migration from a reliance on the output of the historical predicted
probability based on performance data from one or more different
promotion programs to reliance on the output of the promotion
program predicted probability based on performance data from the
promotion program.
26. The method of claim 25, further comprising: providing, for
display on the consumer device, the particular offer via an
electronic communication upon a determination that the final
predicted probability that the particular consumer will accept the
particular offer is within a predefined delta.
27. The method of claim 25 further comprising: determining that the
final predicted probability that the particular consumer will
accept the particular offer is not within the predefined delta; and
determining whether the real performance data indicates that a
desired number of consumers in a selected grouping have been
provided the particular offer, wherein the real performance data is
indicative of a number of consumers with a first promotion program
attribute value of the derived first promotion program attribute
that received the particular offer from the promotion program,
wherein determining whether the real performance data is sufficient
comprises comparing the number of consumers with the first
promotion program attribute value that received the particular
offer from the promotion program to a predetermined number.
28. The method of claim 27, further comprising: determining whether
there are any other consumers to consider in the selected grouping
that have not yet been provided the particular offer from the
promotion program; and in an instance in which the determination is
made that there are not other consumers to consider in the selected
grouping that have not yet been provided the particular offer from
the promotion program, increasing the delta.
29. The method of claim 27, further comprising: determining whether
there are any other consumers to consider in the selected grouping
that have not yet been provided the particular offer from the
promotion program; in an instance in which the determination is
made that there are not other consumers to consider in the selected
grouping that have not yet been provided the particular offer from
the promotion program; determining a new final predicted
probability that a new particular consumer will accept the
particular offer; and determining that the new final predicted
probability that the new particular consumer will accept the
particular offer is within the predefined delta; and providing, for
display on a consumer device associated with the new particular
consumer, the particular offer via the electronic
communication.
30. The method of claim 25, wherein the real performance data
includes a number of consumers that accepted the particular offer
in a previous time period or a percentage of consumers that
accepted the particular offer.
31. The method of claim 25, further comprising: dynamically
updating the promotion program predictive model at one or more
discrete times during the duration of the promotion program.
32. A computer program product for determining whether to provide a
particular offer, from a promotion program, to a consumer device
associated with a particular consumer, the computer program
product, stored on a non-transitory computer readable medium,
comprising instructions that when executed on one or more computers
cause the one or more computers to perform operations the
operations comprising: deriving, via a processor, from the
promotion program, a first promotion program attribute and a
duration, wherein the duration is comprised of a plurality of time
periods; generating, using a historical predictive model, a
historical predicted probability, wherein the historical predicted
probability is based on performance data from one or more different
promotion programs and consumer attribute data associated with the
particular consumer; generating, using a promotion program
predictive model, a promotion program predicted probability,
wherein the promotion program predicted probability is based on
real performance data from the promotion program and the consumer
attribute data; determining sufficient of real performance data
based on whether an amount of the real performance data is greater
than each of a first threshold and a second threshold; determining
a final predicted probability that the particular consumer will
accept the particular offer based on the determination of whether
the amount of real performance data is sufficient, wherein the
final predicted probability is a function of the historical
predicted probability, the promotion program predicted probability,
or the historical predicted probability and the promotion program
predicted probability; dynamically and iteratively updating the
promotion program predictive model as additional real performance
data from the promotion program is available, wherein the
additional real performance data includes performance data from a
previous time period; and as a result of the updating of the
promotion program predictive model, re-determining sufficiency of
the real performance data and re-determining the final predicted
probability that the particular consumer will accept the particular
offer, resulting in gradual migration from a reliance on the output
of the historical predicted probability based on performance data
from one or more different promotion programs to reliance on the
output of the promotion program predicted probability based on
performance data from the promotion program.
33. The computer program product of claim 32, the operations
further comprising: providing, for display on the consumer device,
the particular offer via an electronic communication upon a
determination that the final predicted probability that the
particular consumer will accept the particular offer is within a
predefined delta.
34. The computer program product of claim 32, the operations
further comprising: determining that the final predicted
probability that the particular consumer will accept the particular
offer is not within the predefined delta; and determining whether
the real performance data indicates that a desired number of
consumers in a selected grouping have been provided the particular
offer, wherein the real performance data is indicative of a number
of consumers with a first promotion program attribute value of the
derived first promotion program attribute that received the
particular offer from the promotion program, wherein determining
whether the real performance data is sufficient comprises comparing
the number of consumers with the first promotion program attribute
value that received the particular offer from the promotion program
to a predetermined number.
35. The computer program product of claim 34, the operations
further comprising: determining whether there are any other
consumers to consider in the selected grouping that have not yet
been provided the particular offer from the promotion program; and
in an instance in which the determination is made that there are
not other consumers to consider in the selected grouping that have
not yet been provided the particular offer from the promotion
program, increasing the delta.
36. The computer program product of claim 34, the operations
further comprising: determining whether there are any other
consumers to consider in the selected grouping that have not yet
been provided the particular offer from the promotion program; in
an instance in which the determination is made that there are not
other consumers to consider in the selected grouping that have not
yet been provided the particular offer from the promotion program;
determining a new final predicted probability that a new particular
consumer will accept the particular offer; and determining that the
new final predicted probability that the new particular consumer
will accept the particular offer is within the predefined delta;
and providing, for display on a consumer device associated with the
new particular consumer, the particular offer via the electronic
communication.
37. The computer program product of claim 32, wherein the real
performance data includes a number of consumers that accepted the
particular offer in a previous time period or a percentage of
consumers that accepted the particular offer.
38. The computer program product of claim 32, the operations
further comprising: dynamically updating the promotion program
predictive model at one or more discrete times during the duration
of the promotion program.
39. An apparatus for determining whether to provide a particular
offer, from a promotion program, to a consumer device associated
with a particular consumer, the apparatus comprising: one or more
memories configured to store one or more consumer profiles,
promotion program data of offers relating to the promotion program,
and historical data of offers relating to different promotion
programs; and a processor in communication with the one or more
memories, the processor configured to: derive, via a processor,
from the promotion program, a first promotion program attribute and
a duration, wherein the duration is comprised of a plurality of
time periods; generate, using a historical predictive model, a
historical predicted probability, wherein the historical predicted
probability is based on performance data from one or more different
promotion programs and consumer attribute data associated with the
particular consumer; generate, using a promotion program predictive
model, a promotion program predicted probability, wherein the
promotion program predicted probability is based on real
performance data from the promotion program and the consumer
attribute data; determine sufficient of real performance data based
on whether an amount of the real performance data is greater than
each of a first threshold and a second threshold; determine a final
predicted probability that the particular consumer will accept the
particular offer based on the determination of whether the amount
of real performance data is sufficient, wherein the final predicted
probability is a function of the historical predicted probability,
the promotion program predicted probability, or the historical
predicted probability and the promotion program predicted
probability; dynamically and iteratively update the promotion
program predictive model as additional real performance data from
the promotion program is available, wherein the additional real
performance data includes performance data from a previous time
period; and as a result of the update of the promotion program
predictive model, re-determine sufficiency of the real performance
data and re-determine the final predicted probability that the
particular consumer will accept the particular offer, resulting in
gradual migration from a reliance on the output of the historical
predicted probability based on performance data from one or more
different promotion programs to reliance on the output of the
promotion program predicted probability based on performance data
from the promotion program.
40. The apparatus of claim 39, wherein the processor is further
configured to: provide, for display on the consumer device, the
particular offer via an electronic communication upon a
determination that the final predicted probability that the
particular consumer will accept the particular offer is within a
predefined delta.
41. The apparatus of claim 39, wherein the processor is further
configured to: determine that the final predicted probability that
the particular consumer will accept the particular offer is not
within the predefined delta; and determine whether the real
performance data indicates that a desired number of consumers in a
selected grouping have been provided the particular offer, wherein
the real performance data is indicative of a number of consumers
with a first promotion program attribute value of the derived first
promotion program attribute that received the particular offer from
the promotion program, wherein determining whether the real
performance data is sufficient comprises comparing the number of
consumers with the first promotion program attribute value that
received the particular offer from the promotion program to a
predetermined number.
42. The apparatus of claim 41, wherein the processor is further
configured to: determine whether there are any other consumers to
consider in the selected grouping that have not yet been provided
the particular offer from the promotion program; and in an instance
in which the determination is made that there are not other
consumers to consider in the selected grouping that have not yet
been provided the particular offer from the promotion program,
increase the delta.
43. The apparatus of claim 41, wherein the processor is further
configured to: determine whether there are any other consumers to
consider in the selected grouping that have not yet been provided
the particular offer from the promotion program; in an instance in
which the determination is made that there are not other consumers
to consider in the selected grouping that have not yet been
provided the particular offer from the promotion program; determine
a new final predicted probability that a new particular consumer
will accept the particular offer; determine that the new final
predicted probability that the new particular consumer will accept
the particular offer is within the predefined delta; and provide,
for display on a consumer device associated with the new particular
consumer, the particular offer via the electronic
communication.
44. The apparatus of claim 39, wherein the processor is further
configured to: dynamically update the promotion program predictive
model at one or more discrete times during the duration of the
promotion program.
Description
REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/427,380, filed May 31, 2019, which is a
continuation of U.S. patent application Ser. No. 13/839,360, filed
Mar. 15, 2013, now U.S. Pat. No. 10,346,870 issued Jul. 9, 2019,
which claims the benefit of U.S. Provisional Application No.
61/644,352, filed May 8, 2012, the entire contents of each of which
are incorporated by reference herein.
FIELD OF THE INVENTION
[0002] The present description relates to offering promotions or
deals associated with a product or a service. This description more
specifically relates to dynamically analyzing consumer feedback to
offers for promotions.
DESCRIPTION OF THE RELATED ART
[0003] Merchants typically offer promotions or deals to consumers
from time to time in order to generate more business. The
promotions offered may be in the form of discounts, deals, rewards
or the like. When offering a promotion, a merchant may seek to
focus the offer to a specific subset of consumers that may have a
higher likelihood of accepting the offered promotion. However,
determining which promotional offers will have a higher likelihood
of acceptance by a consumer may prove to be difficult.
SUMMARY
[0004] A system and method for selecting a promotion program offer
to present to a specific consumer is disclosed.
[0005] According to an aspect of the present invention, a method of
determining whether to present an offer from a promotion program to
a consumer is disclosed, where the promotion program includes a
promotion program duration during which offers from the promotion
program are presented. The method includes: accessing a value of an
attribute, the attribute comprising or derived from a consumer
attribute or a promotion attribute (such as a promotion attribute,
a consumer attribute or an attribute derived from one or both);
generating, using a historical predictive model, a historical
predicted probability of the consumer's acceptance of the offer,
the historical predictive model configured to input the value of
the attribute and to output the historical predicted probability,
the historic predictive model using performance data of offers from
different promotion programs in order to correlate values of the
attribute to respective historical predicted acceptances;
generating, using a promotion program predictive model, a promotion
program predicted probability of the consumer's acceptance of the
offer, the promotion program predictive model configured to input
the value and to output the promotion program predicted
probability, the promotion program predictive model using
performance data from previous offers from the promotion program to
correlate values of the attribute to respective promotion program
predicted acceptances, wherein the promotion program predictive
model is dynamically updated multiple times during the promotion
program duration; combining the historical predicted probability of
acceptance and the promotion program predicted probability of
acceptance in order to generate a predicted probability of the
consumer's acceptance of the offer; and using the predicted
probability of acceptance in order to determine whether to present
an offer from the promotion program to the consumer.
[0006] According to another aspect of the present invention, a
system for determining whether to present an offer from a promotion
program to a consumer is disclosed, where the promotion program
includes a promotion program duration during which offers from the
promotion program are presented. The system includes: one or more
memories configured to store one or more consumer profiles,
promotion program data of offers relating to the promotion program,
and historical data of offers relating to different promotion
programs; and a processor in communication with the memory. The
processor is configured to: access a value of an attribute, the
attribute comprising or derived from a consumer attribute or a
promotion attribute; use a historical predictive model in order to
generate a historical predicted probability of the consumer's
acceptance of the offer, the historical predictive model configured
to input the value of the attribute and to output the historical
predicted probability, the historic predictive model using
performance data of offers from different promotion programs in
order to correlate values of the attribute to respective historical
predicted acceptances; use a promotion program predictive model to
generate a promotion program predicted probability of the
consumer's acceptance of the offer, the promotion program
predictive model configured to input the value and to output the
promotion program predicted probability, the promotion program
predictive model using performance data from previous offers from
the promotion program to correlate values of the attribute to
respective promotion program predicted acceptances; combine the
historical predicted probability of acceptance and the promotion
program predicted probability of acceptance in order to generate a
predicted probability of the consumer's acceptance of the offer;
use the predicted probability of acceptance in order to determine
whether to present an offer from the promotion program to the
consumer; and dynamically update the promotion program predictive
model multiple times during the promotion program duration.
[0007] Other systems, methods, and features will be, or will become
apparent to one with skill in the art upon examination of the
following figures and detailed description. It is intended that all
such additional systems, methods, and features included within this
description, be within the scope of the disclosure, and be
protected by the following claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The present invention may be better understood with
reference to the following drawings and description. Non-limiting
and non-exhaustive descriptions are described with reference to the
following drawings. The components in the figures are not
necessarily to scale, emphasis instead being placed upon
illustrating principles. In the figures, like referenced numerals
may refer to like parts throughout the different figures unless
otherwise specified.
[0009] FIG. 1A illustrates a representation of a network and a
plurality of devices that interact with the network to achieve deal
optimizing, according to the present invention;
[0010] FIG. 1B illustrates a block diagram of the promotion program
predictive model;
[0011] FIG. 1C illustrates a block diagram of the historical
predictive model;
[0012] FIG. 2 illustrates a flow chart describing an overview of
dynamic analysis of performance data (such as consumer feedback) to
offers for promotions;
[0013] FIG. 3A illustrates an expanded flow chart describing one
example to determine whether to use performance data for promotion
program analytics;
[0014] FIG. 3B illustrates an expanded flow chart describing
another example to determine whether to use performance data for
deal analytics;
[0015] FIG. 3C illustrates an expanded flow chart describing still
another example whether to use performance data for deal
analytics;
[0016] FIG. 4A illustrates a flow chart to determine which
consumers to select in order to ensure a minimum number of
consumers are presented with an offer from a promotion program;
[0017] FIG. 4B illustrates a flow chart of a part of the logic flow
of FIG. 4A.
[0018] FIG. 5 illustrates a flow chart to determine whether and how
to interpolate performance data;
[0019] FIG. 6 illustrates a table identifying data generated from
performance data;
[0020] FIG. 7A illustrates a flow chart in which subsequent days of
performance data are referenced when determining a probability of
acceptance;
[0021] FIG. 7B illustrates a flow chart in which subsequent days of
performance data are referenced when determining a probability of
acceptance;
[0022] FIG. 8 is a general computer system, programmable to be a
specific computer system, which may represent any of the computing
devices referenced herein.
DETAILED DESCRIPTION
[0023] The present invention as described herein may be embodied in
a number of different forms. Not all of the depicted components may
be required, however, and some implementations may include
additional, different, or fewer components from those expressly
described in this disclosure. Variations in the arrangement and
type of the components may be made without departing from the
spirit or scope of the claims as set forth herein.
[0024] A promotion may include any type of reward, discount,
coupon, credit, deal, voucher or the like used toward part (or all)
of the purchase of a product or a service. The promotion may be
offered as part of a promotion program. The promotion program may
include one or more attributes, such as the merchant offering the
promotion (e.g., "XYZ coffee shop), the location of the promotion,
the amount of the promotion, the category of the promotion (such as
a restaurant promotion), the subcategory of the promotion (such as
a sushi restaurant), or the like.
[0025] When a promotion system has multiple promotion programs, it
may be difficult to determine which promotion (or promotions) to
offer a specific consumer. In order to assist in the determination,
the promotion system may evaluate one, some, or all of the
available promotion programs by a metric, and select the promotion
program with the best metric. One metric is the probability that a
consumer will accept an offer for the promotion program (termed the
conversion rate). Another metric is the expected revenue if a
consumer is given an offer for the promotion program (e.g., the
probability that the consumer will accept the offer for the
promotion program multiplied by the revenue generated from
acceptance of the offer). The above metrics are merely for
illustration purposes. Other metrics are contemplated.
[0026] One or more sources of data may be used to evaluate the
promotion program for the metric, including data related to the
consumer and data related to promotion programs. Data related to
the consumer may be organized according to consumer attributes.
Attributes of the consumer include, but are not limited to, age,
gender, location, or the like.
[0027] With regard to data related to promotion programs, one
source of data is performance data related to promotion programs
other than the promotion program being evaluated. Performance data
may include any type of feedback associated with a promotion
program, such as a number of offers from the promotion program that
were presented to consumers, a number of acceptances of an offer
from the promotion program, an indication of interest in the
promotion program (e.g., selecting the promotion program for
examination on a website or opening an email with an offer for the
promotion), or the like. The performance data may be manipulated in
one of several different forms, such as a look-up table, a
predictive analytical model, one or more distributions, or the
like. For example, the performance data may be segmented according
to certain attributes, such as attributes of the promotion program
(discussed above) and/or attributes of the consumers. Further,
attributes based on both the consumer and the promotion program may
be generated, such as distance from promotion program (e.g., the
distance between the consumer's location, such as the consumer's
home, and the place of business of the promotion program) or
geographic direction (e.g. north, south, east, west) of the
consumer from the location of the promotion program. In this way,
the performance data may be segmented according to attributes of
the promotion program, attributes of the consumer, and/or
attributes based on both the consumer and the promotion
program.
[0028] Another source of data is performance data. The performance
data may relate to promotions programs other than the promotion
program being evaluated. Likewise, the performance data may relate
to the promotion program being evaluated. The performance data may
be organized in one of several ways. One way is in a model that
correlates performance data to one or more consumer attributes. For
example, performance data for promotions programs other than the
promotion program being evaluated may be organized in a historical
predictive model. Similarly, performance data for the promotion
program being evaluated may be organized in a promotion program
predictive model.
[0029] The performance data may be derived from the results of
offering promotions. For example, the performance data may be
organized into conversion rates, which is indicative of the number
of purchases of the deal divided by the number of time the deal is
offered to users. The conversion rate may take several forms, such
as a percentage. As discussed above, the performance data may be
correlated based on one or more attributes of the promotion program
and/or attributes of the consumers. For example, the performance
data may be correlated based on a consumer's age, a consumer's
gender, a consumer's distance from the promotion, or the like. As
another example, the performance data may include the number of
consumers that have received offers for the promotion and/or the
number of consumers that have accepted offers for the promotion.
The performance data may be manipulated in one of several different
forms. In this regard, the predictive models, including the
historical predictive model and the promotion program predictive
model, may be in one of several forms, such as a look-up table.
[0030] The promotion program may be include a promotion program
duration, during which offers from the promotion program are
presented. The promotion program duration may span a predetermined
period of time, such as from a start period to an end period, and
may be divided into discrete periods of time, such as a first
period during which first period offers are issued, a second period
during which second period offers are issued, etc. The
predetermined period of time may be a twenty four (24) hour time
period, a twelve (12) hour time period, or any other similarly
describable time period. For example, a promotion program may be
offered for a period of time lasting five days, and the five days
may further be segmented into five individual time periods, one day
for each of the five days. Performance data may be collected in
response to offers for the promotion program sent in one, some or
all of the time periods within the five day period of time.
[0031] The performance data collected from a previous time period
may be used in order to determine whether to offer a promotion to a
consumer in a subsequent time period. In this regard, the promotion
program predictive model may be updated dynamically multiple times
during the promotion program duration. For example, the performance
data collected from offers sent in day 1 of the specific promotion
program may be used for the promotion program predictive model.
More specifically, first period offers sent during the first period
may be accepted during the first period, during the second period,
etc. Likewise, second period offers sent during the second period
may be accepted during the second period, during the third period,
etc. The promotion program predictive model may be updated
dynamically at discrete points in the promotion program duration
and/or in response to receiving an acceptance of one of the offers
(such as first period offers, second period offers, etc.). For
example, the promotion program predictive model may be updated to
account for first period offers accepted during the first period.
As another example, the promotion program predictive model may be
updated to account for first period offers, outstanding during the
first period, and accepted during the second period and/or account
for second period offers accepted during the second period. Thus,
the promotion program predictive model may be iteratively updated
throughout part or all of the promotion program duration for the
promotion program. In this regard, the promotion program predictive
model may be updated with data prior to using the promotion program
predictive model to determine whether to offer promotions from the
specific promotion program to a particular consumer in the second
period, the third period, etc. An example of using performance data
is illustrated in U.S. Provisional Application No. 61/593,262,
herein incorporated by reference in its entirety.
[0032] As discussed in more detail below, the promotion program
predictive model may be used to predict the acceptance of offers by
consumers in different periods of the promotion program duration,
such as in the second period, in the third period, etc. More
specifically, the promotion program predictive model may be
accessed in order to determine a conversion rate correlated to a
specific attribute. For example, the promotion program predictive
model may correlate performance data with the distance attribute
(i.e., distance of the consumer from the promotion location). In
this regard, during the different periods of the promotion program
duration, the promotion program predictive model may output the
conversion rate. For example, the promotion program predictive
model, using performance data received during the first period, may
be accessed to estimate the conversion rate for consumers 0-2
miles. The estimated conversion rate accessed from the promotion
program predictive model may be used in determining whether to
present an offer in any of the subsequent periods of the promotion
program duration, such as determining whether to present an offer
in the second period, in the third period, etc.
[0033] In one embodiment, the estimated conversion rate accessed is
used to determine whether to present the offer in one of the
subsequent periods. For example, the promotion program predictive
model may output the estimated conversion rate for consumers 0-2
miles, with the estimated conversion rate for consumers 0-2 miles
being combined with an output from the historical predictive model.
In an alternate embodiment, an adjusted estimated conversion rate
(adjusted based on the estimated conversion rate accessed) is used
to determine whether to present the offer in one of the subsequent
periods. The estimated conversion rate as indicated by the
promotion program predictive model is, in effect, a snapshot in
time. As discussed above, offers from the promotion program may
span across multiple periods of the promotion program duration.
Acceptances to offers received in the first period are not
indicative of all of the acceptances of first period offers.
Rather, first period offers may be accepted during subsequent
periods. In this regard, the promotion program predictive model is
updated dynamically, as discussed above. Further, the performance
data, accessed during one period, may be adjusted in order to
reflect estimated acceptances in subsequent periods. For example,
the promotion program predictive model may be accessed in the
second period to determine the conversion rate, which is based on
the acceptances of first period offers received during the first
period. The accessed conversion rate is adjusted in order to
account for estimated acceptances of the first period offers during
later periods.
[0034] The adjustment of the estimated conversion rate may be based
on several factors. One factor is the price to accept an offer from
the promotion program. Typically, the higher the price to accept
the offer, the longer it takes for consumers to accept the offer
(e.g., a first period offer for a vacation will typically be
accepted later than an offer for a restaurant). In this regard, the
estimated conversion rate may be multiplied by a correction factor
to indicate the likelihood of subsequent acceptances of offers.
Another factor is the category and/or subcategory of a promotion
program. As discussed above, promotion programs may be categorized
by category and/or subcategory. The conversion rate may be adjusted
based on the category and/or subcategory in order to indicate the
likelihood of subsequent acceptances of offers.
[0035] In one embodiment, after the performance data is accessed,
the performance data in the promotion program predictive model may
be analyzed to determine whether the performance data is
insufficient to be relied upon. Determination of sufficiency may be
performed in one of several ways, as discussed in detail below. One
way is to examine the number of offers that were issued from the
promotion program for an attribute or a set of attributes. As
discussed above, the performance data may be correlated to a
specific attribute (such as age) or set of attributes (such as age
and gender) and may comprise a number of offers in addition to the
conversion rate. If the number of offers is less than a
predetermined number, the performance data may be deemed
insufficient. Another way to determine sufficiency of the
performance data is to examine the number of acceptances that were
received for an attribute or a set of attributes. As discussed
above, the performance data may be correlated to a specific
attribute (such as age) or set of attributes (such as age and
gender) and may comprise a number of acceptance in addition to the
conversion rate. If the number of acceptance is less than a
predetermined number, the performance data may be deemed
insufficient. Other factors are discussed in U.S. application Ser.
No. 13/839,036, entitled "Promotion System For Determining and
Correcting for Insufficiency of Promotion Data", filed on Mar. 15,
2013, hereby incorporated by reference herein in its entirety.
[0036] In one embodiment, in response to determining that the
performance data in the promotion program predictive model is
insufficient, the performance data in the promotion program
predictive model is not used in estimating acceptance of the offer
by the consumer. Instead, the estimated acceptance may be based on
the historical predictive model.
[0037] In an alternate embodiment, in response to determining that
the performance data in the promotion program predictive model is
insufficient, part or all of the performance data from the
promotion program predictive model may still be used in determining
an estimated acceptance of the offer by the consumer.
[0038] In one embodiment, the performance data in the promotion
program predictive model may be analyzed for sufficiency, with a
confidence level in the performance data resulting from the
analysis. A high confidence level indicates sufficiency in the
performance data whereas a low confidence level indicates
insufficiency of the data. The confidence level may then be applied
to the performance data (e.g., the conversion rate) in order to
determine the estimate acceptance of the offer by the consumer. For
example, a high confidence level may result in greater weighting of
the performance data, so that, when combined with the performance
data resulting from historical predictive model, the performance
data has a greater effect on the ultimate determination of the
estimated acceptance of the offer by the consumer. Conversely, a
low confidence level may result in lower weighting of the
performance data, so that, when combined with the performance data
resulting from historical predictive model, the performance data
has a lesser effect (or no effect) on the ultimate determination of
the estimated acceptance of the offer by the consumer.
[0039] In an alternate embodiment, in response to determining that
the performance data in the promotion program predictive model is
insufficient, other performance data may be accessed and combined
with the performance data deemed insufficient. For example,
performance data may be correlated to position in the electronic
communication (e.g., a first position in an email correspondence).
An example of this is discussed in U.S. application Ser. No.
13/839,786, entitled "Promotion Suppression System", filed on Mar.
15, 2013, incorporated by reference herein in its entirety. In
addition, the performance data may be correlated to age (such as
30-39 years old). In the event that the performance data for the
offer in the first position is insufficient (or the performance
data for the offer in the first position to consumers 30-39 years
old is insufficient), other performance data may be accessed and
combined with the insufficient performance data. More specifically,
performance data for the offer in a position other than the first
position (such as the second position, third position, etc.) may be
accessed. Likewise, performance data for the offer in a position
other than the first position to consumers 30-39 years old may be
accessed. The accessed performance data may then be combined with
the insufficient performance data in order to increase the
reliability of the desired performance data. In the example given,
the accessed performance data for the offer in the second position,
third position, etc. may be modified by one or more weighting
factors in order to combine with the insufficient performance data.
The weighting factors may account for a decreased likelihood of a
consumer accepting an offer in the second or third position of the
electronic communication. In this regard, conversion rates for the
offer in the third position of the electronic communication may be
increased, reflecting the higher likelihood of acceptance of the
offer if the offer had been placed in the first position.
[0040] As another example, performance data may be correlated to
multiple attributes, such as age (e.g., 30-39) and distance (e.g.,
0-2 miles from the promotion location. In the event that the
performance data correlated to both attributes is insufficient, the
performance data for different subsets of the attributes may be
accessed and combined. In particular, performance data may be
correlated to three attributes: "A", "B", and "C". In the event
that the performance data is deemed insufficient correlated to "A",
"B", and "C", different subsets of performance data may be
accessed, and combined, including performance data correlated to:
"A", "B", "C", "AB", "AC", "BC". Combining of the performance data
to the different subsets is described in detail below.
[0041] As discussed in more detail below, the promotion offering
system 102 as illustrated in FIG. 1A, may initially analyze the
performance data for the specific promotion program to determine
whether to use the performance data in evaluating whether to send
an offer for the specific promotion program. Further, if it is
determined to use the performance data in evaluating whether to
send an offer for the specific promotion program, the promotion
offering system may determine how to use the performance data for
the specific promotion program.
[0042] In practice, when a specific promotion program is first
offered to consumers (i.e. launch day, or at the start time
period), the promotion offering system 102 has no performance data
from the specific promotion program on which to rely. In such
situations, the promotion offering system 102 may still predict the
reaction of a particular consumer to an offer for the specific
promotion program. In this instance, the promotion offering system
102 may rely on data identifying how similar promotion programs
have fared with other consumers sharing similar attributes as the
particular consumer, which may include the particular consumer
himself, in the past.
[0043] In the case where the promotion offering system 102 is
configured to determine the probability that a particular male
consumer will accept an offer from a specific promotion program
that provides a discount at an Italian restaurant, a predictive
analytical model may be accessed that is able to generate a
probability that the particular male consumer will accept the
specific promotion program based on data describing how other male
consumers behaved when presented with a similar offer to receive a
discount on a meal from an Italian restaurant. The predictive
analytical model may access a database containing data identifying
a number of male consumers that were presented with offers to
receive a discount at an Italian restaurant, and data identifying
how many of those male consumers accepted such offers to receive a
discount at an Italian restaurant. From this data, the predictive
analytical model may generate a predicted conversion rate that
identifies the percentage of male consumers that have accepted
similar offers to receive a discount at an Italian restaurant given
a total number of male consumers that were offered similar
discounts at an Italian restaurant. The predicted conversion rate
is an indicator of a probability that the particular male consumer
will accept the offer to receive a discount at the specific Italian
restaurant. A similar predicted conversion rate may be generated
for a particular female consumer.
[0044] Further, the predictive analytical model may account for
other consumer attributes, promotion program attributes, or
attributes based on the consumer and the promotion program. For
example, the predictive analytical model may account for distance
of the consumer from the promotion program (e.g., 0-2 miles, 2-4
miles, etc.). So that in the above example, the predictive
analytical model may generate a probability that the particular
male consumer will accept the specific promotion program based on
data describing how other male consumers 2-4 miles from the
location of the specific promotion program behaved when presented
with a similar offer to receive a discount on a meal from an
Italian restaurant. Therefore, a number of attributes may be
considered simultaneously when generating a predicted conversation
rate to be used when determining the probability that a consumer
will accept an offer from a specific promotion program.
[0045] As previously described, in situations where a specific
promotion program is still in its start time period for being
offered (e.g., first launching day), the promotion offering system
102 may rely on predicted conversion rates when determining the
probability that a particular consumer will accept the offer from
the specific promotion program. However in subsequent time periods,
the promotion offering system 102 may rely on both the predicted
conversion rate and on performance data of the specific promotion
program from a previous time period.
[0046] This performance data of the specific promotion program that
is gathered from a previous time period may be more probative in
estimating whether consumers will accept offers from the specific
promotion program. Such performance data may include data
identifying a number of offers from the particular promotion
program that were presented to consumers, as well as data
identifying a number of offers from the particular promotion
program that were accepted by consumers in the previous time
period. Performance data may then be accessed by a dynamic
analytical model (discussed below) to generate a dynamic conversion
rate or other indicator of acceptance of an offer.
[0047] During an initial time period, the performance data for a
particular promotion program may be collected. Then in a subsequent
time period, the dynamic analytical model may access the gathered
performance data for the particular promotion program from the
previous time period and generate a dynamic conversion rate that is
based on the real performance of the particular promotion program.
Whereas the predicted conversion rate is based only on the past
performance of promotion programs that are categorized as being
similar to the particular promotion program, the dynamic conversion
rate generated following an initial time period is based on real
performance data of the particular promotion program.
[0048] Performance data may be organized in one of several ways,
such as based on one or more attributes of the consumers that
received the offer, based on one or more attributes of the
promotion program, based on one or more attributes of the
electronic communication, based on the date when the offers were
sent, etc. Further, the performance data may be used in one of
several ways. One way is to use the performance data in order to
calculate a conversion rate regardless of any attribute of the
consumer. Another way is to use the performance data to calculate
the conversion rate based on one, or both, of attribute(s) of the
consumer and attribute(s) of the electronic communication. Example
attributes of the consumer may include gender, age, distance from
the promotion, and the like. Attributes of the electronic
communication include, but are not limited to, position of the
promotion in the electronic communication, type of electronic
communication (e.g., email, web search download, etc.). In this
regard, one example of organization may comprise conversion rate
for consumers 0-2 miles from the promotion, where the promotion is
listed as the first promotion in the email.
[0049] FIG. 1A illustrates an overview for a promotion system 100
configured to offer and accept promotions for promotion programs.
The promotion system 100 includes a promotion offering system 102,
which communicates via one or more networks 122 with consumers,
such as consumer 1 (124) to consumer N (126), and with merchants,
such as merchant 1 (118) to merchant M (120). The promotion
offering system 102 includes an analytical model 104, which may
include one or more analytical models. As illustrated in FIG. 1A,
the analytical model 104 is shown to include a promotion program
predictive model 106 and a historical predictive model 108.
Although FIG. 1A is illustrated to show separate analytical models
within the analytical model 104, FIG. 1A is merely provided for
illustrative purposes and it is within the scope of the invention
to have the functionality of all the analytical models be performed
by a same computing machine, or separate computing machines.
[0050] The analytical models 104 communicate with multiple
databases that are part of the promotion offering system 102 such
as a promotion program database 110, consumer profiles database
112, historical data database 114 and dynamic data database 116.
Specifically, the historical predictive model 108 and the promotion
program predictive model 106 may access the databases 110, 112, 114
and 116 in order to generate information to be used in determining
a probability that a consumer will accept an offer from a promotion
program, as described in this disclosure.
[0051] The promotion program database 110 is responsible for
storing data detailing various promotion programs that are
available for offer in the promotion offering system 102. In order
to input promotion program information into the promotion program
database 110, merchants may communicate via the networks 122 with
the promotion offering system 102 to input the information
detailing the various promotion program offerings.
[0052] The consumer profiles database 112 includes profiles for the
consumers, consumer 1 (124) to consumer N (126), that are included
in the promotion system 100. The information detailed for a
consumer stored in the consumer profiles database 112 may include,
but is not limited to, name, age, address, occupation, educational
background, previously accepted promotion program offerings,
previously rejected promotion program offerings, gender and the
like. Any one, some or all of the attributes of the consumer may be
used by the promotion offering system 102 in determining whether to
offer a promotion to a consumer.
[0053] The historical data database 114 includes information
detailing the past performance of promotion program offerings that
have been presented in the promotion program system 102 in previous
time periods. The historical data database 114 may include, but is
not limited to, a number of offers from a promotion program that
were presented to consumers, a number of acceptances of an offer
from a promotion program, rates of acceptances of specific
promotion programs, attributes of consumers that accepted or
rejected specific promotion programs, and the like.
[0054] The dynamic data database 116 includes information detailing
the past performance of a promotion program offering that is
currently active in the promotion offering system 102. So that
while a promotion program referenced in the dynamic data database
116 is currently active, the data stored in the dynamic data
database 116 pertains to performance data of the active promotion
program from a previous time period. The analytical model 104 may
access dynamic data database 116 in order to configure promotion
program predictive model 106. Likewise, the analytical model 104
may access historical data database 114 in order to configure
historical predictive model 108.
[0055] Although FIG. 1A has been illustrated to show separate
databases 110, 112, 114 and 116, FIG. 1A has been illustrated for
demonstrative purposes only, and it is within the scope of the
present invention to have the databases 110, 112, 114 and 116
arranged in any combination of one or more memories/storage
units.
[0056] FIG. 1B illustrates a block diagram of the promotion program
predictive model 106. The promotion program predictive model 106 is
configured to organize the data from dynamic data database 116,
including organizing the data based on one or more consumer
attributes, based on one or more promotion program attributes,
based on one or more electronic communication attributes, based on
the date when the offers were sent, etc. The organized data may
take several forms including without limitation: the conversion
rate; the number of consumers who have accepted the offer; and/or
the number of consumers who have been presented the offer. Further,
the promotion program predictive model 106 is configured to receive
one or more inputs. As shown in FIG. 1B, the inputs include one or
more consumer attributes; one or more electronic communication
attributes (such as position of the offer in the email; the
date/time when the electronic communication with be sent); one or
more promotion program attributes (such as the price of accepting
the offer); and one or more derived attributes (such as distance of
the consumer from the promotion program, derived from the location
attribute for the consumer and the location attribute for the
promotion program). Further, the promotion program predictive model
106 is configured to generate one or more outputs. As illustrated
in FIG. 1B, an estimate of acceptance (such as conversion rate) may
be output based on the performance data. Alternatively, or in
addition, the promotion program predictive model 106 may be
configured to generate a weighting factor, which may be applied to
the estimate of acceptance. As discussed above, a weighting factor
may be used depending on the confidence level in the performance
data. In still an alternate embodiment, the estimate of acceptance
output from the promotion program predictive model 106 may already
be weighted. In still an alternate embodiment, in addition to the
weighting factor (or instead of the weighting factor), the
promotion program predictive model 106 may output an indication of
the error associated with the estimate of acceptance, such as an
estimate of the error associated with the estimate of acceptance.
The estimate for the error associated with the estimate of
acceptance for the promotion program predictive model 106 may be
based on one or both of the conversion rate and the number of
offers, as discussed in U.S. application Ser. No. 13/839,036,
entitled "Promotion System for Determining and Correcting for
Insufficiency of Promotion Data".
[0057] FIG. 1C illustrates a block diagram of the historical
predictive model 108. The historical predictive model 108 is
configured to organize the data from historical data database 114,
including organizing the data based on one or more consumer
attributes, based on one or more promotion program attributes,
based on one or more electronic communication attributes, etc.
Similar to the promotion program predictive model 106, the
historical predictive model 108 includes inputs of one or more
consumer attributes, one or more electronic communication
attributes, one or more promotion program attributes, and one or
more derived attributes. The historical predictive model 108 is
also configured to generate an estimate of acceptance by the
consumer. In an alternate embodiment, in addition to the estimate
of acceptance, the historical program predictive model 108 may
output an indication of the error associated with the estimate of
acceptance, such as an estimate of the error associated with the
estimate of acceptance. The historical predictive model 108 (with
the estimate of acceptance and error associated with the estimate
of acceptance) may be characterized in Bayesian statistical terms
as a "prior model". The promotion program predictive model 106
(with the estimate of acceptance and error associated with the
estimate of acceptance) may be characterized in Bayesian
statistical terms as a "predictive data model". In operation, the
output of the "prior model" and the output of the "predictive
model" may be combined to generate a "posterior" estimate.
[0058] FIG. 2 illustrates a flow chart 200 of a dynamic analysis of
performance data (such as consumer feedback) to offers for
promotions. The flow chart 200 is configured to determine a
probability of acceptance for an offer from a promotion program by
a consumer that is based on attribute information pertaining to the
consumer and other data that may be accessed from the databases
110, 112, 114, 116 illustrated in FIG. 1A.
[0059] At 201, the historical predictive model 108 is accessed. The
historical predictive model 108 may have as inputs one or more
attributes of the promotion program, one or more attributes of the
consumer, and/or one or more derived attributes (such as a distance
between the consumer and the promotion location). As discussed
above, the historical predictive model 108 organizes historical
performance data from promotion programs in order to generate an
output indicative of the likelihood that a consumer will accept a
promotion that is offered (such as a predicted conversion
rate).
[0060] The historical predictive model 108 may be organized into
different categories of users correlated with different categories
(and subcategories) of promotion types. For example, the historical
predictive model 108 may include an aggregation of the historical
data from previously-run promotions, organizing features of
consumers (such as gender and distance from a deal) with the
conversion rates for categories/subcategories of deals. In this
way, the historical predictive model 108 may be segmented by
consumers (such as males 0-2 miles from the deal, males 2-4 miles
from the deal, etc.) and segmented by promotions in different
categories/subcategories (such as the category of restaurants, and
the subcategories of Italian restaurants, Greek restaurants, etc.).
The historical predictive model 108 may therefore provide an
aggregation of the data from the previous promotions in order to
generate the conversion rates for the consumers in the different
categories (such as the conversion rate for users that are males
2-4 miles from a Greek restaurant deal in Chicago). The examples of
the categories of users and the categories/subcategories of deals
are merely for illustration purposes only. Other categories are
contemplated. For example, the categories of users may be
subdivided into gender, distance, and age. As another example, the
historical predictive model 108 may be subdivided based on price of
the promotion program.
[0061] In practice, data describing a consumer's attributes stored
in the consumer profile database 112 is input to the historical
predictive model 108. Likewise, the historical predictive model 108
receives as input one or more promotion program attributes, such as
the category of the promotion program, the location of the
promotion program, etc. In this way, the data accessed from the
promotion program database 110 may identify the specific promotion
program under consideration as belonging to a category of "Food and
Drink", and further belonging to a sub-category of "Italian Food".
The location of the Italian restaurant merchant that is offering
the specific promotion program may also be identified from the data
stored in the promotion program database 110. Also, the data
describing the consumer's attributes may include a location of the
consumer, from which the historical predictive model 108 may
generate a distance of the consumer from the specific promotion
program to be between 2-4 miles.
[0062] With the information accessed from the promotion program
database 110 and the consumer profile database 112, the historical
predictive model 108 may then access the historical data database
114 to obtain data that identifies a statistical indication of the
consumers that have accepted similar offers for Italian restaurant
promotion programs located 2-4 miles away. Then at 202, a
determination based on the data obtained by the historical
predictive model 108 may be made to output information related to
an indication of acceptance. One example of the output of the
historical predictive model 108 is a predicted conversion rate
which will be described in further detail below. Another example of
an output of the historical predictive model 108 may be a score
based on the predicted conversion rate.
[0063] At 203, the promotion program predictive model 106 is
accessed, which may include (or be based on) the performance data
of the particular promotion program gathered from a previous time
period. As discussed in more detail below, the promotion program
predictive model 106 may take one of several forms.
[0064] At 204, a determination is optionally made whether to use
the performance data pertaining to the specific promotion program.
In the case where no performance data for the specific promotion
program is available (e.g., it is currently the initial time period
(e.g. launch day) for the specific promotion program), the process
continues to 205. At 205, the probability of acceptance of the
offer from the specific promotion program is determined to be based
on the predicted conversion rate.
[0065] Where performance data on the particular promotion program
exists from a previous time period, the performance data is
analyzed, such as to determine a measure of reliability of the
performance data. FIGS. 3A-3C describe different analytical
measures of the reliability of the performance data gathered during
a previous time period.
[0066] If it is determined that the performance data is to be used,
at 206, a probability of acceptance of the offer from the specific
promotion program is determined based on the predicted conversion
rate and the performance data of the specific promotion program
gathered in a previous time period.
[0067] As discussed above, the promotion program predictive model
106 may generate an indication of acceptance of the specific
promotion program by the particular consumer using performance data
of the specific promotion program from a previous time period. The
output of the promotion program predictive model 106 may take one
of several forms. For example, the output of the promotion program
predictive model 106 may be a ratio of x/y, where x is the subset
of acceptances for the attributes of the particular consumer (such
as consumers who accepted that were 2-4 miles away from the
promotion), and where y is the number of offers for the attributes
of the particular consumer. As another example, the output of the
promotion program predictive model 106 may be a combination of
multiple expected conversion rates.
[0068] Regardless, the performance data for the specific promotion
program may be limited. So that, it may be difficult to obtain a
reliable indication of acceptance if the performance data is
subdivided into multiple attributes. For example, if the attributes
include a male consumer 2-4 miles away from the promotion location,
the performance data may not be sufficient to examine the
performance data to determine the results of consumers similarly
situated (e.g., other male consumers 2-4 miles away from the
promotion location).
[0069] As discussed above, in order to avoid an insufficient amount
of performance data, the promotion program predictive model 106 may
perform multiple analyses of the performance data. For example, the
promotion program predictive model 106 may generate multiple
indications of acceptance based on one or more analyses of the
performance data. In particular, the performance data may be
analyzed for one attribute (or one set of attributes), thereby
generating a first indication of acceptance. The performance data
may also be analyzed for another attribute (or another set of
attributes), thereby generating a second indication of acceptance.
As discussed above, attributes include, but are not limited to,
gender of the consumer, distance of the consumer from the specific
promotion program and a geographical direction of the consumer from
the specific promotion program. In the example given above, the
performance data may be analyzed with respect to male consumers
(e.g., how did male consumers react to the promotion program) to
generate a first indication, and the performance data may again be
analyzed with respect to consumers 2-4 miles away from the
promotion to generate a second indication. The two indications may
be combined in order for the promotion program predictive model 106
to generate the indication of acceptance of the specific
promotion.
[0070] For example, if the particular consumer is located 0-2 miles
from the specific promotion program that is being evaluated, a
first indication of acceptance may consider a dynamic conversion
rate for all users at 0-2 miles from the specific promotion. If the
distance attribute is the only attribute under consideration, the
promotion program predictive model 106 uses the first indication of
acceptance as the indication of acceptance of the specific
promotion program. If additional attribute(s) are to be considered,
additional analysis of the performance data with respect to the
additional attributes is performed, with the output(s) being
factored into the first indication of acceptance. For instance, if
the gender of the particular consumer is further considered and the
particular consumer is a male, the performance data may be examined
to determine the dynamic conversion rate for males that accepted
the specific promotion program in the previous time period. The
dynamic conversion rate that considers the second attribute (male)
may be a second indication of acceptance. In this way, the dynamic
conversion rate of the specific promotion program for males in the
previous time period is generated separately from the dynamic
conversion rate of the specific promotion program for all users
located a distance of 0-2 miles from the location of the specific
promotion program. The two dynamic conversion rates based on the
consumer's distance from the specific promotion program and
consumer's gender are generated separately in order to preserve the
pool of performance data from which the dynamic conversion rates
rely on. So that, the dynamic conversion rates that are generated
for each considered attribute (or each set of attributes) will be
generated separately.
[0071] After the separate dynamic conversion rates that consider
individual attributes (or different sets of attributes) have been
generated, the separate dynamic conversion rates may be combined to
generate a single dynamic conversion rate. For example, for the
particular consumer that is a male, if it is found that men
converted the specific promotion program at twice the rate of the
overall population (i.e., considering both male and females), then
the first indication of acceptance that is based on the distance
attribute may be adjusted by a correction factor that is based on
the gender attribute (such as multiplying the first indication of
acceptance by 2 for the particular consumer).
[0072] As another example, considering the same particular consumer
as above that is located 0-2 miles from the specific promotion
program and taking the first indication of acceptance as discussed
above, consider the example where a second attribute of the
consumer is a direction (e.g., north, south, east, west) of the
particular consumer from the specific promotion program. So that,
after generating the first indication of acceptance for the
particular consumer and the specific promotion program based on the
particular consumer being located 0-2 miles away, a second
indication of acceptance (such as a separate dynamic conversion
rate) may be generated based on the direction of the particular
consumer (such as the consumer being located north of the specific
promotion program). The second indication of acceptance may be
generated based on performance data from a previous time period of
the specific promotion program that identified a percentage of all
consumers that accepted the deal from the specific promotion
program that are located north of the specific promotion program.
After analyzing the first and second indication of acceptances, if
it is found that consumers north of the specific promotion program
converted at twice the rate of the overall population, the first
indication of acceptance that is based on the distance attribute
may be adjusted by a correction factor that is based on the
geographical direction attribute (such as by multiplying the first
indication of acceptance by 2 for the particular consumer that is
located north of the specific promotion program). Thus, the
promotion program predictive model 106 may generate an indication
of acceptance of the specific promotion using multiple indications
of acceptance.
[0073] As discussed above, the historical predictive model 108 is
configured to output a prediction that the consumer will accept an
offer for a promotion, with the prediction based on performance
data from other promotion programs. The prediction from the
historical predictive model 108 may be in one of several forms,
such as a single number, a distribution of numbers, or the like. In
particular, the predicted conversion rate may be represented by a
distribution, which may reflect conversion rates for past promotion
programs that share similar attributes as the specific promotion
program. For example, the distribution may be based on performance
data from a same or similar promotion program
category/sub-category, price, distance, and/or other various
attributes related to the particular consumer and the like. In one
implementation, the output of the historical predictive model 108
is a distribution (D) with an average and a variance
[0074] Generally speaking, the output of the historical predictive
model 108 is an indicator of how the specific promotion program
should perform vis-a-vis other similarly situated promotions. More
specifically, the historical predictive model 108 is an indicator
of what the distribution of conversion rates for this type of
promotion program is. The output of the promotion program
predictive model 106 is based on actual performance data from the
promotion program (such as x conversions from y offers). The
promotion offering system 102 is configured to use statistical
calculations to determine a likely overall conversion rate given
the distribution and the x conversions from y offers. Examples of
general guidelines for the statistical calculations include: the
overall probability is typically not x/y or the average of D; if y
is very large (e.g., the amount of performance data for the
specific promotion program is great), the overall probability is
closer to x/y; if D has a small variance (e.g., the conversion
rates for deals in the category/subcategory are similar), the
overall probability is closer to the average of D; and if D has a
large variance, (e.g., the conversion rates for deals in the
category/subcategory are dissimilar), the overall probability is
closer to x/y.
[0075] Given the general guidelines, the prediction from the
promotion program predictive model 106 and from the historical
predictive model 108 may be combined (such as statistically or
mathematically combined). One type of combination is a Bayesian
statistical combination. In particular, one may assume that the
distribution D is a beta distribution. Bayesian statistics relies
on the interpretation of probabilities, which in this case is the
probability of a consumer accepting a deal offered from the
specific promotion program. The Bayesian combination utilized
relies on a dataset of distributions that describes a distribution
of conversion rates for past promotion programs that share similar
attributes as the specific promotion program, as described in
further detail below.
[0076] Given an example where the selected distribution of
conversion rates for similar past promotion programs is a beta
distribution, two variables may be considered: alpha and beta. Beta
distributions may be effectively utilized when relying on Bayesian
statistical models, as is the current invention. The beta
distribution is used to describe the distribution of an unknown
probability value, which in this case is the use of the
distribution of past conversion rates for promotion programs that
are similar to the specific promotion program in order to determine
a probability that the particular consumer will accept an offer
from the specific promotion program.
[0077] So that, alpha and beta are fit to the score of the
predictive analytical model and the variance of conversion rates
that make up the score. Specifically, method of moments may be used
for the distribution D. In statistics, the method of moments is a
method of estimation of population parameters such as mean,
variance, median, etc. (which need not be moments), by equating
sample moments with unobservable population moments and then
solving those equations for the quantities to be estimated. Thus,
the method of moments may estimate a population of parameters,
which in this case may be seen as the estimation for the predicted
conversion rate from the beta distribution of previous conversion
rates for similar promotion programs.
[0078] Given the beta distribution of the selected distribution,
alpha/(alpha+beta)=score. Further, the most likely overall
probability of acceptance may be represented by:
(x+alpha)/(y+alpha+beta).
[0079] An example of the implementation is a specific promotion
program belonging to the category of "Health and Beauty", and
further the sub-category of "Skin Care & Facials". Given the
above category and sub-category of the specific promotion program,
assume 2992 offers were presented to consumers for similar
promotion programs in a previous time period, (y). And assume that
for those 2992 offers, 1 offer was actually accepted by a consumer,
(x). Also assume an alpha value of 1.67, and a beta value of 1180.
From this data set and assuming a beta distribution of past
conversion rates, a most likely predicted conversion rate can be
calculated according to the relationship of:
(x+alpha)/(y+alpha+beta)=0.0006. This calculation for the likely
predicted conversion rate can be seen to be different from if
simply (x)/(y)=0.0003 were calculated. Then by referencing the
calculated predicted conversion rate (0.0006), the score may be
calculated according to: alpha/(alpha+beta)=0.0014. The standard
deviation for this situation is 0.0011.
[0080] As another example, a specific promotion program may belong
to the category of "Food & Drink", and further the sub-category
of "Thai food". Given the above category and sub-category of the
specific promotion program, assume 2991 offers were presented to
consumers for similar promotion programs in a previous time period,
(y). And assume that for those 2991 offers, 5 offers were actually
accepted by a consumer, (x). Also assume an alpha value of 1.07,
and a beta value of 267.5. From this data set and assuming a beta
distribution of past conversion rates, a most likely predicted
conversion rate can be calculated according to the relationship of:
(x+alpha)/(y+alpha+beta)=0.00186. This calculation for the likely
predicted conversion rate can be seen to be different from if
simply (x)/(y)=0.00167 were calculated. Then by referencing the
calculated predicted conversion rate (0.00186), the score may be
calculated according to: alpha/(alpha+beta)=0.004. The standard
deviation for this situation is 0.0038.
[0081] Alternatively, according to one embodiment, instead of
referencing the performance data of the specific promotion program
gathered from a previous time period to generate a dynamic
conversion rate, the performance rate may be referenced to generate
a correction factor that may be applied to the predicted conversion
rate. The predicted conversion rate may be generated according to
the disclosure above. The predicted conversion rate with the
correction factor applied to it may then be used to determine the
probability of acceptance of the promotion program offer by the
consumer.
[0082] A value of the correction factor may be dependent upon the
performance data that is gathered for the specific promotion
program in the previous time period. In particular, the correction
factor may be dependent upon a reliability of the performance data.
A more detailed description for how the reliability of the
performance data stored in the dynamic data database 116 is
determined is provided below.
[0083] FIGS. 3A-3C illustrates flow charts determining a
reliability of performance data gathered for a promotion program in
a previous time period. Each of FIGS. 3A-3C are an expansion of the
step 204 from FIG. 2, where a determination is made as to whether
to use the performance data or not. FIGS. 3A-3C expands upon this
point by correlating the decision to use the performance data to a
determination as to the reliability of the performance data.
[0084] According to flow chart 204-A, performance data
corresponding to the number of offers from a specific promotion
program that were presented to consumers in a previous time period
is obtained from the dynamic data database 116. This performance
data is essentially the number of offers from the specific
promotion program that were transmitted to consumers, or the number
of impressions made.
[0085] At 302 a determination is made as to whether the number of
offers from the specific promotion program that were sent out to
consumers is greater than a minimum threshold amount. An example of
the minimum threshold is 3000. Other minimum thresholds are
contemplated. If the number of offers is not greater than the
minimum threshold amount, then the performance data is considered
to be unreliable at 303. Unreliable performance data need not be
used or referenced. However, if the number of offers is greater
than the minimum threshold amount, then the performance data may be
considered to be reliable at 304, and the performance data may be
used later when determining the probability that the consumer will
accept the specific promotion program if offered. At 304, the
probability of acceptance may be a prediction that takes into
account both a predicted conversion rate and the performance data
that was determined to be reliable.
[0086] Optionally, a second, higher, threshold may be utilized in
determining whether the performance data is considered reliable. At
305, a second (optional) determination is made as to whether the
number of offers sent out to consumers is greater than a second
threshold. The second threshold may be greater than the previous
minimum threshold referenced in 302.
[0087] If the number of offers from the specific promotion program
transmitted to consumers in the previous time period is not greater
than the second threshold amount, then at 306, it is seen that no
further adjustments are made to the figure obtained for the
probability of acceptance that the consumer accepts the offer from
the specific promotion program described in 304.
[0088] However if the number of offers from the specific promotion
program transmitted to consumers in the previous time period is
found to be greater than the second threshold amount, then at 307 a
dynamic conversion rate is generated based on the performance data
that was found to be reliable. The description for how a dynamic
conversion rate is generated is provided above. After a dynamic
conversion rate is generated at 307, the probability of acceptance
of the offer from the specific promotion program by the consumer
may be adjusted to only rely on the dynamic conversion rate at 308.
So unlike the probability of acceptance that is determined at 304
when the number of offers is found only to be greater than a
minimum threshold where both a predicted conversion rate and
performance data is relied on, when the number of offers is found
to be greater than a second threshold value as in 307 only the
dynamic conversion rate that is based on the reliable performance
data may be relied on when determining the probability of
acceptance. The adjustment made in 308 is taken into account during
the determination made in 206 of flow chart 200.
[0089] According to the flow chart 204-B, performance data
corresponding to the specific promotion program is referenced to
generate a dynamic conversion rate at 310.
[0090] At 311, a determination is made as to whether the dynamic
conversion rate is greater than a minimum threshold amount. If the
dynamic conversion rate is not greater than the minimum threshold
amount, then the performance data is considered to be unreliable at
312. Unreliable performance data is not used or referenced.
However, if the dynamic conversion rate is greater than the minimum
threshold amount, then the performance data may be considered to be
reliable at 313, and the performance data may be used later when
determining the probability that the consumer will accept the
specific promotion program if offered. At 313, the probability of
acceptance may be a prediction that takes into account both a
predicted conversion rate and the performance data that was
determined to be reliable.
[0091] Optionally, a second, higher, threshold may be utilized
during the determination of whether the performance data may be
considered reliable. At 314, a second (optional) determination is
made as to whether the dynamic conversion rate is greater than a
second threshold. The second threshold may be greater than the
previous minimum threshold referenced in 311.
[0092] If the dynamic conversion rate of the specific promotion
program in the previous time period is not greater than the second
threshold amount, then at 315 it is seen that no further
adjustments are made to the figure obtained for the probability of
acceptance that the consumer accepts the offer from the specific
promotion program described in 313.
[0093] However if the dynamic conversion rate is found to be
greater than the second threshold amount, then at 316 the
probability of acceptance of the offer from the specific promotion
program by the consumer is adjusted to only rely on the dynamic
conversion rate. So that, unlike the probability of acceptance that
is determined at 313 when the dynamic conversion factor is found
only to be greater than a minimum threshold where both a predicted
conversion rate and performance data is relied on, when the dynamic
conversion factor is greater than a second threshold value as in
316 only the dynamic conversion rate that is based on the reliable
performance data will be relied on when determining the probability
of acceptance. The adjustment made at 316 is taken into account
during the determination made in 206 of flow chart 200.
[0094] According to the flow chart 204-C, performance data
corresponding to the specific promotion program is referenced to
generate information detailing revenue collected from the
acceptance of the specific promotion program at 320.
[0095] At 321, a determination is made as to whether the revenue
collected is greater than a minimum threshold amount. If the
revenue collected is not greater than the minimum threshold amount,
then the performance data is considered to be unreliable at 322.
Unreliable performance data will not be used or referenced
according to the present invention. However, if the revenue
collected is greater than the minimum threshold amount, then the
performance data may be considered to be reliable at 323, and the
performance data may be used later when determining the probability
that the consumer will accept the specific promotion program if
offered. At 323, the probability of acceptance may be a prediction
that takes into account both a predicted conversion rate and the
performance data that was determined to be reliable.
[0096] Optionally, a second, higher, threshold may be utilized
during the determination of whether the performance data may be
considered reliable. At 324, a second (optional) determination is
made as to whether the revenue collected is greater than a second
threshold. The second threshold may be greater than the previous
minimum threshold referenced in 321.
[0097] If the revenue collected of the specific promotion program
in the previous time period is not greater than the second
threshold amount, then at 325 it is seen that no further
adjustments are made to the figure obtained for the probability of
acceptance that the consumer accepts the offer from the specific
promotion program described in 323.
[0098] However if the revenue collected is found to be greater than
the second threshold amount, then at 326 a dynamic conversion rate
based on the reliable performance data is generated. The
description for how a dynamic conversion rate is generated is
provided above. Then at 327 the probability of acceptance of the
offer from the specific promotion program by the consumer is
adjusted to only rely on the dynamic conversion rate. So unlike the
probability of acceptance that is determined at 323 when the
revenue collected is found only to be greater than a minimum
threshold where both a predicted conversion rate and performance
data is relied on, when the revenue collected is found to be
greater than a second threshold value as in 326 only the dynamic
conversion rate that is based on the reliable performance data will
be relied on when determining the probability of acceptance. The
adjustment made at 327 is taken into account during the
determination made at 206 in the flow chart 200.
[0099] As previously described in detail above, performance data
for a specific promotion program gathered from a previous time
period may optionally be validated as being reliable before the
performance data is used. One variable in determining the
reliability of the performance data is the number of consumers that
were presented with offers from the specific promotion program in
the previous time period. The greater the number of consumers that
were presented with the offer from the specific promotion program,
the larger the data pool and therefore the more reliable the
performance data. For example, a data pool that tracked the
performance of a promotion program that was offered to 1000
consumers is considered more reliable than a promotion offer that
was only offered to 10 consumers. This is because the larger data
pool decreases the likelihood of outlying data corrupting the
"normal" performance data.
[0100] In an attempt to increase the likelihood that the
performance data for a promotion program is considered reliable,
the promotion offering system 102 is configured to present offers
for the promotion program to a minimum number of consumers in a
given time period (such as the initial time period). The minimum
number of consumers may correlate to a minimum threshold number to
determine the performance data reliable (such as 3000, as discussed
above).
[0101] FIG. 4A illustrates a flow chart 400 to determine which
consumers to select in order to ensure a minimum number of
consumers are presented with an offer from a promotion program.
[0102] At 401, an attribute of a consumer may be selected. For
example, the attribute may correspond to a distance of the consumer
from the specific promotion program.
[0103] At 402, groupings within the selected attribute may be
determined. For example, if the attribute is a distance of the
consumer from the specific promotion program, the groupings may be
determined to be the intervals of 0-2 miles, 2-4 miles, 4-6 miles,
6-8 miles, and so on.
[0104] At 403, a grouping within the attribute may be further
selected. For example, if the attribute is a distance of the
consumer from the specific promotion program, a grouping that
concentrates on a distance of 2-4 miles may be selected.
[0105] At 404, the consumer profiles database 112 is accessed to
select a consumer profile that matches the criteria of the
attribute selected at 401 and the grouping selected at 403.
[0106] At 405, a determination is made as to whether to present the
selected consumer with an offer from the specific promotion
program.
[0107] If it is determined that an offer is to be presented to the
consumer, a counter is incremented at 406. Then at 409 a
determination is made as to whether the counter is equal to the
desired number of consumers in the selected grouping that are to be
presented the offer from the specific promotion program. If it is
determined at 409 that the counter is equal to the desired number
of consumers in the selected grouping that are to be presented the
offer from the specific promotion program, then the process will
move on to 410. From 410, if there is another grouping to be
selected, the next grouping will be selected at 411. Or else if
there are no more groupings within the selected attribute to be
selected, then the process will end at 410.
[0108] If it is determined that an offer from the specific
promotion program is not to be made to the consumer at 405, or if
it is determined at 409 that the counter is not equal to the
desired number of consumers in the selected grouping that are to be
presented the offer from the specific promotion program, then
another determination is made at 407. The determination at 407
examines whether there are any other consumers to consider in the
selected grouping that has not yet been offered the specific
promotion program. If there are no consumers left in the grouping
left to consider ("No" at block 407), at 412, the delta is
increased. As discussed in more detail below in FIG. 4B (and
discussed in U.S. Provisional Application 61/593,262, incorporated
by reference herein in its entirety), the delta is a measure of
whether to offer the pre-feature promotion to a consumer. If the
consumer is outside of the delta, the consumer is not offered the
pre-feature promotion. If there are not enough consumers that have
been tagged to be offered the promotion, the delta may be
increased, thereby increasing the number of consumers offered the
pre-feature promotion. If at 407 it is found that consumers are
left in the selected grouping that have not yet been considered for
presentation of the offer from the promotion program, a next
consumer profile within the selected grouping is accessed at 408
and the logic flow repeats again from 405.
[0109] Alternatively, without selecting a consumer attribute and
grouping beforehand (such as at 401-403), flow chart 400 may be
accomplished by ensuring that a minimum number of random consumers
within the promotion program system 100 are presented with the
offer from the particular promotion program. Or after selecting a
consumer attribute, without selecting a specific grouping within
the attribute, a minimum number of consumers anywhere within the
attribute may be selected for being presented with the offer from
the promotion program. Thus variations of the logic flow provided
by the flow chart 400 illustrated in FIG. 4A are within the scope
of the present invention as long as a minimum number of consumers
are presented with the offer from the specific promotion
program.
[0110] FIG. 4B shows a logic flow of block 405 of the logic 400 in
which a single specific promotion program is evaluated. At block
413, "X" promotions are scored for the selected consumer. The
consumer may be offered promotions from multiple promotion
programs, including the specific promotion program. The multiple
promotion programs may be scored as a first step in determining
which promotion(s) to offer to the consumer. The scoring may be
based on historical data, such as: data gathered from the promotion
program under consideration (if data has already been gathered on
the promotion program under consideration); data gathered from
promotion programs with similar attributes (such as similar
locations, similar rewards, etc.); and data gathered from promotion
promotions that the consumer has accepted or rejected. At 414, the
top "Y" scores may be ranked. For example, if 20 full-feature
promotions are scored, the top 5 full-feature promotions, according
to score, may be ranked.
[0111] At 415, the specific promotion program for the selected
consumer may be scored. Similar to the scoring of the other
promotion programs, the scoring for the specific promotion program
may be based on historical data, such as data gathered from other
promotion programs with similar attributes and/or data gathered
from promotions that the consumer has accepted or rejected. At 416,
it is determined whether the difference between the score of the
specific promotion program and the top ranked "Y" score is less
than "delta". If the difference is less than delta, the ordering of
the scores may be changed. For example, at 417, the top ranked "Y"
score is replaced by the specific promotion program, and at 418,
the selected consumer is tagged as receiving the specific promotion
program.
[0112] The promotion offering system 102 may seek to offer the
specific promotion program to consumers. However, the offer of the
specific promotion program is made using "delta" in order to
confirm that the offer is made to a consumer who is, within the
"delta", considered likely to be interested in the promotion. As a
general matter, the greater the "delta", the less likely the
consumer may be interested when offered the pre-feature promotion.
Conversely, the smaller the "delta", the more likely the consumer
may be interested when offered the pre-feature promotion.
[0113] FIG. 5 illustrates a flow chart 500 for generating estimated
conversion rate data when the gathered performance data is
insufficient. As discussed above, the performance data is generated
for a specific promotion program. The performance data may be
segmented based on one or more attributes, such as attributes of
the promotion program, attributes of the consumer (e.g., gender),
and/or attributes based on both the promotion program or consumer
(such as distance between the consumer and the promotion location).
After segmenting the performance data, there may be insufficient
data. Table 1 in FIG. 6 illustrates an example in which the
performance data from a previous time period for two different
promotion programs are insufficient. The attribute in Table 1 is a
distance of a consumer from the particular promotion program, and
the attribute has been further specified into groupings of 0-2
miles, 2-4 miles, 4-6 miles, 6-8 miles, 8-10 miles, 10-12 miles,
12-14 miles, 14-16 miles and so on.
[0114] As shown in the example illustrated in FIG. 6, offers from
the first promotion program were not presented to consumers (or
were not presented to enough consumers) located at 4-6 and 12-14
miles from the promotion program. Therefore, the performance data
may be insufficient for distances of 4-6 miles and 12-14 miles.
[0115] For the second promotion program, offers from the second
promotion program were not presented to consumers located at 2-4,
8-10 and 12-14 miles from the second promotion program. Therefore
dynamic conversion rates at distances of 2-4, 8-10 and 12-14 were
not able to be generated.
[0116] In order to compensate for insufficient data, interpolation
is used. The interpolation may be on the performance data itself,
or on the results from analyzing the performance data (such as on
the conversion rate that is determined by analyzing the performance
data). For example, interpolation of known dynamic conversion rates
may be used to generate interpolated conversion rates that are an
estimation of what such conversion rates would have been in the
gaps had consumers in those distance ranges been presented with
offers from the respective promotion programs.
[0117] At 501, for a given attribute, it is determined whether the
performance data is insufficient. One indication of insufficiency
is whether there are any gaps in dynamic conversion rate data that
was able to be generated from performance data for any attribute
and grouping (e.g. distance of consumer from a promotion program,
and distance range) that is available. If there are no gaps, then
interpolation is not required as seen at 502.
[0118] If the performance data is insufficient, at 503 a first
dynamic conversion rate is referenced. The first dynamic conversion
rate is a dynamic conversion rate that has been generated based on
gathered performance data from a previous time period. For example
in Table 1, if the missing dynamic conversion rate is located at
4-6 miles for the first promotion program, the first dynamic
conversion rate may be the dynamic conversion rate at 2-4
miles.
[0119] At 504, a second dynamic conversion rate is referenced.
Following the same example where the missing dynamic conversion
rate is located at 4-6 miles for the first promotion program, the
second dynamic conversion rate may be the dynamic conversion rate
at 6-8 miles.
[0120] At 505, the first dynamic conversion rate and the second
dynamic conversion rate is interpolated to generate an interpolated
conversion rate for the missing grouping. In the same example
above, the first dynamic conversion rate at 2-4 miles may be
interpolated with the second dynamic conversion rate at 6-8 miles
to obtain the estimated interpolated conversion rate for the
missing dynamic conversion rate at 4-6 miles.
[0121] Alternatively, instead of a missing dynamic conversion rate,
interpolation may be performed whether or not the performance data
is considered sufficient. As illustrated in Table 1, the data is
segmented into ranges. Interpolation may be performed within one of
the ranges. For example, a distance range may be 6-8 miles. If the
distance of the consumer to the place of the promotion is 7.5
miles, the consumer falls within the designated 6-8 miles range. An
interpolation may be performed, using values from multiple ranges.
In particular, when the distance falls within a particular range,
another range may be selected that is closer to the distance. In
the example of 7.5 mile distance, the particular range is 6-8
miles. The other range selected closer to the distance is 8-10
miles. So that, the values from each of the ranges (the 6-8 mile
range and the 8-10 mile range) are weighted. In this instance,
because the distance is within the 6-8 mile range, the value from
the 6-8 mile range is given more weight than the value from the
8-10 mile range. As another example, the consumer is 8.00 miles
from the promotion program. The ranges, as shown are 6-8 miles and
8-10 miles. In this example, the predicted conversion rate may be
generated using interpolation with the conversion rates for 6-8
miles and 8-10 miles (such as with equal weighting for the values
from both ranges).
[0122] In this case, at 503 a first dynamic conversion rate that is
referenced may correspond to the dynamic conversion rate at 6-8
miles for the first promotion program.
[0123] Then at 504, a second dynamic conversion rate is referenced
may correspond to the dynamic conversion rate at 8-10 miles for the
first promotion program.
[0124] At 505, the first dynamic conversion rate and the second
dynamic conversion rate is interpolated to generate an interpolated
conversion rate for the insufficient grouping, which in this case
is for a consumer located 8 miles from the first promotion
program.
[0125] When interpolating the first and second dynamic conversion
rates, a weighting function may also be applied. For instance if
the insufficient performance data was for a consumer located 6.5
miles from the first promotion program, the first dynamic
conversion rate for the distance range 6.5 miles may be given a
higher weighting value during an interpolation of two known dynamic
conversion rates because the consumer is located within the
distance range of the first dynamic conversion rate.
[0126] When referencing known dynamic conversion rates to generate
an interpolated conversion rate estimation for a missing dynamic
conversion rate, it is preferable to reference dynamic conversion
rates that neighbor the missing dynamic conversion rate.
[0127] In some situations, a promotion program may be offered in
multiple time periods. In such situations, a promotion program
provider may have access to performance data of the promotion
program from more than one time period. FIGS. 7A and 7B illustrate
flow charts that describe two methods for dealing with reliable
performance data from more than one time period.
[0128] FIG. 7A illustrates a flow chart 700-A that describes a
process for handling reliable performance data of a promotion
program that is made available from more than one time period. The
following description will be made assuming the current time period
is time period N.
[0129] At 701 a first dynamic conversion rate is generated based on
performance data of a promotion program from a previous time period
(N-1).
[0130] At 702, the first dynamic conversion rate will be referenced
when determining a probability that a consumer will accept an offer
from a promotion program, where the promotion program is the same
promotion program from which the performance data is derived from
in 701.
[0131] At 703, a determination is made as to whether the same
promotion program is made available (i.e. offered) in a subsequent
time period (N+1). If the promotion program is not offered in a
subsequent time period, then the process is seen to end. However if
the promotion program is determined to be offered in a subsequent
time period, then the performance data from the current time period
(N) should be gathered for later use.
[0132] At 704 the performance data from the current time period (N)
is gathered and referenced when generating a dynamic conversion
rate for the current time period.
[0133] Then at 705 the time period is moved on to the subsequent
time period (N++).
[0134] At 706, on the subsequent time period all previously
determined dynamic conversion rates may be referenced when
determining whether the offer from the promotion program will be
accepted by the consumer in the now current time period at 706.
[0135] FIG. 7B illustrates a flow chart 700-B that describes a
process according to some embodiments of the present invention for
handling reliable performance data of a promotion program that is
made available from more than one time period. The following
description will be made assuming the current time period is time
period N.
[0136] At 710 a first dynamic conversion rate is generated based on
performance data of a promotion program from a previous time period
(N-1).
[0137] At 711, the first dynamic conversion rate will be referenced
when determining a probability that a consumer will accept an offer
from a promotion program, where the promotion program is the same
promotion program from which the performance data is derived from
in 710.
[0138] At 712, a determination is made as to whether the same
promotion program is made available (i.e. offered) in a subsequent
time period (N+1). If the promotion program is not offered in a
subsequent time period, then the process is seen to end. However if
the promotion program is determined to be offered in a subsequent
time period, then the performance data from the current time period
(N) should be gathered for later use.
[0139] At 713 the performance data from the current time period (N)
is gathered and referenced when generating a dynamic conversion
rate for the current time period.
[0140] Then at 714 the time period is moved on to the subsequent
time period (N++).
[0141] Then at 715 one dynamic conversion rate from any available
previous time period is selected.
[0142] At 716, on the subsequent time period the selected dynamic
conversion rate may be referenced when determining whether the
offer from the promotion program will be accepted by the consumer
in the now current time period at 716.
[0143] FIG. 8 illustrates a general computer system 800,
programmable to be a specific computer system 800, which can
represent any server, computer or component, such as consumer 1
(124), consumer N (126), merchant 1 (118), merchant M (120),
promotion offering system 102, promotion program predictive model
106, and historical predictive model 108. The computer system 800
may include an ordered listing of a set of instructions 802 that
may be executed to cause the computer system 800 to perform any one
or more of the methods or computer-based functions disclosed
herein. The computer system 800 can operate as a stand-alone device
or can be connected, e.g., using the network 122, to other computer
systems or peripheral devices.
[0144] In a networked deployment, the computer system 800 can
operate in the capacity of a server or as a client-user computer in
a server-client user network environment, or as a peer computer
system in a peer-to-peer (or distributed) network environment. The
computer system 800 may also be implemented as or incorporated into
various devices, such as a personal computer or a mobile computing
device capable of executing a set of instructions 802 that specify
actions to be taken by that machine, including and not limited to,
accessing the Internet or Web through any form of browser. Further,
each of the systems described can include any collection of
sub-systems that individually or jointly execute a set, or multiple
sets, of instructions to perform one or more computer
functions.
[0145] The computer system 800 can include a memory 803 on a bus
810 for communicating information. Code operable to cause the
computer system to perform any of the acts or operations described
herein can be stored in the memory 803. The memory 803 may be a
random-access memory, read-only memory, programmable memory, hard
disk drive or any other type of volatile or non-volatile memory or
storage device.
[0146] The computer system 800 can include a processor 801, such as
a central processing unit (CPU) and/or a graphics processing unit
(GPU). The processor 801 may include one or more general
processors, digital signal processors, application specific
integrated circuits, field programmable gate arrays, digital
circuits, optical circuits, analog circuits, combinations thereof,
or other now known or later-developed devices for analyzing and
processing data. The processor 801 may implement the set of
instructions 802 or other software program, such as manually
programmed or computer-generated code for implementing logical
functions. The logical function or any system element described
can, among other functions, process and convert an analog data
source such as an analog electrical, audio, or video signal, or a
combination thereof, to a digital data source for audio-visual
purposes or other digital processing purposes such as for
compatibility for computer processing.
[0147] The computer system 800 can also include a disk or optical
drive unit 804. The disk drive unit 804 may include a
computer-readable medium 805 in which one or more sets of
instructions 802, e.g., software, may be embedded. Further, the
instructions 802 may perform one or more of the operations as
described herein. The instructions 802 may reside completely, or at
least partially, within the memory 803 or within the processor 801
during execution by the computer system 800. Accordingly, the
databases 110, 112, 114, or 116 may be stored in the memory 803 or
the disk unit 804.
[0148] The memory 803 and the processor 801 also may include
computer-readable media as discussed above. A "computer-readable
medium," "computer-readable storage medium," "machine readable
medium," "propagated-signal medium," or "signal-bearing medium" may
include any device that has, stores, communicates, propagates, or
transports software for use by or in connection with an instruction
executable system, apparatus, or device. The machine-readable
medium may selectively be, but not limited to, an electronic,
magnetic, optical, electromagnetic, infrared, or semiconductor
system, apparatus, device, or propagation medium.
[0149] Additionally, the computer system 800 may include an input
device 807, such as a keyboard or mouse, configured for a user to
interact with any of the components of system 800. It may further
include a display 806, such as a liquid crystal display (LCD), a
cathode ray tube (CRT), or any other display suitable for conveying
information. The display 806 may act as an interface for the user
to see the functioning of the processor 801, or specifically as an
interface with the software stored in the memory 803 or the drive
unit 804.
[0150] The computer system 800 may include a communication
interface 808 that enables communications via the communications
network 122. The network 122 may include wired networks, wireless
networks, or combinations thereof. The communication interface 808
network may enable communications via any number of communication
standards, such as 802.11, 802.17, 802.20, WiMax, 802.15.4,
cellular telephone standards, or other communication standards, as
discussed above. Simply because one of these standards is listed
does not mean any one is preferred.
[0151] Further, the promotion offering system 102, as depicted in
FIG. 1 may comprise one computer system or multiple computer
systems. Further, the flow diagrams illustrated in FIGS. 2-7B may
use computer readable instructions that are executed by one or more
processors in order to implement the functionality disclosed.
[0152] The present disclosure contemplates a computer-readable
medium that includes instructions or receives and executes
instructions responsive to a propagated signal, so that a device
connected to a network can communicate voice, video, audio, images
or any other data over the network. Further, the instructions can
be transmitted or received over the network via a communication
interface. The communication interface can be a part of the
processor or can be a separate component. The communication
interface can be created in software or can be a physical
connection in hardware. The communication interface can be
configured to connect with a network, external media, the display,
or any other components in system, or combinations thereof. The
connection with the network can be a physical connection, such as a
wired Ethernet connection or can be established wirelessly as
discussed below. In the case of a service provider server, the
service provider server can communicate with users through the
communication interface.
[0153] The computer-readable medium can be a single medium, or the
computer-readable medium can be a single medium or multiple media,
such as a centralized or distributed database, or associated caches
and servers that store one or more sets of instructions. The term
"computer-readable medium" can also include any medium that can be
capable of storing, encoding or carrying a set of instructions for
execution by a processor or that can cause a computer system to
perform any one or more of the methods or operations disclosed
herein.
[0154] The computer-readable medium can include a solid-state
memory such as a memory card or other package that houses one or
more non-volatile read-only memories. The computer-readable medium
also may be a random access memory or other volatile re-writable
memory. Additionally, the computer-readable medium may include a
magneto-optical or optical medium, such as a disk or tapes or other
storage device to capture carrier wave signals such as a signal
communicated over a transmission medium. A digital file attachment
to an email or other self-contained information archive or set of
archives may be considered a distribution medium that may be a
tangible storage medium. The computer-readable medium is preferably
a tangible storage medium. Accordingly, the disclosure may be
considered to include any one or more of a computer-readable medium
or a distribution medium and other equivalents and successor media,
in which data or instructions can be stored.
[0155] Alternatively or in addition, dedicated hardware
implementations, such as application specific integrated circuits,
programmable logic arrays and other hardware devices, may be
constructed to implement one or more of the methods described
herein. Applications that may include the apparatus and systems of
various embodiments may broadly include a variety of electronic and
computer systems. One or more embodiments described herein may
implement functions using two or more specific interconnected
hardware modules or devices with related control and data signals
that may be communicated between and through the modules, or as
portions of an application-specific integrated circuit.
Accordingly, the present system may encompass software, firmware,
and hardware implementations.
[0156] The methods described herein may be implemented by software
programs executable by a computer system. Further, implementations
may include distributed processing, component/object distributed
processing, and parallel processing. Alternatively or in addition,
virtual computer system processing may be constructed to implement
one or more of the methods or functionality as described
herein.
[0157] Although components and functions are described that may be
implemented in particular embodiments with reference to particular
standards and protocols, the components and functions are not
limited to such standards and protocols. For example, standards for
Internet and other packet switched network transmission (e.g.,
TCP/IP, UDP/IP, HTML, and HTTP) represent examples of the state of
the art. Such standards are periodically superseded by faster or
more efficient equivalents having essentially the same functions.
Accordingly, replacement standards and protocols having the same or
similar functions as those disclosed herein are considered
equivalents thereof.
[0158] The illustrations described herein are intended to provide a
general understanding of the structure of various embodiments. The
illustrations are not intended to serve as a complete description
of all of the elements and features of apparatus, processors, and
systems that utilize the structures or methods described herein.
Many other embodiments can be apparent to those of skill in the art
upon reviewing the disclosure. Other embodiments can be utilized
and derived from the disclosure, such that structural and logical
substitutions and changes can be made without departing from the
scope of the disclosure. Additionally, the illustrations are merely
representational and cannot be drawn to scale. Certain proportions
within the illustrations may be exaggerated, while other
proportions may be minimized. Accordingly, the disclosure and the
figures are to be regarded as illustrative rather than
restrictive.
[0159] The above disclosed subject matter is to be considered
illustrative, and not restrictive, and the appended claims are
intended to cover all such modifications, enhancements, and other
embodiments, which fall within the true spirit and scope of the
description. Thus, to the maximum extent allowed by law, the scope
is to be determined by the broadest permissible interpretation of
the following claims and their equivalents, and shall not be
restricted or limited by the foregoing detailed description.
* * * * *