U.S. patent application number 12/309261 was filed with the patent office on 2011-11-10 for communication system and method for narrowcasting.
This patent application is currently assigned to NEXT JUMP, Inc.. Invention is credited to Isabella Chung, Thomas Fuller, Yong-Chul C. Kim, Nettana Samroengraja.
Application Number | 20110276377 12/309261 |
Document ID | / |
Family ID | 38957347 |
Filed Date | 2011-11-10 |
United States Patent
Application |
20110276377 |
Kind Code |
A1 |
Kim; Yong-Chul C. ; et
al. |
November 10, 2011 |
Communication system and method for narrowcasting
Abstract
A communication system with client devices in communication with
at least one communication network. User data stores are also in
communication with the communications network and store user data
of users using respective ones of the client devices. Offer data
stores also in communication with the communications network store
offers from merchants. A narrowcasting engine includes an active
data gathering module to collect the user data, and an active
learning module to generate a user profile based on the user data.
The communication engine selects dynamically offers from the offer
data store based on the profile, and communicates the selected
offers in the offer data store to the users.
Inventors: |
Kim; Yong-Chul C.; (New
York, NY) ; Chung; Isabella; (New York, NY) ;
Fuller; Thomas; (New York, NY) ; Samroengraja;
Nettana; (New York, NY) |
Assignee: |
NEXT JUMP, Inc.
New York
NY
|
Family ID: |
38957347 |
Appl. No.: |
12/309261 |
Filed: |
July 17, 2007 |
PCT Filed: |
July 17, 2007 |
PCT NO: |
PCT/US07/16287 |
371 Date: |
April 21, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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60831193 |
Jul 17, 2006 |
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60850263 |
Oct 10, 2006 |
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60924591 |
May 22, 2007 |
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60924592 |
May 22, 2007 |
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Current U.S.
Class: |
705/14.17 ;
705/14.25; 705/14.43; 705/14.66; 705/14.71; 707/748; 707/769;
707/E17.014; 715/764; 715/772 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 30/0269 20130101; G06Q 30/0226 20130101; G06Q 30/0224
20130101; G06Q 30/0275 20130101; G06Q 30/0215 20130101; G06N 20/00
20190101; G06Q 30/0244 20130101 |
Class at
Publication: |
705/14.17 ;
705/14.66; 705/14.71; 705/14.25; 705/14.43; 707/748; 715/772;
707/769; 715/764; 707/E17.014 |
International
Class: |
G06Q 30/00 20060101
G06Q030/00; G06F 3/048 20060101 G06F003/048; G06F 17/30 20060101
G06F017/30 |
Claims
1. A communication system, comprising: (a) one or more client
devices in communication with at least one communication network;
(b) one or more user data stores in communication with the
communications network and being to store user data of one or more
users using respective ones of the client devices; (c) one or more
offer data stores in communication with the communications network
and being to store one or more offers from one or more merchants;
and (d) a narrowcasting engine including (i) an active data
gathering module to collect the user data, and (ii) an active
learning module to generate a user profile based on the user data,
the user profile including inferred preference of the one or more
users, wherein the narrowcasting engine is to select dynamically
one or more offers from the offer data store based on the user
profile, and communicate the selected one or more offers in the
offer data store to the one or more users.
2. The communication system of claim 1, wherein the user data
collected by the active data gathering module includes demographic,
behavioral, and preference data.
3. The communication system of claim 2, wherein the preference data
includes a request for future reminders of past lost
opportunities.
4. The communication system of claim 2, wherein the behavioral data
includes at least one of click-throughs, hovers, and search terms
of offers presented on the one or more client devices.
5. The communication system of claim 1, wherein the active data
gathering module includes a preference game for obtaining
preference data of the one or more users.
6. The communication system of claim 1, wherein the profile
generated by the active learning module includes a persona type,
selected from a predetermined set of personas, for the one or more
users.
7. The communication system of claim 1, wherein the profile
generated by the active learning module includes an indication of a
life stage of the one or more users.
8. The communication system of claim 1, wherein an initial set of
user data of the one or more users is received from a sponsor of a
loyalty or rewards program.
9. A communication system, comprising: (a) one or more client
devices in communication with at least one communication network;
(b) one or more offer data stores in communication with the
communications network to store one or more offers from one or more
merchants; and (c) an offer ranking module to rank the offers in
the one or more offer data stores based on popularity.
10. A communication system, comprising: (a) one or more client
devices in communication with at least one communication network;
(b) one or more offer data stores in communication with the
communications network to store one or more offers from one or more
merchants; and (c) an offer bidding module useable by the one or
more merchants via the one or more client devices to modify the
merchant's offer based on a rank of the merchant's offer.
11. A method for communication, comprising: (a) collecting user
data of one or more users in a user data store; (b) storing one or
more merchant offers in an offer data store; (c) generating a
persona of one or more users based on the user data; (d) storing
the persona in a persona data store; (e) segmenting the one or more
offers in the offer data store based on the persona stored in the
persona data store; (f) segmenting the one or more users into one
or more segmentation cells; (g) matching the one or more offer
mixes with the one or more user segmentation cells based on rules
associated with each cell; and (h) transmitting the offer mix to
the one or more users.
12. The method of claim 11, wherein the rules include at least one
of suppression rules, designation rules, and offer mix integration
rules.
13. The method of claim 12, wherein the offer mix integration rules
determine which offers are to be combined to form the offer
mix.
14. A system for creating a merchant offer, comprising: (a) one or
more client devices in communication with a communication network;
(b) an enrollment module to solicit and receive merchant
information and an offer information; (c) a heat map module to
display on the one or more client devices consumer activity on the
communication network; and (d) a data store to store the merchant
information and the offer information.
15. The system of claim 14, wherein the display of the consumer
activity generated by the heat map module includes a graphical
representation depicting varying levels of activity over a period
of time based on at least one of product, product type, and
merchant.
16. The system of claim 15, wherein the graphical representation
includes at least one of varying shapes, sizes, or color in
proportion to the varying levels of activity.
17. The system of claim 14, further comprising an offer ranking
module to rank the offers in the data store based on
popularity.
18. The system of claim 17, further comprising an offer bidding
module accessible by the one or more merchants to modify the
merchant's offer based on the rank of the merchant's offer from the
offer ranking module.
19. A system, comprising: (a) a card transaction processing module
to generate purchase transaction data associated with a payment
card; (b) an offer data store including one or more offers from one
or more merchants; and (c) a transaction matching module to receive
the purchase transaction data associated with the payment card and
match the purchase transaction with the one or more merchants in
the offer data store.
20. The system of claim 19, further comprising a rewards module to
determine an incentive to be applied to the payment card based on
any offer associated with the matched merchant and to generate a
qualified transaction data to be transmitted to an issuer of the
payment card.
21. A system, comprising: (a) an offer data store including one or
more offers from one or more merchants; (b) a registered card
module to register one or more payment cards to be used for a
purchase transaction; (c) a transaction matching module to match
purchase transaction data resulting from the purchase transaction
with the one or more merchants in the offer data store; and (d) a
rewards module to determine an incentive to be applied to the one
or more payment cards based on any offer associated with the
matched merchant and to generate a qualified transaction data to be
transmitted to an issuer of the one or more payment cards.
22. The system of claim 21, further comprising a card processing
module to determine the amount to be credited back to the one or
more payment cards identified in the qualified transaction
data.
23. The system of claim 22, further comprising a statement
generator to generate a card statement including an itemized
listing of the purchase transaction and the amount credited back to
the one or more payment cards.
24. The system of claim 21, further comprising a points module to
convert a designated number of points into a monetary value and
apply the converted monetary value to the purchase transaction.
25. The system of claim 21, further comprising a points module to
convert a designated number of points into a monetary value and
apply the converted monetary value to a saving account.
26. The system of claim 21, further comprising a points module to
convert a designated number of points into a monetary value and
apply the converted monetary value to a charity account.
27. A method for testing a market segmentation, comprising:
segmenting users into one or more user segmentation cells, the user
segmentation cells being associated with one or more market
segments; generating one or more messages for the one or more user
segmentation cells associated with the users, the one or more
messages including an offer mix; sending the one or more messages
to a subset of the users associated with the one or more user
segmentation cells; analyzing one or more responses by the users
receiving the one or more messages to identify a type of message
eliciting a high response rate; refining the messages based on the
identified type of message; and sending the refined messages to all
of the users of the one or more segmentation cells.
28. The method of claim 27, wherein generating includes generating
one or more first messages for a first subset of the users, and one
or more second messages for a second subset of the users, wherein
the first and second messages are different.
29. The method of claim 28, wherein the generating, sending and
analyzing are repeated for a predetermine number of times.
30. A method for preference building, comprising: presenting one or
more questions and one or more offers available to a user on a user
interface, wherein the one or more questions and one or more offers
are dynamically created for the user based on initial user data;
receiving answers to the questions through the user interface;
processing the answers to generate preference data of the user;
dynamically changing offers presented on the user interface in near
real-time based on the preference data.
31. The method of claim 30, wherein the initial user data is
supplied by a network to which the user belongs.
32. The method of claim 31, wherein the network is a sponsor of a
loyalty/rewards program.
33. A method for preference building, comprising: presenting a
calendar interface to a user, the calendar interface including
indicia indicative of one or more past offers from one or more
merchants; and presenting a selection interface to the user, the
selection interface including an input field to designate past
offers that the user wishes to be reminded of in future
offerings.
34. The system of claim 10 further comprising: (e) an offer ranking
module to rank the merchant's offer to other offers in the one or
more offer data stores based on popularity.
35. The system of claim 10 further comprising: (e) a heat map
module to display on the one or more client devices consumer
activity on the communication network.
36. The system of claim 19, wherein the purchase transaction data
is included in a partially qualified transaction (PQT) issued by
the card transaction processing module.
37. The system claim 36, wherein the purchase transaction data
includes stock keeping unit (SKU) level data.
38. The system of claim 20, wherein the qualified transaction data
is included in a fully qualified transaction (FQT) issued by the
rewards module
39. The system of claim 21, wherein the purchase transaction data
is included in a partially qualified transaction (PQT) issued by
the card transaction processing module.
40. The system claim 39, wherein the purchase transaction data
includes stock keeping unit (SKU) level data.
41. The system of claim 21, wherein the qualified transaction data
is included in a fully qualified transaction (FQT) issued by the
rewards module
Description
[0001] This application claims the benefit of U.S. Provisional
Patent Application Nos. 60/831,193 filed on Jul. 17, 2006,
60/850,263 filed on Oct. 10, 2006, 60/924,591 filed on May 22,
2007, and 60/924,592 filed on May 22, 2007, which are incorporated
herein by reference.
BACKGROUND
[0002] 1. Field
[0003] The present invention relates to a communication system and
method, and more particularly, to a communication system and method
for narrowcasting information based on active data gathering and
active learning, providing merchant network services, and
transaction processing for accumulating and redeeming rewards on a
registered card with a spend, save, and give feature.
[0004] 2. Discussion of the Related Art
[0005] Traditional communication systems and methods of reaching
desired audiences among the masses mainly rely upon the
broadcasting model, such as mass mailings and television/radio
advertisements to inform potential consumers of various offerings
in the hopes of increasing business.
[0006] But, the traditional broadcasting model is highly
inefficient and research has shown that only a small fraction of
the population pays any attention to these broadcasts. Using free
standing inserts ("FSI") as an example, it has been estimated that
merchants spend about $3 billion annually on marketing campaigns,
while the amount actually redeemed by the consumers (i.e., those
that have responded) is estimated to be only about $30 million, or
only about a 1% response rate. Response rates to other forms of
broadcast are generally unknown and difficult to quantify.
[0007] Merchants also use emails as a cheaper marketing channel in
an effort to make the marketing process more efficient. But,
consumers become overwhelmed with unsolicited advertisement emails
(i.e., "spam") and all of its various forms of unsolicited
advertisements, such as "pop-ups" (e.g., unsolicited advertisements
that pop up during Internet use) and discard these indiscriminant
communications. The result is tremendous waste in marketing spend
for a miniscule return.
[0008] In an attempt to focus the communications to be more
relevant, the communications industry has recently been developing
ways to match information to recipients to find more information of
interest. This is often referred to as targeted marketing. This can
be based on demographic data. But, this information, for example
gender and location of residence, is generally insufficient to
assure offers that may be interesting to the user. To supplement
this information, others try to obtain information to profile the
users' interests.
[0009] Unfortunately, profiles are only as useful as the
information provided by the user. If the user provides false
information or does not provide any information during
registration, the targeted information will be irrelevant and
therefore useless. Usually profiling is achieved by presenting
users with vague questions to elicit the required information. For
example, a typical "general interest" category may be listed as
"outdoors." If a user designates "outdoors" as an interest during
registration, the user may get advertisements and/or offers ranging
from hiking shoes to picnic accessories to travel magazines because
such a preference is so vague. These so-called targeted
communications are only slightly more effective than general
broadcasting.
[0010] Some service providers have begun to supplement the vague
user preference categories with tracked user activities, such as
purchases made by the user. But, targeted advertisements and offers
from these known systems are ineffective because the offered
contents are almost always done in hindsight, i.e., based on past
activities and, therefore, tend to be too late.
[0011] In addition to targeting communications, certain business
use incentives to attract customers. For example, coupons are one
of the vehicles used to encourage consumers to purchase specific
products and/or spend at a particular business. Currently, some 300
billion coupons are distributed annually in the United States
through an approximately $6 billion national coupon industry.
Approximately half of the estimated $6 billion goes towards the
actual incentive with the other half going towards administration.
Combining this estimate with the fact that approximately 99% of the
coupons end up in the trash, unused, and unredeemed, consumers only
benefit from approximately $30 million in redeemed incentives
(i.e., only about 0.5% of the $6 billion actually goes to the
consumers).
[0012] What is needed, therefore, is a cost-efficient and
convenient incentive redemption system and method that would
provide benefits to both the consumers and merchants.
[0013] Another type of incentive typically used to draw consumers
to usage is a rewards/loyalty program. The main marketing thrust of
a rewards/loyalty program is to register, maintain, and increase
consumer usage of a particular merchant or service provider by
offering various incentives to the members of the program.
Approximately 160 million people belong to an airline loyalty
program, and approximately 32 million people belong to a credit
card rewards program. Businesses spend an estimated $25 billion on
rewards and incentives. Companies spend an estimated $50 million on
employee rewards and recognition programs.
[0014] One of the most popular incentives used by rewards/loyalty
programs is the "points" system. The idea is the member accumulates
certain number of points for specified activities defined by the
sponsor of the program (e.g., 1 point for every $1 spent, 1 point
for visiting a sponsoring merchant, etc., 1 point for every mile
traveled, etc.). Then, the member is given the opportunity to
redeem the accumulated points for a "reward." The reward may be a
product, service, or even cash that can be obtained by redeeming a
specified number of points (e.g., 1 back for every point, a free
camera for 3,400 points, a free plane ticket for 50,000 points,
etc.).
[0015] There are various disadvantages of the current points based
incentive programs. First, the rewards available for redemption are
extremely limited. Typically, products available for redemption are
generally products that are overstocked or outdated and are sitting
in warehouses, either purchased by the sponsors at a discount or
contracted by the warehouse vendors to help move the products.
Therefore, majority of the members find themselves ordering
products they do not need or do not find very appealing to burn the
points before losing them or letting them go to waste.
[0016] Second, the redeemable price and the cost spent are
generally disproportionate. That is to say, the amount the member
must spend to accumulate a point is far greater than the point is
worth when the time comes to redeem it. For example, many reward
programs equate 1 point for every $1 spent. But, a typical rewards
catalog will list a camera, for example, with a street price of
$150 to be redeemable with 3,400 points. As another example,
typical airlines equate 1 point for every 1 mile traveled. But, to
obtain a free plane ticket to a destination within the continental
United States, typical airlines require 50,000 points or more.
Given that the distance between the east coast and the west coast
is only about 3,000 miles; such an "incentive" does not necessarily
encourage a consumer to become a member just for the reward. Even
cash back reward programs typically only give back 1% of the amount
spent, and many sponsors push to apply the cash back as credit
against the bill or issue the cash as gift certificates.
[0017] Third, the redeeming process is extremely inefficient and
inconvenient. The typical wait time between requesting for
redemption of the points to actually receiving the reward is about
4 to 8 weeks, depending on the requested reward. Even when cash
back is requested, the processing time generally takes about 4 to 6
weeks, especially when a check or gift certificate is to be issued.
Because of the delay in the processing, the points total will not
reflect the pending redemption amount until the points are
redeemed. Accordingly, the offered "rewards" do not appeal to
consumers who understand that the economics behind the rewards
program are not only inconvenient but are not really incentives at
all.
SUMMARY OF THE INVENTION
[0018] Accordingly, one aspect of this invention provides for a
communication system with client devices in communication with at
least one communication network. user data stores are also in
communication with the communications network and store user data
of users using respective ones of the client devices. offer data
stores also in communication with the communications network store
offers from merchants. A narrowcasting engine includes an active
data gathering module to collect the user data, and an active
learning module to generate a user profile based on the user data.
The communication engine selects dynamically offers from the offer
data store based on the profile, and communicates the selected
offers in the offer data store to the users.
[0019] The user data collected by the active data gathering module
can include demographic, behavioral, and preference data. The
preference data can include a request for future reminders of past
lost opportunities; and the behavioral data can include at least
one of click-throughs, hovers, and search terms of offers presented
on the client device.
[0020] In addition, the active data gathering module allows a user
to participate in a preference game for obtaining preference data
of the user.
[0021] Preference building can be accomplished by presenting
questions and offers available to a user on a user interface, in
which the questions and offers are dynamically created for the user
based on initial user data. Answers to the questions are received
through the user interface and processed to generate preference
data of the user. Offers presentation can then be changed
dynamically on the user interface in near real-time based on the
preference data.
[0022] In some instances the initial user data is supplied by a
network to which the user belongs. Such network could be a sponsor
of a loyalty/rewards program.
[0023] Preference building can also be accomplished by presenting
to a user a calendar interface that includes indicia for past
offers from merchants. A selection interface then allows the user
to designate past offers that the user wishes to be reminded of in
future offerings.
[0024] Further, the profile generated by the active learning module
includes a persona type, selected from a predetermined set of
personas, for users. The profile can also includes an indication of
a life stage of users.
[0025] The system can also extend to an offer datastore for storing
offers from merchants and an offer ranking module for rank the
offers in the offer datastore based on popularity. Merchants can
also use an offer bidding module to modify the offers based on the
rank of the offer from the offer ranking module.
[0026] The invention also encompasses a method for communication,
including collecting user data of users in a user datastore;
storing merchant offers in an offer datastore; generating a persona
of users based on the user data; storing the persona in a persona
datastore; segmenting the offers in the offer datastore based on
the persona stored in the persona datastore; segmenting users into
segmentation cells; matching the offer mixes with the user
segmentation cells based on rules associated with each cell; and
transmitting the offer mix to the users.
[0027] The rules can include at least one of suppression rules,
designation rules, and offer mix integration rules and the offer
mix integration rules determine which offers are to be combined to
form the offer mix. the persona can be defined as before.
[0028] In addition, the invention extends to a system for creating
a merchant offer that uses an enrollment module to solicit and
receive merchant information and offer information; a heat map
module to display on the client devices consumer activity on the
communication network; and a datastore to store the merchant
information and the offer information.
[0029] The display of the consumer activity generated by the heat
map module includes a graphical representation depicting varying
levels of activity over a period of time based on at least one of
product, product type, and merchant. The graphical representation
includes at least one of varying shapes, sizes, or color in
proportion to the varying levels of activity.
[0030] The system also extends to a card transaction processing
module that generates purchase transaction data associated with a
payment card. This operates with an offer datastore including
offers from merchants; and a transaction matching module that
receives the purchase transaction data associated with the payment
card and match the purchase transaction with the merchants in the
offer datastore. A rewards module determines an incentive to be
applied to the payment card based on any offer associated with the
matched merchant and generates a qualified transaction data to be
transmitted to an issuer of the payment card.
[0031] Also, the system can include an offer datastore including
offers from merchants; and a registered card module to register
payment cards to be used for a purchase transaction. These work
together with a transaction matching module that matches the
purchase transaction with the merchants in the offer datastore; and
a rewards module that determines an incentive to be applied to the
payment cards based on any offer associated with the matched
merchant and generates a qualified transaction data to be
transmitted to an issuer of the payment cards.
[0032] A card processing module can be added to determine the
amount to be credited back to the payment cards identified in the
qualified transaction data. Another addition is a statement
generator that generates a card statement including itemized
listing of the purchase transaction and the amount credited back to
the payment cards. A points module can be used to convert a
designated number of points into a monetary value and apply the
converted monetary value to the purchase transaction. The points
module could also convert a designated number of points into a
monetary value and apply the converted monetary value to a saving
account. Alternatively, the points module could convert a
designated number of points into a monetary value and apply the
converted monetary value to a charity account.
[0033] The invention also extends to testing a market segmentation
by segmenting users into user segmentation cells, in which the user
segmentation cells being associated with market segments. Messages
including an offer mix are generated for the user segmentation
cells associated with the users. Messages are then sent to a subset
of the users associated with the user segmentation cells. These
users' responses are then analyzed to identify a type of message
eliciting a high response rate. The messages can then be refined
based on the identified type of message; and the refined messages
can then be sent to all of the users of the segmentation cells.
[0034] It is possible to generate a first message for a first
subset of the users, and a different second message for a second
subset of the users. Typically, the generating, sending and
analyzing processes are repeated for a predetermine number of
times.
[0035] Thus, the systems, sub-systems and methods of this invention
have numerous facets, many of which can be combined in different
configurations.
[0036] Additional features and advantages of the invention will be
set forth in the description which follows, and in part will be
apparent from the description, or may be learned by practice of the
invention. The objectives and other advantages of the invention
will be realized and attained by the structure particularly pointed
out in the written description and claims hereof as well as the
appended drawings.
[0037] It is to be understood that both the foregoing general
description and the following detailed description are exemplary
and explanatory and are intended to provide further explanation of
the invention as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The accompanying drawings, which are included to provide a
further understanding of the invention and are incorporated in and
constitute a part of this specification, illustrate embodiments of
the invention and together with the description serve to explain
the principles of the invention. In the drawings:
[0039] FIG. 1A is an overview of the various components of the
present invention;
[0040] FIG. 1B is a block diagram view of the loyalty/rewards
system;
[0041] FIG. 2 is a system diagram illustrating an exemplary
embodiment of the present invention;
[0042] FIG. 3 is a block diagram illustrating member
segmentation;
[0043] FIG. 4 is a block diagram illustrating segmentation
cells;
[0044] FIGS. 5A-5B are block diagrams illustrating offer
segmentation and an exemplary offer mix;
[0045] FIGS. 6A-6B are block diagrams illustrating a branding
approach and an exemplary branding result;
[0046] FIGS. 7-11 are views illustrating the testing and launch
process according to the present invention;
[0047] FIG. 12 is a block diagram that illustrates active data
gathering according to an exemplary embodiment of the present
invention;
[0048] FIG. 13 is a block diagram that illustrates active learning
according to an exemplary embodiment of the present invention;
[0049] FIG. 14 is a diagram that illustrate the registration and
activation of benefit sites in accordance with the present
invention;
[0050] FIG. 15 is a diagram that illustrates the preference
questions generated based on segmented cells;
[0051] FIG. 16 is an exemplary web page illustrating intelligent
questioning according to an exemplary embodiment of the present
invention;
[0052] FIGS. 17A-17G are exemplary illustrations of the data
collection process according to the present invention;
[0053] FIG. 18 is a chart illustrating an exemplary behavioral
analysis according to the present invention;
[0054] FIG. 19 is an exemplary profile of a persona in accordance
with the present invention;
[0055] FIGS. 20-22 are exemplary illustrations of the various
reports generated in accordance with the present invention;
[0056] FIG. 23 is a diagram illustrating a purchase funnel of the
present invention;
[0057] FIG. 24 is a block diagram illustrating an exemplary data
flow according to the present invention;
[0058] FIGS. 25-30 show an exemplary embodiment of segmentation and
preference gathering/learning according to the present
invention;
[0059] FIG. 31 is a view of an exemplary embodiment of the
auto-enroll module of the present invention;
[0060] FIG. 32 is a flowchart describing an exemplary enrollment
process according to the present invention;
[0061] FIG. 33 is a flowchart describing an exemplary offer
management process according to the present invention;
[0062] FIG. 34 is an exemplary view of a heat map according to an
exemplary embodiment of the present invention;
[0063] FIG. 35 is an exemplary view of various examples of heat
maps;
[0064] FIG. 36 is an exemplary view of an offer rank module of the
present invention;
[0065] FIG. 37 is an exemplary view of an offer bid module of the
present invention;
[0066] FIG. 38 is an exemplary view of merchant mapping;
[0067] FIG. 39 is a merchant workflow diagram of an exemplary
embodiment of the present invention;
[0068] FIGS. 40A-40D are exemplary screenshots shown during an
auto-enroll process in accordance with the present invention;
[0069] FIG. 41 is an exemplary view of an enrollment notification
in accordance with the present invention;
[0070] FIG. 42 is an exemplary view of a login page in accordance
with the present invention;
[0071] FIGS. 43A-43D are exemplary screenshots shown during a
merchant setup process in accordance with the present
invention;
[0072] FIGS. 44A-44E are exemplary screenshots of various merchant
tools in accordance with the present invention;
[0073] FIG. 45 is a block diagram illustrating an exemplary
embodiment of a payment processing system in accordance with the
present invention;
[0074] FIG. 46 is a block diagram illustrating an exemplary
embodiment of a registered card processing system in accordance
with the present invention;
[0075] FIG. 47 is a workflow diagram illustrating an exemplary
registered card process in accordance with the present
invention;
[0076] FIG. 48 is a view of an exemplary registered card statement
in accordance with the present invention;
[0077] FIG. 49 is a view of exemplary rules available through the
registered card system;
[0078] FIGS. 50-51 are illustrations of exemplary embodiments for
processing card transactions;
[0079] FIG. 52 is a flow diagram illustrating an exemplary process
for earning points in accordance with the present invention;
[0080] FIGS. 53-54 are flow diagrams illustrating exemplary
processes for burning points in accordance with the present
invention;
[0081] FIG. 55 is a diagram illustrating an exemplary process for a
registered card purchase transaction; and
[0082] FIGS. 56A-56F are exemplary views of a portal for accessing
a rewards/loyalty program.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE
INVENTION
[0083] Reference will now be made in detail to the preferred
embodiments of the present invention, examples of which are
illustrated in the accompanying drawings.
[0084] Overview of the Narrowcasting System and Method
[0085] The present invention is directed to presenting relevant
communications to relevant audiences at the relevant time. In
particular, the system and method for narrowcasting is directed
towards presenting communications in a discrete manner via
closed-loop marketing. As used herein, "closed-loop" marketing
refers to a marketing channel where a marketing campaign can be
traced from its launch to the end user. The narrowcasting system
and method of the present invention is directed to communicating
relevant offers from providers of goods and services to relevant
potential consumers at relevant times, although the system and
method of the present invention may be applied to other venues and
applications without departing from the scope of the invention.
[0086] Relevance of the communicated offers is only as accurate as
the preference data provided by the users. While users provide some
type of preference data to merchants, accurate preference data is
difficult to obtain without an established trust. As mentioned
above, prior art solutions of obtaining customer preference data
have relied on formal surveys or questionnaires. Preference data
elicited from these prior art mechanisms are generally of lower
relevance and marketing value. By contrast, preference data
provided by customers in affirmatively requesting for a specific
merchant offer during the course of online shopping as a matter of
customer service, for example, is highly relevant.
[0087] Accordingly, the present invention is directed to
establishing a system and method that provides "customer service"
experience to the users rather than the prior art approach of
"selling" products and services to the users. To illustrate, in
accordance with the present invention, a consumer that is shopping
for a computer laptop may view an expired offer for a computer
laptop from a specific merchant (e.g., Dell). The present invention
allows for the consumer to request a reminder regarding similar
offers in the future. At this point, the consumer has made an
explicit request about a specific merchant offer regarding a
specific product. Hence, similar offers in the future will likely
result in a higher rate of purchase (i.e., a conversion rate). The
higher the rate of conversion, the more profitable the customer
interaction becomes, and the more valuable the marketing service is
to the merchant.
[0088] As users realize that they are being "serviced" by relevant
offers and/or choices at the most opportune times, trust is
increased in the system's ability. As trust increases, usage of the
system increases. In turn, as usage increases, more accurate
preference data are obtained, which then act to provide even more
relevant offers at more relevant times.
[0089] The present invention increases the "trust" aspect of the
users' experience by leveraging the relationship already
established between the users and their affiliated "networks"
(e.g., employers, financial institutions, institutions, etc.) by
implementing the system and method of the present invention on a
"rewards/loyalty" platform. However, the system and method of the
present invention may be implemented on other types of business
models without departing from the scope of the invention. In the
exemplary embodiment of the present invention, the system and
method of the present invention operates on a rewards/loyalty
platform of networks of which the users are already members,
instant trust is already created between the system of the present
invention and that of the users. Initial sets of data for the
potential users (e.g., demographic, behavioral, preference
information) are provided by the networks already in a trust
relationship with the users, thereby making the initial preference
analysis already highly relevant and accurate even before usage by
the users. Therefore, the first impression of the system of the
present invention to first time users is one of relevance and
trust, thereby setting the tone for high usage.
[0090] While "relevance" of offers can increase usage by increasing
trust, breadth (i.e., quantity) and depth (i.e., quality) of
products and services available on the system are integral to
increasing usage of the system. Moreover, as explained in the
Background of the Invention, quality of the incentives as well as
convenience of redeeming the incentives are also significant
factors in increasing the usage of the rewards/loyalty program. In
accordance with the present invention, the narrowcasting system of
the present invention includes various payment and redemption
modules to increase the usage of the rewards/loyalty program by
benefiting the members, participating merchants, and the program
sponsors alike.
[0091] FIG. 1A shows an overview of the various components of the
present invention. As shown, the system of the present invention
generally includes a user network 10, merchant network 20, and a
loyalty/rewards system 30. As explained in detail below, the
loyalty/rewards system 30 integrates with other existing,
components such as the credit, debit, point-of-sales (POS) systems
40, cellular phone systems 50, charity organizations 60, and
financial/asset management systems 70.
[0092] FIG. 1B shows a block diagram view of the loyalty/rewards
system 30 of FIG. 1A. As shown in FIG. 1B, the loyalty/rewards
system 30 includes customer network technology component 32,
merchant network technology component 34, payment services
technology 36, and narrowcasting technology 38. Each of these
components is described in more detail below.
[0093] Narrowcasting
[0094] "Narrowcasting," as used in the exemplary embodiment of the
present invention, is a scientific approach to marketing using
computer, behavioral, and statistical science to target a market.
Narrowcasting is a communications model that provides the right
product to the right customer at the right time through the right
communications channel. The narrowcasting system and method of the
present invention accomplishes this task by using information
obtained from trusted relationships, explicit preferences
designated by the users, and inferred behavioral preferences
obtained by tracking users' activities to dynamically provide
relevant offers at relevant times to the right users. Unlike prior
art systems, the narrowcasting system of the present invention
creates synthetic behavioral profiles referred to herein as
"personas" with associated rules to match offers to the users. The
matched offers are then sent via the most effective communications
channel. An exemplary embodiment of the narrowcasting system and
method of the present invention is described below.
[0095] Narrowcasting System
[0096] As shown in FIG. 1B, the narrowcasting system 38 includes an
active data gathering component 38a and active learning component
38b. The details of these components are described with reference
to FIG. 2.
[0097] FIG. 2 shows an overall diagram of an exemplary embodiment
of the present invention. In its simplest form, narrowcasting
engine 250 dynamically matches the most relevant offers from
various providers of goods and services, referred to herein as
"merchants," (260a, 260b) to the most relevant users enrolled with
the narrowcasting system (290a, 290b) at the most relevant time. To
accomplish this end, the narrowcasting system of the present
invention includes various components.
[0098] As shown in FIG. 2, the narrowcasting system of the present
invention includes a user network member database 210 and a
merchant offer database 260. The term "user network" as used herein
refers to a group to which the users belong. Examples of user
networks include employers (i.e., HR), institutions (e.g.,
universities, credit card companies), affinity groups (e.g., trade
groups), and other entities with members who are networked through
the entity. Although not shown, networks register with the
narrowcasting system of the present invention to setup
narrowcasting services for their members. For example, the
narrowcasting system 38 may be used by the networks to provide
benefit services (e.g., perks, loyalty, or reward programs) to
their members. The narrowcasting system 38 may provide the offers
in the offer database 260 to the registered networks' members as
network membership benefits. Accordingly, the network member
database 210 contains information about the members provided by the
registering networks including demographic and other personal
information. Therefore, most of the users of the narrowcasting
system 38 are members of a registered network. In another exemplary
embodiment, access to the narrowcasting system 38 may also be
granted to non-network members, such as network members' family
members and associated individuals.
[0099] Merchant Offer Database
[0100] Merchant offer database 260 contains various offers, such as
incentives and discounts, offered by various merchants (260a,
260b). In the exemplary embodiment of FIG. 2, merchants may be
divided into two categories: (1) hosted merchants 260a, and (2)
network affiliated merchants 260b. Hosted merchants 260a are a
class of merchants who register with the narrowcasting system 38 to
provide offers to the users. Network affiliated merchants 260b area
class of merchants who have a working relationship with the
members' networks. For example, a particular merchant (e.g., a
flower shop) may be a sponsored merchant for a particular network
(e.g., an affiliated credit card network) while another merchant
(e.g., bookstore) has no affiliation with any of the member
networks. Therefore, the sponsored merchant is classified as a
network affiliated merchant 260b while the non-affiliated merchant
is classified as a host merchant 260a.
[0101] Narrowcasting Engine
[0102] The narrowcasting engine 250 is a computer programmed to
dynamically match the users (290a, 290b) with the offers in the
merchant offer database 260 while updating and facilitating various
transactions and activities provided by the narrowcasting system
38. In particular, the narrowcasting engine 250 is connected to a
user preference data store 240a, a user behavioral data store 240b,
rules/personas data store 240c, and a demographic data store 240d.
The data stores 240a, 240b, 240c, and 240d may be any type of
appropriate data storage device known in the art. The data stores
240a-240d may each be independent storage devices, sub-portions of
a single storage device, or any combination thereof without
departing from the scope of the invention. Moreover, the user
preference data, behavioral data, rules/personas, and demographic
data may be stored as flat files or as records in a relational
database or databases without departing from the scope of the
invention.
[0103] Demographic data includes personal information about the
user, such as name, company, title, location, gender, age, marital
status, etc. obtained from the network member database 210 and
directly communicated by the users during an enrollment stage.
Behavior data include such information as general interests,
viewing and transaction activities on the system (e.g.,
click-throughs), and inferred specific items of interest of the
user. The user preference data, as described in more detail below,
are directed to specific requests from the users that indicate the
users' future preferences. The narrowcasting engine 250 also
dynamically updates the user preference data from users'
activities, constantly supplementing the users' preference data
with newly obtained information. The types of data and how they are
obtained are explained in further detail below.
[0104] Based on the users' demographic, behavioral, and preference
information, the narrowcasting engine 250 creates synthetic
behavioral profiles (i.e., "personas") and stores/updates the
profiles in the rules/personas data store 240c. Using the personas
and rules associated thereto, the narrowcasting engine 250 selects
the most appropriate offers from the offer database 260 for each of
the users (290a, 290b) and presents the dynamically generated
communications to the users (290a, 290b) through the network
program portal (280a, 280b). Personas are explained in more detail
below.
[0105] The narrowcasting engine 250 tracks users' activities on the
system and dynamically updates user preference, behavioral, and
demographic data into the appropriate data stores 240a, 240b, and
240d, respectively, based on the users' activities. Some of the
activities facilitated and tracked by the narrowcasting engine 250
include user selections and viewing activities on the network
program portal (280a, 280b). If the user takes advantage of the
offers presented in the narrowcasted communication, the
narrowcasting engine 250 tracks the users' transaction regarding
the accepted offer through the transaction module 270.
[0106] Transaction Module
[0107] The transaction module 270 tracks the transaction between
the merchant related to the accepted offer and the user to report
the users' activities. The transaction module 270 also calculates
the payments due to or from the networks, merchants, and to the
narrowcasting system. The transaction module 270 is described in
more detail below with regard to the registered card
implementation.
[0108] Member Segmentation
[0109] The member segmentation module 220 processes information
regarding the members to initially set up membership registration
including assignment of member identification ("member ID"),
membership validations, offer suppression/designation, etc. Upon
log in, the registered members are then directed to the program
enrollment module 230. The program enrollment module 230 creates
and/or updates profile information of the user, such as designation
of preferences as well as information missing from the network
member database 210. The member segmentation module 220 and program
enrollment module 230 are explained in further detail below.
[0110] As explained above, an exemplary embodiment of the present
invention is a system and method of narrowcasting various offers
and incentives to members of affinity groups/networks. For
instance, one example is an employer sponsored portal accessible by
an employee to access company benefits hosted by the narrowcasting
system 38. Such employer-based portals may be set up, in part, to
allow employees to obtain perks and benefits, such as negotiated
discounts from affiliated merchants 260b, for example. Another
example is a financial network, such as credit card companies, who
set up portals accessible by card members to obtain loyalty rewards
and perks.
[0111] To host such benefits programs, a network registers with the
narrowcasting system 38 and provides information about their
members. As explained above, member information is stored in the
network member database 210. The member segmentation module 220
sets up member user access accounts by, for example, validating the
member users, assigning member user IDs, and offer
suppression/designation/integration rules. The offer
suppression/designation/integration rules are a set of rules set by
the user networks to suppress offers from specified merchants
and/or service providers, to designate specified merchants and/or
service providers to provide offers, and integrate various offers
together for narrowcasting. An example of offer suppression is a
sponsoring network (e.g., a product company) may not want their
members to receive offers from their competitors. An example of
offer designation is a sponsoring network (e.g., a credit card
company) may want their members to specifically take advantage of
offers from affiliated merchants.
[0112] More specifically, as shown in FIG. 3, the member
segmentation module 220 obtains members' information from the
network member database 210. Member data generally includes
demographic data and preference data provided to the user network.
For instance, if the user network is an employer-sponsored website
for its employees, the employer already has substantial information
about the employee, such as name, address, and position data. If
the user network is a credit card company, the credit card company
has the card member's information, such as name, address, income,
and credit history including debt information. Moreover, credit
card companies may also have past purchase history information
collected from their members' interaction with specific merchants
and special interest areas, such as interest in electronics, music,
sports, etc. The level of detail of the members' information
depends on the nature of the user network.
[0113] The member segmentation module 220 processes the member
users' data based on the demographic and preference data using
eligibility rules and existing marketing models to create a market
segmented member database 320. In particular, for descriptive
purposes only, each horizontal row of the segmented member database
320 represents members associated with a particular market segment
based on their demographic and preference information. As explained
further below, this segmentation is dynamically adjusted as the
users' preference information changes over a period of time. The
initial segmentation is made based on rules and models applied to
the information provided by the networks.
[0114] Member Enrollment
[0115] To create more accurate segments of members, the members are
directed to access program enrollment module 230 through an
enrollment website, for example, to create, update, and/or
supplement the members' information obtained from the user network.
Additional and/or missing demographic and preference data may be
collected through the program enrollment module 230 to supplement
the data from the member user database 210. The gathered preference
and demographic data are stored in the user preference data store
240a and the user demographic data store 240d, respectively.
[0116] Pilot Groups
[0117] Once the segmented member database 320 is populated, a pilot
group 330 of the segmented member database 320 is generated. The
pilot group 330 is a sampling of members across all the market
segments (denoted in the vertical dotted line) to obtain a workable
subset that accurately represents the entire collection of members.
This pilot group 330 is processed to create initial rules and
member cells to be tested and verified as accurate representation
of the members' personas, the process of which is explained further
below. Once the sampled set of members in the pilot group 330 has
been validated, the determined rules and personas are applied to
the entire body of members.
[0118] As shown in FIG. 4, a sample subset 400 of the pilot group
330 is used to create member segmentation cells 410. Member
segmentation cells 410 are generally categorized into three types
of cells: (1) custom cells 420, (2) advanced cells 430, and (3)
basic cells 440. Custom cells 420 refer to member segmentation
categories with rules customized in accordance with the directions
from the affinity group or network for select members. For example,
a credit card company may want to categorize certain segments of
its members as "luxury travel" members. As another example, an
employer may want to categorize certain segments of its employees
as "officers."
[0119] Advanced cells 430 are directed to member segmentation
categories and associated rules generated by the narrowcasting
system 38. Advanced cells 430 are created based on demographic,
preference, and behavioral data collected about the members. For
example, a certain combination of demographic, preference, and
behavioral data suggests that a particular segment of member users
are "workaholics" while another segment of member users are
preparing for a "wedding."
[0120] Basic cells 440 refer to member segmentation categories
generated from basic demographic information. For example, one
basic cell may be designated as "males" in the "national" region
while another basic cell may be designated as "females" in the
state of "New York." Specific segmentation cells are generally
created based on the needs of the network and the benefit services
it wants to provide for its members. However, segmentation cells
may be created as marketing testing beds for merchants requesting
to move certain types of items, for example. This process will be
explained in further detail below.
[0121] Once the member segmentation cells and associated rules have
been determined, the members in the sampled subset 400 of pilot
group 330 in the segmented member database 320 are assigned to
appropriate member segmentation cells. A member user may be
associated exclusively to a particular cell or end up being
associated to multiple cells based on the segmentation rules. The
member segmentation cells and rules associated thereto are stored
in rules/personas data store 240c (FIG. 2).
[0122] Offer Segmentation
[0123] The member segmentation cells 420, 430, and 440 have
associated rules that decide which offers in the offer database 260
will be associated with which segmentation cell. As explained above
with respect to FIG. 2, the offer database 260 is populated with
various types of offers from hosted merchants 260a and
network-affiliated merchants 260b. As explained above,
network-affiliated merchants 260b may be groups of merchants that
provide unique offers to members of particular networks due to
their affiliation to the particular network. For instance, a
particular employer may have a negotiated discount with a specific
clothing store for its employees to encourage loyalty to that
particular clothing store. As another example, a particular credit
card company may have negotiated incentives with a particular
merchant if the member user uses the particular credit card to
transact with the merchant. Other merchants who wish to use the
narrowcasting system of the present invention to provide various
offers to the member users are generally classified as hosted
merchants 260a with no particular affiliation to the user
networks.
[0124] The offers in the offer database 260 undergo offer
segmentation based on rules stored in the rules/persona data store
240c. As shown in FIG. 5A, various rules may be applied to segment
the offers in the offer database 260. For example, the offer
segmentation rules may include suppression rules 510, designation
rules 520, and offer mix integration rules 530. In particular, the
networks may have reasons to exclude certain offers from being made
available to their members. Some examples of suppression rules 510
may include suppressing certain offers from being assigned to
specified segmentation cells by categories (e.g., offers related to
"flowers"), by merchant (e.g., competitors), by offers (e.g.,
"instant rebates," "free shipping"), and other suppression criteria
requested by the user networks. Conversely, networks may have
reasons to specifically designate certain offers to be made
available to their members. Some examples of designation rules 520
may include offers based on strategic relationships, business
partnerships, and other designation criteria requested by the
affinity groups/networks. The offer mix integration rules 530
determine which offers are to be mixed together to form an offer
mix. For example, offer mix integration rules 530 may designate
certain hosted offers, network exclusive offers, and jointly
sourced offers into an integrated mix of offers. The offer mix
resulting from the offer mix integration rules 530 are segmented
and designated to specific cells in the offer segmentation module
540.
[0125] More specifically, the narrowcasting engine 250 (FIG. 2)
matches various offer mixes resulting from the offer mix
integration 530 with the member segmentation cells 420, 430, and
440 based on the rules assigned to each cell stored in the
rules/persona data store 240c (FIG. 2). FIG. 5B shows exemplary
offer segmentations matched with some of the exemplary member
segmentation cells.
[0126] Branding
[0127] To draw interest and make an impression of relevance to the
members who wish to enroll and eventually use the programs created
by the narrowcasting system of the present invention, the
narrowcasting system 38 provides branded site customization for
each member based on their affiliated network and assigned
segmentation cells. The narrowcasting system 38 applies branding
strategies based on a two-phase approach. For example, as shown in
FIG. 6A, Phase 1 block 610 compiles results from primary research
to leverage members of the segment and/or segment experts to obtain
informed branding. Research is conducted by posing various
preference questions to member users associated with the member
segment for which the branding strategy is being built. Some
examples of primary research resources include feedback from
segment members, information from industry experts, and opinions
from focus groups.
[0128] In Phase 2 block 620, results from secondary research are
compiled to augment and validate primary research results. Some
examples of secondary research resources include data/analytics,
market research, industry periodicals, and inspiration screens from
other websites. The results of Phase 1 and Phase 2 are combined to
obtain a branding strategy 630. FIG. 6A shows an example of the
branding approach using the "Weddings" member segment. As shown in
FIG. 6B, the obtained branding strategy for a member segment (e.g.,
"Weddings") is associated with a segment cell. Thereafter, the
obtained branding strategy is applied and provided to members who
are associated with the segment cell.
[0129] CABR (Credibility, Affinity, Benefit, and Redemption)
[0130] Once members and offers have been segmented, the
narrowcasting system 38 tests the segmentations using the active
learning approach. The active learning approach according to the
present invention includes a two phased approach. As shown in FIG.
7, the first phase (Phase I) includes testing a sampling of members
and teaming their response to the offer segmented mix developed for
the segments associated to the tested members. The responses are
analyzed and results are "learned" for refinement. The results of
Phase I are applied to Phase II which includes launch of the
programs and initiatives developed by the narrowcasting system
using real-time optimization and feedback loop.
[0131] FIG. 8 shows Phase I of the active learning process
according to the present invention. In the test planning stages of
Phase I, test member segmentation, offer segmentation, and CABR
messages are developed for each segment of the test members. "CABR"
stands for credibility, affinity, benefit, and redemption.
"Credibility" messages focus on the networks' value-proposition,
business model, and/or third party validation (e.g., review
articles from reputable entities). "Affinity" messages focus on the
relationship between the members and their networks. "Benefit"
messages focus on value-added offers. "Redemption" messages focus
on how to redeem the offers emphasizing on ease, time-savings, and
convenience. The following are examples of CABR messages directed
to the same benefit program:
[0132] Credibility--"You already enjoy the many benefits that only
[Network] members have access to, now you have another--the ability
to save 10%-70% at your favorite brand name merchants every day
through the [Network's] program."
[0133] Affinity--"Because you are a valued [Network] member, you're
eligible to enjoy savings of 10%-70% at your favorite brand name
merchants every day, as well as gain access to private events and
product launches through the [Network's] program."
[0134] Benefit--"Visit the [Network's] program today and save
10%-70% at your favorite brand name merchants every day."
[0135] Redemption--"Save 10%-70% at your favorite brand name
merchants every time you purchase--simply by making your purchases
through the [Network's] program."
[0136] CABR messages are messages that emphasize one of the four
categories to determine what type of messages the users in each of
the segmented cells respond to more readily. For instance, users in
a particular segmented cell may respond to messages geared towards
"credibility" and "redemption" while users in a different segmented
cell may respond better to messages geared towards "affinity" and
"benefit."
[0137] Once the test planning is complete, the developed CABR
messages are sent to the test member segments with various offer
mixes determined from the offer segmentation in the calibration
stage. The purpose of the calibration stage is to gather response
data from the test members receiving the CABR messages. Once the
responses have been gathered and analyzed, a second CABR message is
sent with different offer mixes in the validation stage. The
purpose of the validation stage is to verify the results of the
analysis gathered during the calibration stage and to further
refine the CABR messages based on the responses during the
validation stage. FIG. 9 shows in more detail examples of the
calibration and validation CABR messaging.
[0138] In the last stage, all of the data gathered through the
calibration and validation stages are analyzed and summarized. The
information learned during the testing stages is then incorporated
into the launch of the messages to all of the members. Depending on
the learning, launch methodology is tailored for each member
segment. For instance, FIG. 10 shows an example of launching email
messages to the members in segments A-J based on the learning. As
shown, messages related to launch of messages for members of
segment A may include a teaser, invitation 1, and invitation 2 in
successive periods. For members of segment B, the teaser and
invitation 1 messages are sent in successive periods with a delayed
invitation 2 message by one period.
[0139] In this way, the narrowcasting system 38 dynamically
gathers, analyzes, and adjusts the effectiveness of each
narrowcasted message. As shown in FIG. 11, the narrowcasting system
of the present invention uses the active learning process in a
continuous feedback loop to build/launch, gather, analyze, and
refine each narrowcasted message such that the next message is more
accurate and effective in eliciting responses from the users.
[0140] "Active Data Gathering" (ADG) and "Active Learning" (AL)
[0141] Having described some of the components of the narrowcasting
system of the present invention, the narrowcasting communications
in accordance with the present invention is implemented using a
two-prong approach to target the right product to the right person
at the right time: (1) active data gathering, and (2) active
learning model. To this end, using artificial intelligence, for
example, the narrowcasting engine 250 (FIG. 2) includes an active
data gathering ADG and active learning AL that performs the active
data gathering process and the active learning process to obtain
future buying data of each member.
[0142] As shown in FIG. 2, the active data gathering includes three
aspects: (1) data quality 251, (2) trust 252, and (3) data
availability 253. The data quality 251 is directed to the
effectiveness of particular data elements in regard to predicting
future buying behavior (i.e., "forward looking" data). For example,
reminder data has the highest data quality, while behavioral click
data is of a lower quality.
[0143] The trust 252 is directed to the quantity of preference data
per user and works to increase the amount gathered per user. As
used herein, "preference" data refers to data that is communicated
by the user to indicate future buying preferences. The most trusted
communications are those that have been specifically requested by
the user. Therefore, preference data are the highest quality data
to determine the most relevant offers for the user. For example,
reminder data is used to send emails to members, alerting them to
the offer that they asked to be alerted about. Untargeted marketing
emails diminish trust, in that the user may stop believing that
giving preference information will result in a more relevant and
customized experience. For this reason, preference data is
preferably gathered continually so as to maintain trust. One
process used to gather preference data is called "intelligent
questioning." Intelligent questioning, described in further detail
below, uses algorithms from the active learning AL to infer a
user's preferences and gives the user an opportunity to confirm
those inferences. For example, the active learning AL may infer
that a particular user is likely to be interested in purchasing at
a particular merchant. The narrowcasting engine 250 will confirm
this inferred preference by dynamically presenting the user with a
preference question (e.g., a reminder) about that merchant and
detecting the user's reaction.
[0144] The data availability 253 is directed to evaluation of raw
data and ensures that the data is normalized for marketing
purposes. For example, reminder data is binary (i.e., the user
accepts or rejects the offered reminder) and therefore can be
easily used, while suggestion and search data (e.g., names of
products, merchants, etc.) must be normalized before use.
[0145] The active learning AL includes three aspects: (1) targeting
management 254, (2) fatigue management 255, and (3) content
optimization 256. The targeting management 254 is directed to
control and tracking of response rates to various algorithms for
inferring a user's interest in a particular merchant. Important
factors in generating high response may include recency of data and
customer type. Recency is a rating of how "old" the data is. The
more recently the data is collected, the higher quality the data is
and the stronger the response rate. Customer type is a
classification of the shopping patterns of a user. For example,
"type A" customers may be defined as people who are infrequent
shoppers but spend a large amount when they do shop. "Type B"
customers may be defined as people who shop frequently but spend
less during each purchase. Based on the customer type, a user will
be marketed to accordingly. Additional factors may be incorporated
into the targeting management 254, such as behavioral and
demographic data. The fatigue management 255 is directed to
monitoring of response rates of individual users and alters the
frequency of communication to that user. The content optimization
256 is directed to testing and monitoring of response rates of
users to messages with different CABR positionings described above.
These aspects of the active learning module AL combine to optimize
response rates.
[0146] For example, a typical "type A" customer may be an
investment banker and a typical "type B" customer may be a bank
teller. The investment banker (type A) is extremely busy and has
little time available to read marketing messages. Accordingly, the
fatigue management 255 is used to limit the quantity of emails to
this user to only the most relevant offerings resulting in
infrequent, but highly responded to emails. The bank teller (type
B), by contrast, will receive more frequent and consistent emails,
as they tend to enjoy reading the messages and enjoy a variety of
offers. The content optimization 256 is used to determine that the
"Credibility" and "Redemption" messages (from the CABR framework
described above) are most effective for "type A" as these customers
desire assurance of quality and a fast redemption while "Benefit"
messages are most effective for "type B" as these customers are
frequent shoppers with the knowledge and time to price-compare. In
this manner, the active learning AL "learns" to optimize response
rates for individual users.
[0147] Communication Management
[0148] The communication management CM is directed to managing the
schedule of the narrowcasted communications, such as email and
website communications sent to the users. The communication
management CM is used to interface with the active learning AL to
match users and offers, for example, and is used to interface with
the active data gathering ADG to determine what additional
preference data should be gathered from a particular user, for
example.
[0149] FIG. 12 illustrates the data gathered during the active data
gathering process according to the present invention. As shown in
FIG. 12, the active data gathering process of the present invention
includes obtaining data about each member provided by the networks
and the merchants obtained directly from the members. These
explicit data include information such as names, physical
addresses, email addresses, gender, age, and specified preference
information that is obtained during registration and completed
transactions. The active data gathering process also includes
obtaining data inferred from members' activities on the
narrowcasting system of the present invention. For instance, gender
and location information may be inferred based on activities and
choices made by the members while accessing their portals. For
example, if the user searches for items and offers generally
attributable for males (e.g., men's clothes, men's shoes, power
tools, electronic gadgets, etc.), the narrowcasting engine 250 may
infer that the user is a male. If the user searches or selects
items and offers from walk-in stores in a particular region, the
narrowcasting engine 250 may infer that the user lives in that
particular region. In this example, it is possible that the user is
a female in one region who is looking for a gift item for a male
who lives near the stores of interest. Accordingly, the active data
gathering process according to the present invention is performed
on a continual basis, constantly updating the users' activities to
modify the explicit and inferred data to obtain an accurate
profile. As more information about the user is gathered, the
information presented to the user, including preference questions
and offers, is refined to be more relevant to the user. As the
information presented to the user becomes more relevant, the user
is induced to provide more information about the user as the user
will spend more time viewing and selecting the information
presented, thus triggering even more preference data gathering
about the user. Therefore, this feedback loop continues to allow a
significant amount of preference data gathering of the user.
[0150] FIG. 13 illustrates the active learning model according to
the present invention. The narrowcasting system of the present
invention takes the traditional marketing approach used by
networks, which traditionally identifies "who" the buyers are, and
approaches used by the merchants, which traditionally identifies
"what" the consumers buy, and optimizes the effectiveness of a
marketing campaign by identifying "why" a buyer purchases a
particular product.
[0151] In particular, as shown in FIG. 13, networks typically
collect demographic data and cluster like-minded individuals based
on their demographic data (i.e., "demographic clusters").
Accordingly, the networks use demographic-based algorithms to
target information to the users. On the other hand, merchants
typically collect transactional data and cluster like-minded
individuals based on their transaction data (i.e., "transaction
clusters"). Accordingly, the merchants use transaction-based
algorithms for their targeted marketing initiatives. In contrast,
the narrowcasting system of the present invention combines the
demographic clusters from the networks with their marketing
response (i.e., "marketing response clusters") to increase the
response from marketing initiatives from the right customer.
Additionally, the narrowcasting system of the present invention
combines the transaction clusters from the merchants with the
marketing response clusters to target the right product for
profitability.
[0152] Moreover, the narrowcasting system of the present invention
leverages the demographic clusters and the transaction clusters
with the marketing response clusters to create "personas" that
target the right product to the right customer at the right time.
The active learning module AL leverages known user data (i.e.,
preference, behavioral and demographic data) to infer a particular
user's future buying preferences. For example, based on personas
and other segmentations, it is inferred that a certain user, such
as a member of the persona or segment, will have interest in a
particular merchant. To confirm this interest, the active data
gathering module ADG may dynamically present the user with a
preference question (e.g., a reminder) or with an offer (e.g., a
prominent link on the website portal or email) and monitor if the
user responds. In this mariner, the system uses behavioral data to
infer a user's future buying preferences and uses the website to
confirm that interest, generate more preference data, and refine
the algorithm.
[0153] Communications
[0154] As shown in FIG. 2, based on the results of the active data
gathering module ADG and active learning module AL, the
narrowcasting engine 250 controls two types of communication:
direct and indirect communications. Direct communications refer to
narrowcasted communications, such as emails, based on preference
data (i.e., "forward looking" data). All communications sent by the
narrowcasting engine 250 are targeted, but direct communications,
such as emails, leverage the highest quality data--i.e., preference
data--which is most predictive of future purchasing behavior. These
messages build trust, as they are "customer service" driven (i.e.,
responding to a users request) and help to generate more preference
data. Indirect communications are communications, such as
newsletters and website personalization (i.e., website placements)
that are based on behavioral data (i.e., "looking back" data).
Indirect communications may be used to drive users to the portal
website and capture additional preference data.
[0155] Preference Building
[0156] The narrowcasting system of the present invention collects
and builds preference profiles of a user in a continuous, dynamic
process. The narrowcasting system of the present invention employs
several mechanisms for collecting and building user preference
profiles. As already described above, the narrowcasting system of
the present invention initially obtains highly specific and
relevant information from the networks who register with the
narrowcasting system of the present invention to host benefit
programs for their members (i.e., network member database 210). As
briefly described with reference to FIG. 2-4 above, building the
initial preference profile of the member user according to the
present invention begins with member user's data that is already
comprehensive and reliable.
[0157] As a preface to describing the preference building aspect of
the present invention, it is important to note that because users
of the present invention are members of the registered networks,
the users' demographic data are more detailed and reliable than
those collected by prior art systems. Some examples of networks
include employers, financial institutions, such as banks, lenders,
and credit card companies, and other trusted groups, such as trade
groups (e.g., American Automotive Associations) and institutional
organizations (e.g., American Bar Association). Because the
narrowcasting system of the present invention provides network
members access to offers from hosted/affiliated merchants and
service providers, the network provides member information to the
narrowcasting system of the present invention to provide benefit
services for their member. Accordingly, the narrowcasting system of
the present invention begins with data that prior art systems
strive to obtain.
[0158] As described above, member information provided by the
networks is analyzed and segmented and stored in the user
preference data store 240a and the user demographic data store 240d
along with member segmentation rules stored in rules/personas data
store 240c. The narrowcasting engine 250 uses the data in the user
preference data store 240a, the user demographic data store 240d,
and the rules/personas data store 240c to create initial member
segmentation cells and offer segmentations. Therefore, even if a
user who enrolls with the narrowcasting system of the present
invention without providing any further information, the user
receives highly relevant offers to the user from the moment the
user activates his or her account.
[0159] In addition to the information provided by the networks, the
narrowcasting system uses the registration/activation process to
obtain even more relevant information about the users. As described
in detail below, the narrowcasting engine 250 uses the initial data
about the user stored in the user preference data store 240a and
the user demographic data store 240d to generate questions to
refine/supplement information about each user who enrolls with the
narrowcasting system. This "intelligent questioning" process allows
the narrowcasting engine 250 to validate, modify, and/or refine the
member's data. The information collected during the
registration/activation process is added to the user preference
data store 240a and the user demographic data store 240d, and
member segmentation cells and associated rules stored in the
rules/personas data store 240c are refined. Thus, while prior art
systems begin the collection of user information during the
registration process, the narrowcasting system of the present
invention uses the registration process to verify and/or supplement
the information already stored in the user preference data store
240a and the user demographic data store 240d.
[0160] In particular, FIG. 14 shows a more step by step view of the
registration/activation process. As shown in FIG. 14, a user
(already associated with a member segment cell) accesses the
network program portal 280a, 280b (FIG. 2) to register and activate
the benefits program. If the user is responding to an invitation
message, the portal may be accessed via a link embedded in the
invitation. If the user is already enrolled, the network program
portal (280a, 280b) may be accessed manually by typing in the
assigned address to the portal or via pre-established links on the
users' computer, such as through an Intranet site. When the user
logs in for the first time, the user is guided through an initial
preference building process. In reality, as described above, the
narrowcasting system of the present invention already has
preference data initially provided by the networks stored in the
user preference data store 240a and the user demographic data store
240d. However, to the user, the process of identifying preferences
at this stage is a first time for the user. Therefore, while the
preference selection process during activation may be perceived as
an initial preference setup to the user, in reality, the preference
building process during registration for the narrowcasting system
of the present invention is a process to refine the preference
profile for the particular user.
[0161] To engage the member user to assist in refining the
preference building process, the narrowcasting engine 250 begins
the intelligent questioning process by presenting the user with
various questions through the portal 280a, 280b. The questions are
dynamically generated to be cell-specific to the user, the cells
being assigned during the member segmentation process as shown in
FIG. 4. The types of questions may be dichotomous questions (e.g.,
"yes" or "no"), multiple choice questions (e.g., selection from a
set of answers), rank order questions (e.g., rank a list of answers
based on level of interest), or multiple choice/battery matrix
questions. Other types and methods of presenting the questions may
be used without departing from the scope of the present invention.
For instance, instead of directly asking questions, the questions
may be offered as an interactive interface, such as a game, to
input the member user's preference selections. Because the
narrowcasting engine 250 has access to the initial preference data
for the user as stored in the user preference data store 240a and
the initial demographic data stored in the user demographic data
store 240d, the questions presented are preferably designed to be
more specific to the user than broad questions generally employed
by prior art systems, thereby presenting less but more pertinent
questions about the user.
[0162] For instance, FIG. 15 illustrates examples of the type of
customized questions generated for users based on their member
segmentation profiles. As shown, members in the network member
database 210 are segmented into the relevant markets presented by
their initial preference information. From the segmentation
information, various segmented cells 410 (custom cells 420,
advanced cells 430, basic cells 440) are created and associated
with each user. When a user logs in to register/access the benefit
page through network program portal (280a, 280b), the user is
presented with dynamic questions related to the user's associated
cell. As described below, these preference questions may be asked
during registration/activation of the user's benefit site as well
as during continual use of the site. All of the information
provided during the registration process is then stored in the user
preference data store 240a and the user demographic data store
240d.
[0163] FIG. 16 shows an example of a preference building page
engaging the user in intelligent questioning. As shown in FIG. 16,
various questions are presented to the user. These questions are
dynamically created for the specific user based on the information
already obtained from the user's network in setting up the account.
In addition, this page also displays some of the offers available
to the user "waiting inside." Again, these presented offers are
dynamically created for this particular user based on the
information obtained from the network. As the user begins to answer
the questions, the presented offers dynamically change based on the
user's answers. For instance, as shown in FIG. 16, if the user
selects "buying a new home," the offers shown on the left will
change to include items related to homes (e.g., offers related to
mortgages, furniture, etc.). Moreover, if the user moves the cursor
over to one of the offers displayed, the offer pointed to by the
cursor enlarges the offer to display specific information regarding
the offer (e.g., jewelry, shown in FIG. 16). During this time, the
narrowcasting engine 250 keeps track of all the activities being
performed by the user. For example, the narrowcasting engine 250
may keep track of which offers the user appears to be interested in
by tracking the offers viewed and how long the offer is viewed
(e.g., by measuring the time of the cursor hovering over a
particular offer to view the details). The narrowcasting engine 250
may also keep track of the answers being selected/de-selected. All
of the collected information is used to add, modify, or refine the
information about the user already stored in the user's preference
data store 240a, the user's behavioral data store 240b, and the
user's demographic data store 240d.
[0164] Once the member user has been given the chance to designate
his/her preferences, the member user is given access to the
benefits site. The benefits site is dynamically generated by the
narrowcasting engine 250 customized for the user based on the
user's associated member segment cell generated from the preference
data provided by the network. If the user has provided further
information during the registration process, whether explicitly
(e.g., answers to the questions) or implicitly (e.g., by hovering
over an offer), the narrowcasting engine 250 dynamically adjusts
the user's associated preferences based on the information provided
and dynamically adjusts the offer mix to be presented to the user.
In this way, the narrowcasting system of the present invention
instantly provides relevant offers from the first time the user
accesses the benefit program hosted by the narrowcasting
system.
[0165] Once the preference data provided by the networks and by the
user during the registration/activation process is processed, the
narrowcasting system of the present invention tracks users'
activities throughout the user's access to the narrowcasting system
to further collect and analyze preference information of each user.
The three types of data collected by the narrowcasting system of
the present invention are demographic data, preference data, and
behavioral data. For exemplary purposes only, the preference data
is stored in the user preference data store 240a, the demographic
data is stored in the user demographic data store 240d, and
behavioral data is stored in the user behavioral data store 240b.
As already described, the information may be stored in separate
databases or stored in different portions of the same database
without departing from the scope of the invention. The demographic
data includes, but is not limited to, home and work locations,
gender, income level, job title, and marital status. The data may
be obtained from employee data files. Preference data includes, but
is not limited to, current and future purchase decisions obtained
from user suggestions, requests, and selections. Behavioral data
includes, but is not limited to, shopping habits and purchasing
behavior over a period of time.
[0166] As described above, all of the contents presented to the
user are dynamically generated and tracked in a real-time,
continuous feedback loop. In general, the intelligent questioning
during the active data gathering process includes reminders,
searches, suggestions, and calendaring features. The reminder
feature notifies users of up-coming offers or missed offers and
asks whether the user would like to be reminded of the offer or
similar offers in the future. For instance, if the user missed a
2-hour special sale or promotion, the user can request the system
to notify the user if another or similar offer comes up in the
future. If there is an offer a month away that the user does not
want to miss, the user may request a reminder prior to the offer
(e.g., days, hours, or minutes before the offer takes effect). To
facilitate the notification of offers, the narrowcasting system of
the present invention displays offers (past, present, and future)
on a calendar such that the user can view important offers at a
glance. The reminder feature may be connected to the calendar
feature to optimize the opportunity for the user to interact with
the system.
[0167] The search feature allows the user to search for specific
offers available to the user. The user may search for offers
specific to a product or category of products, merchant or type of
merchant, time, and the like. The specific search parameters may be
varied without departing from the scope of the present invention.
All aspects of the search performed and the results viewed are
stored as preference data.
[0168] The suggestion feature allows a user to suggest specific
merchants, products, and/or services not found in the search to be
added to the narrowcasting system of the present invention. The
suggestions made by the user are also stored as preference data.
The users' activities related to all of these features are
collected and analyzed to validate, update, modify, and/or refine
the users' preference and behavioral data.
[0169] Data Collection
[0170] FIGS. 17A-17G illustrates an example of how these different
types of data are collected. FIGS. 17A and 17B show activities of a
user who is responding to an invitation to register and activate
the benefit program hosted by the narrowcasting system of the
present invention for the user's network. At the time the user is
taken to the registration page through the network program portal
(280a, 280b), the user's demographic information is already in the
user demographic data store 240d as provided by the network. As
described above, the registration process may be used to add or
update any of the demographic information. FIG. 17B shows that once
the user logs onto the portal, the user is invited to select his or
her preference of interest as well as administrative items (e.g.,
communications channel). The preference information provided by the
user is then stored in the user preference data store 240a as
preference data.
[0171] FIGS. 17C and 17D show a user beginning to use the benefit
program. For instance, the user may start searching for particular
items and offers from a particular merchant stored in the offer
database 260. The search terms used in the search are tracked and
stored as preference data as shown in FIG. 17C. In addition, any
selections made by the user as showing interest in particular
offers from particular merchants are also stored as shown in FIG.
17D. FIG. 17D also illustrates the calendar and reminder features
discussed above.
[0172] FIGS. 17E-17G show collection of user's behavioral data
during activity on the benefit site. For instance, FIG. 17E shows
that the user selected a particular offer (e.g., shoes) from a
particular merchant (e.g., shoe store) to view further information
about the offer (e.g., Interest Level 1). This information is
stored in the user behavioral data store 240b. FIG. 17F shows that
the user, upon viewing a detailed description of the offer, adds
the item in the offer to the shopping cart, for example (e.g.,
Interest Level 2). This information, again, is stored in the user
behavioral data store 240b. FIG. 17G shows the data stored in the
user behavioral data store 240b after the user has viewed,
selected, and clicked to a particular item for purchase (e.g.,
Interest Level 3) from a particular merchant (e.g., an engagement
ring from a jewelry store). This information may include the actual
purchase of the item. As described, the narrowcasting system of the
present invention not only collects demographic and preference data
about a user before and during activities on the benefit site, but
the narrowcasting system of the present invention also collects
behavioral data to build an accurate profile of each user to be
used by the narrowcasting engine 250 to present relevant offers to
the users at relevant times.
[0173] In addition to collecting and building demographic,
preference, and behavioral data, the narrowcasting engine 250
analyzes the information to determine correlations and most
pertinent variables. As an example, as shown in FIG. 18, the
narrowcasting engine 250 is able to analyze the behavior of males
and females in a particular demographic profile. According to the
analysis, the most highly-correlated items for high-end flower
purchasers are flat-screen televisions and luxury suits, while
location of the correlated purchasers does not appear to be
significant. Such highly-correlated information generated by the
narrowcasting engine 250 is used to create rules for the segmented
cells 410 as well as analysis reports, explained in detail below.
Throughout the data gathering process, the narrowcasting engine 250
collects not only the explicit data provided by the networks,
users, and merchants, but collects inferred data as well to
supplement the explicit data to more accurately predict the users'
preferences. As briefly mentioned above, inferred data refers to
information about the user that is inferred from the explicit data
gathered. For example, if a user does not furnish where the user
lives, the explicit data gathered about the user, such as repeated
transactions at a particular store, is used to infer that the user
lives near the store. Moreover, the preference data gathered by the
narrowcasting system of the present invention represents future
buying data rather than past purchase histories and behaviors
characterized by the prior art systems.
[0174] Personas and Life Events
[0175] The demographic data, preference data, and behavioral data
collected for each user in accordance with the present invention
allows for narrowcasting engine 250 to present highly relevant
information to the users at relevant times. As described above, the
narrowcasting engine 250 provides relevant information by
segmenting the members and offers into associated segment cells 410
and delivering the offer mix 530 to the users associated with the
specific segmented cells 420, 430, and 440. As described above,
members of custom cells 420 are segmented in accordance with
specified parameters from the network. That is to say, custom cells
420 and the rules associated thereto are customized in accordance
with the networks' requests. Members of basic cells 440 are
segmented in accordance with basic demographic data (e.g., gender
and location, such as males living in New York). Basic cells 440
are used as "targeted broadcast" communications by the
narrowcasting system of the present invention. For example, basic
cells 440 may be defined by rules used to send communications to
users across networks or to large segments for newly provided
programs and services. The targeted users can be narrowed or
broadened based on the demographic data selected as the
communications criteria for the segmented members for the cell.
Members of the advanced cell 430 are segmented based on "personas"
and/or "life events," as further explained below.
[0176] A "persona" is a synthetic personality and rules associated
thereto based on specific demographic, preference, and behavioral
data, all within the context of time. More specifically, an
individual's behavior and preferences, especially related to
purchasing habits, are related to a specific time period in the
individual's life. For example, an individual who is single and at
the beginning stages of his or her career will exhibit a particular
purchasing behavior that is different from the behavior of an
individual who is established in his or her career and is newly
married. An individual who has recently had a baby will display
still yet a different purchasing behavior than the other two
individuals. The "personas" generated by the narrowcasting engine
250 are based on the premise that an individual will be in a
particular life style for a finite amount of time. By detecting
particular trigger events (based on preference and/or behavioral
data--e.g., purchase of an engagement ring, purchasing a home,
purchasing a minivan, etc.) and observing the following purchase
preferences and behaviors, a more accurate profile, and eventually
future purchasing data, can be obtained.
[0177] Accordingly, a persona in accordance with the narrowcasting
system of the present invention is characterized by a defined
event, a trigger of the event, duration of the event, and the
user's location in the timeline of events. In particular, the
personas generated by the narrowcasting engine 250 relate to the
"directional" (i.e., future trend of purchases) of the user in his
or her purchasing behavior rather than the "data point" (i.e., item
of purchase) of the user's purchases.
[0178] As an example, a "workaholic" persona is defined by rules
that look for users who work in a particular industry, have a high
level of education, and have an income above a particular
threshold. "Workaholics" tend to purchase expensive items such as
high end electronics and jewelry and tend to travel frequently.
FIG. 19 shows a fictitious user who is segmented into a
"workaholic" cell. Accordingly, rules developed for the
"workaholic" cell are used to create offer segmentation for the
workaholics, and those who are segmented into the workaholics cell
are presented with the appropriate offers relevant to this persona.
The narrowcasting engine 250 segments the offers based on the
workaholic persona and delivers the most appropriate offers to
those in this cell, such as offers related to flat screen
televisions and diamond jewelry, as shown in FIG. 19.
[0179] Similarly, "life events" are cells segmented based on
demographic, preference, and behavioral data that indicate a
particular stage of life that the user is in. For example, a
"wedding" life event is defined by rules that look for users who
are single or divorced, who recently researched and/or purchased
engagement rings, or viewed offers from wedding dress vendors.
These patterns indicate that the user may be planning for a wedding
that may occur in a short period of time. Another example may be a
"baby" life event that is defined by rules that look for users who
are married (or users who fit the "weddings" profile) who recently
researched and/or purchased baby necessities. These patterns
indicate that the user may be expecting a baby in a short time.
Accordingly, the offers segmented by the narrowcasting engine 250
according to the life events may include travel offers for their
honeymoon or offers for family-friendly vehicles, such as minivans.
Therefore, the narrowcasted communications according to the present
invention proactively communicate offers that are relevant and
timely. Prior art systems, by contrast, are reactive, and thus
present offers that are irrelevant and or too late to be of use to
a user.
[0180] In each case as described above, a defined event (e.g., new
job, wedding) is detected based on a trigger of the event (e.g.,
business apparel, engagement ring). Using data gathered from other
individuals, a duration of these events can be approximated (e.g.,
1-5 years for the workaholic, 6-12 months for the wedding). The
individual's position within this time frame can be determined
based on the preference and behavior data (e.g., luxury car may
indicate the latter stages of the workaholic while purchase of a
wedding gown may indicate the wedding date is near). By using these
established personas, future purchasing data can be determined and
appropriate offers (e.g., vacation or honeymoon packages) may be
presented to the appropriate individuals at the most relevant times
in their life stage.
[0181] Reports
[0182] The narrowcasting engine 250 dynamically tracks, collects,
and updates all data that is communicated between the users and the
merchants. In addition, the narrowcasting engine 250 analyzes the
responses from the user to provide various services to the networks
and/or merchants hosted by the narrowcasting system of the present
invention. From the networks' perspective, the offers provided by
the affiliated merchants 260b are generally based on contractual
terms that provide financial incentives for both parties.
Therefore, results of the users' activities and any transactions
that results need to be accounted for. Moreover, if the network is
a financial institution, such as a credit card company, analysis of
user responses and activities may be used to pursue the networks'
own marketing campaign to draw more members.
[0183] From the merchants' perspective, affiliated merchants 290
are interested in the users' activities/transactions as well as the
other side of the contractual obligations with the networks hosted
by the narrowcasting system of the present invention. Moreover,
whether the merchant is an affiliated merchant 260b or just a
hosted merchant 260a, the merchants 260a, 260b may be on a
contractual relationship with the narrowcasting system of the
present invention for the offers made to the users. Additionally,
analysis of user activities and responses may provide valuable
marketing information that may be important in developing the
merchants' own marketing campaigns.
[0184] The narrowcasting engine 250 provides, but is not limited
to, the following levels of analysis and corresponding reports to
the networks and/or merchants: (1) Basic, (2) Analyzer, (3)
Forecaster, (4) Scenario Builder, (5) Advisor, (6) Custom.
[0185] (1) Basic--The Basic level analysis provides quarterly
reports of the users' activities on the system. The Basic report is
useful in reporting of network members' activities as well as
marketing campaigns, such as sales reports, leads, and campaign
summaries.
[0186] (2) Analyzer--The Analyzer level analysis provides weekly,
monthly, quarterly, and yearly reports that provide, in addition to
the Basic report, usage by segments and leads/sales by merchant.
The Analyzer report provides historical trends. (FIG. 20)
[0187] (3) Forecaster--The Forecaster level analysis provides
weekly, monthly, quarterly, and yearly reports that provide, in
addition to the Basic report, forecasts by segments, forecast
leads, forecast sales, and forecast response. The Forecaster report
also provides historical trends. (FIG. 21)
[0188] (4) Scenario Builder--The Scenario Builder level analysis
provides weekly, monthly, quarterly, and yearly reports that
provide, in addition to the Basic report, "what-if" scenarios,
profitability analysis, demand curves, conversions, and buy rates.
(FIG. 22)
[0189] (5) Advisor--The Advisor level analysis provides weekly,
monthly, quarterly, and yearly reports that provide, in addition to
the Basic report, executive level usage and activity
recommendations, diagnostics, persona development, and user
mappings.
[0190] (6) Custom--The Custom level analysis provides reports
according to frequency and level of detail specified by the
network/merchant.
[0191] Purchase Funnel
[0192] As shown in FIG. 23, the "purchase funnel" refers to the
process of optimizing the narrowcasted communication. The
narrowcasting system, according to the present invention, tracks
the marketing-to-purchase process from end-to-end (i.e.,
"closed-loop"). In other words, the narrowcasting system of the
present invention tracks users' responses from the delivery of the
communication via the network (e.g., HR communication), site
communication (e.g., web portal placements) and narrowcasting, all
the way through to the transaction ("TRN"). Accordingly, the
narrowcasting system according to the present invention can analyze
and break down all the data obtained from the user from the
beginning of the marketing initiative to the resulting purchase
into granular detail. For example, conversion rates (i.e., user's
favorable response of a communication) are tracked from marketing
to portal-website, from portal homepage to offer detail page, from
offer detail page to "go shop" (i.e., clicking to a merchant's
website, coupon, etc), and from "go shop" to transaction (i.e.,
purchase). In addition, the present invention tracks these response
rates for merchants, allowing the system to calculate averages for
categories, subcategories, groups of similar merchants and the
like. These calculations are benchmarks that can be used to
diagnose where problems (i.e., marketing breakdown) in the purchase
funnel exist. For example, a merchant may have an offer detail page
to go shop conversion of 23%, but the average for like merchants is
45%. In addition to identifying the problem areas, the analysis
allows for the development of products and services that are
designed to fix specific steps in the purchase funnel.
[0193] FIG. 24 is a block diagram illustrating an exemplary data
flow according to the present invention. As discussed in detail
above, data collected from the network, the merchants, and the user
are input to the narrowcasting engine of the present invention. The
active data gathering ADG collects the data from these three
sources through the active data gathering process as described
above, and parses this data into the appropriate database (e.g.,
demographic, preference, and behavioral databases) in a format
readily available for marketing. As discussed above, the data is
also evaluated as to effectiveness in predicting future buying
behavior (i.e., "forward looking" data). The active learning AL
processes the collected data as described above and applies rules
and algorithms that will determine what offers to present to a
particular user (i.e., determine the right product for the right
person at the right time). Based on results from the AL process,
the communication management CM will send customer service emails
(based on reminder and suggestion preference data, for example)
and/or marketing/advertising communication (based on inferred data,
for example). The communication of marketing/advertising messages
occurs utilizing one or more mediums: Internet (e.g., website
portal), newsletter insertion (e.g., HR newsetters) and emails
(email newsletters). The response is fed back into the system in
real-time to collect and refine the data to be even more accurate
and relevant.
Example
[0194] FIGS. 25-30 illustrate a non-limiting example of the
narrowcasting system and method according to the present invention.
In particular, FIGS. 25-30 show an exemplary embodiment of
segmentation and preference gathering/learning in accordance with
the present invention. As shown in FIG. 25, an existing user
network (e.g., "BookStore") wants to establish a rewards program on
the narrowcasting system of the present invention. In block 2510,
the current membership are grouped and segmented as described above
into RFM buckets. In this example, 4 target groups and 15 segments
have been established and analyzed, as shown in FIG. 26. These
segments can be grouped based on the target group or segment
objective. For example, as shown in FIG. 26, BookStore is
interested in retaining members of the Gold and Silver segments.
Thus, the incentives and rewards for these segments can be designed
around retaining these segments.
[0195] Once the groups/segments have been determined, a call to
action messaging is developed and disseminated to the groups. As
shown in block 2520, using target group objectives and themes, CABR
is used to develop a message to promote the incentives (rewards),
retention, and renewal of the members into the rewards program.
Once the members, or potential members, receive the messaging, they
are invited to register into the rewards perks program by providing
a link, for example, to the rewards perks website.
[0196] In block 2530, the user is then guided through the
registration/login process. FIGS. 27-29 show the preference
building interface (i.e., "preference game") to ascertain the
preference data of the user during registration. As described
above, the right side of the screen (FIG. 27) is populated with
some of the offers found "inside" (i.e., once registration is
complete). These offers, while appearing random, are actually
populated based on data already known about the user. As the user
answers the intelligent questions on the left side of the screen,
the offers displayed on the right side of the screen dynamically
changes, as shown in FIG. 29. Further, as the user interacts with
some of the offers (e.g., hovers over a particular offer), the
information of the user's interest is also gathered to build the
user's preference profile. FIG. 28 is an exemplary flow of the
preference building process during registration. Once the user's
preference data is gathered during the registration process through
the preference game, for example, the segmented content and
preference based content are collected and dynamically arranged on
the user's website as personalized content. (e.g., FIG. 29).
[0197] Based on the demographic, behavioral, and preference data
gathered during the registration/prior activities, the
narrowcasting system of the present invention migrates the analysis
to lifestyle focused marketing. As shown in block 2540, based on
the gathered data of the user, the user is profiled into personas
to determine the user's lifestyle. Focusing on the user's lifestyle
segmentation, the offers available in the system are segmented into
the most relevant categories for the user, as shown in block 2550.
In block 2560, the offers and the website are dynamically generated
and branded and presented to the user to begin usage of the
BookStore's rewards perks program.
[0198] Silent Marketing
[0199] As described above, the narrowcasting system according to
the present invention has many applications. In particular, the
narrowcasting system of the present invention in the exemplary
embodiment as described above serves as a rewards/loyalty platform
for networks while serving as a targeted marketing platform for
merchants.
[0200] From the standpoint of the members, the narrowcasting system
of the present invention provides relevant information to the users
from the time the user first accesses the user portal. Because the
users are members of a network and the portals are setup on behalf
of the networks, the networks provide information about their users
when registering to be hosted by the narrowcasting system of the
present invention. Therefore, the narrowcasting system of the
present invention begins with information that prior art systems
strive to collect over a long period of time. Because the
information provided to the users is relevant from the beginning of
their experience on the portal, the users' first impression is that
of credibility and trust of the communications provided by the
narrowcasting system of the present invention. This reduces the
number of ignored/deleted communications, such as emails and the
like. More importantly, because the offers being made through the
narrowcasting system of the present invention are branded through
the network to which the users are members, the offerings are
viewed as benefits or perks from a trusted entity and not perceived
as spam. As the offerings become more relevant through usage, the
trust relationship is increased, thereby perpetuating the
collection of more data points to refine the users' future buying
data even more.
[0201] From the standpoint of the networks, as the members find the
offerings through the narrowcasting system of the present invention
more relevant and useful, the network strengthens the relationship
with the members. Accordingly, the members' loyalty to the network
increases, thereby obtaining more business.
[0202] From the standpoint of the merchants, as the offerings are
matched with relevant members, their marketing efforts become more
efficient and productive. Rather than inundating the public with
offerings that may not even get viewed, the narrowcasting system of
the present invention provides accurate and productive results. To
this end, the narrowcasting system of the present invention
provides a "silent marketing" option as a test bed for merchants to
perform market analysis of their products.
[0203] In particular, the narrowcasting system of the present
invention is a "closed loop" network with proven marketing rules
(e.g., personas). The term "closed loop" as used herein refers to a
closed environment with a specific audience and defined rules. In
contrast, an "open loop" network is the general public that has no
boundaries and unspecified audience (e.g., anyone can access the
network and identity is not authenticated). Because all of the
potential customers in the narrowcasting system are members of the
hosted networks that have been segmented and analyzed, products
and/or marketing campaigns can be tested with accurate results with
no public dissemination of information about the test.
[0204] For example, a merchant may wish to determine a market for a
particular product. Traditionally, the merchant would pay for
broadcast advertisements for the product to the general public to
determine the market for the product. However, because the general
public is an "open loop" network, the response is unpredictable and
sporadic. Even if the products are sold, the data obtained is
extremely diverse to determine the marketability of the product
with any accuracy. However, using the narrowcasting system of the
present invention, members have been already segmented and
analyzed. Accordingly, a sampling of members using the preference,
behavioral, and persona data can be generated and the product
marketability tested. Because the narrowcasting system of the
present invention uses the Internet and email, the results are
almost immediate. Based on the returned results, a different or
larger sample can be generated for either re-testing or validating
the results. Moreover, prototype offers and other marketing
campaigns can also be tested to determine their efficacy.
[0205] In another aspect, if the market is too small, traditional
methods for testing marketability exhaust the test pool. In other
words, the people that would have found the product useful have
been used up for the test. Accordingly, there is no one else to
market after the testing is performed. However, in the
narrowcasting system of the present invention, because the audience
consists of members of large networks (e.g., employers,
institutions, affinity groups) and the market pool can be segmented
and sampled over members of different networks, the total pool is
not exhausted after testing. In other words, the narrowcasting
system of the present invention creates a controlled sampling of a
market in a controlled environment to obtain accurate results that
can be re-tested without exhausting the marketable pool. This
results in the ability to run additional tests without exhausting
the market pool for the actual marketing campaign.
[0206] The narrowcasting system of the present invention can be
used in this fashion to test marketability of products, cost
valuation of products, effectiveness of marketing strategies, and
other valuable marketability analysis in a controlled, efficient
manner with near instant results. Moreover, because of the
effectiveness of the narrowcasting system of the present invention,
merchants can also use the narrowcasting engine 250 to move surplus
products more effectively.
[0207] Merchant Network Services
[0208] As discussed above, while "relevance" of offers can increase
usage by increasing trust, breadth (i.e., quantity) and depth
(i.e., quality) of products and services available on the system
are integral to increasing usage of the system. To increase the
quantity and quality of the products/services, the system must be
capable of increasing and deepening relationships with merchants
that provide the products/services. Accordingly, the narrowcasting
system of the present invention includes various merchant network
modules to increase the number of merchants, thereby the number of
products and/or services, and deepen the relationship to increase
the quality of offers for the products and/or services available
from the merchants.
[0209] Auto-Enroll Module
[0210] A key barrier for increasing the number of merchants,
thereby increasing products and services on the system, is the ease
of enrollment with the rewards/loyalty program. Typically,
rewards/loyalty programs require that participating merchants offer
discounts and/or other incentives to be able to market to the
members of the rewards/loyalty program members. This can pose
challenges to mainstream merchants who do not need discounts to
draw customers, and particularly to those offering discounts that
are administratively challenging to provide (e.g., non-public or
"private" offers, offers customized to specific communities or
types of users, or any other offers that deviate from existing
promotional plans). Hence, merchants may be dissuaded from
participating in reward/loyalty programs that require discounts as
a condition for participation.
[0211] On the other hand, a key barrier to increasing the quality
of merchants is the challenge of increasing the number of high-end
merchants (e.g., retailers of name brand designers) who participate
in the rewards/loyalty program. High-end merchants can generally
sell their products/services at market price without any discounts
since the consumers of those products are typically affluent
individuals. Hence, merchants with high quality products/services
are dissuaded from participating in reward/loyalty programs that
require discounts as a condition for participation.
[0212] Conversely, the merchants that offer the steepest discounts
and/or incentives tend to be merchants with products/services that
are less than desirable (e.g., out-dated products, unknown/unproven
products, overstocked items, etc.). Hence, typical merchants
participating in rewards/loyalty programs tend to be unknown or low
end merchants, which tends to discourage users of long term usage
as the quality and quantity of offers become sub par.
[0213] As shown in FIG. 31, an exemplary embodiment of the present
invention includes an auto-enroll module 3100 that is implemented
on a rewards/loyalty platform such that the system does not require
merchants to provide discounts and/or incentives to become a
participant. Rather, the incentives are provided by a rewards
module, discussed in detail below, of the system of the present
invention rather than by the merchants. In an exemplary embodiment,
the rewards are provided out of the marketing spend accounts of the
merchants. Hence, while the users are incentivized to purchase from
participating merchants, the merchants do not have to offer
discounts/incentives to be a participating member. In this way, the
system and method of the present invention increases the number of
merchants, especially the high-end merchants that do not typically
join rewards/loyalty programs as discussed above. Moreover, the
system and method of the present invention includes features, as
discussed further below, to make the registration and
administration of the participation easy and simple for the
merchants to further increase the number of merchants participating
in the network.
[0214] In the exemplary embodiment, the auto-enroll module 3100 is
a secure communications module on the system of the present
invention, such as an Intranet or Internet access portal as shown
in FIG. 31. As shown, the auto-enroll module 3100 includes a
website-like interface with instructions to guide the merchant
through the registration/enrollment process. As shown in FIG. 31,
the auto-enroll module 3100 includes an offer wizard 3110 that
guides the merchant through a quick and easy process of creating an
offer to be displayed to the user of the narrowcasting system of
the present invention. The offer wizard 3100 may be a webpage
having fields that allow entry of the merchant's information, the
description of the offer, link to the merchant's website, and
merchant's logo and/or other images regarding the offer. Other
types of fields and/or interface may be used without departing from
the scope of the present invention.
[0215] The auto-enroll module 3100 also includes options for
participation. For instance, FIG. 31 illustrates a performance
based pricing ("Rev-Share") module 3120 that allows the merchant to
sign up for a budget-based enrollment for continuous, periodically
recurring offers. In particular, when the amount budgeted by the
merchant for a particular offer is spent within a cycle set by the
merchant, the offer is suspended until the next cycle begins. The
performance based pricing module 3120 allows the merchant to
set/change the budget levels, set/change frequency of the display
of the offers, as well as other administrative tasks regarding the
offers made available to program members. The auto-enroll module
3100 further includes a quick start ("Pay and Go") module 3130. The
quick start module 3130 allows a one time payment and activation of
an offer to be made available to the program members.
[0216] FIG. 32 illustrates an exemplary flowchart describing the
enrollment procedure. For instance, merchant 3201 accesses the
auto-enroll module 3210 through a website, for example, to sign up
with the system of the present invention. (Step 1) During the
enrollment process, the merchant provides information about the
merchant and the offer to be made available to the program members.
Once the merchant has provided all the necessary information
regarding the merchant, the offer, and the like, the merchant
information is forwarded to the registered card module 3220. (Step
2) The registered card module 3220 sets up the merchant with
identification information (e.g., merchant ID), type of offer
including any discount/incentive information, and stores the
information into the merchant database (not shown). Once the
merchant enrollment is confirmed, the merchant's offer or offers
are added to the offer administration module 3230. (Step 4)
[0217] The offer administration module 3230 adds and/or updates the
offers into the offer database 3235 to be used by the narrowcasting
engine (FIG. 2) to make the offer to the most relevant member at
the most relevant time. (Step 5) If the offer does not include any
discounts and/or incentives as the merchant is not required to do
so as discussed above, the system of the present invention may add
a default incentive, such as a reward point for a predetermined
amount spent. The default offer may be in lieu of, or in addition
to, any discounts/incentives offered by the merchant.
[0218] In addition, the offer administration module 3230 sends
notification, such as an email, for example, to the merchant 3201
once the enrollment process has been completed. (Step 6) The
notification may further include login instructions to allow the
merchant access to the offer administration module 3230 for
administering the merchant's offers, as described further
below.
[0219] FIG. 33 illustrates a diagram describing exemplary
administrative functions and tools of the offer administration
module 3230 made available to the merchants. For instance, after
enrollment, a merchant 3201 togs into the account manager module
3310 using the information sent to the merchant after enrollment.
(Step 1) The account manager module 3310 allows the merchants to
perform various administrative tasks, such as changing
login/password information, changing merchant information, viewing
and changing account information, and the like. Moreover, the
account manager module 3310 provides various marketing tools to the
merchant 3201 such as click volume data, transaction data,
discount/offer redemption data, and the like. (Step 2) The
marketing data and information available to the merchant may be
varied depending on the level of service in which the merchant has
enrolled. Some of the different levels of analysis and data that
may be made available to the merchant are described above.
[0220] Furthermore, the account manager module 3310 provides
various tools to the merchant to manage the offer or offers
enrolled in the system of the present invention. (Step 3) In
particular, the account manager module 3310 includes a heat map
module 3320, an offer rank module 3330, and offer bid module
3340.
[0221] Heat Map Module
[0222] The heat map module 3320 is a tool that conveys activity
levels of various aspects of the marketplace to merchants using the
system of the present invention. For instance, the merchant 3201 or
a user can use the heat map module 3320 to view the most active
category of merchandise over a specified time period. (Step 4) FIG.
34 illustrates an exemplary heat map that indicates the most
popular type of merchandise being viewed/purchased on the system of
the present invention. As shown in FIG. 34, "apparel" is the most
actively viewed/purchased by the members on the system of the
present invention followed by "electronics." While the exemplary
heat map of FIG. 34 displays the activities of products/services
based on type, the parameters may be customized by the user. For
instance, the heat map may be configured to show the name of the
most popular products rather than by product type. As another
example, the heat map may be configured to show activity based on
merchant name.
[0223] Moreover, the heat map may be "clickable" to show various
levels of granularity of the information. For example, FIG. 34
shows activity based on product type (e.g., "apparel"). The heat
map module 3320 may include the function of allowing the user to
click on the "apparel" section of the heat map and a new heat map
may be displayed showing activity levels broken down by categories
of apparel (e.g., men, women, children, etc.) or show popularity
over a period of time. Moreover, the user may click on one of these
categories to generate yet another heat map that displays
popularity based on the specific type of product (e.g., shirts,
pants, suits, casuals, etc.). The heat map module 3320 may be
configured to display any level of granularity for any type of
parameter without departing from the scope of the present
invention.
[0224] Furthermore, while the exemplary heat map of FIG. 34
illustrates a colored area graph to convey levels of activity,
other graphical representations may be used without departing from
the scope of the invention. For instance, FIG. 35 illustrates
various exemplary representations that may be used, such as (a)
heat, (b) speed/cluster, (c) sound/vibrations, and (d) color/size.
These exemplary representations are meant only to provide examples
and not as limitations. Hence, other graphical representations may
be used without departing from the scope of the invention.
[0225] Offer Rank Module
[0226] One of the challenges in marketing is to induce merchants to
increase the amount of "variable" marketing (i.e., offers,
discounts, incentives, etc.) offered to users. Unlike "fixed"
marketing fees (e.g., periodic advertisements), variable marketing
fees are often proportionate to the ultimate purchase price. Hence,
this generally offers a more attractive or predictable return on
investment to merchants. However, merchants are often unwilling to
increase their variable marketing spending unless they have a
better understanding of what impact the added spending will have on
their traffic and how the change in traffic (if any) compares to
their competitors. Unfortunately, prior art rewards/loyalty systems
do not enable merchants to readily compare themselves to other
merchants participating in the system. Moreover, prior art systems
do not enable merchants to readily change the value of their
variable marketing spending (e.g., increase/decrease the offer,
discount, or incentive to users) to compete with other merchants
using the system. Additionally, prior art systems do not enable
merchants to analyze how their variable marketing impacts their
traffic or how their offers compare with those offers from other
merchants. Hence, merchants are generally reluctant to increase the
value of the discounts and/or incentives they offer to users
participating in the rewards/loyalty program.
[0227] In accordance with an exemplary embodiment of the present
invention, another tool available to the merchant on the account
manger module 3310 includes an offer rank module 3330. As shown in
FIG. 33, the merchant 3201 may view where the merchant's offer
ranks among other active offers in the same category in terms of
redemption and effectiveness. For instance, FIG. 36 shows an
exemplary view of an offer rank. As shown, the offer rank for the
offer from merchant 3201 is ranked in popularity with other offers
from merchants in a similar category of products/services. In this
example, the offer from merchant 3201 is 6th in popularity when
compared with other offers from competitors.
[0228] In this example, the offer rank module 3330 provides a
pull-down menu to select a product/service category as well as the
time periods for comparison. Other types of parameters may be used
for comparison without departing from the scope of the invention.
While the exemplary embodiment of FIG. 36 displays the names of
competitors, the names of merchants other than the viewing merchant
may be removed to provide an anonymous offer rank. In this manner,
the merchant 3201 may be able to assess what types of offers are
popular among the users as well as the effect of the merchant's own
offer in the marketplace. Based on this information, the merchant
may create more effective offers to get better results.
[0229] Offer Bid Module
[0230] In conjunction with the offer rank module 3330, the account
manager module 3310 includes an offer bid module 3340. In
particular, the merchant 3201 may change its offer based on the
information obtained from the offer rank module 3330 and/or other
marketing information (e.g., click volume data) to increase the
effectiveness of the offer. (FIG. 33: Step 6) The offer bid module
3340 may be accessed from the account manager module 3310 or from
the offer rank module 3330 (e.g., via buttons 3610) as shown in
FIG. 36. More specifically, using the offer rank information from
FIG. 36, the merchant 3201 may want to increase the offer level
(e.g., higher discount) to make the offer more desirable. If it
appears from the click volume data from the account manager module
3310 that there is little click traffic, then the merchant 3201 may
want to increase or change the offer parameters to create more
traffic.
[0231] FIG. 37 illustrates an exemplary view of the offer bid
module 3340. As shown, the offer bid module 3340 allows the
merchant 3201 to change the offer to be more attractive to
potential consumers. For example, the offer bid module 3340 shows a
portion of the offer rank to compare the current offer to those
that are more successful from competitors. The offer bid module
3340 allows the merchant 3201 to change the level of discount, for
example, give free shipping, an additional gift, and/or change the
offer type (e.g., from "limited time" to "ongoing"). Other types of
offers/incentives may be used without departing from the scope of
the present invention.
[0232] Once the offer has been changed through the offer bid module
3340, the new offer is updated in the offer database 3335. (Step 7)
The new offer is then updated to be matched and distributed to the
relevant users. (Step 8) The offer bid module 3340 allows the
merchants to benefit by being able to change the offer parameters
to create a more effective offer and users benefit by receiving
better offers due to the competitive marketplace created by the
offer rank module 3330.
[0233] Merchant Mapping
[0234] Merchant mapping allows for the exponential collection of
preference data. As discussed above, reminder data is captured from
a customer. The active data gathering module (ADG) may dynamically
present a user with a preference question (e.g., a reminder) or
with an offer and monitor if the user responds. This reminder data
is collected and analyzed. The analysis may include filtering
merchants based on the recency, frequency, and magnitude of the
reminder data. The recency of the data is a rating of how old the
data is. The frequency of the data is how often a particular offer
is requested. The magnitude of the data is how many times an offer
has been requested or the dollar amount of an offer. The reminder
data is then used to send similar types of offers to customers in
the future. For example, the similar types of offers may include
offers from the same vendor or related vendors. FIG. 38 is an
example of a merchant mapping. The example shows that customers
that respond to offers from a retail store, such as Target.RTM., or
shop at the store may also be interested in offers from various
other merchants displayed in the mapping. The closer in proximity a
merchant is to the center of the circle, the more likely a customer
is to respond to an offer from that merchant. For example, as shown
in FIG. 38, customers shopping at Target.RTM. may respond to offers
from Kmark.RTM. or SmartBargains.com more than offers from Buy.com.
Merchant mapping allows for the selling of a marketing campaign
that applies to one merchant to be sold or used by another merchant
that is found in the merchant mapping.
Example
[0235] The following describes an exemplary workflow of a merchant
interacting with the merchant network services module in accordance
with the present invention. As shown in FIG. 39, a merchant
accessing the system of the present invention for the first time is
guided through a series of screens, such as an auto-enroll wizard
the examples of which are shown in FIGS. 40A-40D, to setup an
account to begin offering a product or service.
[0236] In particular, in step 3902, the merchant is first guided to
a screen that allows the merchant to pick a category that best
describes the merchant. FIG. 40A shows an exemplary embodiment of a
category interface through which the merchant selects a category.
In step 3904, the merchant is then guided through a screen to
create an advertisement/information about the merchant to be
displayed on the system of the present invention. FIG. 40B shows an
exemplary embodiment of an ad creation interface. As shown, the ad
creation interface may be a pre-designed template with various
fields that can be customized by the merchant to create an
advertisement about the merchant. In step 3906, the merchant is
guided through a screen to enter the contact information needed to
send the activation information as well as to setup a password for
accessing the merchant services module of the present invention.
(FIG. 40C) In step 3908, the merchant is notified that the initial
enrollment process is complete and to expect an email message to
confirm enrollment (FIG. 40D).
[0237] Once the initial enrollment has been completed, the merchant
waits for approval. When the approval process is completed, an
email message containing a link to the account manager module 3310
(FIG. 33) and instructions on how to login is sent to the merchant.
(Step 3910) FIG. 41 shows an exemplary embodiment of the email
message that is sent to the merchant to confirm enrollment. When
the merchant activates the link (e.g., a URL to the account manager
module 3310), the merchant is guided through a screen for logging
into the merchant services module. FIG. 42 shows an exemplary
embodiment of the login interface. During the login procedure, the
system of the account manager module 3310 determines if the
merchant is logging in for the first time. (Step 3914) If this is
the first time logging in, the merchant is guided through a series
of screens for setting up the account and creating an offer.
[0238] In step 3916, the merchant is guided through a screen for
setup up of the merchant's account. FIG. 43A shows an exemplary
embodiment of the account setup interface. In step 3918, the
merchant is guided through the heat map screen so that the merchant
may get a sense of users' activities on the system. FIG. 43B shows
an exemplary embodiment of a heat map showing users' activities
based on merchant categories, for example. In the exemplary heat
map of FIG. 43B, the "hottest" category appears to be in the
"electronics" category, followed by "cell phone/wireless" and
"apparel." As described above, the merchant may also "drill down"
into each of the categories (e.g., into sub-categories) to obtain a
display with higher granularity. In step 3920, the merchant is
guided through the offer rank screen so that the merchant may get a
sense of the most popular offers in a particular category of
merchants. FIG. 43C shows an exemplary embodiment of an offer rank
showing the offers ranked in popularity (i.e., traffic) for a
particular category of merchants. The merchant's offer will not be
shown when the merchant is logging in for the first time as no
offer has been created. The other merchants' identities are kept
anonymous to ensure privacy. In step 3922, the merchant is guided
through the offer tool screen so that the merchant may create an
offer based on the information obtained from the heat map and the
offer rank. FIG. 43D shows an exemplary embodiment of an offer tool
interface. The merchant can designate, in part, the offer, length
of the offer, and any descriptions of the offer. Once the offer has
been created, a confirmation screen is displayed informing the
merchant that the created offer will be made available to the
users. (Step 3924).
[0239] Once the merchant has set up the account and created an
offer for the first time, the merchant is taken to the account
manager module homepage. All subsequent logins occur at step 3912,
bypassing the auto-enroll wizard (i.e., steps 3902-3910). The login
at step 3912, once registered, directs the merchant to a homepage
3310a on the account manager module 3310. FIG. 44A illustrates an
exemplary embodiment of the homepage 3310a. In the example shown,
FIG. 44A displays an account summary, online lead generation
information, and account activity. However, other information may
be displayed without departing from the scope of the invention.
[0240] As shown in FIG. 39, the account manager module 3310
includes access to various management tools (3310a-3310e). Access
to these tools is depicted as tabs 4410 in FIG. 44A. However, other
interfaces, such as buttons, for example, may be used without
departing from the scope of the present invention. In the example
shown in FIG. 44A, the tabs 4410 may include access to "Home"
(3310a), "Create Offer" (3310b), "Increase Marketing" (3310c),
"Reporting" (3310d), "My Account" (3310e), and "Tutorial/FAQ"
(3310f) tools. FIGS. 44A-44E show exemplary displays and interfaces
of some of these tools.
[0241] In particular, as shown in FIG. 39, various marketing tools
may be accessed through the Increase Marketing tool 3310c. As
shown, the Increase Marketing tool gives the merchant access to
heat map module 3320, offer rank module 3330, and the offer tool
module 3340 with functionalities as explained above. Furthermore,
the merchant is also given the option of selecting between a
"Revenue Sharing" 3950 and "Fixed Marketing" 3960 tool.
Additionally, the selection of the Create Offer 3310b tool guides
the merchant through steps 3902-3910 to create another offer.
[0242] Payment/Registered Card Module
[0243] An exemplary embodiment of the system of the present
invention includes the following components and entities:
[0244] Users: Users are potential customers of goods and services.
The users may be members of a loyalty program through which the
incentives are offered.
[0245] Card Issuers (partners): The card issuers are entities that
offer credit or debit cards to the users. Examples of card issuers
may be American Express, Bank of America, Chase, CitiBank, etc.
[0246] Processors: The processors are entities that process
transactions from credit or debit card purchases. The processors
may be separate entities from the card issuers.
[0247] Registered Cards: Registered cards are credit or debit cards
issued to the users by the card issuers that have been registered
with the user's loyalty program to be used as the main transaction
vehicle for purchases of goods and services. Examples of cards that
are registered include, but are not limited to, MasterCard.RTM.,
Visa.RTM., American Express.RTM., Discover.RTM., Diner's Club.RTM.,
and the like.
[0248] Merchants: Merchants are entities who offer goods and
services to users. The merchants may offer incentives to the users
through the loyalty program to which the users' may be members. The
incentives may range from discounts to free offers as well as other
perks intended to entice the users to purchase merchants' goods
and/or services.
[0249] Sponsors (User Networks): Sponsors are entities that provide
loyalty or perks programs to the users. Sponsors may be employers,
institutions (e.g., alumni or bar associations), and companies.
Sponsors may also be merchants or card issuers as well.
[0250] Registered Card ("RC") Processing System: The RC processing
system is a middle system that is the backbone of the present
invention. The RC processing system provides the registration of
the cards, processes transaction data from the card issuers,
matches users' transactions with the incentives/discounts offered
from the merchants, and distributes awards to users.
[0251] Registered Card Processing System
[0252] FIG. 45 shows an exemplary embodiment of a payment
processing system in accordance with the present invention. As
shown in FIG. 45, the payment processing system of the present
invention includes a Registered Card ("RC") Processing System 4510.
The RC processing system includes a data capture module 4510a,
rules management module 4510b, and instruction module 4510c. The
data capture module 4510a captures, among other things, the
enrollment information such as the user information, registered
card information, and loyalty program to which the user is
enrolled. The rules management module 4510b includes access to the
rules management database (e.g., FIG. 2: 240c) that stores the
business rules to be applied for each user in determining the
incentive to be applied. Based on the business rules, the rules
management module 4510b, among other things, calculates the
discount/incentive due to the user. The instruction module 4510c
sends instructions to the issuer or other third party processing
entity of the registered card for proper processing and applies the
discount/incentive due to the user. The discounts/incentives
include accumulating reward points (i.e., earning the points),
redeeming the accumulated points (i.e., spending the points),
depositing the points in an account (i.e., saving the points), or
giving the points to a charitable account. Some or all of the
components of the RC processing system 4510 may be implemented in
conjunction with or independent of the narrowcasting system 38
described above.
[0253] The RC processing system 4510 also includes a points module
4520 and a spend, save, and give module 4530 to be described in
detail below. In general, the points module 4520 maintains an
accounting of accumulated and redeemed points based on the users'
activities. The spend, save, and give module 4530 processes the
various accounts to which the user has designated the redemption of
the points to flow.
[0254] The RC system of the present invention may be implemented on
a computer network using Internet or Intranet portals. The users
may be given access to the portal that is specific to the users'
enrolled loyalty program. The RC system of the present invention
may be accessed by the user at any end-user client device, such as
computers, kiosks, and mobile devices that is connected to the
system via a local area network (LAN), wide area network (WAN),
Internet, peer-to-peer connections (i.e., direct connections via
modem, for example), or wireless networks. The portals may be
implemented on the system of the present invention or may be
implemented on separate systems.
[0255] RC System Workflow
[0256] A loyalty or reward program for a sponsor, such as an
employer who wants to provide a benefits program to provide
incentives for its employees, may created in conjunction with or
independent of the narrowcasting system 38 as described above. A
loyalty program portal, for example, may be implemented on the
system of the present invention or may be implemented by a separate
system. The sponsors may be any entity, including merchants and
card issuers, who want to provide benefits and incentives to its
members in exchange for their loyalty to the sponsor.
[0257] Once a loyalty program is set up, the sponsor notifies its
intended users regarding the loyalty/perks/rewards program and
encourages the users to enroll in the loyalty program. The loyalty
program provides incentives to the users by making available offers
from merchants that would peak the users' interests. The matching
of the incentives from various merchants to the most appropriate
users is explained above.
[0258] To enroll, the user accesses the portal and provides the
necessary information to become a member of the loyalty program.
During enrollment, the user is provided the opportunity to register
a payment card to be used in purchase transactions resulting from
the incentives provided by the loyalty program. The user
information and the payment card information are captured by the
data capture module 4510a. In particular, the users' personal
information and the card information associated with the user are
stored in a user database. In addition, the portal, as shown in
FIGS. 56A-56F, allows a user to access and view discount/incentives
that is available to the user or the user has received, including
any reward points earned or redeemed by the user.
[0259] FIG. 46 shows an exemplary embodiment of the RC processing
system 4510 in accordance with the present invention. FIG. 47 shows
a workflow diagram of an exemplary process according to the present
invention. FIG. 55 shows an example of the overall registered card
purchase transaction flow. As shown in FIG. 46, the RC processing
system 4510 is an interface between the rewards/loyalty program,
the card issuer, the processor, and the merchant. In particular, a
user accesses the rewards/loyalty program through a portal as shown
in FIGS. 56A-56F, for example. The rewards/loyalty portal includes
an enrollment module 4610 through which the user may register as a
member to the rewards/loyalty program. During enrollment, the user
is asked to register a payment card to be used for performing
transactions to take advantage of the merchant incentives offered
through the rewards/loyalty program (FIG. 47: step 4701). While the
card may be registered during enrollment, the user may register a
card, add additional cards, or change a registered card with
another, at any time through the rewards/loyalty program portal
without departing from the scope of the invention.
[0260] The enrollment information and/or the card information,
including the cardholder name and card number, are captured in the
data capture module 4510a. The card may be registered by passing
the card information directly to the data capture module 4510a or
through a surrogate. That is to say, instead of passing the card
information directly to the data capture 4510a, in an alternate
embodiment, the enrollment module 4610 may contact the card-issuer
to receive a surrogate ID to be used in place of the actual card
information. Once the data capture module 4510a receives the user
information including the card information (either the actual card
information or a surrogate ID), the data capture module 4510a sends
to the transaction reporting module 4620 the registered card
information (FIG. 47: step 4702). Optionally, a list of
participating merchants may also be sent (FIG. 47: step 4702). The
transaction reporting module 4620 updates the cardholder data files
and participating merchant list (FIG. 47: step 4703). In this way,
the card issuer or processor monitors any transactions occurring at
the participating merchant associated with the registered card.
[0261] Acceptance of Offer
[0262] Once the user has enrolled in the loyalty program (now a
"member"), the user is provided with a list of incentives and
offers from merchants that would most interest the user on the
loyalty/rewards program portal. In an exemplary embodiment, the
incentives and offers may be narrowcasted to the user through the
narrowcasting system 38 as described above. However, the
narrowcasted incentives/offers are not required. The incentives
offered to the user are stored in the rules management module
4510b. In one exemplary embodiment, the user can take advantage of
the incentive/offer made available to the user on the portal by
simply going to the particular vendor related to the incentive and
making a purchase. The purchase may be made at the physical store,
on-line, over the phone, through the mail, or any other purchase
channel as long as the user uses the registered card.
[0263] In another exemplary embodiment, the user can sign up (i.e.,
reserve) to take advantage of the incentive or offer through the
loyalty/rewards program portal. This is a form of "RSVPing" (i.e.,
reserving) the offer or incentive for use in the future. By signing
up for the offer or incentive, the payment processing system of the
present invention can accumulate analytics of the user's purchasing
behavior. These analytics may be input into the narrowcasting
system 38 as additional data sets to further enhance the relevance
of future offers to be made to the user as well as for market
reporting features for the merchant who made the offer.
Accordingly, this tool is useful for proving incrementality of an
offer. This tool is also useful in limiting the redemption of an
incentive to a present number of people or consumers (i.e., "offer
control).
[0264] Partially Qualified Transactions (PQTs)
[0265] During a purchase from the merchant offering an incentive,
the user uses the payment card registered with the RC processing
system 4510. As briefly discussed above, the purchase may be made
on-line (i.e., through the merchant's website), in person at a
physical location of the merchant, through a mail order catalog, by
phone, or any other method without departing from the scope of the
invention (FIG. 47: step 4704). The transaction reporting module
4620 monitors the registered card activities and identifies
transactions made with the participating merchant using a
registered card as a partially qualified transaction ("PQT") (FIG.
47: step 4705). Identified PQTs are then sent to the rules
management module 4510b of the RC processing system 4510 (FIG. 47:
step 4706). In an exemplary embodiment, the RC processing system
4510 may track both an offer that is reserved as discussed above
and an offer that has been used by a customer.
[0266] In the rules management module 4510b, a matching engine 4624
matches the PQTs with the associated merchants and passes the
information to a rewards calculation module 4626 to determine the
type and amount of the incentives/reward, if any, based on stored
business rules (FIG. 47: step 4709). In this regard, the matching
engine 4624 may analyze the PQT to match the transaction based on
merchants and/or products. Product based matching will be further
explained in detail below. Moreover, the rewards calculation module
4626 applies stored business rules from a business rules database
(e.g., FIG. 2: 240c) to the received PQTs.
[0267] The business rules may be defined by the merchants to
specify the terms of the offer to be made to the user. The rules
management module 4510b processes the PQT based on rules associated
with the user. The business rules database (e.g., FIG. 2: 240c)
connected to the rules management module 4510b contains all of the
business rules associated with all the incentives made available to
the users. FIG. 49 describes examples of the different types of
rules that are available through the registered card system.
[0268] For example, the rules may include time-based criteria
(e.g., time period in which the incentives offered are valid),
user-specific criteria (e.g., incentive only available to members
of a specific loyalty program), and terms of the
incentive/discount. Other business rules related to the incentive
are stored in the rules database to be applied in processing the
transaction data For example, the conditions may include, but are
not limited to, the type of incentive, the amount of incentive, to
whom the incentive applies, the time frame for the offer, and any
other conditions of the offer. Moreover, the same merchant may
target a specific type of user by customizing the amount of the
incentive or terms of the offer down to the individual level. In
order words, an offer by the same merchant may be different between
users based on the users' profiles. In this way, the merchants can
customize the offer as generally or as detailed as the merchant
desires. The rules management module 4510b automatically applies
the rules to the user's PQT to determine the level of
discount/incentive based on these rules.
[0269] Offer Types
[0270] The following types of incentives or offer types may be made
by merchants. These offer types are associated with the rules
specified by the merchants. A first offer type is the delivery of
discounts or savings, such as a percentage or dollar amount off, a
percentage or dollar amount off of a purchase greater than a set
dollar amount, or a percentage or dollar amount off up to a certain
maximum discount. A second offer type may be used to attract new
customers or bring back loyal customers. The offer may include
allowing each user to utilize the offer one time for a purchase.
For example, this offer type may state "50% off Next Purchase" or
"$100 Savings on First purchase." A third offer type may include
making offer "A" available the first time a customer makes a
purchase and making offer "B" available for all subsequent
purchases. A fourth offer type may include allowing for repeating
charges to be discounted for a set period of time. An example may
include "25% off your first six months of delivery." A fifth offer
type may include tiered offers or offers based on dollar ranges of
merchandise. An example may include "10% off purchases up to $99,
20% off purchases $100-$999, and 30% off purchases $1000+." A sixth
offer type may include an offer that is available a certain number
of times per period of time.
[0271] In addition, a seventh offer type may include an offer that
will be available on a specific day of the week. An eighth offer
type may include requiring a user to view the Offer Detail Page
within a specified window of time or to view and take active steps
(i.e., clicking on a web page) to take advantage of the offer. This
type of offer eliminates accidental discounts for a customer who
would have paid full price. This offer type is discussed above with
regards to reserving an offer. A ninth offer type may include
imposing minimum and/or maximum spending and discounts. A tenth
offer type may include an offer that is individualized for a
particular user. For example, this user-level offer may include
giving person A 10% off and person B 20% off. An eleventh offer
type may include network level offers that allow a merchant to make
an offer available to an entire network (or segments of the
network). For example, company A employees get 10% off and company
B employees get 20% off. These offer types can be combined to
create customized solutions and all offer types can be based on
dollar or percentage calculations. In addition, an offer type can
be used to create cross promotion of products.
[0272] By way of example, a flower vendor may create an offer for
free shipping to all men who live in New York City that purchase 5
dozen roses on February 13 between the hours of 8:00 am and 10:00
am using a specific type of credit card. Another example is a
merchant creating an offer that only a small number of consumers
can take advantage of. Once the offer has been used by a set number
of consumers, a merchant can create a second offer. The second
offer is typically a lesser offer, but still provides consumers
with an incentive to make a purchase. Any business rule for the
offer may be made without departing from the scope of the present
invention. In particular, criteria for determining the business
rule to maximize the merchant's marketability may be performed by
the narrowcasting system of the present invention as described
above. Other types of offers and any combinations thereof may be
used without departing from the scope of the invention.
[0273] Fully Qualified Transactions (FQTs)
[0274] Once the PQTs have been processed by the matching engine
4624 and rewards calculation module 4626 to verify and determine
the type and amount of the incentive, if any, the PQTs are
converted to fully qualified transactions ("FQT"s). The FQTs and
the determined rewards associated thereto are sent to the
instruction module 4510c. The instruction module 4510c sends
instructions to the card issuer to credit back the user based on
the calculated discount, for example, offered by the merchant for
this user (FIG. 47: step 4709). For example, the instruction module
4510c sends credit data to a user crediting module 4630, which
applies the credit data to a user statement module 4640 to reflect
the credit given to the user on the user's monthly statements. The
instruction module 4510c also updates the pending credit due to
user in the user's account accessible through the rewards/loyalty
program portal (FIG. 47: step 4708). The card issuer then credits
the user's account in the amount identified by the instruction
module 210c (FIG. 47: step 4710) and the credit amount is reflected
in the user's monthly card statement (FIG. 47: step 4711).
[0275] The card issuer receives the discount information from the
instruction module 4510c and directly applies the credit to the
user's registered card account. The original transaction amount and
the credited discount amount are reflected as separate transactions
in the user's card statement. FIG. 48 shows an exemplary statement
generated in accordance with the present invention. Accordingly,
the user is only obligated to repay the card issuer at the
discounted price. In this regard, the card issuer has several
options for processing the discount. The card issuer may withhold
the amount of the discount before paying the merchant, especially
if the transactions are batched over a period of time (e.g.,
monthly basis). If the merchant has already been paid at the
regular price of the goods or services, the card issuer obtains the
amount of the discount from the merchant directly. In an
alternative embodiment, the RC processing system 4510 of the
present invention may act as an intermediary, thereby paying the
credit amount to the card issuer and recovering the same amount
from the merchant by billing the merchant. Other payment options
between the card issuer and the merchant may be made without
departing from the scope of the present invention.
[0276] Merchant Matching
[0277] In order to create PQTs and FQTs, the card transaction data
must be matched with the merchants to determine the
incentives/discounts, if any. FIGS. 50 and 51 illustrate exemplary
embodiments for processing the card transaction data to match the
transactions to the proper merchants. Merchant matching allows for
the identification of merchant transactions from data feeds
provided by the card issuer or a third party processor. The
merchant matching software (i.e., matching engine 4624) allows for
the inclusion of any merchant that is registered with the
registered card system. The merchant matching process also solves
an issue of identifying merchants from datasets that do not include
unique merchant identifications as is typical with transaction data
from issuers or processors.
[0278] The process of merchant matching includes the following
steps as shown in FIGS. 50 and 51: [0279] 1. A user makes a
purchase at a merchant 5110 and the transaction data, such as card
member information and merchant information, and identifying
information for the item or items, is forwarded to a processor
5120. [0280] 2. The merchant transaction data from the processor is
forwarded to the registered card system 4510. [0281] 3. A card
issuer 5130 receives the card member transaction data from the
processor 5120. [0282] 4. The card issuer 5130 sends an identified
PQT to the rules management module 4510b of the registered card
system 4510 as discussed above. The PQT may include the card member
information and the purchase made. [0283] 5. The registered card
system 4510 normalizes the transaction data received from the
processor 5120. The data normalization process may be any type of
appropriate normalization process known in the art. [0284] 6. The
registered card system 4510 identifies the merchant data in the
transaction data. The registered card system 4510 verifies that the
merchant is registered with the registered card system 4510. This
step may include determining whether the merchant's name or other
merchant identifying information, such as the merchant location,
store identification, or industry code, in the transaction data
matches or correlates with a merchant name or other merchant
identifying information that is stored within the registered card
system. This step may also include verifying that the merchant is
actually offering a particular incentive that is found in the
transaction data. [0285] 7. The matching engine 4624 of the
registered card processing system 4510 matches the PQT with the
associated normalized merchant data and passes the information to a
rewards calculation module 4626 to determine the type and amount of
the reward, if any, based on stored business rules (FIG. 47: step
4709). [0286] 8. Finally, if the PQT is a qualified transaction,
then a FQT is transmitted from the registered card system 4510 to
the card issuer 5130. The remaining steps are similar to those
discussed above with regards to the processing of FQTs.
[0287] In accordance with the exemplary embodiment of the payment
processing described above, the user does not have to clip coupons,
remember coupon codes, or perform any other extraneous activities
to take advantage of an offered incentive. Once the user registers
a card with the rewards/loyalty program, all the user has to do is
use the registered card to make purchases at participating
merchants. Because the card issuer authorizes the transaction at
the regular price of the offered goods or services at the time of
purchase, the sales/service representatives do not have any
indication that the user is obtaining a discount. From the
merchant's perspective, the user is a regular customer making a
regular purchase. Moreover, because the incentives are
automatically processed after the purchase, the users are notified
immediately of the pending discounts or rewards. Finally, any
discounts are automatically applied to the card account before
issuing the card statement, thereby receiving the benefits of any
savings directly. Some of the benefits of the present invention are
listed below:
[0288] From the customers' perspective, the registered card
automated incentive redemption system provides:
[0289] Faster redemption of incentives: Incentives are processed
automatically by the card issuer and the incentives are applied
directly to the card transaction.
[0290] Easier redemption: There are no coupons or certificates to
clip, print, carry, and produce at the time of purchase.
[0291] Better purchase experience: A customer cannot forget to take
the coupon or certificate, or suffer the embarrassment of producing
the coupon or certificate at the register.
[0292] From the merchants' perspective, the registered card
automated incentive redemption system provides:
[0293] Cheaper promotions: Eliminates administrative cost to
produce and process paper coupons or promotions.
[0294] Easier promotions: Less administrative cost for processing
redemptions with the paperless transactions and efficient sales
processing. The system also provides better tracking of promotions
and effectiveness.
[0295] Secure promotions: No coupon leakage or viral distributions
of promotion codes. The system allows for private sales that are
discreet and exclusive with complete control of intended
customers.
[0296] Tracking of purchases: Merchants are able to track the
purchases of all customers using a coupon (i.e., purchase
funnel).
[0297] From the card issuer and the rewards/loyalty sponsor's
perspective, the registered card automated incentive redemption
system provides:
[0298] Concentrated purchases: All purchases are on one card
allowing increased usage of the card.
[0299] Irreplaceable transaction mechanism: Combining huge
discounts on larger purchases with good discounts on everyday
spending makes the card invaluable to the user (i.e., the user will
always use the card in the off chance that the user will get a
discount).
[0300] Super-charges the rewards program: The rewards/loyalty
program gets a boost of usage as users discover the convenience and
benefits of using the card.
[0301] SKU Level Discount Processing
[0302] As briefly described above, another exemplary embodiment of
the present invention includes a SKU (i.e., stock keeping unit)
discount processing. Currently, many grocery chains, as well as
other merchandise-based vendors offer membership or club cards to
customers to provide discounts (e.g., "membership price") and
rewards. These loyalty programs are intended to offer discount
prices to members while keeping track of the types of goods
purchased by the customers.
[0303] The RC processing system 4510 of the present invention
receives SKU level discount information from merchants and
manufacturers and stores the information in the rules database (not
shown). The user, who has registered a transaction card with a
loyalty program of a merchant (e.g., grocery store), makes
purchases at the merchant's store (either on-line or in the store)
using the registered card. As described above in reference to FIGS.
46 and 47, the transaction reporting module 4620 of the card issuer
monitors the transaction activity of the registered card and
identifies PQTs (i.e., transactions on the registered card
associated with the merchants participating on the loyalty
program). The identified PQTs are communicated to the rules
management module 4510b of the RC processing system 4510. In
addition, the merchant sends SKU level purchase data to the rules
management system 4510b of the RC processing system 4510. Similar
to identifying the merchants associated with the PQTs described
above, the matching engine 4624 matches the PQTs with the SKU level
purchase data from the merchant to identify the specific products
associated with the PQTs purchased with a particular merchant. In
this regard, the business rule database (not shown) has stored
therein SKU level purchase data received either from the merchant
or, more typically, from a third party marketing institution that
gathers and processes SKU level purchase data for the merchant. The
matching engine 4624 applies the SKU level discount rules to the
SKU level transaction data to identify, validate, and verify the
PQTs made by the user and converts them into FQTs. The rewards
calculation module 4626 applies the stored business rules, such as
the discounted purchases corresponding to the card transaction data
(e.g., by date, location, transaction amount, etc.). The rewards
calculation module 4626 then calculates the amount of discount or
points for each qualified SKU item and determines the total amount
of discount or points due to the user.
[0304] The total discount amount to be credited to the user or the
amount of points earned by the user for the purchase of an item is
sent to the card issuer through the instruction module 4610c. The
card issuer then credits the discount amount or points amount to
the user's registered card account, for example, and generates a
monthly card statement. The monthly statement may list the SKU
information with the associated discounts or points to provide a
record of the items to which the discounts or points were applied
and the amount of savings associated with each item. As the
discount has already been applied before issuing the statement, the
user is only responsible for repaying the card issuer the
discounted transaction amount. As discussed before, the card issuer
may obtain the discounted amount from the merchant directly or the
RC processing system 4510 may act as an intermediary. In this
manner, the user does not have to clip any coupons or keep track of
a separate club/rewards card in order to take advantage of the
loyalty based savings. Moreover, because the card issuer has
processed the FQTs including the SKU level discounts, the processed
information may be used by the merchant to recover any
reimbursements from manufacturer based discounts, thereby
simplifying the incentives offered by the manufactures while
reducing fraudulent coupon redemptions by the merchants from the
manufacturers.
[0305] Reward Points Module
[0306] In an exemplary embodiment of the present invention, a
points module 4520 adds enhanced features to the RC processing
module 4510. The points module 4520 may be implemented
independently or in conjunction with the RC processing system 4510
without departing from the scope of the present invention. In
general, the points module 4520 allows users to accumulate points
based on specified activities and redeem the accumulated points for
various rewards, such as to purchase goods or services or apply the
points to spend and save accounts described in further detail
below.
[0307] Earning Points
[0308] Generally, an enrolled member accumulates reward points
based on reward rules set by the sponsors and/or merchants (e.g.,
number of visits, qualified purchases, performance award, etc.).
The accumulated points are maintained and tracked through the
user's account on the loyalty/rewards program portal as shown in
FIGS. 56A-56F, for example. In the exemplary embodiment of the
present invention, the points module 4520 defines a specified
amount of points (e.g., 1 point) to represent a specified monetary
amount (e.g., $0.01). The points module 4520 allocates a specified
number of points for a specified type of activity based on reward
rules. As discussed above, these reward rules may be defined by the
sponsors and/or merchants. For instance, the merchants may add
points that correspond to the amount of discount for various
products and services rather than giving monetary discounts (e.g.,
100 points for every $1 spent, 10 points/$ for 10% discounts,
etc.).
[0309] FIG. 52 shows an exemplary process for accumulating points.
As shown in FIG. 52, a user ("Bob") visits a participating merchant
and makes a $100 purchase using his registered card. As explained
above with reference to FIGS. 46 and 47, the transaction is
authorized and settled by the card issuer as a normal credit card
transaction. During the card transaction processing, the card
issuer realizes that the transaction is a registered card
transaction with a participating merchant. The transaction is
flagged as a PQT (partially qualified transaction) and sent to the
RC processing system 4510. The RC processing system 4510 performs
the merchant matching then applies the business rules and reward
rules to determine incentives/discounts that Bob is entitled to. In
this example, the business rules also indicate that the merchant is
offering points for spending a set amount with the merchant. In
this example, 1 point is to be allocated for every $1 spent on a
qualified transaction. Because Bob spent $100 with the merchant,
the RC processing system 4510 adds 100 points to Bob's account
loyalty/reward account. While the exemplary embodiment of FIG. 52
is described with the standard point accumulation process,
additional points may be allocated to the user if the sponsor of
the rewards/loyalty program has reward rules set to provide an
incentive to the user to use the program. Additional points may be
added by reward rules set by the merchants to further encourage
usage. The reward rules for adding points may be customized for
each user without departing from the scope of the present
invention.
[0310] Burning Points
[0311] Once the points have been earned, the points module 4520
maintains a points balance for each user. The user accesses the
points balance through the rewards/loyalty program portal, for
example. In accordance with the present invention, the accumulated
points may be redeemed in various ways. For instance, the points
may be redeemed for cash, applied against a purchase, or designated
into various savings vehicles through the spend, save, and give
module 4530. Regardless of how the points are to be redeemed, the
user accesses the points module 4520 through the user's account on
the rewards/loyalty program portal. Under the user's account
accessed through the portal, the points module 4520 displays the
total balance of the accumulated points available for redemption.
Once the user decides to redeem the points, the user designates the
number of points to be redeemed and where the points are to be
applied (e.g., cash, merchant, spend and save, etc.)
[0312] Spending Points
[0313] If the user decides to cash in the points, the user
designates the amount of points to redeem and selects the "Cash"
option. The points module 4520 sends the request to the rules
management module 4510b. The rules management module 4510b applies
the stored conversion rate (e.g., 1 point=1 ) to determine the
monetary value of the points. Once the amount of the reward is
calculated, the instruction module 4510c sends an instruction to
issue payment to the user.
[0314] If the user decides to spend the points at a merchant, the
user designates the amount of points to redeem and selects the
merchant where the points will be used. More than one merchant may
be designated without departing from the scope of the invention.
Thereafter, the user can visit the merchant (e.g., on-line, in
store, by phone, through catalog, by mail, etc.) to make a
purchase. When making the purchase, the user uses the registered
card and the transaction occurs like any other purchase at the
regular price. As discussed above, the card issuer then identifies
the transaction as a PQT (i.e., a registered card transaction at a
participating merchant). The PQT is sent to the RC processing
system 4510 to be processed as described above to determine any
discounts and/or incentives are to be applied. During the
transaction/product matching stage the rules management module
4510b recognizes that points are designated to the identified
merchant. Accordingly, the predesignated amount of points is
converted to a monetary equivalent and the value is deducted from
the transaction price. If the rules management module 4510b
identifies additional discounts/incentives offered by the merchant,
those discounts/incentives are also applied. Once all of the
discounts, incentives, and rewards have been applied, the PQT is
converted to an FQT and passed to the instruction module 4510c. As
explained above, the instruction module 4510c then issues the FQT
to the card issuer to instruct the amount of credit to be applied
to the user's account. If the purchase price exceeds the amount of
points redeemed in addition to any other incentives/discounts, then
the difference is charged against the registered card account. If
the purchase price is less than the amount of points redeemed, then
no charge is made against the registered card account and any
points left over are kept as designated for redemption at the
specified merchant. Each transaction is then reflected in the
user's registered card statement. In addition, the pending
redemption of the points and any pending discounts/savings are
calculated by the rules management module 4510b and displayed under
the user's account in the rewards/loyalty program portal. After the
FQT has been processed and the user notices any left over points,
the user may de-designate the points for redemption (i.e., put the
points back into the total points balance) or use the left over at
the merchant at another time.
[0315] FIGS. 53 and 54 illustrate an example of the points
redemption process that includes a discount offer from the
designated merchant. As shown in FIG. 53, a user ("Bob") accesses
his account through the rewards/loyalty program portal and sees a
total points balance of 85,000. Bob browses through the portal to
see various offers made to his rewards/loyalty program members and
notices that one of the merchants ("Star Trac") is offering a 25%
discount on a $1000 item. Bob decides he wants to pay for the item
with his points. Bob designates 75,000 points (i.e., $750 in this
example) to redeem and selects the merchant ("Star Trac") where he
will redeem the points. After designating the points to be
redeemed, Bob goes to the merchant (on-line, in store, by phone, or
by mail order) and uses his registered card to make the purchase.
The card issuer authorizes the transaction for the full price of
the item (i.e., $1000). The card issuer recognizes the transaction
as being a PQT (i.e., a registered card transaction at a
participating merchant) and forwards the PQT to the RC processing
system 4510. The PQT is matched to the corresponding merchant
("Star Trac") and the business rules for the merchant are applied
to the PQT. At this time, the RC processing system 4510 recognizes
that Bob had designated 75,000 points to be applied to transactions
from this merchant. The RC processing system 4510 also identifies
that the merchant is offering a 25% discount (i.e., $250) to
members of the rewards/loyalty program. Accordingly, the PQT (i.e.,
$1000) is reduced by the discount (i.e., $1000-$250=$750). In
reality, the RC processing system 4510 charges the merchant for the
discount amount (i.e., $250). The remaining balance (i.e., $750) is
paid by the RC processing system 4510 from the redeemed reward
points. From an accounting perspective, because the card issuer, in
effect, has paid the merchant the full purchase price (i.e.,
$1000), the FQT generated by the RC processing system 4510
authorizes the card issuer to debit the account maintained by the
RC processing system 4510, thereby bringing the user's registered
card account to $0. Accordingly, the RC processing system 4510
updates the user's account in the rewards/loyalty program portal
with the discount/redemption information to reflect a new points
balance (i.e., 10,000). The card issuer, likewise, reflects in the
registered card user's statement the initial purchase ($1000), the
applied discount from the merchant ($250), and the amount from the
redeemed points ($750), each as a separate line item on the
statement.
[0316] FIG. 54 illustrates another example of the points redemption
process. In this example, the points are redeemed at two different
merchants with no discounts offered by the merchants. As explained
above, Bob accesses his account on the rewards/loyalty program
portal and notices his points balance (i.e., 10,000). He decides to
burn all of his points and designates 5,000 points at Merchant A
and 5,000 points at Merchant E. Thereafter, Bob makes purchases at
Merchant A (i.e., $100 purchase) and at Merchant E (i.e., $75
purchase) using his registered card. The card issuer authorizes the
charge each time for the full amount (i.e., $100 and $75,
respectively). The card issuer identifies these transactions as
PQTs and issues two PQTs. The RC processing system 4510 matches
these PQTs and identifies the first PQT as being with Merchant A
and the second PQT as being with Merchant E. When applying the
business rules of these merchants, the RC processing system 4510
recognizes that there are no offers outstanding. The RC processing
system 4510 also recognizes that the user has designated 5,000
points to be redeemed at Merchant A and 5,000 points to be redeemed
at Merchant E. The RC processing system 4510 converts the
designated points to monetary values (i.e., $50 for each merchant)
and generates FQTs. As explained above, the card issuer has already
paid the merchants for the full amount (i.e., $100, $75).
Therefore, each FQT authorizes the card issuer to debit the account
maintained by the RC processing system 4510 by the points
redemption amount (i.e., $50+$50=$100). The remaining balance is
applied to Bob's registered card account. Accordingly, the RC
processing system 4510 reflects Bob's account as having 0 point
balance with any pending transactions as being completed (i.e.,
displays that 5,000 points have been redeemed at Merchant A and
5,000 points have been redeemed at Merchant E). Furthermore, the
card issuer issues a statement reflecting the full purchase price
of the purchases (i.e., $100, $75), the amount of points redeemed
and applied to the purchase price (i.e., $50, $50), and the total
balance due to the card issuer (i.e., $50+$25=$75). Each of these
transactions will be reflected as separate line items on the card
statement.
[0317] Saving and Giving Points
[0318] In an alternative embodiment, the RC processing system 4510
includes a spend, save, and give module 4530. In particular, the
spend, save, and give module 4530 manages various accounts to which
the points designated in the points module 4520 can be sent. The
various accounts include, but are not limited to, checking/savings
accounts, investment accounts, loan repayment accounts, and even
charitable accounts. As shown in FIG. 1, the loyalty/reward system
30 of the present invention interfaces with charity organization 60
and Asset Management system 70. The operation of the spend, save,
and give module 4530 may operate in conjunction with the points
module 4520 or operate as a separate module. The allocation of the
points operates in the same manner as the points module 4520
designating points to be burned at specified merchants. That is,
rather than merchants, the user will designate the various accounts
in which the values of the points will be transferred. For
instance, the user may designate that a predetermined minimum
number of points, once accumulated, be transferred to one or more
accounts designated in the spend, save, and give module 4530.
Accordingly, the accumulated points may be directly deposited to
checking accounts, savings accounts, investment accounts (e.g.,
IRAs, mutual funds, education, etc.), loan repayment accounts
(e.g., mortgages, equity loans, line of credit, credit cards,
etc.), and even charitable accounts (e.g., Red Cross, Goodwill,
etc.). Other accounts may be designated without departing from the
scope of the invention. Moreover, the user can access the spend,
save, and give module 4530 through the rewards/loyalty program
portal to view/designate/modify the savings information including
disbursements and balances. More than one account may be designated
without departing from the scope of the invention.
[0319] As discussed above, traditional reward redemption programs
require the user to select offered items for redemption from a
"rewards catalog." These items are generally overstock or outdated
items that are sold in bulk to clearinghouses that contract with
the sponsors of the reward program to accept the reward points as
consideration for the items. Accordingly, users have extremely
limited selections of items to redeem with their points.
Furthermore, because the reward points must be subjected to a
claims process, the user must wait several days, if not weeks,
before the item is delivered. The points module according to the
present invention has no such limitations. The user may purchase
any item from any merchant. Moreover, because the payment with
points is transparent to the merchant (i.e., the merchant is paid
outright by the registered card), the transaction and delivery is
processed and fulfilled as with any other sales. Accordingly, the
loyalty program benefits as users find more value in the reward
points, and therefore use the loyalty program more frequently. The
card issuers benefit because more transactions are placed on the
registered card, thereby generating more revenue while
administrative processing is performed by the RC processing system
of the present invention. The users benefit because the points, in
whole or in part, may be used with any merchant for any item. When
the points are used to purchase items with merchant incentives, the
discounts are automatically processed and combined with the points
usage. All of the savings are then reflected conveniently on the
users' card statements. Moreover, the points may be used as savings
vehicles to be applied to various accounts designated by the user
to further enhance the usefulness and convenience of the
rewards/loyalty program, thereby generating even more usage.
[0320] Mobile Device Messaging and Transactions
[0321] As shown in FIG. 1A, in an exemplary embodiment, users are
able to use cell phones or any other mobile device 50 to search for
offers and to receive merchant offers and incentives. A user may be
required to register the mobile device 50 with the RC processing
system 4510. If a mobile device 50 is registered with the RC
processing system 4510, then targeted offers can be delivered to
the registered users' mobile devices 50. Registration includes
registering a user's mobile device phone number and associating
this number with the user's registered card or cards. The mobile
device 50 must be registered to determine whether a search for an
offer is associated with a registered card holder.
[0322] After the user registers the mobile device, a user is able
to search for an offer based on criteria such as location,
category, brand, and type of discount. For example, if a user is
shopping in a particular geographic area of New York City, the user
could determine whether any incentives or offers were available
based on the location of the user.
[0323] To use the registered card system from a mobile device, the
RC processing system 4510 must determine whether the mobile device
phone number associated with a message or request for a search is
being sent from a registered mobile device. The RC processing
system 4510 determines whether the phone number is associated with
a particular registered card or cards. If the phone number is
associated with a registered card or cards, the RC processing
system 4510 will send an offer or incentive to the mobile device.
This offer or incentive sent can be based on the user's search
criteria. The user is then able to take advantage of the offer at
the merchant associated with the offer. The registered card system
and process then proceed as discussed above.
[0324] Having described the various exemplary embodiments of the
present invention, it will be apparent to those skilled in the art
that various modifications and variations can be made to the
communication system and method for narrowcasting based on the
active learning system and to the system and method for merchant
network services of the present invention without departing from
the spirit or scope of the invention. Thus, it is intended that the
present invention cover the modifications and variations of this
invention provided they come within the scope of the appended
claims and their equivalents.
* * * * *