U.S. patent application number 11/450252 was filed with the patent office on 2006-10-12 for consumer profiling and advertisement selection system.
This patent application is currently assigned to Prime Research Alliance E., Inc.. Invention is credited to Charles Eldering.
Application Number | 20060230053 11/450252 |
Document ID | / |
Family ID | 36576628 |
Filed Date | 2006-10-12 |
United States Patent
Application |
20060230053 |
Kind Code |
A1 |
Eldering; Charles |
October 12, 2006 |
Consumer profiling and advertisement selection system
Abstract
A consumer profiling and advertisement selection system is
presented in which consumers or subscribers can be characterized
based on their purchase or viewing habits. The result of this
process is a consumer characterization vector describing the
probabilistic demographics and product preferences of the
subscriber or viewer. Advertisement characterization vectors
describing an actual or hypothetical market for a product or
desired viewing audience can be determined. The ad characteristics
including an ad demographic vector, an ad product category and an
ad product preference vector is transmitted along with a consumer
ID. The consumer ID is used to retrieve a consumer characterization
vector which is correlated with the ad characterization vector to
determine the suitability of the advertisement to the consumer. A
price for displaying the advertisement can be determined based on
the results of the correlation of the ad characteristics with the
consumer characterization vector.
Inventors: |
Eldering; Charles;
(Doylestown, PA) |
Correspondence
Address: |
TECHNOLOGY, PATENTS AND LICENSING, INC./PRIME
2003 SOUTH EASTON RD
SUITE 208
DOYLESTOWN
PA
18901
US
|
Assignee: |
Prime Research Alliance E.,
Inc.
|
Family ID: |
36576628 |
Appl. No.: |
11/450252 |
Filed: |
June 9, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
09807887 |
Apr 19, 2001 |
7062510 |
|
|
11450252 |
Jun 9, 2006 |
|
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Current U.S.
Class: |
1/1 ;
707/999.101 |
Current CPC
Class: |
Y10S 707/99942 20130101;
Y10S 707/99948 20130101; Y10S 707/99944 20130101; G06Q 30/02
20130101; Y10S 707/99945 20130101; Y10S 707/99943 20130101 |
Class at
Publication: |
707/101 |
International
Class: |
G06F 17/00 20060101
G06F017/00 |
Claims
1. A method of profiling users in an computer environment, said
method comprising: (a) receiving a purchase history corresponding
to a unique user and including information related to the purchase
of at least one item by said unique user; (b) receiving demographic
information corresponding to said unique user; (c) receiving
product characterization information describing a statistical
relationship between a particular product and demographic
characteristics of purchasers of the product; (d) extrapolating
additional demographic information from said purchase history and
said product characterization information; and (e) updating said
demographic information based on said additional demographic
information.
2. The method of claim 1, wherein said product characterization
information is developed based on a market study of a user
population that visits a particular website.
3. The method of claim 1, wherein said demographic information is
received from a user demographic profile corresponding to said
unique user.
4. The method of claim 3, further comprising: (f) identifying
missing demographic information in said user demographic
profile.
5. The method of claim 5, wherein at least a portion of said
missing demographic information in said user demographic profile is
obtained through said additional demographic information.
6. The method of claim 5, further comprising: (g) returning said
updated demographic information of step (e) to said user
demographic profile.
7. The method of claim 1, wherein said product characterization
information does not include information about specific users.
8. The method of claim 1, further comprising: (f) targeting
advertisements based on said updated demographic information.
9. The method of claim 1, further comprising: (f) comparing said
updated demographic information to a target expression; (g)
generating a score based on said comparing; and (h) delivering an
advertisement based on said score.
10. A method of targeting ads in an computer environment based on
user segmentation, said method comprising: (a) receiving user
profile information corresponding to a unique user; (b) assigning,
based on said user profile information, said unique user to a
population segment; and (c) comparing said population segment of
said unique user to an ad segment characterization corresponding to
at least one advertisement.
11. The method of claim 10, further comprising: (d) calculating a
correlation factor between said ad segment characterization and
said population segment of said unique user; and (e) targeting an
ad based on said correlation factor.
12. The method of claim 10, wherein said assigning is realized by
creating a population segment vector that describes the population
segment of said unique user.
13. The method of claim 12, wherein said ad segment
characterization is represented by an ad segment vector.
14. The method of claim 13, wherein said comparing is realized by
comparing said population segment vector to said ad segment
vector.
15. The method of claim 14, further comprising: (d) calculating a
correlation factor between said ad segment vector and said
population segment vector; and (e) targeting an ad based on said
correlation factor.
16. The method of claim 10, wherein said user profile information
includes purchase history information.
17. The method of claim 16, wherein said purchase history
information includes a record of at least one purchase.
18. The method of claim 10, wherein said user profile information
includes demographic information.
19. A method of presenting cross-sell products in a computer
environment, said method comprising: (a) receiving a purchase
history corresponding to a unique user and including information
related to the purchase of at least one product; (b) receiving
product characterization information for said at least one product
wherein said product characterization describes a relationship
between said at least one product and demographic characteristics
of purchasers of said at least one product; (c) calculating a
consumer characterization vector based on said purchase history and
said product characterization information; and (d) suggesting items
based on said consumer characterization vector.
20. The method of claim 19, wherein said consumer characterization
vector is based on a combination of a demographic characterization
vector and a product preference vector.
21. The method of claim 20, wherein the suggestion made in step (d)
is based on the product preference vector.
22. A method of profiling users in a computer environment, said
method comprising: (a) receiving a purchase history wherein said
purchase history corresponding to a unique user and said purchase
history including information related to the purchase of at least
one item by said unique user; (b) receiving demographic information
corresponding to said unique user; (c) receiving product
characterization information describing a statistical relationship
between a particular product and demographic characteristics of
purchasers of the product; (d) calculating additional demographic
information from said purchase history and said product
characterization information; and (e) updating said demographic
information based on said additional demographic information.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 09/807,887, filed Apr. 19, 2001, and entitled
Consumer Profiling and Advertisement Selection System, the entire
disclosure of which is incorporated herein by reference.
[0002] This application claims the benefit of International
Application No. PCT/US99/28628, filed Dec. 2, 1999, entitled
Consumer Profiling and Advertisement Selection System, which claims
the benefit of co-pending U.S. patent application Ser. No.
09/204,888, filed Dec. 3, 1998, entitled Subscriber
Characterization System; U.S. patent application Ser. No.
09/268,526, filed Mar. 12, 1999, entitled Advertisement Selection
System Supporting Discretionary Target Market Characteristics, now
U.S. Pat. No. 6,216,129; and U.S. patent application Ser. No.
09/268,519, filed Mar. 12, 1999, entitled Consumer Profiling
System, now U.S. Pat. No. 6,298,348.
BACKGROUND OF THE INVENTION
[0003] The advent of the Internet has resulted in the ability to
communicate data across the globe instantaneously, and will allow
for numerous new applications which enhance consumer's lives. One
of the enhancements which can occur is the ability for the consumer
to receive advertising which is relevant to their lifestyle, rather
than a stream of ads determined by the program they are watching.
Such "targeted ads" can potentially reduce the amount of unwanted
information which consumers receive in the mail, during television
programs, and when using the Internet. Examples of editorial
targeting can be found on the World Wide Web, where banners are
delivered based on the page content. The product literature from
DoubleClick, "Dynamic Advertising Reporting and Targeting (DART),"
printed from the World Wide Web site
http://www.doubleclick.net/dart on Jun. 19, 1998 discloses
DoubleClick's advertising solution for matching advertiser's
selected targeted profiles with individual user profiles and
deliver an appropriate banner. The user and advertisements are
matched based on geographic location or keywords on the page
content. The product literature from Imgis, "Ad Force," printed
from the World Wide Web site http://www.starpt.com/core on Jun. 30,
1998 discloses an ad management system for targeting users and
delivering advertisements to them. Users are targeted based on the
type of content they are viewing or by keywords.
[0004] From an advertiser's perspective the ability to target ads
can be beneficial since they have some confidence that their ad
will at least be determined relevant by the consumer, and therefore
will not be found annoying because it is not applicable to their
lifestyle. Different systems for matching a consumer profile to an
advertisement have been proposed such as the U.S. Pat. No.
5,774,170, which discloses a system for delivering targeted
advertisement to consumers. In this system, a set of advertisements
is tagged with commercial identifier (CID) and, from the existing
marketing database, a list of prospective viewers is also
identified with CID. The commercials are displayed to the consumers
when the CIDs match.
[0005] Other systems propose methods for delivering programming
tailored to subscribers' profile. U.S. Pat. No. 5,446,919 discloses
a communication system capable of targeting a demographically or
psychographically defined audience. Demographic and psychographic
information about audience member are downloaded and stored in the
audience member receiver. Media messages are transmitted to
audience member along with a selection profile command, which
details the demographic/psychographic profile of audience members
that are to receive each media message. Audience members which fall
within a group identified by the selection profile command are
presented with the media message.
[0006] U.S. Pat. No. 5,223,924 discloses a system and method for
automatically correlating user preferences with a TV program
information database. The system includes a processor that performs
"free text" search techniques to correlate the downloaded TV
program information with the viewer's preferences. U.S. Pat. No.
5,410,344 discloses a method for selecting audiovideo programs
based on viewers' preferences, wherein each of the audiovideo
programs has a plurality of programs attributes and a corresponding
content code representing the program attributes. The method
comprises the steps of storing a viewer preference file, which
includes attributes ratings, which represents the degree of impact
of the programs attributes on the viewer and, in response to the
comparison of viewer preference file with the program content
codes, a program is selected for presentation to the viewer.
[0007] In order to determine the applicability of an advertisement
to a consumer, it is necessary to know something about their
lifestyle, and in particular to understand their demographics (age,
household size and income). In some instances, it is useful to know
their particular purchasing habits. Purchasing habits are being
used by E-commerce to profile their visitors. As an example, the
product literature from Aptex software Inc., "SelectCast for
Commerce Servers," printed from the World Wide Web site
http://www.aptex.com/products-selectcast-commerce.htm on Jun. 30,
1998 discloses the product SelectCast for Commerce Servers. The
product personalizes online shopping based on observed user
behavior. User interests are learned based on the content they
browse, the promotions they click and the products they
purchase.
[0008] Knowledge of the purchasing habits of a consumer can be
beneficial to a product vendor in the sense that a vendor of soups
would like to know which consumers are buying their competitor's
soup, so that they can target ads at those consumers in an effort
to convince them to switch brands. That vendor will probably not
want to target loyal customers, although for a new product
introduction the strategy may be to convince loyal customers to try
the new product. In both cases it is extremely useful for the
vendor to be able to determine what brand of product the consumer
presently purchases.
[0009] There are several difficulties associated with the
collection, processing, and storage of consumer data. First,
collecting consumer data and determining the demographic parameters
of the consumer can be difficult. Surveys can be performed, and in
some instances the consumer will willingly give access to normally
private data including family size, age of family members, and
household income. In such circumstances there generally needs to be
an agreement with the consumer regarding how the data will be used.
If the consumer does not provide this data directly, the
information must be "mined" from various pieces of information
which are gathered about the consumer, typically from specific
purchases.
[0010] A relatively intrusive method for collecting consumer
information is described in U.S. Pat. No. 4,546,382, which
discloses a television and market research data collection system
and method. A data collection unit containing a memory, stores data
as to which of the plurality of TV modes are in use, which TV
channel is being viewed as well as input from a suitable optical
scanning device for collecting consumer product purchases.
[0011] Once data is collected, usually from one source, some type
of processing can be performed to determine a particular aspect of
the consumer's life. As an example, processing can be performed on
credit data to determine which consumers are a good credit risk and
have recently applied for credit. The resulting list of consumers
can be solicited, typically by direct mail. Although information
such as credit history is stored on multiple databases, storage of
other information such as the specifics of grocery purchases is not
typically performed. Even if each individual's detailed list of
grocery purchases was recorded, the information would be of little
use since it would amount to nothing more than unprocessed shopping
lists.
[0012] Privacy concerns are also an important factor in using
consumer purchase information. Consumers will generally find it
desirable that advertisements and other information is matched with
their interests, but will not allow indiscriminate access to their
demographic profile and purchase records.
[0013] The Internet has spawned the concept of "negatively priced
information" in which consumers can be paid to receive advertising.
Paying consumers to watch advertisements can be accomplished
interactively over the Internet, with the consumer acknowledging
that they will watch an advertisement for a particular price.
Previously proposed schemes such as that described in U.S. Pat. No.
5,794,210, entitled "Attention Brokerage," of which A. Nathaniel
Goldhaber and Gary Fitts are the inventors, describe such a system,
in which the consumer is presented with a list of advertisements
and their corresponding payments. The consumer chooses from the
list and is compensated for viewing the advertisement. The system
uses also software agents representing consumers to match the
consumer interest profiles with advertisements. The matching is
done using "relevance indexing" which is based on hierarchical tree
structures. The system requires real-time interactivity in that the
viewer must select the advertisement from the list of choices
presented.
[0014] The ability to place ads to consumers and compensate them
for viewing the advertisements opens many possibilities for new
models of advertising. However, it is important to understand the
demographics and product preferences of the consumer in order to be
able to determine if an advertisement is appropriate.
[0015] Although it is possible to collect statistical information
regarding consumers of particular products and compare those
profiles against individual demographic data points of consumers,
such a methodology only allows for selection of potential consumers
based on the demographics of existing customers of the same or
similar products.
[0016] U.S. Pat. No. 5,515,098, entitled "System and method for
selectively distributing commercial messages over a communications
network," of which John B. Carles is the inventor, describes a
method in which target household data of actual customers of a
product are compared against subscriber household data to determine
the applicability of a commercial to a household. Target households
for a product or service are characterized by comparing or
correlating the profile of the customer household to the profile of
all households. A rating is established for each household for each
category of goods/services. The households within a predefined
percentile of subscribers, as defined by the rating, are targeted
by the advertiser of the product or service.
[0017] It will also frequently be desirable to target an
advertisement to a market having discretionary characteristics and
to obtain a measure of the correlation of these discretionary
features with probabilistic or deterministic data of the
consumer/subscriber, rather than being forced to rely on the
characteristics of existing consumers of a product. Such
correlation should be possible based both on demographic
characteristics and product preferences.
[0018] Another previously proposed system, described in U.S. Pat.
No. 5,724,521, entitled "Method and apparatus for providing
electronic advertisements to end users in a consumer best-fit
pricing manner," of which R. Dedrick is the inventor, utilizes a
consumer scale as the mechanism to determine to which group an
advertisement is intended. A consumer scale matching process
compares the set of characteristics stored in a user profile
database to a consumer scale associated with the electronic
advertisement. The fee charged to the advertiser is determined by
where the set of characteristics fall on the consumer scale. Such a
system requires specification of numerous parameters and weighting
factors, and requires access to specific and non-statistical
personal profile information.
[0019] For the foregoing reasons, there is a need for a consumer
profiling system which can profile the consumer, provide access to
the consumer profile in a secure manner, and return a measurement
of the potential applicability of an advertisement. There is also a
need for an advertisement selection system which can match an
advertisement with discretionary target market characteristics, and
which can do so in a manner which protects the privacy of the
consumer data and characterizations.
SUMMARY OF THE INVENTION
[0020] The present invention supports the receipt of consumer
purchase information with which consumer characterization vectors
are updated based on product characterization information. The
consumer characterization vectors include a consumer demographic
vector which provides a probabilistic measure of the demographics
of the consumer, and a product preference vector which describes
which products the consumer has typically purchased in the past,
and therefore is likely to purchase in the future. The product
characterization information includes vector information which
represents probabilistic determinations of the demographics of
purchasers of an item, heuristic rules which can be applied to
probabilistically describe the demographics of the consumer based
on that purchase, and a vector representation of the purchase
itself.
[0021] In a preferred embodiment a computer-readable detailed
purchase record is received, along with a unique consumer
identifier. A demographic characterization vector corresponding to
the consumer can be retrieved. In the event that there is no
existing demographic characterization vector for that consumer, a
new demographic characterization vector can be created. In a
preferred embodiment the new demographic characterization vector
contains no information. A set of heuristic rules is retrieved and
contains a probabilistic measure of the demographic characteristics
of a typical purchaser of an item. A new demographic
characterization vector is calculated based on the purchase, the
existing demographic characterization vector, and the heuristic
rules.
[0022] In a preferred embodiment the calculation of the demographic
characterization vector is performed by calculating a weighted
average of a product demographics vector and the existing
demographic characterization vector. A weighting factor is used in
which the weighting factor is determined based on the ratio of the
current product purchase amount to a cumulative product purchase
amount. The cumulative product purchase amount can be measured as
the amount spent on a particular category of items (e.g. groceries,
clothes, accessories) over a given period of time such as one month
or one year.
[0023] In a preferred embodiment the heuristic rules are in the
form of a product demographics vector which states the demographics
of known purchasers of an item. Each product can have an associated
product demographics vector.
[0024] The present invention can be used to develop product
preference descriptions of consumers which describe the brand and
size product that they purchase, and which provide a probabilistic
interpretation of the products they are likely to buy in the
future. The product preference description can be generated by
creating a weighted average of an existing product preference
vector describing the consumer's historical product preferences
(type of product, brand, and size) and the characteristics of
recent purchases.
[0025] The present invention can be realized as a data processing
system or computer program which processes consumer purchase
records and updates their demographic and product preference
profiles based on the use of product characterization information.
The data processing system can also be used to receive information
regarding an advertisement and to perform a correlation between the
advertisement and the consumer's demographic and product
preferences.
[0026] The present invention can be realized as software resident
on one or more computers. The system can be realized on an
individual computer which receives information regarding consumer
purchases, or can be realized on a network of computers in which
portions of the system are resident on different computers.
[0027] One advantage of the present invention is that it allows
consumer profiles to be updated automatically based on their
purchases, and forms a description of the consumer including
demographic characteristics and product preferences. This
description can be used by advertisers to determine the suitability
of advertisements to the consumer. Consumers benefit from the
system since they will receive advertisements which are more likely
to be applicable to them.
[0028] The present invention can be used to profile consumers to
support the correlation of an advertisement characterization vector
associated with an advertisement with the consumer characterization
vector to determine the applicability of the advertisement to the
consumer.
[0029] Another feature of the present invention is the ability to
price access to the consumer based on the degree of correlation of
an advertisement with their profile. If an advertisement is found
to be very highly correlated with a consumer's demographics and
product preferences, a relatively high price can be charged for
transmitting the advertisement to the consumer. From the consumer's
perspective, if the correlation between the advertisement and the
consumer's demographics or product preferences is high the consumer
can charge less to view the ad, since it is likely that is will be
of interest.
[0030] The present invention also describes a system for
determining the applicability of an advertisement to a consumer,
based on the reception of an ad characterization vector and use of
a unique consumer ID. The consumer ID is used to retrieve a
consumer characterization vector, and the correlation between the
consumer characterization vector and the ad characterization vector
is used to determine the applicability of the advertisement to the
consumer. The price to be paid for presentation of the
advertisement can be determined based on the degree of
correlation.
[0031] The price to present an advertisement can increase with
correlation, as may be typical when the content/opportunity
provider is also the profiling entity. The price can decrease with
correlation when the consumer is the profiler, and is interested
in, and willing to charge less for seeing advertisements which are
highly correlated with their demographics, lifestyle, and product
preferences.
[0032] The present invention can be used to specify purchasers of a
specific product. In a preferred embodiment the advertisement
characterization vector contains a description of a target market
including an indicator of a target product, i.e., purchasers of a
particular product type, brand, or product size. The advertisement
characterization vector is correlated with a consumer
characterization vector which is retrieved based on a unique
consumer ID. The correlation factor is determined and indicates if
the consumer is a purchaser of the product the advertisement is
intended for. This feature can be used to identify purchasers of a
particular brand and can be used to target ads at those consumers
to lure them away from their present product provider. Similarly,
this feature can be used to target ads to loyal consumers to
introduce them to a new product in a product family, or different
size of product.
[0033] One advantage of the present invention is that discretionary
target market parameters can be specified and do not necessarily
need to correspond to an existing market, but can reflect the
various market segments for which the advertisement is targeted.
The market segments can be designated by demographic
characteristics or by product preferences.
[0034] Another advantage of the present invention is that
demographic samples of present purchasers of a product are not
required to define the target market.
[0035] The present invention can be used to determine the
applicability of an advertisement to a consumer based on
demographics, product preferences, or a combination of both.
[0036] In a preferred embodiment of the present invention the
correlation is calculated as the scalar product of the ad
characterization vector and the consumer characterization vector.
The ad characterization vector and consumer characterization vector
can be composed of demographic characteristics, product purchase
characteristics, or a combination of both.
[0037] In a preferred embodiment pricing for the displaying of said
advertisement is developed based on the result of the correlation
between the ad characterization vector and the consumer
characterization vector. In a first embodiment the pricing
increases as a function of the correlation. This embodiment can
represent the situation in which the party which determines the
correlation also controls the ability to display the
advertisement.
[0038] In an alternate embodiment the price for displaying the
advertisement decreases as a function of the degree of correlation.
This embodiment can represent the situation in which the consumer
controls access to the consumer characterization vector, and
charges less to view advertisements which are highly correlated
with their interests and demographics. A feature of this embodiment
is the ability of the consumer to decrease the number of unwanted
advertisements by charging a higher price to view advertisements
which are likely to be of less interest.
[0039] One advantage of the present invention is that it allows
advertisements to be directed to new markets by setting specific
parameters in the ad characterization vector, and does not require
specific statistical knowledge regarding existing customers of
similar products.
[0040] Another advantage is that the system allows ads to be
directed at consumers of a competing brand, or specific targeting
at loyal customers. This feature can be useful for the introduction
of a new product to an existing customer base.
[0041] Another advantage of the present invention is that the
correlation can be performed by calculating a simple scalar (dot)
product of the ad characterization and consumer characterization
vectors. A weighted sum or other statistical analysis is not
required to determine the applicability of the advertisement.
[0042] The present invention can be realized as a data processing
system and as a computer program. The invention can be realized on
an individual computer or can be realized using distributed
computers with portions of the system operating on various
computers.
[0043] An advantage of the present invention is the ability to
direct advertisements to consumers which will find the
advertisements of interest. This eliminates unwanted
advertisements. Another advantage is the ability of advertisers to
target specific groups of potential customers.
[0044] These and other features and objects of the invention will
be more fully understood from the following detailed description of
the preferred embodiments which should be read in light of the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0045] The accompanying drawings, which are incorporated in and
form a part of the specification, illustrate the embodiments of the
present invention and, together with the description serve to
explain the principles of the invention.
[0046] In the drawings:
[0047] FIGS. 1A and 1B show user relationship diagrams for the
present invention;
[0048] FIGS. 2A, 2B, 2C and 2D illustrate a probabilistic consumer
demographic characterization vector, a deterministic consumer
demographic characterization vector, a consumer product preference
characterization vector, and a storage structure for consumer
characterization vectors respectively;
[0049] FIGS. 3A and 3B illustrate an advertisement demographic
characterization vector and an advertisement product preference
characterization vector respectively;
[0050] FIG. 4 illustrates a computer system on which the present
invention can be realized;
[0051] FIG. 5 illustrates a context diagram for the present
invention;
[0052] FIGS. 6A and 6B illustrate pseudocode updating the
characteristics vectors and for a correlation operation
respectively;
[0053] FIG. 7 illustrates heuristic rules;
[0054] FIGS. 8A and 8B illustrate flowcharts for updating consumer
characterization vectors and a correlation operation respectively;
and
[0055] FIG. 9 represents pricing as a function of correlation.
[0056] FIG. 10 illustrates a representation of a consumer
characterization as a set of basis vectors and an ad
characterization vector.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0057] In describing a preferred embodiment of the invention
illustrated in the drawings, specific terminology will be used for
the sake of clarity. However, the invention is not intended to be
limited to the specific terms so selected, and it is to be
understood that each specific term includes all technical
equivalents which operate in a similar manner to accomplish a
similar purpose.
[0058] With reference to the drawings, in general, and FIGS. 1
through 10 in particular, the method and apparatus of the present
invention is disclosed.
[0059] FIG. 1A shows a user relationship diagram which illustrates
the relationships between a consumer profiling system and various
entities. As can be seen in FIG. 1, a consumer 100 can receive
information and advertisements from a consumer personal computer
(PC) 104, displayed on a television 108 which is connected to a
settop 106, or can receive a mailed ad 182.
[0060] Advertisements and information displayed on consumer PC 104
or television 108 can be received over an Internet 150, or can be
received over the combination of the Internet 150 with another
telecommunications access system. The telecommunications access
system can include but is not limited to cable TV delivery systems,
switched digital video access systems operating over telephone
wires, microwave telecommunications systems, or any other medium
which provides connectivity between the consumer 100 and a content
server 162 and ad server 146.
[0061] A content/opportunity provider 160 maintains the content
server 162 which can transmit content including broadcast
programming across a network such as the Internet 150. Other
methods of data transport can be used including private data
networks and can connect the content sever 160 through an access
system to a device owned by consumer 100.
[0062] Content/opportunity provider 160 is termed such since if
consumer 100 is receiving a transmission from content server 162,
the content/opportunity provider can insert an advertisement. For
video programming, content/opportunity provider is typically the
cable network operator or the source of entertainment material, and
the opportunity is the ability to transmit an advertisement during
a commercial break.
[0063] The majority of content that is being transmitted today is
done so in broadcast form, such as broadcast television programming
(broadcast over the air and via cable TV networks), broadcast
radio, and newspapers. Although the interconnectivity provided by
the Internet will allow consumer specific programming to be
transmitted, there will still be a large amount of broadcast
material which can be sponsored in part by advertising. The ability
to insert an advertisement in a broadcast stream (video, audio, or
mailed) is an opportunity for advertiser 144. Content can also be
broadcast over the Internet and combined with existing video
services, in which case opportunities for the insertion of
advertisements will be present.
[0064] Although FIG. 1A represents content/opportunity provider 160
and content server 162 as being independently connected to Internet
150, with the consumer's devices being also being directly
connected to the Internet 150, the content/opportunity provider 160
can also control access to the subscriber. This can occur when the
content/opportunity provider is also the cable operator or
telephone company. In such instances, the cable operator or
telephone company can be providing content to consumer 100 over the
cable operator/telephone company access network. As an example, if
the cable operator has control over the content being transmitted
to the consumer 100, and has programmed times for the insertion of
advertisements, the cable operator is considered to be a
content/opportunity provider 160 since the cable operator can
provide advertisers the opportunity to access consumer 100 by
inserting an advertisement at the commercial break.
[0065] In a preferred embodiment of the present invention, a
pricing policy can be defined. The content/opportunity provider 160
can charge advertiser 144 for access to consumer 100 during an
opportunity. In a preferred embodiment the price charged for access
to consumer 100 by content/opportunity provider varies as a
function of the applicability of the advertisement to consumer 100.
In an alternate embodiment consumer 100 retains control of access
to the profile and charges for viewing an advertisement.
[0066] The content provider can also be a mailing company or
printer which is preparing printed information for consumer 100. As
an example, content server 162 can be connected to a printer 164
which creates a mailed ad 182 for consumer 100. Alternatively,
printer 164 can produce advertisements for insertion into
newspapers which are delivered to consumer 100. Other printed
material can be generated by printer 162 and delivered to consumer
100 in a variety of ways.
[0067] Advertiser 144 maintains an ad server 146 which contains a
variety of advertisements in the form of still video which can be
printed, video advertisements, audio advertisements, or
combinations thereof.
[0068] Profiler 140 maintains a consumer profile server 130 which
contains the characterization of consumer 100. The consumer
profiling system is operated by profiler 140, who can use consumer
profile server 130 or another computing device connected to
consumer profile server 130 to profile consumer 100.
[0069] Data to perform the consumer profiling is received from a
point of purchase 110. Point of purchase 110 can be a grocery
store, department store, other retail outlet, or can be a web site
or other location where a purchase request is received and
processed. In a preferred embodiment, data from the point of
purchase is transferred over a public or private network 120, such
as a local area network within a store or a wide area network which
connects a number of department or grocery stores. In an alternate
embodiment the data from point of purchase 110 is transmitted over
the Internet 150 to profiler 140.
[0070] Profiler 140 may be a retailer who collects data from its
stores, but can also be a third party who contracts with consumer
100 and the retailer to receive point of purchase data and profile
consumer 100. Consumer 100 may agree to such an arrangement based
on the increased convenience offered by targeted ads, or through a
compensation arrangement in which they are paid on a periodic basis
for revealing their specific purchase records.
[0071] Consumer profile server 130 can contain a consumer profile
which is determined from observation of the consumer's viewing
habits on television 108 or consumer PC 104. Determination of
demographic and product preference information based on the
consumer's use of services such as cable television and Internet
access can be performed by monitoring the channel selections that a
subscriber makes, and determining household demographics based on
the subscriber selections and information associated with the
programming being watched.
[0072] In one embodiment the channel selections are recorded, and
based on the time of day during which the programming is watched
and duration of viewing, heuristic rules are applied to make
probabilistic determinations regarding the household demographics
including age, gender, household size and income, as illustrated in
FIG. 2A. This can be accomplished by applying heuristic rules which
associate the programming with known and assumed characteristics
for viewers of the programming. As an example, it is known that the
probability that the viewer of a cartoon in the morning is in the
3-8 year old age group is high, thus if the household viewing
habits consistently record viewing of cartoons the probability that
the household will contain one or more viewers in the 3-8 year old
age group is high.
[0073] In one embodiment information regarding the program is
extracted from the Electronic Program Guide (EPG) which contains
information regarding the scheduled programming. In another
embodiment information regarding the programming is retrieved from
the closed caption channel transmitted in the broadcast signal.
[0074] The volume at which the program is watched can also be
stored and forms an additional basis for subscriber
characterization, wherein the muting of a channel indicates limited
interest in a particular program or advertisement. In the case of
an advertisement, muting of the advertisement can be used as a
measure of the effectiveness (or ineffectiveness) of the
advertisement and can serve as part of the basis for the subscriber
characterization. The muting of a program, as well as the duration
for which the program is watched, can also be used in the
determination of the subscriber characterization vector.
[0075] By processing the recorded viewing habits in conjunction
with programming related information and heuristic rules similar to
those illustrated in FIG. 7 but related to programming rather than
purchases, it is possible to construct a subscriber
characterization vector which contains a probabilistic demographic
profile of the household.
[0076] When used herein, the term consumer characterization vector
also represents the subscriber characterization vector previously
described. Both the consumer characterization vector and the
subscriber characterization vector contain demographic and product
preference information which is related to consumer 100.
[0077] FIG. 1B illustrates an alternate embodiment of the present
invention in which the consumer 100 is also profiler 140. Consumer
100 maintains consumer profile server 130 which is connected to a
network, either directly or through consumer PC 104 or settop 106.
Consumer profile server 130 can contain the consumer profiling
system, or the profiling can be performed in conjunction with
consumer PC 104 or settop 106. A subscriber characterization system
which monitors the viewing habits of consumer 100 can be used in
conjunction with the consumer profiling system to create a more
accurate consumer profile.
[0078] When the consumer 100 is also the profiler 140, as shown in
FIG. 1B, access to the consumer demographic and product preference
characterization is controlled exclusively by consumer 100, who
will grant access to the profile in return for receiving an
increased accuracy of ads, for cash compensation, or in return for
discounts or coupons on goods and services.
[0079] FIG. 2A illustrates an example of a probabilistic
demographic characterization vector. The demographic
characterization vector is a representation of the probability that
a consumer falls within a certain demographic category such as an
age group, gender, household size, or income range.
[0080] In a preferred embodiment the demographic characterization
vector includes interest categories. The interest categories may be
organized according to broad areas such as music, travel, and
restaurants. Examples of music interest categories include country
music, rock, classical, and folk. Examples of travel categories
include "travels to another state more than twice a year," and
travels by plane more than twice a year."
[0081] FIG. 2B illustrates a deterministic demographic
characterization vector. The deterministic demographic
characterization vector is a representation of the consumer profile
as determined from deterministic rather than probabilistic data. As
an example, if consumer 100 agrees to answer specific questions
regarding age, gender, household size, income, and interests the
data contained in the consumer characterization vector will be
deterministic.
[0082] As with probabilistic demographic characterization vectors,
the deterministic demographic characterization vector can include
interest categories. In a preferred embodiment, consumer 100
answers specific questions in a survey generated by profiler 140
and administered over the phone, in written form, or via the
Internet 150 and consumer PC 104. The survey questions correspond
either directly to the elements in the probabilistic demographic
characterization vector, or can be processed to obtain the
deterministic results for storage in the demographic
characterization vector.
[0083] FIG. 2C illustrates a product preference vector. The product
preference represents the average of the consumer preferences over
past purchases. As an example, a consumer who buys the breakfast
cereal manufactured by Post under the trademark ALPHABITS about
twice as often as purchasing the breakfast cereal manufactured by
Kellogg under the trademark CORN FLAKES, but who never purchases
breakfast cereal manufactured by General Mills under the trademark
WHEATIES, would have a product preference characterization such as
that illustrated in FIG. 2C. As shown in FIG. 2C, the preferred
size of the consumer purchase of a particular product type can also
be represented in the product preference vector.
[0084] FIG. 2D represents a data structure for storing the consumer
profile, which can be comprised of a consumer ID field 237, a
deterministic demographic data field 239, a probabilistic
demographic data field 241, and one or more product preference data
fields 243. As shown in FIG. 2D, the product preference data field
243 can be comprised of multiple fields arranged by product
categories 253.
[0085] Depending on the data structure used to store the
information contained in the vector, any of the previously
mentioned vectors may be in the form of a table, record, linked
tables in a relational database, series of records, or a software
object.
[0086] The consumer ID 512 can be any identification value uniquely
associated with consumer 100. In a preferred embodiment consumer ID
512 is a telephone number, while in an alternate embodiment
consumer ID 512 is a credit card number. Other unique identifiers
include consumer name with middle initial or a unique alphanumeric
sequence, the consumer address, social security number.
[0087] The vectors described and represented in FIGS. 2A-C form
consumer characterization vectors that can be of varying length and
dimension, and portions of the characterization vector can be used
individually. Vectors can also be concatenated or summed to produce
longer vectors which provide a more detailed profile of consumer
100. A matrix representation of the vectors can be used, in which
specific elements, such a product categories 253, are indexed.
Hierarchical structures can be employed to organize the vectors and
to allow hierarchical search algorithms to be used to locate
specific portions of vectors.
[0088] FIGS. 3A and 3B represent an ad demographics vector and an
ad product preference vector respectively. The ad demographics
vector, similar in structure to the demographic characterization
vector, is used to target the ad by setting the demographic
parameters in the ad demographics vector to correspond to the
targeted demographic group. As an example, if an advertisement is
developed for a market which is the 18-24 and 24-32 age brackets,
no gender bias, with a typical household size of 2-5, and income
typically in the range of $20,000-$50,000, the ad demographics
vector would resemble the one shown in FIG. 3A. The ad demographics
vector represents a statistical estimate of who the ad is intended
for, based on the advertisers belief that the ad will be beneficial
to the manufacturer when viewed by individuals in those groups. The
benefit will typically be in the form of increased sales of a
product or increased brand recognition. As an example, an "image
ad" which simply shows an artistic composition but which does not
directly sell a product may be very effective for young people, but
may be annoying to older individuals. The ad demographics vector
can be used to establish the criteria which will direct the ad to
the demographic group of 18-24 year olds.
[0089] FIG. 3B illustrates an ad product preference vector. The ad
product preference vector is used to select consumers which have a
particular product preference. In the example illustrated in FIG.
3B, the ad product preference vector is set so that the ad can be
directed a purchasers of ALPHABITS and WHEATIES, but not at
purchasers of CORN FLAKES. This particular setting would be useful
when the advertiser represents Kellogg and is charged with
increasing sales of CORN FLAKES. By targeting present purchasers of
ALPHABITS and WHEATIES, the advertiser can attempt to sway those
purchasers over to the Kellogg brand and in particular convince
them to purchase CORN FLAKES. Given that there will be a payment
required to present the advertisement, in the form of a payment to
the content/opportunity provider 160 or to the consumer 100, the
advertiser 144 desires to target the ad and thereby increase its
cost effectiveness.
[0090] In the event that advertiser 144 wants to reach only the
purchasers of Kellogg's CORN FLAKES, that category would be set at
a high value, and in the example shown would be set to 1. As shown
in FIG. 3B, product size can also be specified. If there is no
preference to size category the values can all be set to be equal.
In a preferred embodiment the values of each characteristic
including brand and size are individually normalized.
[0091] Because advertisements can be targeted based on a set of
demographic and product preference considerations which may not be
representative of any particular group of present consumers of the
product, the ad characterization vector can be set to identify a
number of demographic groups which would normally be considered to
be uncorrelated. Because the ad characterization vector can have
target profiles which are not representative of actual consumers of
the product, the ad characterization vector can be considered to
have discretionary elements. When used herein the term
discretionary refers to a selection of target market
characteristics which need not be representative of an actual
existing market or single purchasing segment.
[0092] In a preferred embodiment the consumer characterization
vectors shown in FIGS. 2A-C and the ad characterization vectors
represented in FIGS. 3A and 3B have a standardized format, in which
each demographic characteristic and product preference is
identified by an indexed position. In a preferred embodiment the
vectors are singly indexed and thus represent coordinates in
n-dimensional space, with each dimension representing a demographic
or product preference characteristic. In this embodiment a single
value represents one probabilistic or deterministic value (e.g. the
probability that the consumer is in the 18-24 year old age group,
or the weighting of an advertisement to the age group).
[0093] In an alternate embodiment a group of demographic or product
characteristics forms an individual vector. As an example, age
categories can be considered a vector, with each component of the
vector representing the probability that the consumer is in that
age group. In this embodiment each vector can be considered to be a
basis vector for the description of the consumer or the target ad.
The consumer or ad characterization is comprised of a finite set of
vectors in a the vector space that describes the consumer or
advertisement.
[0094] FIG. 4 shows the block diagram of a computer system for a
realization of the consumer profiling system. A system bus 422
transports data amongst the CPU 203, the RAM 204, Read Only
Memory--Basic Input Output System (ROM-BIOS) 406 and other
components. The CPU 203 accesses a hard drive 400 through a disk
controller 402. The standard input/output devices are connected to
the system bus 422 through the I/O controller 201. A keyboard is
attached to the I/O controller 201 through a keyboard port 416 and
the monitor is connected through a monitor port 418. The serial
port device uses a serial port 420 to communicate with the I/O
controller 201. Industry Standard Architecture (ISA) expansion
slots 408 and Peripheral Component Interconnect (PCI) expansion
slots 410 allow additional cards to be placed into the computer. In
a preferred embodiment, a network card is available to interface a
local area, wide area, or other network. The computer system shown
in FIG. 4 can be part of consumer profile server 130, or can be a
processor present in another element of the network.
[0095] FIG. 5 shows a context diagram for the present invention.
Context diagrams are useful in illustrating the relationship
between a system and external entities. Context diagrams can be
especially useful in developing object oriented implementations of
a system, although use of a context diagram does not limit
implementation of the present invention to any particular
programming language. The present invention can be realized in a
variety of programming languages including but not limited to C,
C++, Smalltalk, Java, Perl, and can be developed as part of a
relational database. Other languages and data structures can be
utilized to realize the present invention and are known to those
skilled in the art.
[0096] Referring to FIG. 5, in a preferred embodiment consumer
profiling system 500 is resident on consumer profile server 130.
Point of purchase records 510 are transmitted from point of
purchase 110 and stored on consumer profile server 130. Heuristic
rules records 530, pricing policy 570, and consumer profile 560 are
similarly stored on consumer profile server 130. In a preferred
embodiment advertisement records 540 are stored on ad server 146
and connectivity between advertisement records 540 and consumer
profiling system 500 is via the Internet or other network.
[0097] In an alternate embodiment the entities represented in FIG.
5 are located on servers which are interconnect via the Internet or
other network.
[0098] Consumer profiling system 500 receives purchase information
from a point of purchase, as represented by point of purchase
records 510. The information contained within the point of purchase
records 510 includes a consumer ID 512, a product ID 514 of the
purchased product, the quantity 516 purchased and the price 518 of
the product. In a preferred embodiment, the date and time of
purchase 520 are transmitted by point of purchase records 510 to
consumer profiling system 500.
[0099] The consumer profiling system 500 can access the consumer
profile 560 to update the profiles contained in it. Consumer
profiling system 500 retrieves a consumer characterization vector
562 and a product preference vector 564. Subsequent to retrieval
one or more data processing algorithms are applied to update the
vectors. An algorithm for updating is illustrated in the flowchart
in FIG. 8A. The updated vectors termed herein as new demographic
characterization vector 566 and new product preference 568 are
returned to consumer profile 560 for storage.
[0100] Consumer profiling system 500 can determine probabilistic
consumer demographic characteristics based on product purchases by
applying heuristic rules 519. Consumer profiling system 500
provides a product ID 514 to heuristic rules records 530 and
receives heuristic rules associated with that product. Examples of
heuristic rules are illustrated in FIG. 7.
[0101] In a preferred embodiment of the present invention, consumer
profiling system 500 can determine the applicability of an
advertisement to the consumer 100. For determination of the
applicability of an advertisement, a correlation request 546 is
received by consumer profiling system 500 from advertisements
records 540, along with consumer ID 512. Advertisements records 540
also provides advertisement characteristics including an ad
demographic vector 548, an ad product category 552 and an ad
product preference vector 554.
[0102] Application of a correlation process, as will be described
in accordance with FIG. 8B, results in a demographic correlation
556 and a product correlation 558 which can be returned to
advertisement records 540. In a preferred embodiment, advertiser
144 uses product correlation 558 and demographic correlation 556 to
determine the applicability of the advertisement and to determine
if it is worth purchasing the opportunity. In a preferred
embodiment, pricing policy 570 is utilized to determine an ad price
572 which can be transmitted from consumer profiling system 500 to
advertisement records 540 for use by advertiser 144.
[0103] Pricing policy 570 is accessed by consumer profiling system
500 to obtain ad price 572. Pricing policy 570 takes into
consideration results of the correlation provided by the consumer
profiling system 500. An example of pricing schemes are illustrated
in FIG. 9
[0104] FIGS. 6A and 6B illustrate pseudocode for the updating
process and for a correlation operation respectively. The updating
process involves utilizing purchase information in conjunction with
heuristic rules to obtain a more accurate representation of
consumer 100, stored in the form of a new demographic
characterization vector 562 and a new product preference vector
568.
[0105] As illustrated in the pseudocode in FIG. 6A the point of
purchase data is read and the products purchased are integrated
into the updating process. Consumer profiling system 500 retrieves
a product demographics vector obtained from the set of heuristic
rules 519 and applies the product demographics vector to the
demographics characterization vector 562 and the product preference
vector 564 from the consumer profile 560.
[0106] The updating process as illustrated by the pseudocode in
FIG. 6A utilizes a weighting factor which determines the importance
of that product purchase with respect to all of the products
purchased in a particular product category. In a preferred
embodiment the weight is computed as the ratio of the total of
products with a particular product ID 514 purchased at that time,
to the product total purchase, which is the total quantity of the
product identified by its product ID 514 purchased by consumer 100
identified by its consumer ID 512, purchased over an extended
period of time. In a preferred embodiment the extended period of
time is one year.
[0107] In the preferred embodiment the product category total
purchase is determined from a record containing the number of times
that consumer 100 has purchased a product identified by a
particular product ID.
[0108] In an alternate embodiment other types of weighting factors,
running averages and statistical filtering techniques can be used
to use the purchase data to update the demographic characterization
vector. The system can also be reset to clear previous demographic
characterization vectors and product preference vectors.
[0109] The new demographic characterization vector 566 is obtained
as the weighted sum of the product demographics vector the
demographic characterization vector 562. The same procedure is
performed to obtain the new product preference vector 568. Before
storing those new vectors, a normalization is performed on the said
new vectors. When used herein the term product characterization
information refers product demographics vectors, product purchase
vectors or heuristic rules, all of which can be used in the
updating process. The product purchase vector refers to the vector
which represents the purchase of a item represented by a product
ID. As an example, a product purchase vector for the purchase of
Kellogg's CORN FLAKES in a 32 oz. size has a product purchase
vector with a unity value for Kellogg's CORN FLAKES and in the 32
oz. size. In the updating process the weighted sum of the purchase
as represented by the product purchase vector is added to the
product preference vector to update the product preference vector,
increasing the estimated probability that the consumer will
purchase Kellogg's CORN FLAKES in the 32 oz. size.
[0110] In FIG. 6B the pseudocode for a correlation process is
illustrated. Consumer profiling system 500, after receiving the
product characteristics and the consumer ID 512 from the
advertisement records retrieves the consumer demographic
characterization vector 562 and its product preference vector 564.
The demographic correlation is the correlation between the
demographic characterization vector 562 and the ad demographics
vector. The product correlation is the correlation between the ad
product preference vector 554 and the product preference vector
564.
[0111] In a preferred embodiment the correlation process involves
computing the dot product between vectors. The resulting scalar is
the correlation between the two vectors.
[0112] In an alternate embodiment, as illustrated in FIG. 10, the
basis vectors which describe aspects of the consumer can be used to
calculate the projections of the ad vector on those basis vectors.
In this embodiment, the result of the ad correlation can itself be
in vector form whose components represent the degree of correlation
of the advertisement with each consumer demographic or product
preference feature. As shown in FIG. 10 the basis vectors are the
age of the consumer 1021, the income of the consumer 1001, and the
family size of the consumer 1031. The ad characterization vector
1500 represents the desired characteristics of the target audience,
and can include product preference as well as demographic
characteristics.
[0113] In this embodiment the degree of orthogonality of the basis
vectors will determine the uniqueness of the answer. The
projections on the basis vectors form a set of data which represent
the corresponding values for the parameter measured in the basis
vector. As an example, if household income is one basis vector, the
projection of the ad characterization vector on the household
income basis vector will return a result indicative of the target
household income for that advertisement.
[0114] Because basis vectors cannot be readily created from some
product preference categories (e.g. cereal preferences) an
alternate representation to that illustrated in FIG. 2C can be
utilized in which the product preference vector represents the
statistical average of purchases of cereal in increasing size
containers. This vector can be interpreted as an average measure of
the cereal purchased by the consumer in a given time period.
[0115] The individual measurements of correlation as represented by
the correlation vector can be utilized in determining the
applicability of the advertisement to the subscriber, or a sum of
correlations can be generated to represent the overall
applicability of the advertisement.
[0116] In a preferred embodiment individual measurements of the
correlations, or projections of the ad characteristics vector on
the consumer basis vectors, are not made available to protect
consumer privacy, and only the absolute sum is reported. In
geometric terms this can be interpreted as disclosure of the sum of
the lengths of the projections rather than the actual projections
themselves.
[0117] In an alternate embodiment the demographic and product
preference parameters are grouped to form sets of paired scores in
which elements in the consumer characterization vector are paired
with corresponding elements of the ad characteristics vector. A
correlation coefficient such as the Pearson product-moment
correlation can be calculated. Other methods for correlation can be
employed and are well known to those skilled in the art.
[0118] When the consumer characterization vector and the ad
characterization vector are not in a standardized format, a
transformation can be performed to standardize the order of the
demographic and product preferences, or the data can be decomposed
into sets of basis vectors which indicate particular attributes
such as age, income or family size.
[0119] FIG. 7 illustrates an example of heuristic rules including
rules for defining a product demographics vector. From the product
characteristics, a probabilistic determination of household
demographics can be generated. Similarly, the monthly quantity
purchased can be used to estimate household size. The heuristic
rules illustrated in FIG. 7 serve as an example of the types of
heuristic rules which can be employed to better characterize
consumer 100 as a result of their purchases. The heuristic rules
can include any set of logic tests, statistical estimates, or
market studies which provide the basis for better estimating the
demographics of consumer 100 based on their purchases.
[0120] In FIG. 8A the flowchart for updating the consumer
characterization vectors is depicted. The system receives data from
the point of purchase at receive point of purchase information step
800. The system performs a test to determine if a deterministic
demographic characterization vector is available at deterministic
demographic information available step 810 and, if not, proceeds to
update the demographic characteristics.
[0121] Referring to FIG. 8A, at read purchase ID info step 820, the
product ID 514 is read, and at update consumer demographic
characterization vector step 830, an algorithm such as that
represented in FIG. 6A is applied to obtain a new demographic
characterization vector 566, which is stored in the consumer
profile 560 at store updated demographic characterization vector
step 840.
[0122] The end test step 850 can loop back to the read purchase ID
info 820 if all the purchased products are not yet processed for
updating, or continue to the branch for updating the product
preference vector 564. In this branch, the purchased product is
identified at read purchase ID info step 820. An algorithm, such as
that illustrated in FIG. 6A for updating the product preference
vector 564, is applied in update product preference vector step
870. The updated vector is stored in consumer profile 560 at store
product preference vector step 880. This process is carried out
until all the purchased items are integrated in the updating
process.
[0123] FIG. 8B shows a flowchart for the correlation process. At
step 900 the advertisement characteristics described earlier in
accordance with FIG. 5 along with the consumer ID are received by
consumer profiling system 500. At step 910 the demographic
correlation 556 is computed and at step 920 the product preference
correlation 558 is computed. An illustrative example of an
algorithm for correlation is presented in FIG. 6b. The system
returns demographic correlation 556 and product preference
correlation 558 to the advertisement records 540 before exiting the
procedure at end step 950.
[0124] FIG. 9 illustrates two pricing schemes, one for
content/opportunity provider 160 based pricing 970, which shows
increasing cost as a function of correlation. In this pricing
scheme, the higher the correlation, the more the
content/opportunity provider 160 charges to air the
advertisement.
[0125] FIG. 9 also illustrates consumer based pricing 960, which
allows a consumer to charge less to receive advertisements which
are more highly correlated with their demographics and
interests.
[0126] As an example of the industrial applicability of the
invention, a consumer 100 can purchase items in a grocery store
which also acts as a profiler 140 using a consumer profiling system
500. The purchase record is used by the profiler to update the
probabilistic representation of customer 100, both in terms of
their demographics as well as their product preferences. For each
item purchased by consumer 100, product characterization
information in the form of a product demographics vector and a
product purchase vector is used to update the demographic
characterization vector and the product preference vector for
consumer 100.
[0127] A content/opportunity provider 160 may subsequently
determine that there is an opportunity to present an advertisement
to consumer 100. Content/opportunity provider 160 can announce this
opportunity to advertiser 144 by transmitting the details regarding
the opportunity and the consumer ID 512. Advertiser 144 can then
query profiler 140 by transmitting consumer ID 512 along with
advertisement specific information including the correlation
request 546 and ad demographics vector 548. The consumer profiling
system 500 performs a correlation and determines the extent to
which the ad target market is correlated with the estimated
demographics and product preferences of consumer 100. Based on this
determination advertiser 144 can decide whether to purchase the
opportunity or not.
[0128] Although this invention has been illustrated by reference to
specific embodiments, it will be apparent to those skilled in the
art that various changes and modifications may be made which
clearly fall within the scope of the invention. The invention is
intended to be protected broadly within the spirit and scope of the
appended claims.
* * * * *
References