WO2000033228A1 - Systeme de profilage de consommateur et de selection de publicite - Google Patents

Systeme de profilage de consommateur et de selection de publicite Download PDF

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Publication number
WO2000033228A1
WO2000033228A1 PCT/US1999/028628 US9928628W WO0033228A1 WO 2000033228 A1 WO2000033228 A1 WO 2000033228A1 US 9928628 W US9928628 W US 9928628W WO 0033228 A1 WO0033228 A1 WO 0033228A1
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WO
WIPO (PCT)
Prior art keywords
consumer
vector
characterization vector
characterization
product
Prior art date
Application number
PCT/US1999/028628
Other languages
English (en)
Inventor
Charles A. Eldering
Original Assignee
Expanse Networks, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US09/204,888 external-priority patent/US7150030B1/en
Priority claimed from US09/268,519 external-priority patent/US6298348B1/en
Priority claimed from US09/268,526 external-priority patent/US6216129B1/en
Application filed by Expanse Networks, Inc. filed Critical Expanse Networks, Inc.
Priority to US09/807,887 priority Critical patent/US7062510B1/en
Priority to CA002353393A priority patent/CA2353393A1/fr
Priority to EP99962991A priority patent/EP1133745A4/fr
Priority to JP2000585801A priority patent/JP2002531897A/ja
Priority to AU19319/00A priority patent/AU768680B2/en
Publication of WO2000033228A1 publication Critical patent/WO2000033228A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the product literature from Doubleclick, "Dynamic Advertising Reporting and Targeting (DART),” printed from the World Wide Web site http://www.doubleclick.net/dart on June 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 June 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.
  • U.S. Patent 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.
  • U.S. Patent 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. Patent 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.
  • a relatively intrusive method for collecting consumer information is described in U.S. Patent 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.
  • processing can be performed to determine a particular aspect of the consumer's life.
  • 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.
  • 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 .
  • 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 .
  • 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. Patent 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.
  • 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 . 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 .
  • 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.
  • 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.
  • 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.
  • 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.
  • a new demographic characterization vector can be created.
  • 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.
  • 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.
  • 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.
  • 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 .
  • 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 .
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 .
  • Another advantage of the present invention is that demographic samples of present purchasers of a product are not required to define the target market.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. 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. 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.
  • 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.
  • 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 .
  • FIGS. 1A and IB show user relationship diagrams for the present invention
  • 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;
  • FIGS. 3A and 3B illustrate an advertisement demographic characterization vector and an advertisement product preference characterization vector respectively
  • FIG. 4 illustrates a computer system on which the present invention can be realized
  • FIG. 5 illustrates a context diagram for the present invention
  • FIGS. 6A and 6B illustrate pseudocode updating the characteristics vectors and for a correlation operation respectively;
  • FIG. 7 illustrates heuristic rules
  • FIGS 8A and 8B illustrate flowcharts for updating consumer characterization vectors and a correlation operation respectively
  • FIG. 9 represents pricing as a function of correlation.
  • FIG. 10 illustrates a representation of a consumer characterization as a set of basis vectors and an ad characterization vector.
  • FIGS. 1 through 10 in general, and FIGS. 1 through 10 in particular, the method and apparatus of the present invention is disclosed.
  • FIG. 1A shows a user relationship diagram which illustrates the relationships between a consumer profiling system and various entities.
  • 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.
  • PC consumer personal computer
  • 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.
  • 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. 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.
  • broadcast form such as broadcast television programming (broadcast over the air and via cable TV networks), broadcast radio, and newspapers.
  • broadcast television programming broadcast over the air and via cable TV networks
  • broadcast radio broadcast radio
  • newspapers 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 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.
  • 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.
  • a pricing policy can be defined.
  • the content/opportunity provider 160 can charge advertiser 144 for access to consumer 100 during an opportunity.
  • 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.
  • consumer 100 retains control of access to the profile and charges for viewing an advertisement.
  • the content provider can also be a mailing company or printer which is preparing printed information for consumer 100.
  • content server 162 can be connected to a printer 164 which creates a mailed ad 182 for consumer 100.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the data from point of purchase 110 is transmitted over the Internet 150 to profiler 140.
  • 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.
  • 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.
  • 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.
  • information regarding the program is extracted from the Electronic Program Guide (EPG) which contains information regarding the scheduled programming.
  • EPG Electronic Program Guide
  • information regarding the programming is retrieved from the closed caption channel transmitted in the broadcast signal .
  • 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.
  • 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.
  • 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.
  • FIG. IB 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.
  • 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 .
  • 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.
  • 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.”
  • 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. 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.
  • FIG. 2C illustrates a product preference vector.
  • the product preference represents the average of the consumer preferences over past purchases.
  • 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.
  • the preferred size of the consumer purchase of a particular product type can also be represented in the product preference vector.
  • 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.
  • the product preference data field 243 can be comprised of multiple fields arranged by product categories 253.
  • 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.
  • the consumer ID 512 can be any identification value uniquely associated with consumer 100.
  • 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.
  • 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 .
  • 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.
  • 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 .
  • FIG. 3B illustrates an ad product preference vector.
  • the ad product preference vector is used to select consumers which have a particular product preference.
  • 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.
  • the advertiser can attempt to sway those purchasers over to the Kellogg brand and in particular convince them to purchase CORN FLAKES .
  • the advertiser 144 desires to target the ad and thereby increase its cost effectiveness.
  • the ad characterization vector can be set to identify a number of demographic groups which would normally be considered to be uncorrelated.
  • 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.
  • discretionary refers to a selection of target market characteristics which need not be representative of an actual existing market or single purchasing segment.
  • 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.
  • the vectors are singly indexed and thus represent coordinates in n-dimensional space, with each dimension representing a demographic or product preference characteristic.
  • 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) .
  • a group of demographic or product characteristics forms an individual vector.
  • age categories can be considered a vector, with each component of the vector representing the probability that the consumer is in that age group.
  • 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 .
  • 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.
  • ISA expansion slots 408 and Peripheral Component Interconnect (PCI) expansion slots 410 allow additional cards to be placed into the computer.
  • 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.
  • 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.
  • 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 530, pricing policy 570, and consumer profile 560 are similarly stored on consumer profile server 130.
  • 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.
  • FIG. 5 are located on servers which are interconnect via the Internet or other network.
  • 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.
  • the date and time of purchase 520 are transmitted by point of purchase records 510 to consumer profiling system 500.
  • 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.
  • 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. 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.
  • a correlation process results in a demographic correlation 556 and a product correlation 558 which can be returned to advertisement records 540.
  • 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.
  • pricing policy 570 is utilized to determine an ad price 570 which can be transmitted from consumer profiling system 500 to advertisement records 540 for use by advertiser 144.
  • 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
  • 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.
  • the point of purchase data are read and the products purchase 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.
  • 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.
  • 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.
  • the extended period of time is one year.
  • 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.
  • 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.
  • 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.
  • 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 .
  • 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.
  • the correlation process involves computing the dot product between vectors.
  • the resulting scalar is the correlation between the two vectors.
  • the basis vectors which describe aspects of the consumer can be used to calculate the projections of the ad vector on those basis vectors.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 .
  • FIG. 8B shows a flowchart for the correlation process.
  • the advertisement characteristics described earlier in accordance with FIG. 5 along with the consumer ID are received by consumer profiling system 500.
  • 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.
  • FIG. 9 illustrates two pricing schemes, one for content/opportunity provider 160 based pricing 970, which shows increasing cost as a function of correlation.
  • this pricing scheme the higher the correlation, the more the content/opportunity provider 160 charges to air the advertisement .
  • 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 .
  • 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.
  • 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.
  • 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 .

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Abstract

L'invention concerne un système de profilage de consommateur et de sélection de publicité (500), qui permet de caractériser des consommateurs (100) en fonction de leurs habitudes d'acheteurs ou de téléspectateurs. On établit ainsi un vecteur de caractérisation de consommateur (562) décrivant la démographie probabiliste et les préférences de produits de l'abonné ou du téléspectateur (100). On peut établir des vecteurs de caractérisation de publicité (548) décrivant un marché réel ou fictif pour un produit ou un public ciblé. Les caractéristiques de publicité comprenant un vecteur démographique de publicité (548), une catégorie de produit publicitaire (552) et un vecteur de préférence de produit publicitaire (554) sont transmises avec un identificateur de consommateur (512), lequel est utilisé pour récupérer le vecteur de caractérisation de consommateur (562), et une corrélation est établie entre ce vecteur et le vecteur démographique de publicité (548) pour déterminer la correspondance entre la publicité et le consommateur (100). Un tarif d'affichage de la publicité peut être déterminé en fonction de la corrélation entre les caractéristiques de publicité et le vecteur de caractérisation de consommateur (562). On peut utiliser le système pour améliorer l'efficacité et la rentabilité des publicités, et aussi pour déterminer le prix de la transmission ou de l'affichage d'une publicité, sur la base de la corrélation entre la publicité et le profil du consommateur.
PCT/US1999/028628 1998-12-03 1999-12-02 Systeme de profilage de consommateur et de selection de publicite WO2000033228A1 (fr)

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US09/807,887 US7062510B1 (en) 1999-12-02 1999-12-02 Consumer profiling and advertisement selection system
CA002353393A CA2353393A1 (fr) 1998-12-03 1999-12-02 Systeme de profilage de consommateur et de selection de publicite
EP99962991A EP1133745A4 (fr) 1998-12-03 1999-12-02 Systeme de profilage de consommateur et de selection de publicite
JP2000585801A JP2002531897A (ja) 1998-12-03 1999-12-02 消費者調査および広告選択システム
AU19319/00A AU768680B2 (en) 1998-12-03 1999-12-02 Consumer profiling and advertisement selection system

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US09/204,888 1998-12-03
US09/204,888 US7150030B1 (en) 1998-12-03 1998-12-03 Subscriber characterization system
US09/268,519 US6298348B1 (en) 1998-12-03 1999-03-12 Consumer profiling system
US09/268,519 1999-03-12
US09/268,526 1999-03-12
US09/268,526 US6216129B1 (en) 1998-12-03 1999-03-12 Advertisement selection system supporting discretionary target market characteristics

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EP1133745A4 (fr) 2005-09-28
AU1931900A (en) 2000-06-19

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