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WO2002015454A2 - Procede et systeme permettant de generer de maniere automatique des offres personnalisees optimisees - Google Patents

Procede et systeme permettant de generer de maniere automatique des offres personnalisees optimisees Download PDF

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Publication number
WO2002015454A2
WO2002015454A2 PCT/IL2001/000753 IL0100753W WO0215454A2 WO 2002015454 A2 WO2002015454 A2 WO 2002015454A2 IL 0100753 W IL0100753 W IL 0100753W WO 0215454 A2 WO0215454 A2 WO 0215454A2
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WO
WIPO (PCT)
Prior art keywords
offers
customer
personalized
offer
sets
Prior art date
Application number
PCT/IL2001/000753
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English (en)
Other versions
WO2002015454A3 (fr
Inventor
Noam Ron
Yaron Caspi
Micha Bodor
Shmuel Kaplan
Original Assignee
Sagarmatha Ltd.
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
Application filed by Sagarmatha Ltd. filed Critical Sagarmatha Ltd.
Priority to AU2001280071A priority Critical patent/AU2001280071A1/en
Publication of WO2002015454A2 publication Critical patent/WO2002015454A2/fr
Publication of WO2002015454A3 publication Critical patent/WO2002015454A3/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 present invention relates to marketing. More particularly, it relates to automatically generating sets of personalized offers.
  • a system for generating and distributing personal offers is generally composed of 3 elements (subsystems):
  • the present invention focuses on step (ii).
  • the present invention performs a match of personalized offers based, mainly, on the customer's behavior history, in a completely automatic manner.
  • personalized offers refers to several levels of personalization, most of them unknown before.
  • the most popular way to do so is by producing and distributing a uniform set of offers (through various means of offer distribution such as, direct mail, web sites, e-mail, etc.) to the customers.
  • the problem with this approach is that each person has different interests and needs, making the uniform set of offers very limited in its effectiveness.
  • the present invention solves all the above-mentioned problems and enables the accomplishment of true "1 to 1" marketing in a completely automatic way. This is not the case in prior art, some of which is summarized below.
  • U.S. Pat. No. 5,353,218 discloses a "focused coupon system". This is a system for distributing coupons with a machine readable code (barcode) containing both customer and coupon identifications.
  • the consumer code may be replaced with a generic code used in a look-up table for coupon verification and information.
  • the entire machine-readable code may be captured and uploaded to a central database for determining coupon and consumer identification.
  • the uploaded information may be used for marketing purposes (to determine which coupons to send next to the consumer) and/or for rebate purposes.
  • U.S. Patent No. 5,832,457 discloses a "Method and apparatus for selective distribution of discount coupons based on prior customer behavior."
  • pre-selected combination appears several times in the invention and is further emphasized by pre filtering at the store level, which prevents the usage of data later on, for example, when one replaces the type of coupons he/she intends to distribute.
  • Jermyn U.S. Pat. No. 6,026,370 discloses a "Method and apparatus for generating purchase incentive mailing based on prior purchase history".
  • Jermyn's method describes in length a method for collecting customer purchase data, including particular implementation for identifying the customers, the products that he buys, and the coupons that he uses. Only a few words about the mechanism for selecting customers are mentioned, however.
  • Jermyn's invention selects a given number of customers, based on their categorization into 3 levels of loyalty to the brand that gives the incentives: ⁇ Very loyal, not loyal, and loyal to the competitive brand ⁇ .
  • ⁇ Very loyal, not loyal, and loyal to the competitive brand ⁇ Here we see for the first time a limited attempt to personalize the incentives but only up to 3 categories, and not up to full scale personalization. The usage of past data remains limited.
  • No cross product information or purchase of product combinations is used. So, for example, a distributor that gives as an incentive a package of sugar, if a package of coffee is bought will look only at the loyalty of the customer to the coffee brand and will not look at the effect of giving away the sugar.
  • Jermyn's method does not deal with the question of which coupons should be given to a particular customer, but only selects a predetermined number of suitable candidates.
  • the present invention is capable of choosing in a completely automatic manner an optimal set of personalized offers for each specific customer out of a larger set of optional offers, as opposed to all previous inventions, which either selected customers for a given offer or gave (or did not give) an offer to a given customer.
  • the personal offers can be of different types such as: coupons, banners, advertisement, and so on. And may be used in different applications, while all the above inventions are tailored for a particular application.
  • the personal set of offers is optimal in the sense that the target function is predefined by the campaign operator (for example, maximum sales, maximum profitability, maximum response to the offers, etc.). This new concept is not evident in any of the prior inventions.
  • the present invention uses data regarding each customer's behavior history and characteristics.
  • the fact that it uses large sets of data and uses data extending relatively far back in time enables the prediction and compensation for seasonal effects such as Christmas shopping or beach products in the summer time. It can both predict the usage before the season and stop the attractiveness of the product with the season's end and without a time delay (as a short history system might do).
  • the system can also use customer demographic data such as age, sex, home address, etc, but does not rely on its existence in order to work.
  • customer demographic data such as age, sex, home address, etc.
  • the system uses data mining and artificial intelligence (Al) technologies and uses the feedback created from personal offers given in the past.
  • the present invention goes through a stage of determining the rules for choosing personal offers, this stage is usually done manually while using diverse statistical tools. For example, it is possible to predetermine a rule saying that all those who have purchased diapers in the past will receive a coupon for beer.
  • the present invention is able to implement this rule on customers. Meaning that in the example cited above, the system will identify which customers bought diapers in the past and will issue them the coupon for beer.
  • a novel aspect of the present invention is that once the set of personal offers and the target function are defined, it is possible to produce, by one click, a personal set of coupons for each customer, all this without the need to define any particular rules.
  • the system functions in a completely automatic manner and there is no need for human resources of any kind to create rules so that the personal offers will be issued to the customers according to these rules.
  • the system functions in accordance with the campaign target function once it is fed into the system the system will produce, automatically, the sets of personal offers that will optimize the target function. A change in the target function will enable, with one click, to produce a new personal set of offers suitable to the new target function.
  • the system is able to create a true "1 to 1" marketing system: almost every customer receives a unique personal set of offers. (During a trial of the system seven personal coupons were produced for 10,000 customers (out of 80 possible coupons); 8,000 unique sets of coupons were created.)
  • the system can produce in a completely automatic manner, personalization of each aspect in the personal offer. For example, in a coupon that includes a privilege and the conditions to receive such privilege (for example, get an $X discount if you buy over $Y of goods). Both the privilege (X) and the condition to receive the privilege (Y) and the choice of who will receive the coupon are treated individually in a completely automatic manner.
  • a method for fully automatically producing optimized personalized sets of offers according to a target function, using customer and product or service data previously collected comprising: analyzing the collected data and automatically deriving at least one of a plurality of behavior models for every offer or set of offers;
  • the method further comprises aggregating the data prior to said analyzing;
  • the method further comprises pruning the data prior to said analyzing
  • the method further comprises a feedback loop between responses to the offers and said implementing of said behavior models;
  • the previously collected data comprises demographic data
  • the target function is user-defined for typical marketing goals, such as increasing redemption rates, increasing revenues, increasing sales, decreasing costs while maintaining impact, or any combination of such goals;
  • the method further comprises applying constraints regarding valid customer-offer combinations with said optimal matching being subject to said constraints;
  • the method further comprises adding real-time information about the customer;
  • said adding of real-time information about the customer includes the current time and current customer location
  • the method further comprises configuring said matching, such that it will select the optimal customers for each set of offers in terms of the target function;
  • the method further comprises configuring of said matching, such that it will select as desirable an offer for each customer or as appropriate a customer for each offer as possible in terms of the target function;
  • a method for fully automatically producing optimized personalized sets of offers according to a target function, using data previously collected comprising:
  • the method further comprises distributing of the sets of personalized offers via at least one of a plurality of automated-teller machines (ATM).
  • ATM automated-teller machines
  • ATM automated-teller machines
  • the method further comprises distributing the sets of personalized offers via interactive televisions.
  • any marketing system in which personalized offers are distributed via interactive televisions.
  • the method further comprises distributing the sets of personalized offers via at least one of a plurality of electronic information kiosks.
  • the method further comprises distributing the sets of personalized offers via cellular telephones.
  • a system for using a computing device to fully automatically produce optimized personalized sets of offers according to a target function, using customer and product or service data previously collected in a database comprising:
  • a model-building software module that analyzes the collected data and automatically derives at least one of a plurality of behavior models for every offer or set of offers;
  • an offers-building software module that implements at least one of said behavior models for every customer and every offer so as to construct personalized offers with maximum predicted impact relative to the target function
  • an offers-matching software module that optimally matches the best set of offers to each customer in terms of the target function
  • the sets of personalized offers are distributed via at least one of a plurality of automated-telling machines.
  • the sets of personalized offers are distributed via at least one of a plurality of interactive televisions.
  • the sets of personalized offers are distributed via at least one of a plurality of electronic information kiosks.
  • the sets of personalized offers are distributed via at least one of a plurality of cellular telephones.
  • FIG. 1 is a general architectural view of the components of a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 2 is a block diagram of the database module of a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 3 is an example of loyalty club records in a customer behavior database in accordance with a preferred embodiment of the present invention.
  • FIG. 4 is a block diagram of the data analysis module of a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 5 is a detailed block diagram of the response prediction module of a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 6 is a sample diagram of a basic classification used in a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 7A is an abstract example of a data structure for a simple learning file for use in a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 7B is an abstract example of a data structure for a simple classification file for use in a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 7C is a detailed example of a simple learning file before data aggregation and pruning for use in a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 7D is a detailed example of a simple learning file after data aggregation and pruning for use in a personalized offers system in accordance with a preferred embodiment of the present invention.
  • FIG. 8 is a block diagram illustrating some applications for a personalized offers system in accordance with a preferred embodiment of the present invention.
  • the present invention is software-based. It can be implemented on any computing device from a microcomputer to a mainframe.
  • the specific hardware required will depend on the size of the system parameters, such as database memory requirements, number of variables, or degree of target function complexity.
  • a personalized offer system contains three sub systems, as illustrated in FIG. 1 :
  • a data collection system 200 An offer matching system 210.
  • An offer distribution system 220 An offer distribution system 220.
  • FIG.1 illustrates these steps.
  • the data collection 102 produces a database 104, and composes the first step data collection 200.
  • This database 104 is used as input for the second step, offer matching 210, where it is analyzed 106. Using the results of the analysis the personalized set of offers is produced 108.
  • the output of the second step, offer matching 210 is a list of personalized offers 110. Based on the list 110, offers are distributed 112 in the third step, offer distribution 220. Each customer 116 receives one or many offers 114.
  • the current invention improves the capabilities of the second step, offer matching 210.
  • the innovative aspect of the invention is therefore, that, in a completely automatic manner, it produces personal offers out of a potential set of offers and under given constraints.
  • the system produces the best personal set of offers in accordance with a predefined target function using the data mining engine and artificial intelligence (Al) algorithms to produce an efficient and accurate system.
  • Al artificial intelligence
  • a personal coupon - The coupon can be aimed at businesses, products or services.
  • the definition of the coupon includes one or more of the following elements:
  • the coupon condition The conditions that the customer had to fulfill in the past in order to be eligible to receive the coupon. (For example, if a certain store issues a coupon only for customers who did not purchase anything in the store in the past 6 months, then the coupon condition is: "Did not purchase anything in the store in the past 6 months").
  • a personalized advertisement or message For example, if a certain store issues a coupon only for customers who did not purchase anything in the store in the past 6 months, then the coupon condition is: "Did not purchase anything in the store in the past 6 months").
  • a personalized banner (a banner which is generated specifically for the user and which is displayed when the user opens a Web page from the Internet.)
  • a personal sample (a product sample distributed freely to the appropriate customers.)
  • the present invention can perform personalization for any or all of the different offer types, and for any or all elements of an offer type, in a completely automatic manner.
  • the personalization might mean not only choosing different coupons for different customers but also:
  • the coupon for the same cereal might give a 10 cent discount, a 20 cent discount, or a free bottle of milk.
  • the system will fit, automatically, the most suitable privilege for the specific customer.
  • the preferred embodiment of the present invention focuses on personal coupons while the mechanism remains the same for other embodiments.
  • FIG. 2 is a block diagram of the database module of a personalized offers system in accordance with a preferred embodiment of the present invention.
  • the system is able to function successfully even when the length of the tracked data for some customers is significantly shorter than for others, or contains gaps in the tracked period.
  • the system is able to function successfully even if it has data from few stores/branches, and not from all branches of a chain store.
  • the Customer Behavior database 104.1 contains data that enables the system to learn of the customers' needs, habits and preferences. The contents of this database change depending upon the type of environment in which the system is operated:
  • the database will contain data concerning customers' purchases (the products bought, the prices of the products, dates of purchases, etc.).
  • the data can be obtained when a customer 116 reaches the Point of Sale (POS) 126 or checkout stand.
  • FIG. 3 illustrates an example of the structure of the internal representation of such a database. The figure contains a list of customer purchase records 130 and 131 for every receipt that a store in the chain has issued.
  • Each receipt record 130 and 131 may contain the following information: receipt number 130.1 , issue time and date 130.2, the customer's unique identification 130.3 (e.g., an identification number), and a list of purchased items. For each item purchased, the record includes the product number 130.4, the amount 130.5 and the price 130.6.
  • this database may look very different but it will always link uniquely between the customer and his/her activities, and furthermore to activities that collectively contain information about the customers preferences or needs.
  • the response to offers database 104.2 contains information regarding customer 116 responses to offers 110 given in the past. For example, if the personal offers are coupons for products, the response may be to use the coupon or not to use it, while other parameters connected to the response are measured as well, such as the influence on the customer's purchase basket or the influence on any other predefined target function.
  • a means for collecting such information in the preferred embodiment exists in information sent from the POS 126 to the offers database 104.2 where it can be compared with the list of previous personalized offers 110. In different embodiments, however, this feedback may be collected differently.
  • Receiving the feedback from a customer's response to the offer is not imperative for the operation of the system, but it nonetheless enables the improvement of its performance.
  • the feedback is received from the data collected either directly or indirectly about the customers' behavior.
  • the coupon may contain a barcode.
  • This barcode may be captured in POS 126 and passed on to the response to offers database 104.2 together with other information.
  • the system will conclude that the customer used the coupon if he/she received it and purchased the product applied to the coupon during the time that it remained valid, while the condition to redeem the offer must have been fulfilled. (If the customer received a coupon that granted him/her a free bottle of Coke on the purchase of another 3 bottles, then the system checks if the 4 bottles of Coke were purchased at the same time in order to conclude that the customer used the coupon.)
  • the Business/Products database 104.3 contains data about businesses, products and services that appear in the Customer Behavior Database 104.1.
  • the Customer Behavior Database 104.1 will contain information about the products that the customers have purchased and the Business/Products Database 104.3 will contain information about the products in the chain store. This information will include data such as the prices of products, their connection to categories, etc.
  • the information is provided by the chain store running the marketing campaign (campaign manager 118).
  • the system can function without such a database, but this may harm its performance.
  • Potential Offers Database 104.4 contains data on the campaigns' sets of optional offers and complete details on each offer. For example, in a campaign of coupons, data on each of the possible coupons, including: the range of possible discounts for each coupon (in that range the system finds, automatically, the appropriate discount for each customer), the range of conditions posed in order to receive the coupon privilege, the minimal and maximal number of customers that will receive the coupons, etc.
  • the customer database 104.5 contains:
  • Demographic information 122 such as the customer's age, sex, address, income, etc. This data is usually extracted from customer registration information 128 (for example, a questionnaire that is filled in by the customer 116 when asking to join a loyalty club, or by handing in a request for a credit card, etc.)
  • Contact information 120 information required in order to transfer personal offers (if the means of distribution of coupons is by direct mail, there is a need for an address. If the means of distribution is an e-mail, the e-mail address is needed and so forth). This data is also collected from questionnaires filled in by customers (customer registration information 128).
  • contact information 120 may not be required, for example, the distribution of offers via an automated-teller machine (ATM).
  • ATM automated-teller machine
  • Offer matching 210 consists of two modules: data analysis 106 and choosing personalized sets of offers 108.
  • FIG. 4 is a block diagram of the data analysis 106 module, and its interactions with other system modules.
  • the system begins to work when the campaign manager 118 (i.e., the user of the system, who could be a retailer as in the preferred embodiment or an e- tailer, a credit card company, etc. in other applications) defines the target function 142 (which can be, for example, the receptiveness to offers, or the increase of the total amount of purchases, or the increase of profitability) and the optional set of offers intended to be distributed to the customer.
  • the campaign manager 118 i.e., the user of the system, who could be a retailer as in the preferred embodiment or an e- tailer, a credit card company, etc. in other applications
  • the target function 142 which can be, for example, the receptiveness to offers, or the increase of the total amount of purchases, or the increase of profitability
  • the optional set of offers intended to be distributed to the customer.
  • the system will produce personalized sets of offers based on the data stored in the database 104, and on additional real time data 147 if available.
  • the output of the data analysis 106 and selection of personalized sets of offers 108 is the list of personalized offers 110 that will be given to each customer.
  • the data analysis module 106 has the ability to predict the customer's response to personal offers on the basis of the information collected about him/her (via the response prediction module 134). For example, it can predict the chances that each individual customer will use a given coupon for a certain product.
  • the ability to predict such results enables grading the predicted contribution of all combinations of customer and offer 140 in relation to the target function 138 of the campaign.
  • the optimal sets of personalized offers are chosen 108 for each customer. This optimal set can optionally be subject to predefined constraints 144 set by the campaign manager 118.
  • the system can use a closed-loop feedback system 136 to measure the customer's response to the offers and dynamically adjust accordingly.
  • FIG. 4 illustrates the overall processing steps involved in the production of personalized offers.
  • the process works in two stages: First, in the data analysis stage 106, the system grades all possible offers separately for each customer, taking into consideration the predicted contribution 138 of the offer to the campaign's target function 142. (If the target function is, for example, the increase of a loyalty club member's purchase basket in a chain store, then offers with high chances of inducing the customer to make a large purchase that he/she would not otherwise have made will receive high grades. On the other hand, offers with low chances of inducement or small purchases or inducement for a purchase that would have been made with or without the offer will receive low grades.)
  • the target function is, for example, the increase of a loyalty club member's purchase basket in a chain store
  • offers with high chances of inducing the customer to make a large purchase that he/she would not otherwise have made will receive high grades.
  • the system chooses for each customer a set of offers that are ranked by the highest grades and which deal with the posed constraints. For example, if there is a constraint not to issue two offers of similar products to the same customer then even if there are two offers for cereals that received high grades, only one will be presented to the customer.
  • the system can distribute a changing number of offers for different customers and can deal with various constraints, such as constraints on the relationship of different offers in the same set (both for the general population and for the subpopulation) and constraints on the minimum and maximum distributions for each offer as well as constraints upon the customers that can take a certain decision (only, for example, those who did not purchase a box of cereal in the last 6 months).
  • the choosing of personalized offers 108 stage can also choose the X most appropriate customers for each coupon (by choosing the group of X customers with the best grades for the required coupon) or by picking the suitable customers for the offer by choosing all customers with a grade higher than a certain limit.
  • the response prediction module 134 is the core of the entire process, it is used many times for different purposes, but in all cases it is used to predict customer responses to particular offers.
  • the response prediction module's 134 input is the customer database 104. Its output is a prediction of the customers' expected responses to the offer, or the probability the customers will take a particular action.
  • the combined output from all the response prediction modules 134 is delivered to the predict contribution to target function module 138.
  • such actions may include trivial actions such as the purchase of a particular item or redeeming a coupon. It may also include more sophisticated actions such as the distance that a customer may be expected to be willing to travel in order to buy a particular item or the customer's expected purchase basket in a single visit to a store. It may also include a variety of different actions in different applications (see details later in this detailed description). All of the above examples and more are learned from the customer database 104.
  • the response prediction module 134 is shown in detail in FIG. 5. Before describing this module in detail, however, it will be helpful to understand the principle of learning how to predict customer response, as shown in FIG. 6.
  • the location of the marker is determined by two properties: the amount of cheese the household buys per week (the Y-axis 161) and the amount of meat bought per week (the X-axis 162).
  • Clearly two clusters of dominant behavior, butter-discount cluster 163. a and no-butter-discount cluster 163.b can be identified.
  • the preferred embodiment of this process involves two stages (displayed in the two columns in FIG. 5).
  • learning 150 where the model based on a sufficient sample of data is constructed.
  • classification 152 where the entire population is classified (a detailed implementation will be described later).
  • the structure of the predictive behavior model in 150.3 is not limited to ellipses. Any modeling method may be applied.
  • FIG. 5 is a detailed block diagram of the response prediction module 134, which, as mentioned, includes two stages, learning 150 and classification 152.
  • the learning process 150 is responsible for the construction of the prediction behavior model 150.3. This construction is done in a process of supervised learning, meaning that automatic learning from examples occurs.
  • the learning data record 150.1 contains information about the sample group of customers (or about all the customers). This information, as is seen in FIG. 7A includes fields of class 300 and features 302.1 ,.. ,302. N, the idea of this learning process is to find the rules that will predict the class from the features.
  • the aim is to find rules that will enable to predict the monthly consumption of Carlsberg beer according to a given set of features (the data in this case is taken from the loyalty club database).
  • the output of the learning process applied to the learning data record 150.1 of FIG. 7C could be, for example, the following model, which describes two categories of customers (constructed, of course, in a completely automatic manner).
  • the classifying process 152 is responsible for the implementation of the model that is constructed in the learning process for all customers.
  • the classification data record 152.1 contains information similar to the learning file but without the class field, as can be seen in FIG. 7B.
  • the idea is to examine, according to each customer's features, which of the rules out of the rules defined in the learning process the customer falls into, and to receive from this result the predicted outcomes. To illustrate the classification process, we will continue with the two-category model constructed above.
  • the idea is to connect this customer to a group with the potential for purchasing Carlsberg beer and beer in general. As a result the system will probably offer him/her a coupon for beer, for example, that might induce him/her to start purchasing beer in the chain store running the campaign.
  • the data preparation module 146 edits the information stored in the databases
  • the 104 and computes a set of features for each customer- which can be based on demographic data (age, sex, address, etc.) and on the customer's past behavior. For example, in the case of a chain store's loyalty club database, the features can be based on a customer's total purchases for a given period of time for each one of the chain's products or for different groups of products.
  • the data preparation stage 146 produces the class field
  • This field 300 can be for example the amount of purchases of a particular product such as in the example in FIG. 7C, or a binary field that describes the use or lack of use of a certain coupon etc.
  • the Data Aggregation & Pruning module 148 (FIG. 5) is not imperative in the system but is likely to contribute to the reduction of the amount of features used for each classification 152 and to the improvement of the features that will enable a better performance of the classification.
  • Deleting non-useful properties can be done twice: once, if the property is irrelevant for identifying purchase patterns of a given product, and secondly, if • it contains information that is already known.
  • Fig 7C illustrates a possible learning file 150.1 after data preparation step 146 but without data aggregation and pruning step 148.
  • Fig 7D illustrates a possible learning file 150.1 after both data preparation step 146 and data aggregation and pruning step 148.
  • the next step is to predict contribution to target function 138 by combining the outputs of multiple response prediction modules134.
  • the outputs of the various response predictions 134 are combined into a single value that is stored in a large table containing grades for all offers for every customer 140.
  • the computation of the various response predictions 134 is based mainly on pre-stored data in the database 104. However real time information 147, such as the current time, customer current location, and/or customer interactive response (for example, when the customer uses an ATM) are adjoined to the computation at this point.
  • the target function is to maximize the number of customers that used the offer.
  • a The probability that the customer will respond to a restaurant offer in general.
  • b The probability that the customer will go to a French restaurant.
  • c The additional value of the discount in the customer's decision to select a given restaurant.
  • d The distance the customer will be willing to travel in order to go to a restaurant (this can be done with respect to the places where the customer usually does his/her transactions in the afternoon or with respect to the customer's current location, if it is known (such as could be done where an ATM is used for offer distribution).
  • e The impact of the time at which the customer receives the offer.
  • target functions for example the company that issues the offer may wish not just to increase the number of customers but also to increase its revenues.
  • the invention takes into account different target functions - the definition of the party that issues the offer of a desired impact.
  • the grades produced by this model will illustrate the predicted contribution to the predefined campaign target function.
  • a product manufacturer may wish to increase the penetration rate, i.e., to maximize the number of new customers. Another manufacturer might not want to waste discounts on customers that might purchase his product without the offer.
  • the target function 142 in the first case will be to maximize the total use of offers, or to increase the number of purchases that follow a free sample.
  • the target function 142 will be to increase the overall purchase basket. In this case, those customers that will buy the product in any case will not receive the offer.
  • coupon A will receive a higher grade simply because 70% > 60%.
  • coupon B will receive a higher grade because it has a higher potential of contributing to the income of the purchase basket
  • the particular personalized impact of the offer can be learnt. This is useful as many of the decisions are based on the generalization of a group of people, although such groups always include exceptions.
  • the closed-loop feedback 136 enables the system to recognize and deal with such exceptions.
  • a personalized offer system includes 3 parts:
  • the preferred embodiment of the present invention focuses on collecting data about the history of purchases.
  • the data is collected at the product level (a list of products that every customer bought).
  • the distribution channels are based on the distribution services of the data collecting entity, for example a loyalty club's contact channel.
  • FIG. 8 illustrates how the current invention, offer matching system 210, may be embedded in numerous types of applications, with various channels for collecting data, and for distributing offers.
  • the data collection system 200 can be one or more of the systems in the left column (product purchases 500, businesses where purchases were made 502, banking accounts 504, phone services used (numbers dialed, etc.) 506, TV usage history 508, e-commerce purchases 510, Web sites visited 512, or other data source 514).
  • the offer distribution system 220 can be one or more of the systems in the right column (direct mail 540, kiosk 542, automated teller machine 544, E-mail 546, Web site 548, TV 550, Cellular phone or other electronic device 552, or other offer distribution channel 554).
  • FIG. 8 is by no means exhaustive, it serves to illustrate some of the wide range of applications to which the present invention can be applied.
  • the customer behavior database 104.1 will contain the list of stores where the card holders made purchases 502 (businesses where purchases were made, dates of purchases, etc.). The data can be obtained from the credit card company's database or rebate card company's database or the database of any other company that collects such information in any event.
  • the domain of operation is customer accounts and the offers are likely to be banking offers.
  • the customer behavior database 104.1 contains the detailed surfing history 512 for each surfer (names of the Web sites visited, time of entrance to the site, surfing time duration, etc.)
  • the above does not limit the functionality of the system to provide banners only. For example it can offer books in special domains or special prices based on detailed surfing history. In these cases the data can be obtained from the Internet access suppliers.
  • the features can be the accumulative surfing time in each one of web sites or in different groups of sites, the last date surfed in a certain site, etc.
  • the response can be whether a click through was performed on the banner and so forth.
  • the phone services used (numbers dialed, etc.) 506 and telephone services used (such as voice mail) are a gold mine of information, and are stored in the telephone companies databases (including customer contact information).
  • the data on viewing history 508 is recorded. This database can be used to analyze domains of interest and can therefore induce relevant offers. Note that these offers are not limited to broadcast material only.
  • Credit card holders may receive a personalized offer attached to their invoices.
  • a customer who approaches the ATM 544 at 12:00 p.m. may receive a coupon for lunch in a French restaurant which is close to the specific ATM - this only after the system recognized that the customer likes French cuisine.
  • Another distribution channel is kiosk machines 542 in stores.
  • the customer will introduce a loyalty club card (which can be a credit card) enabling the kiosk to recognize the customer and informing him/her of personalized offers, which the customer can print out on the kiosk machine or receive at the POS 126. If the consumer's e-mail 546 address is known, the personal set of offers can be sent to him/her through this means of offer distribution.
  • a loyalty club card which can be a credit card
  • a personal offer can be presented and the customer can choose to print it out or to redeem it on-line.
  • cellular phones and other electronic devices 552 such as an interactive television, mobile telephones, personal digital assistants, etc. it is possible to send various types of personal offers (including coupons) to the display.
  • the present invention is very flexible is in its capacity to combine any offer distribution channel 220 and data collection channel 200.
  • similar offer distribution 220 and data collection 200 channels might be used, for example, to distribute offers based on Internet surfing habits by E-mail. But if other contact information is available, the regular mail system or the personal cellular phone might be used to distribute the same offers.
  • the data collection 200 resource could be chain stores, retail loyalty clubs, supplier/manufacturer loyalty clubs, or any other company that holds product purchase history data 500 .
  • the data content could be history of purchases at the level of products purchased by each customer, history of purchases regarding customer reaction to past offers, current time, and/or customer's current location
  • the offer distribution 220 channel could be direct mail 540, a kiosk placed at the purchase site 542, small screens on a store's shopping carts or similar instruments, E-mail, 546, banners in an Internet/intranet Web site 548, mobile phones 552, Palm pilots 552, call centers, and/or interactive TV 550.
  • a list of optimal customers can be generated by the present invention and given to the manufacturer/supplier or a third party.
  • the offer set could include regular coupons (discounts on the purchase of a product), coupons with redeeming conditions (e.g., buy two packages and get one free), advertisement offers, samples, gifts, and/or package deals.
  • the data collection 200 resource could be e-commerce Web sites 510, auctions, television sales channels, telemarketing channels, chain stores, retail loyalty clubs, supplier/manufacturer loyalty clubs, or any other company that holds product purchase history data 500 .
  • the data content could be history of time spent at a certain Web site, the areas of interest shown by the customer, history of purchases at the level of products purchased by each customer, history of purchases regarding customer reaction to past offers, current time, and/or customer's current location.
  • the offer distribution 220 channel could be direct mail 540, a kiosk placed at the purchase site 542, small screens on a store's shopping carts or similar instruments, E-mail 546, banners in an Internet/intranet Web site 548, mobile phones or personal digital assistants 552, call centers, and/or interactive TV 550.
  • a list of optimal customers can be generated by the present invention and given to a manufacturer/supplier or a third party.
  • the offer set could include regular coupons (discounts on the purchase of a product), coupons with redeeming conditions (e.g., buy two packages and get one free), advertisement offers, samples, gifts, and/or package deals.
  • the data collection 200 resource could be credit card or rebate card companies, banks, or any other company that holds similar data.
  • the data content could be personal purchase history (e.g., credit card purchases) that is associated with the customer's identity, data regarding the customer's reaction to past offers, and/or the customer's current location (which can be detected by some means of offer distribution, such as ATMs).
  • personal purchase history e.g., credit card purchases
  • data regarding the customer's reaction to past offers e.g., credit card purchases
  • customer's current location e.g., credit card purchases
  • the offer distribution 220 channel could be direct mail 540, ATMs or other types of terminals 544, e-mail 546 banners in an Internet/intranet Web site 548, mobile phones or personal digital assistants 552, call centers, and/or interactive TV 550
  • the offer set could include regular coupons (discount on the purchase of a product or in a store), coupons with redeeming conditions (e.g., buy at more than X$, receive Y% discount, where X and Y can be personalized), advertisement offers, samples, gifts, package deals.
  • regular coupons paid on the purchase of a product or in a store
  • coupons with redeeming conditions e.g., buy at more than X$, receive Y% discount, where X and Y can be personalized
  • advertisement offers samples, gifts, package deals.
  • the data collection 200 resource co ⁇ ld be Internet service providers, portals, e-commerce sites, search engine sites, or any other entity holding surfing history data 512.
  • the data content could be Internet/intranet surfing history (including surfing time, purchases, click-on banners, and other actions) where the data is associated with the customer's identity or that of the computer used for surfing, other personal data , and/or data regarding customer reaction to past offers.
  • the offer distribution 220 channel could be e-mail 546, banners in an Internet/intranet Web site 548, mobile phones or personal digital assistants 552, small screens on a store's shopping carts and similar instruments 542, call centers, interactive TVs 550 ATMs 554, and/or any type of kiosk 542
  • the offer set could include regular coupons (discount on the purchase of a product or in a store), coupons with conditions for redemption (buy at more than $X, receive Y% discount, where X and Y can be personalized), advertisement offers, samples, gifts, package deals.
  • the data collection 200 resource could be banks or any other type of company holding banking data 504 or similar data.
  • the data content could be history of banking actions taken by the customer and associated with the customer's identity, other personal data, and/or customer reaction to past offers.
  • the offer distribution 220 channel could be a personal offer by the banker, direct mail 540, a kiosk placed at the purchase site 542, small screens on a store's shopping carts or similar instruments, E-mail, 546, banners in an Internet/intranet site Web site 548, mobile phones 552, Palm pilots 552, call centers, and/or interactive TV 550.
  • a list of optimal customers can be generated by the present invention and given to the bank or a third party.
  • the offer set could include offers of banking activities with personal incentives, general incentives or no incentives at all; regular coupons (discount on the purchase of a product or in a store); coupons with conditions for redemption (buy at more than X$, receive Y% discount, where X and Y can be personalized), advertisement offers, samples, gifts, and/or package deals
  • the data collection 200 resource could be mobile phone service providers, regular telephone service providers, or any other type of company amassing phone service history data 506.
  • the data content could be the customer's mobile phone use history (length of call, type of call, phone numbers dialed by the customer, other cellular services, etc.), other personal data, and/or data regarding customer reaction to past offers.
  • the offer distribution 220 channel could be mobile phones, call centers, direct mail, ATMs or other types of terminals, e-mail, banners in an Internet/intranet site, and/or interactive TV.
  • the offer set could include price packages, packages of accompanying services, regular coupons (discount on the purchase of a product or in a store), coupons with conditions for redemption (buy at more than X$, receive Y% discount, where X and Y can be personalized), advertisement offers, samples, gifts, and/or package deals.
  • the data collection 200 resource could be interactive TV suppliers or any other type of company which stores TV usage data, such as viewing history 508.
  • the data content could be customer's TV viewing history (programs, viewing hours, viewing time, channel switching, channel switching during commercials, etc.), history of other actions, such as Internet surfing, other personal data, and/or data regarding customer reaction to past offers.
  • TV viewing history programs, viewing hours, viewing time, channel switching, channel switching during commercials, etc.
  • history of other actions such as Internet surfing, other personal data, and/or data regarding customer reaction to past offers.
  • the offer distribution 220 channel could be interactive TV, mobile phone, call centers, direct mail, ATMs or other types of terminals, e-mail, banners in an Internet/intranet site, and/or personal digital assistants.
  • the offer set could include offers for content packages, price packages, packages of accompanying services, regular coupons (discount on the purchase of a product or in a store), coupons with conditions for redemption (buy at more than X$, receive Y% discount, where X and Y can be personalized), advertisement offers, samples, gifts, and/or package deals.

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Abstract

L'invention concerne un procédé et un système permettant de générer de manière entièrement automatique des ensembles d'offres personnalisées optimisées selon une fonction cible, à l'aide des données collectées sur les clients et les produits ou services. Au cours de l'étape (106) d'analyse des données, les offres possibles sont d'abord classées séparément pour chaque client, en tenant compte de la contribution estimée (138) de l'offre à la fonction cible (142) de la campagne. Au cours de l'étape (108) de sélection des offres personnalisées, le système sélectionne ensuite pour chaque client un ensemble d'offres les plus hautement classées et qui répondent à toutes les contraintes posées. Ceci permet de générer de manière automatique des ensembles d'offres personnalisées optimisées pour les clients, sans nécessité d'avoir recours à un expert en statistique ou en base de données.
PCT/IL2001/000753 2000-08-16 2001-08-14 Procede et systeme permettant de generer de maniere automatique des offres personnalisees optimisees WO2002015454A2 (fr)

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AU2001280071A AU2001280071A1 (en) 2000-08-16 2001-08-14 Method and system for automatically producing optimized personalized offers

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US09/639,736 2000-08-16

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US7522186B2 (en) 2000-03-07 2009-04-21 L-3 Communications Corporation Method and apparatus for providing immersive surveillance
US20090177540A1 (en) * 2003-07-08 2009-07-09 Yt Acquisition Corporation High-precision customer-based targeting by individual usage statistics
US7633520B2 (en) 2003-06-19 2009-12-15 L-3 Communications Corporation Method and apparatus for providing a scalable multi-camera distributed video processing and visualization surveillance system
US7836045B2 (en) 2006-01-10 2010-11-16 Approximatch, Inc. Customizing web search results based on users' offline activity
US8412566B2 (en) 2003-07-08 2013-04-02 Yt Acquisition Corporation High-precision customer-based targeting by individual usage statistics
US10699288B2 (en) 2006-10-17 2020-06-30 Inmar—Youtech, Llc Methods and systems for distributing information via mobile devices and enabling its use at a point of transaction
US11032422B1 (en) 2016-05-12 2021-06-08 State Farm Mutual Automobile Insurance Company Heuristic sales agent training assistant
US11544783B1 (en) 2016-05-12 2023-01-03 State Farm Mutual Automobile Insurance Company Heuristic credit risk assessment engine

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US6009411A (en) * 1997-11-14 1999-12-28 Concept Shopping, Inc. Method and system for distributing and reconciling electronic promotions
US20010014868A1 (en) * 1997-12-05 2001-08-16 Frederick Herz System for the automatic determination of customized prices and promotions
AU4481600A (en) * 1999-04-22 2000-11-10 Qode.Com, Inc. System and method for providing electronic information upon receipt of a scannedbar code
US7228285B2 (en) * 1999-12-01 2007-06-05 Catalina Marketing Corporation Automated method and system for automated tracking, charging and analysis of multiple sponsor discount coupons
GB2360371A (en) * 2000-03-14 2001-09-19 Catalina Marketing Uk Ltd Method and system for distributing and redeeming offers and incentives
US20020059100A1 (en) * 2000-09-22 2002-05-16 Jon Shore Apparatus, systems and methods for customer specific receipt advertising
US20020053076A1 (en) * 2000-10-30 2002-05-02 Mark Landesmann Buyer-driven targeting of purchasing entities

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US7522186B2 (en) 2000-03-07 2009-04-21 L-3 Communications Corporation Method and apparatus for providing immersive surveillance
US7633520B2 (en) 2003-06-19 2009-12-15 L-3 Communications Corporation Method and apparatus for providing a scalable multi-camera distributed video processing and visualization surveillance system
US20090177540A1 (en) * 2003-07-08 2009-07-09 Yt Acquisition Corporation High-precision customer-based targeting by individual usage statistics
US8412566B2 (en) 2003-07-08 2013-04-02 Yt Acquisition Corporation High-precision customer-based targeting by individual usage statistics
US10528975B2 (en) 2003-07-08 2020-01-07 Inmar—Youtech, Llc High-precision customer-based targeting by individual usage statistics
US7836045B2 (en) 2006-01-10 2010-11-16 Approximatch, Inc. Customizing web search results based on users' offline activity
US10699288B2 (en) 2006-10-17 2020-06-30 Inmar—Youtech, Llc Methods and systems for distributing information via mobile devices and enabling its use at a point of transaction
US11164238B1 (en) 2016-05-12 2021-11-02 State Farm Mutual Automobile Insurance Company Cross selling recommendation engine
US11032422B1 (en) 2016-05-12 2021-06-08 State Farm Mutual Automobile Insurance Company Heuristic sales agent training assistant
US11164091B1 (en) 2016-05-12 2021-11-02 State Farm Mutual Automobile Insurance Company Natural language troubleshooting engine
US11461840B1 (en) 2016-05-12 2022-10-04 State Farm Mutual Automobile Insurance Company Heuristic document verification and real time deposit engine
US11544783B1 (en) 2016-05-12 2023-01-03 State Farm Mutual Automobile Insurance Company Heuristic credit risk assessment engine
US11556934B1 (en) 2016-05-12 2023-01-17 State Farm Mutual Automobile Insurance Company Heuristic account fraud detection engine
US11734690B1 (en) 2016-05-12 2023-08-22 State Farm Mutual Automobile Insurance Company Heuristic money laundering detection engine
US12020307B2 (en) 2016-05-12 2024-06-25 State Farm Mutual Automobile Insurance Company Heuristic document verification and real time deposit engine
US12131377B2 (en) 2016-05-12 2024-10-29 State Farm Mutual Automobile Insurance Company Heuristic credit risk assessment engine
US12229816B2 (en) 2016-05-12 2025-02-18 State Farm Mutual Automobile Insurance Company Heuristic account fraud detection engine
US12236470B2 (en) 2016-05-12 2025-02-25 State Farm Mutual Automobile Insurance Company Heuristic money laundering detection engine

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