WO2005008421A2 - High-precision customer-based targeting by individual usage statistics - Google Patents
High-precision customer-based targeting by individual usage statistics Download PDFInfo
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- WO2005008421A2 WO2005008421A2 PCT/US2004/022260 US2004022260W WO2005008421A2 WO 2005008421 A2 WO2005008421 A2 WO 2005008421A2 US 2004022260 W US2004022260 W US 2004022260W WO 2005008421 A2 WO2005008421 A2 WO 2005008421A2
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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
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0254—Targeted advertisements based on statistics
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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
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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
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0211—Determining the effectiveness of discounts or incentives
Definitions
- the present invention relates to the targeting of sales announcements, promotions, advertisements, coupons and the like to customers, and delivery of such targeted announcements, etc. to the customers in print or in electronic form, for example by cell phones, email, ATM device, or by any other device capable of printing, displaying or otherwise presenting a commercial message.
- Retailers, wholesalers, marketers, and manufacturers often distribute promotional offers, such as coupons, offering discounts and other incentives in order to reward valuable customers, attract new customers, or promote the sale of specific products or services identified in the promotional offers. (Both products and services may be the subject of promotional offers.
- This form of targeting is intended to identify those who are most likely to buy. In the reverse sense, targeting can exclude those who are least likely to buy. For example, a targeting process should not distribute a promotion for meat to vegetarians. The overall objective of targeting was, and still is, to significantly reduce the number of promotional offers distributed while significantly increasing the number accepted. These forms of targeting might appear to be adequate but they are not.
- a further disadvantage of the past targeting attempts is the inability to effectively control the number of promotional offers delivered to each individual customer while still retaining precision in targeting.
- past methods may be able to establish and enforce several different distribution limits, the manner in which those limits are maintained can also impose extremely severe disadvantages. For example, in the prior art of coupon distribution, there are sometimes limits on the number of coupons distributed in total, the number for each offer-communicating terminal, the number for each store, the number for each offer, and also the maximum number to be delivered to any one customer.
- any or all such limits must result in the reduction of the number of coupons distributed to some customers.
- the selection of which coupons to withhold is typically based upon factors other than the purchase history of the customer, for example the age of the coupon or simply an arbitrary first-come-first-serve policy as the coupons are created.
- Tl us some coupons that might have been distributed to a given customer because of that customer's purchasing statistics may be withheld because of some unrelated limit.
- the disadvantage arises in the fact that those coupons withheld from a customer because of limits might well have been the very coupons most likely to be redeemed by that customer. Therefore, the setting of limits in the past had the major disadvantage of distorting the targeting process.
- a further disadvantage of the past targeting attempts is the statistical bias towards products that are more broadly used, rather than those more likely to be redeemed by each individual customer.
- the bias arises where the probability of purchasing a product in the future is estimated simply by the frequency of similar purchases in the past. For example, an offer of a 10% discount on bread might be distributed to almost all customers because almost all buy bread frequently.
- the statistical analysis is not normalized in the sense that it does not tak ⁇ into consideration the relative purchasing behavior between customers so that offers for bread might be distributed only to those who purchase bread much more often than others.
- the present invention provides a method and apparatus for targeting customers that overcomes the above disadvantages of past targeting approaches. It is particularly beneficial in cases where the number of offers to be distributed must be limited and the appeal of the offer to the individual customer is important. The two requirements of limited distribution and individualized appeal are handled in such a way as to optimize the likelihood of customer acceptance.
- the invention is equally applicable to promotional offers for customers of traditional brick and mortar retail establishments as well as promotions over the Internet or in other channels of trade. It is an object of the invention to provide a targeting method that matches promotional offers to individual customers in such a way that each customer can receive a limited number of offers that are estimated to be most likely to be acceptable by the customer, even when that limited number is much smaller than the total number of offers available for distribution, and where several differing limitations might apply concurrently.
- the invention provides probability estimates based upon such factors as each individual customer's purchasing history as well as other personal data and information relating to the general context of the offer such as events, timing, and location. In prior art targeting methods the individual purchasing history of each customer is used to match each offer to those customers estimated to be the most likely to accept the offer.
- a probability threshold can be set so that each promotion can be offered to a reduced number of customers rather than to all.
- This form of prior art targeting strategy is referred to here as “Product-Based” targeting because it selects the customers for each product.
- the present invention provides a targeting strategy referred to here as “Customer-Based” in that it selects the products for each customer.
- Customer-Based targeting along with other methods of the invention overcomes or greatly diminishes the disadvantages of prior art targeting techniques noted above.
- the Customer-Based targeting distributes only the promotional offers most likely to be personally appealing to each individual customer, and in so doing reduces the annoyance to the customers, increases the rate at which customers accept promoted offers, and reduces the cost of the promotions.
- the Customer-Based targeting technology of the invention accommodates each customer's individual tastes and purchasing proclivities.
- Customer-Based targeting analyzes each customer's past purchasing behavior relative to a master list of promotional offers made available to all customers. From that master list Customer-Based targeting selects a preset limit of promotional offers for each individual customer according to the likelihood that, " given the opportunity to select any offers of the master list, each customer would prefer those few offers selected specifically for him or her. This unique approach to targeting avoids the major disadvantages of conventional Product-Based targeting methods caused by the wide disparity in individual customer tastes.
- a more sophisticated, statistically based Limit Manager process is provided to assure that the customers receive the offers that they are most likely to redeem, even if limits are applied that reduce the number of promotional offers and therefore that withhold some promotional offers from some customers. It is also an objective of the invention to calculate the necessary statistical estimates with very high precision through several methods including but not limited to the use of Bayes techniques for reducing variance. Empirical Bayes techniques are applied to improve the imprecision that results from sparse data. In general, the preprogrammed merchandising ⁇ strategies of the invention serve to declare more precisely which customers are to be targeted and therefore to declare discounts more accurately.
- the result is to improve targeting precision while simplifying the declaration of targeting infonnation. It is a further objective of the invention to distribute promotional offers by hardcopy printing as well as all types of electronic means such as Internet, email, and telephone. It is a further objective of the invention to reduce the negative effects of distributing offers on paper by providing a practical and efficient method for multi-channel distribution electronically on cell phones and other mobile devices that can be carried instead of paper. It is also an objective of the invention to target customers according to Marketing Strategies according to the method of declared distribution rules embodied in the invention. This individualizing nature of the invention through Customer-Based targeting and personal data, the management of limits, the Strategies, and other features of the invention eliminate the greatest disadvantages of current targeting systems.
- the invention comprises a method and apparatus for distributing Limited Lists and Tree-Structured Lists of promotional offers to targeted customers with each List being individualized to the each target customer. All offers on all of the Limited Lists or Structured Lists can be taken from the same Master List of offers.
- Each customer's Limited List or Structured List is generated according to some combination of the given customer's personal shopping history, personal attributes, and other pertinent context such as location, time, and personal data.
- FIG. 1 A simplified example of the process is schematized in FIG. 1, which is offered here only to assist in illustrating the invention and is not to be taken as limiting the invention only to the methods and steps illustrated in the Figure.
- a data structure 10 is provided in which the customers are each modeled in terms of usage and personal data.
- the targeting process 11 uses statistical methods and rule-based inferences to score each promotional offer of the Master List of offers 12 according to the model of each customer X, thereby generating an Ordered List of offers 13 which are ordered by score specifically for the given customer X. Of that list, only the limited number of offers having the highest estimated probability for customer X, and complying with other constraints, are allocated to the Offer Distribution List 14 for distribution to customer X. Such other constraints may include any number of limits on the number of offers to be distributed in total, as groups, individually, by location, or by any other condition.
- the length of the Distribution Lists 14 can be much shorter than those of the Master List 12 or Ordered List 13 while continuing to be the estimated most probable products to be purchased by the given customer relative to all others on the Master List while complying with any imposed distribution constraints.
- the application of certain constraints can introduce complexities that are not represented by FIG. 1.
- the invention further passes the Offer Distribution List to each customer through adaptors to any or all of several communication channels such as email, mobile phone messaging, mobile phone Java based http communications, PDA, printers, kiosks, and other client terminals.
- the invention further assists the enterprise in forming the promotional offers by simplifying the task of targeting through preprogrammed marketing strategies.
- FIG. 1 is a block diagram overview of the methods of the invention.
- FIG. 2 is a block diagram of an apparatus embodying the invention.
- FIG. 3 is a probability matrix showing the probability that the customer in the row will - accept the offer in the column.
- FIG. 4 is a diagram of three tables which demonstrate the difference between "Customer-Based" targeting and traditional "Product-Based” targeting.
- FIG. 5 shows a simplified example of the Market Basket Transaction Database for a loyalty program of a hypothetical supermarket chain.
- FIGS. 6 A and 6B show two tables derived from the Market Basket Transaction Database of FIG. 5.
- FIG. 7 is an illustrative flowchart for calculating the average SKU probabilities given any fonn of customer marketing segmenting.
- FIG. 8 an illustrative flowchart for computing the SKU Probability Matrix FIG.
- FIG. 9 an illustrative flowchart for the calculation of the Offer Score Matrix based upon the probability matrix exemplified by FIG. 3.
- FIG. 10 is a flow diagram illustrating several ways in which the user can define how the offer scores are to be calculated.
- FIG. 11 an illustrative flowchart for effecting readjustment in offer probabilities.
- FIG. 12 an illustrative flowchart for the final calculation of the Offer Distribution Lists, including the Limit Manager.
- FIG. 13 illustrates an example of a loyalty program categorization table. DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
- an embodiment of the invention is described in terms of several distribution channels such as email, cellular telephones, PDAs, Internet, direct mail, voice phone, and others.
- the embodiment connects to customer databases, point of sale systems, lists of promotions, business rules, and other repositories of information.
- Other embodiments of this invention may have different configurations depending upon the differences between installations and usage.
- This embodiment is offered only by way of example and no limitation to only those repositories, those connections, or those channels is thus intended.
- Promotional offers are presented to customers by distributing offers through the channels in formats particular to the channel.
- a large supermarket chain is sometimes used herein to illustrate various aspects of the invention.
- the invention is applicable to any repeatable sales enterprise, (including retail, wholesale, and manufacturing) and no limitation only to supermarkets is thus intended.
- the term "user” is intended to mean the person or people who operate an embodiment of this invention.
- SKU is commonly used in retail to abbreviate the phrase "Stock Keeping Unit,” in other words an identifier for a product that can be sold by the unit to the customer. SKU serves as an operational definition but is not required because some retailers, wholesalers, and manufacturers may use some other term for the basic units of products that they offer for sale. Although SKU is the term used here for a product identifier, other forms of product identifiers may of course also be used.
- POS Point of Sale
- Information from the Customer Database included in the POS system 16 comprises the individual profile infonnation on each customer, for example, name, address, gender, customer segment, loyalty program data and other pertinent information.
- Such information can be used by this embodiment to limit offers to specific segments of users or to better target the group of customers to receive each offer.
- the information can be used to identify segments such as women under 18 years old, men and women of age over 18 years, or those whose spending record is in the upper 20% of all customers, next 20%, and so on.
- Other information in the Customer Database includes the data for all SKUs such as product name, brand, price per unit, and position within the taxonomy such as department or aisle, category, and subcategory. hj some instances, the purchase records of all customers for all SKUs are available from which this embodiment can construct a customer purchase history table.
- the POS systems report each transaction, consisting of the SKU sold, quantity sold, price, customer, market basket id, and other data.
- the POS systems are also used to verify offer validity for the customer, date, and SKU.
- this embodiment can construct the customer purchase history table without data from the Customer Database.
- Software components of this embodiment organize, structure, and store the information in the database 17.
- the optional Analytical Engine 18 of this embodiment uses infonnation from database 17 to form reports and data warehouse views that can be helpful to the user. It is not required in all embodiments.
- the Targeting Engine 19 performs the essential software tasks.
- the apparatus can be configured in other ways to perform the same tasks, for example, different numbers of servers or different deployments of software modules may be used, and all such configurations are considered equivalent.
- the Targeting Engine 19 performs all of the methods illustrated in FIG 2. Its functions utilize data from the database 17 as well as data directly received from external sources.
- the promotion list 21 corresponds to the master Hst ofoffers l0 ofFIG 2.
- FIG. 3 shows a simple example of an offer probability matrix, sometimes referred to as a score matrix, used to target promotional offers according to customer purchasing history. For purposes of illustration the numerical entries in the matrix of FIG. 3 may have been detennined by the methods of the invention or by prior art methods.
- the rows of the matrix correspond to customers and the columns to promotional offers.
- Each cell (i,j) represents the estimated probability that customer i will purchase offer j.
- the offer j consists of an incentive such as a discount for the purchase of a specific SKU. The probability of the offer being accepted is equated to the probability of the specific
- FIG. 4 illustrates the results of conventional Product-Based targeting compared with the Customer-Based targeting of the invention.
- FIG. 4 includes three Tables that display the results of three different promotion distribution strategies. As in FIG. 3, the value in cell (i,j) is the estimated probability that the product(s) promoted by offer j will appear in the next market basket of customer i.
- Conventional targeting corresponds to searching the matrix of FIG 3 vertically to find the customers who are most likely to accept the product offering of the column by purchasing the promoted product.
- the users are looking for the best customers for the product as contrasted with the best product for the customer as with the Customer-Based targeting of the invention.
- the column orientation is the reason for referring to conventional targeting as Product-Based.
- the following limits are set in this example: (a) no more than two of each offer can be distributed, and (b) no customer can receive more than two offers.
- targeting methods can be required to observe limits in the numbers of offers distributed in total, by store, by terminal, by individual offer, by number sent to each customer, and by others. The differences between conventional targeting and the Customer -Based targeting of the invention are most evident when those limits are applied.
- Offer-1 should be delivered to customer-2 and customer- 3 because, of all customers in the offer-1 column, these customers have the highest probabilities of accepting Offer-1 (namely, 0.007 and 0.009).
- Table 1 is seen to comply with the limit (a) of only two of each offer because no offer is distributed to more than two customers. The vertically targeted best probabilities are shown in bold type in FIG. 3.
- the entries in Table 1 are reorganized by customers in Table 2 so as to show the Distribution to Customers resulting from Table 1.
- the reorganized Table 2 illustrates the point that customer- 1 receives offer-3 and offer- 4, customer-2 receives offer-1 and offer-2, etc.
- Table 2 The information in Table 2 is identical to that of Table 1, entry by entry, but organized according to customers rather than products. According to the imposed limit (b), no customer can receive more than two promotional offers. Therefore, in Table 2, offer-3 cannot be delivered to customer-2 and it is struck off the list, even tfiough offer-3 is by far the most likely promotional offer for customer-2 to accept among the offers available. Thus conventional targeting may fail to accommodate the proclivities of the customer.
- the Customer-Based targeting of Table 3 in FIG. 4 is obtained by selecting from the same probability matrix of FIG 3 the two promotional offers of highest probability for each customer. Thus customer-1 receives offer-1 and offer-4 because these offers have the highest probability in the customer-1 row.
- a comparison of the Customer- Based targeting and traditional targeting may be seen by comparing the Product-Based targeting of Table 2 with the Customer-Based targeting of Table 3.
- the promotional offers are fewer by one because customer-2 was targeted for 3 promotional offers when only 2 are permitted by limit (a).
- the method of Product-Based targeting has no way of discovering the relative proclivities of the customer. That particular disadvantage of conventional targeting can result in withholding the promotional offers most likely to appeal to the customers and distributing the less appealing ones.
- the method of the invention eliminates this disadvantage.
- FIG. 5 shows an example of the Market Basket Transaction Database for a frequent buyer or loyalty program of a hypothetical supermarket chain. Similar databases are commonly found at many other kinds of retail chains or outlets, wholesale distributors, manufacturers, or marketers and the invention may also find application to such other databases. For ease of illustration the database is presented in FIG. 5 as a simple table although in general the data may be organized in other data structures, for example, more complex database structures organized according to general principles of relational database organization well known in the art and requiring no elucidation here. The transactions in FIG.
- each row references a SKU that appeared in a market basket of a referenced customer. In this example all purchases are made by customer number 1001.
- An important parameter for calculations is the number of market baskets in which each SKU type appeared, regardless of the quantity. For example, SKU 36 appears only three times in the database, in the rows labeled by reference numerals 26, 27 and 28, even though the quantity purchased was four in row 26 alone.
- FIG. 5 In loyalty program systems the transaction database of FIG. 5 is commonly populated by checkout data electronically gathered from the POS terminal as the customer pays for purchases.
- the customer ID is typically associated with the market basket transaction by scanning the loyalty card or by keying in the phone number of the customer.
- FIGS. 6 A and 6B show two tables derived from the Market Basket Transaction
- the objective is to calculate the entries of a SKU Group Probability Profile for each customer such as shown in FIG. 6B for Customer X.
- Each entry in the probability row of FIG. 6B represents the probability that at least one SKU in the given group appear in a market basket of the given customer.
- the entries are calculated for the Transaction Summary Table of FIG. 6A, which summarizes all market basket transactions for each customer in terms of SKU Groupings. Representative SKU Groupings are shown, and the same SKU Groupings are referenced in FIGS. 6A and 6B.
- the row for customer 1001 is indicated by reference numeral 29 in this example summary table.
- the Transaction Summary Table is then used to estimate the probability that at least one of the SKUs in the SKU Grouping will appear in a market basket of the given customer.
- the SKU Grouping (33,36,42) is represented three times, that is, in three different market baskets of the Transaction Database for Customer 1001. None of the SKUs of the Grouping appears in basket number 2001. All three appear in 2002. SKU 33 and SKU 36 both appear in 2003 and SKU 36 appears in 2004. Altogether at least one of the SKUs in the SKU Grouping appears in three of the four baskets.
- FIG. 7 is a flowchart illustrating an embodiment of a method for calculating the average SKU Group probabilities given any form of customer marketing segmenting.
- the operational definition of Market Segmenting as used herein is the classification of customers intoMnutually exclusive groups having similar marketing characteristics according to predefined intentions, inclusion rules, methods, or algorithms.
- the example described here refers to a specific fo ⁇ n of Market Segmenting, the invention is not intended to be limited to any particular form of segmenting. Segmenting can be based upon any of the several well known clustering algorithms such as K-means Clustering,
- the objective of the present embodiment is to calculate the probability that at least one SKU of a specified SKU Grouping will appear in the next market basket of a given customer of the given Market Segment as represented by a predictive model.
- the collection of such probabilities for a given customer is referred to here as the customer's probability profile, and the collection of all such probabilities for all customers of a given market segment is the segment probability profile.
- the objective of the flowchart of FIG. 7 is to calculate a probability profile model (priors) for each segment, independent of other segments, based upon the purchase history data for the members of that segment or a subset thereof.
- the model is used to predict the probability profile of an individual customer in the segment given the individual's purchase history for the previous m baskets, where m is a parameter that may be set to accommodate such system considerations as computational time and memory capacity.
- the flowchart begins at reference numeral 31 by partitioning all customers into Marketing Segments. Each customer is marked to identify the appropriate Market Segment. As indicated above, a number of schemes are known for market segmentation, the details of which need not be described here. The invention is intended to operate with any appropriate Market Segmenting method.
- a Transaction Summary Table such as illustrated in the simple example of FIG. 6A is then generated for each Market Segment.
- a Market Segment identifier is read, and the Transaction Summary Table is generated for that Market Segment at reference numeral 32 from the data in the Market Basket Transaction Database for all customers of the given Market Segment.
- FIG. 8 is a flowchart illustrating a computation of the SKU Probability Matrix for the Market Segments, which contains the estimated predicted probability of each SKU appearing in the market basket of each customer and from which an offer probability matrix exemplified by FIG. 3 may be generated.
- entries in an offer probability matrix are embellished and referred to more generally as Score, rather than "probability.”
- the flowchart of FIG. 8 begins much as that of FIG. 7.
- a Market Segment identifier is read, and the Transaction Summary Table is generated for that Market Segment at reference numeral 37 from the data in the Market Basket Transaction Database for all customers of the given Market Segment or a previously compiled Transaction Summary Table may be referenced.
- Segment Transaction Summary Table is then stepped through, customer by customer, at reference numeral 38 and the SKU Grouping purchase probability profile is calculated for each customer at reference numeral 39.
- a purchase profile model is then applied to each row of this table at reference numeral 40.
- the estimated probability for SKUj of customer X could be calculated as the numerical average of the number of shopping baskets in which one or more SKUj appear, divided by the total number of X's baskets. This calculation ignores the shopping behavior of the aggregate segment population.
- the calculation of the probability estimates for SKUj for customer X may advantageously use a parametric empirical Bayes model. In such cases the calculation takes into account the statistics calculated over the entire population of customers within the Market Segment as well as those computed only for the individual customer.
- the various forms and means of parameter estimation for empirical Bayes models are generally well known and need not be described in any detail here. See, for example, An Introduction to Mathematical Statistics and its Applications by Richard J. Larsen and Morris L. Marx, Published by Prentice Hall. See also references cited therein for empirical Bayes and other estimator techniques. Different embodiments of the invention may use several different methods for different situations.
- an empirical Bayes estimator it may sometimes be advantageous not to use an empirical Bayes estimator at all, but rather to use another (non-empirical Bayesian) method.
- An example is given here calculating one such empirical Bayes model and making predictions with it.
- the number of baskets x,. out of n t for customer / that contain a given SKU (or any of a group of SKUs) is modeled by a binomial distribution Bin(n, ⁇ ) whose ⁇ parameter is in turn drawn from a Beta ⁇ ,M) distribution.
- This model comes from a class of so-called conjugate models that are preferred because they are particularly amenable to computation.
- the probability that the SKU (or any in a group of SKUs) will ⁇ be in the next basket of customer i is simply ⁇ , .
- FIG. 9 is a flowchart illustrating the calculation of the Offer Score Matrix.
- the objective of the flowchart is to provide a measure of the estimated probability that a given customer will purchase the Promotion SKU of each offer, when the various strategies, rules, multipliers, and all other factors are taken into consideration. No immutable rule applies to estimating the probability that an offer j will be accepted by a customer i.
- the rules and functional relationships are based upon probabilities and functional estimators of probabilities, but they are fonned heuristically as predictors of the actions of the customer.
- Offer Score Matrix An Offer Score Matrix structure is generated at reference numeral 43 having one row per customer and one column per offer.
- the matrix is populated at reference numeral 44 by sequencing through each offer of the Master List of Offers 12 illustrated in FIG. 1. When this is completed, the populated Offer Score Matrix then corresponds to the matrix exemplified in FIG. 3, at which point the Ordered Offer List 13 of each customer, as exemplified by FIG. 1, can be constructed.
- the offer may have expired, the store location might be excluded, or the limit of any and all distributions may have been exceeded.
- FIG. 10 illustrates several ways in which the user can define how the offer scores are to be calculated in FIG. 9. A sample calculation is shown at reference numeral 51. In one embodiment the user expresses scoring intentions through Strategies 52.
- SKU Groupings These are preprogrammed targeting criteria stated in terms of SKU Groupings, which are either implied by the offer or declared explicitly by the user.
- the user provides a taxonomy of all SKUs divided into departments, categories, subcategories, etc.
- the user can then refer to any level, or levels, of the taxonomy in order to target customers by SKU probabilities.
- the preprogrammed strategies reference the taxonomy in an abstract way so that one strategy may apply to any offer. For example, the probability produced by the strategy may be equal to the probability that the customer will purchase any of the SKUs referenced, implied, or explicitly declared, by the strategy.
- the MoveStock strategy applied to an offer for SKU X implicitly declares the score for the customer to be the probability that the customer will purchase any of the SKUs in the subcategory containing SKU X.
- the score from the existing taxonomy of a very large supermarket chain would be the probability that the customer will purchase any SKU in the subcategory called "Cold Cereal,” which is in the category called "Cereal & Breakfast Foods.”
- Other functional relationships between SKU probabilities and offer probabilities can be used in addition to or instead of the combined probabilities of the taxonomical groups referenced by the strategy.
- the MoveStock strategy produces the purchase probabilities of the various brands of cereals such as Wheaties, Bran Flakes, Cheerios and so on through all cereal in the "Cold Cereal" subcategory.
- the purchase probability values of each SKU are not of themselves sufficient for the calculation of combined probabilities.
- the information illustrated by FIG. 5 is needed to calculate the combined probability of purchasing any of several SKUs. The probability is based upon the percentage of market baskets in which any combination of referenced SKUs appears.
- the Strategy 52 might produce the SKU Grouping probability of 0.008.
- Strategies refer to two kinds of SKU, the one or more being promoted, and the one or more used for targeting. The first kind, designated the
- Promoted SKU is always provided by the offer.
- the second kind, designated the Targeting SKU is usually an aggregate of SKUs derived from the SKU taxonomy and declared in different ways for each Strategy.
- the objective of the Strategy is to equate, for each customer, the probability of purchasing the Promoted SKU to the probability of having purchased the Targeting SKU.
- the Strategies are parameterized to support explicit taxonomical references where the Targeting SKU is not implicit. Some strategies require other parameters. For example, the UpSell Strategy requires a set of starting SKUs to "sell up" from. A software utility can reduce that set by eliminating any SKU for which the price is equal or greater than that of the Promoted ⁇ KU.
- the Targeting SKU is a user declared parameter.
- the S trategy is defined by defining the Targeting SKU, since the Promoted SKU is always defined in the offer.
- the CrossSell Strategy attempts to induce customers who purchase the Targeting SKU to also purchase the Promoted SKU. An instance would be a 50% discount on caviar for customers with a proclivity for Vodka.
- the Targeting SKU is a user-declared parameter.
- the Introduction Strategy is an attempt to induce purchasers of a very wide range of Targeting SKUs to try the Promotion SKU.
- the Targeting SKU is implicitly taken to be the category one level above the subcategory to which the Promotion SKU belongs. Using Introduction rather than MoveStock as the Strategy for that example, the Targeting SKU would be all SKUs in the broader "Cereal & Breakfast Foods" category, rather than the "Cold Cereal" subcategory contained within it.
- the purpose of the Reward Strategy is to reward the best customers by simply offering something they like at a meaningful discount. For e ample, a customer's favorite wine might be offered once at 50% discount.
- the Targeting SKU is taken to be the Promoted SKU.
- the BrandChange Strategy attempts to entice the customer from a currently used brand to the promoted brand.
- the Targeting SKU is formed from the subcategory of the Promoted SKU by eliminating any SKU of the Promoted Brand before estimating purchase probabilities. Thus a customer is more likely to be offered the promotion if that customer is a more frequent user of a competing brand.
- the Custom Strategy admits any collection of taxonomical references from SKU to subcategory to category, etc. through the entire taxonomy. The purpose is to permit any arbitrary targeting considered meaningful to he user. From time to time, non-custom Strategies can be added as they are proven to be useful for the specific application of the invention.
- the SKU Grouping probabilities are normalized, indicated at reference numeral 53, in such a way that that the offer scores are not dominated by inexpensive SKU Groupings that appear regularly in most of the market baskets, for example milk and bread.
- the objective of normalization is to take into account the purchasing probabilities of each customer as compared to those of all customers.
- One more easily calculated method of nonnalization is based upon rough estimates of SKU probabilities, rather than detailed calculations of SKU Groupings. For example, for each customer a ratio is formed by dividing the sum of the SKU probabilities of every SKU in each given SKLT Grouping by the average purchase probability of the same set of SKUs for the entire population of customers in the same segment.
- That ratio then provides a rough indication of how different the purchasing probabilities for the given customer are as compared to the whole.
- the normalizing ratio of 1.10 suggests that the customer is more probable than the average to accept the offer.
- Other normalization adjustments are possible. Imposing no normalization is equivalent to a normalization ratio of unity.
- the invention provides for a Discount Demand table 54, which equates discount percentage to a coefficient appearing in the score calculation.
- the discount or other incentive is a parameter of each offer that can be expected to affect the probability of accepting the offer.
- the coefficient can multiply the score automatically, from the table, or manually through a user interface.
- a discount of 20% may increase the probability by 1.3 as in the example of the figure, and by 2.6 in the case of a 40% discount.
- Such tables are prior art in businesses, retail or otherwise, and depend upon various aspects of the particular business. Although the user of this invention must provide the appropriate table, the use of the table for probability calculations is an element of this invention that avoids the disadvantage of failing to distribute offers of relatively unpopular SKU at a vast and seductive discount. For example, a wine normally sold at $42 per bottle may not have a high demand. However, were the wine to be discounted by 50 percent and sold at $21, the demand might be extremely high.
- Another aspect of the invention provides a visual/graphical method for revising the distribution of promotional offers and is indicated at reference numeral 55 in FIG. 10.
- FIG. 10 illustrates how the user's bar chart adjustments set the value of an offer score coefficient, 1.6 in the Figure, thereby changing the offer score of the given offer for each customer.
- FIG. 11 illustrates a method for effecting manually overriding adjustments to the offer distribution.
- a bar chart such as that at reference numeral 55 of FIG. 10 is displayed by request of the user.
- the user then has a choice at reference numeral 58 of either tenninating the session or adjusting an offer probability/score.
- the user adjusts the height of a bar by click-dragging it to a new value.
- the Master List of Offers 12 FIG. 1 is updated to reflect the new adjustment coefficient.
- FIG. 12 illustrates the final calculation of the Offer Distribution Lists. The calculation begins after construction of the Offer Score Matrix illustrated in FIG. 9 and all scoring operations are complete, as illustrated in FIG. 10. A score list is constructed at reference numeral 65 by sorting all offers according to their scores.
- Each entry in the list is a triple of score, offer, and customer to which each offer score belongs, although only the score determines the sort order.
- the offers of the list are distributed list-entry-by-list-entry at reference numeral 66 until all entries have been distributed or discarded.
- the offer is not distributed to the associated customer if prevented by the customer's category at reference numeral 67.
- the customer category may have no relationship to the" Market Segment referenced in FIG. 7 but is usually associated with some recognizable marketing attribute of the customer. Membership in a category is based upon some recognition rule provided by the user. For example, the user may intend to withhold distribution of an offer for an alcoholic beverage from customers of the category, "under 18 years of age.” A commonly used categorization is by customer spending.
- limits are in common use, for example, limits on the number of offers distributed in total, the number for each offer communicator terminal, the number for each store, the number for each offer, and also the maximum to be delivered to any one customer. Each limit is tallied separately. When any of the limits is exceeded, the offer is not distributed to the customer. Otherwise the offer is placed on the Offer Distribution List of the customer at reference numeral 69.
- the setting of limits in the past had the major disadvantage of distorting the targeting process. Some offers that werefe less likely to be redeemed by the customer might have been distributed while some that were more likely to be redeemed might not. This process, referred to as the Limit Manager, avoids that major disadvantage in the nonrial operating situations. Referring once again to FIG.
- the Offer Distribution Lists 14 of FIG. 1 are then passed to channel adapters indicated generally at reference numeral 72 where they are matched with the promotional offer content and conveyed by any printed or electronic means to the customers such as those means indicated at reference numeral 73.
- channel adapters indicated generally at reference numeral 72 where they are matched with the promotional offer content and conveyed by any printed or electronic means to the customers such as those means indicated at reference numeral 73.
Abstract
Description
Claims
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Also Published As
Publication number | Publication date |
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US8412566B2 (en) | 2013-04-02 |
WO2005008421A3 (en) | 2005-06-16 |
US20050010472A1 (en) | 2005-01-13 |
US10528975B2 (en) | 2020-01-07 |
US20130268356A1 (en) | 2013-10-10 |
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