Targeted Incentives Based Upon Predicted Behavior
A computer implemented system and method employs predictive analysis on purchase data to determine product categories for which a consumer is statistically likely to purchase and either has not purchased or has not purchased at the statistically expected level at a particular retail store or chain of retail stores, and responds by generating a purchase incentive offer for that consumer requiring purchase of a product in the target category as a condition for the consumer obtaining the offered incentive.
1 . A computerized method for selecting consumers to which to provide purchase incentive offers for purchase of products, comprising:
(1) storing in computer memory purchase history data for purchases from one or more retail stores during a certain time period, wherein said purchase history data includes CID records, wherein each CID record stores in association with one another at least CID, PIDs of products purchased in transactions associated with said at least one CID, and quantity of product items having said PIDs;
(2) defining a Target Category Correlation Function (TCCF) for a target category, wherein said TCCF is a function of at least quantity of purchase of products in non target categories;
(3) applying statistical analysis to at least a subset of said purchase history records and said TCCF to define values of coefficients for terms of said TCCF, said values corresponding to correlation of purchase of products in non target categories to purchase of products in said target category;
(4) applying said TCCF to purchase history records associated with CIDs to obtain CID TCCF values for said CIDs;
(5) deciding whether to provide purchase incentive offers for purchase of products in said target category to consumers associated with said CIDs based at least in part upon said CID TCCF values.
2 . The method of claim 1 wherein said (5) deciding is also based at least in part upon values for quantity or volume of purchase in said target category associated with said CIDs.
3 . The method of claim 1 wherein said (1) storing also comprises storing in said CID records volume of product items having said PIDs.
4 . The method of claim 1 wherein said (5) deciding further comprises:
determining from said CIDs target category offer CIDs (i) whose purchase history records have no purchases in said target category and (ii) that have relatively high TCCF values; and
associating with at least one of said target category offer CIDs purchase incentive offer data identifying at least one product in said target category.
5 . The method of claim 1 wherein said TCCF is normalized so that its value defines an expected volume of purchase in said target category, wherein volume is a measure of at least one of number of product items purchased and currency value of product items purchased; and
further comprising determining a ratio of actual purchase volume in said target category to CID TCCF value for one of said CIDs.
6 . The method of claim 1 wherein said purchase history data includes a plurality of CID records for a CID, wherein each one of said plurality of CID records storing data corresponding to a single transaction.
7 . The method of claim 1 wherein said purchase history data includes a plurality of CID records for a CID including at least one record storing data from more than one transaction.
8 . The method of claim 1 wherein said purchase history data includes a plurality of CID records for a certain CID, each of said plurality of CID records storing transaction data for transactions in a time period, such that different ones of said plurality of CID records store transaction data from different time periods.
9 . The method of claim 1 wherein said TCCF has the form of a linear equation consisting of a sum of terms, wherein each term is a coefficient multiplied by a variable indicating product or category purchase volume.
10 . The method of claim 1 wherein said coefficient is a measure of statistical correlation of purchase in a non target category to purchase in said target category.
11 . The method of claim 1 wherein said TCCF has the form of a sum of terms Aij*Pj where Aij represents statistical correlation of purchase in an ith non target category to purchase in target category j, and Pj is a variable representing the volume of purchase in category j.
12 . The method of claim 4 wherein said relatively high TCCF values including only values within the top 20 percent of all TCCF values.
13 . The method of claim 5 wherein said determining a ratio comprises determining whether said ratio is less than a specified fraction which is less than one.
14 . The method of claim 1 further comprising defining TCCFs for a plurality of categories, and performing (3) to (5) for each one of those TCCFs.
15 . The method of claim 14 wherein at least one of said purchase incentive offers defined a plurality of products, each one of said plurality of products in a different category, and said purchase incentive offer required a consumer to purchase each one of said plurality of products in order to receive an incentive associated with said at least one of said purchase incentive offers.
16 . The method of claim 1 further comprising transmitting said purchase history data to a central CS and wherein steps (1) occurs at said central CS.
17 . The method of claim 1 wherein steps (3) and (4) occur at said central CS.
18 . The method of claim 1 further comprising transmitting a subset of said CIDs and associated product purchase incentive offers for said target category from a central CS to a POS CS from which transaction data containing CIDs in said subset had been transmitted to said central CS.
19 . The method of claim 1 further comprising associating all CIDs records associated with the same residence address with a single CID.
20 . The method of claim 1 further comprising limiting determining said subset of said purchase history records by selecting from said purchase history records only those records in which block data and retail store customer data from a retail store indicate that all purchasers from the same residence address purchase in said retail store.
21 . A computer system for selecting consumers to which to provide purchase incentive offers for purchase of products, comprising:
at least one central processing unit;
an input device;
an output device;
(1) computer memory storing purchase history data for purchases from one or more retail stores during a certain time period, wherein said purchase history data includes CID records, wherein each CID record stores in association with one another at least CID, PIDs of products purchased in transactions associated with said at least one CID, and quantity of product items having said PIDs;
(2) code stored in computer memory defining a Target Category Correlation Function (TCCF) for a target category, wherein said TCCF is a function of at least quantity of purchase of products in non target categories;
(3) code stored in computer memory for applying statistical analysis to at least a subset of said purchase history records and said TCCF to define values of coefficients for terms of said TCCF, said values corresponding to correlation of purchase of products in non target categories to purchase of products in said target category;
(4) code stored in computer memory for applying said TCCF to purchase history records associated with CIDs to obtain CID TCCF values for said CIDs;
(5) code stored in computer memory for deciding whether to provide purchase incentive offers for purchase of products in said target category to consumers associated with said CIDs based at least in part upon said CID TCCF values.
22 . The system of claim 21 wherein element (1) is stored in a central CS and element (2) is not stored on said central CS.
23 . The system of claim 21 wherein elements (1), (4), and (5) are stored in a central CS.