Techniques For Targeted Offers

• US 20110106607A1
• Filed: 11/30/2007
• Published: 05/05/2011
• Est. Priority Date: 11/30/2006
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Prascak, Brian, Murdock, Wendy, Stanek, Craig, Furman, Brad, Sleeva, Sheryl, Lo Faro, Walter, Merz, Chris, Alfonso, Chris, Cohen, Ruven
• Application Number:
  US12/515,002
• Publication Number:
  US 20110106607A1
• Time in Patent Office: Days
• US Class Current: 705/14.25
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

Systems and methods for analyzing and segregating payment card account profiles into clusters and targeting offers to cardholders. Offers may be targeted based on analyzing customer transactions with merchants from a merchant category as compared with transactions with merchants from a universe of merchants. Customers who have no transaction history with a merchant may be selected for offers based on similarities with respect to other customers of the merchant. Multiple merchant offers may be combined into a single mailing based on a scoring algorithm.

Claims

1. A method for targeting an offer to a plurality of customers comprising:

obtaining a merchant category associated with a merchant;

retrieving transaction data for a plurality of customers from a customer transaction database;

assigning each of said plurality of customers a loyalty ranking with respect to said merchant, based at least in part on comparing each customer'"'"'s transactions at said merchant to said customer'"'"'s transactions with at least one other merchant associated with said merchant category during a predetermined period of time;

assigning at least a portion of each of said plurality of customers a category spend ranking based at least in part on the customer'"'"'s total purchases at least two merchants associated with said merchant category; and

determining whether to send a predetermined incentive offer to each of said at least a portion of each of said plurality of customers based on said loyalty ranking and said category spend ranking.

2. The method of claim 1, wherein said customer loyalty ranking is based at least in party on the ratio of the number of said customer'"'"'s transactions conducted with said merchant to the number of said customer'"'"'s transactions conducted with all merchants associated with said merchant category.

3. The method of claim 1, wherein said determining whether to send a predetermined offer is also based on whether said customer is geographically eligible for said offer.

4. A computer system including one or more processors and memory for targeting an offer to a customer, comprising:

a first database for storing profiles for one or more merchants, each merchant associated with at least one merchant category;

a second database for storing transaction data for said customer;

a loyalty module for determining, for a merchant within said merchant category, a customer loyalty ranking with respect to said merchant based at least in part on comparing said customer'"'"'s transactions at said merchant with said customer'"'"'s transactions with other merchants in said merchant category and assigning said customer to a loyalty category based at least in part on said customer loyalty data;

a category spend module for determining said customer'"'"'s total category purchases at all merchants associated with said merchant category over a predetermined period of time;

an offer module for determining whether to send said offer to said customer, based at least in part on said customer loyalty ranking and said customer'"'"'s total category purchases.

5. A method operable on a computer system for sending customized offers from a plurality of merchants to a plurality of payment cardholders having payment accounts held by at least one payment card issuer, comprising:

determining a cardholder selection criteria corresponding to spending habits of cardholders to which said offer is targeted;

extracting from a first database of cardholder transaction data, a list of a payment card accounts exhibiting said cardholder selection criteria;

scoring each payment card account on said list based on expected future transaction activity of said payment card account at said plurality of merchants;

associating said payment card account with a payment cardholder;

selecting, for each of said plurality of merchants, an offer to be presented to a plurality of said payment cardholders, based on said scoring step;

generating and presenting to the associated cardholder a customized offer for each of said plurality of said payment cardholders.

6. A method for automatically segregating multiple payment card accounts, each having at least one associated cardholder, into a plurality of purchase clusters, comprising:

receiving, from a first database of payment card account data, data relating to transactions conducted using a plurality of payment card accounts at a plurality of merchant categories over a predetermined time period;

altering said payment card account data to correct for seasonal spending variability at said plurality of merchant categories to form adjusted payment card account data;

deriving a candidate cluster solution using at least said adjusted payment card account data, said candidate cluster solution consisting of a plurality of account clusters, each account cluster containing at least one of said payment card accounts;

determining, for each one of said plurality of account clusters derived, at least one merchant category at which the accounts contained in said account cluster have statistically abnormal purchasing activity as compared to the purchasing activity of a plurality of payment card accounts in a plurality of the other account clusters; and

generating a list of payment card accounts contained in at least one of said plurality of account clusters.

7. The method of claim 6, wherein said determining involves comparing the percentage of accounts in said one of a plurality of account clusters with purchasing activity exceeding a predetermined threshold at least one merchant in said merchant category to the percentage of accounts in said plurality of payment card accounts in a plurality of the other account clusters with purchasing activity exceeding said predetermined threshold at least one merchant in said merchant category.

8. The method of claim 7 wherein said predetermined threshold is 0 currency amount.

9. The method of claim 7 wherein said predetermined threshold is 0 transactions.

10. The method of claim 6, wherein said determining involves comparing the total currency amount spent by accounts in said one of a plurality of account clusters at a plurality of merchants in said merchant category to the total currency amount spent by said plurality of payment card accounts in a plurality of the other account clusters at said plurality of merchants in said merchant category.

11. The method of claim 6, wherein said determining involves comparing the total number of transactions performed using by accounts in said one of a plurality of account clusters at a plurality of merchants in said merchant category to the total number of transactions performed using said plurality of payment card accounts in a plurality of the other account clusters at said plurality of merchants in said merchant category.

12. The method of claim 6, wherein said altering said payment card account transaction data further includes identifying at least one inactive account based on an inactivity criteria;

and normalizing at least a portion of said payment card account data associated with said at least one inactive account.

13. The method of claim 12, wherein said normalizing comprises:

obtaining a plurality of profile variables associated with said at least one inactive account;

for at least one of said plurality of profile variables, calculating a normalized profile variable by dividing said profile variable value by the sum of the values of a plurality of other said profile variables associated with said at least one inactive account.