Cardholder Clusters

• US 20100306029A1
• Filed: 08/07/2009
• Published: 12/02/2010
• Est. Priority Date: 06/01/2009
• Status: Abandoned Application

• Current Assignee: Visa International Service Association (Visa, Inc.)
• Original Assignee: Visa International Service Association (Visa, Inc.)
• Inventors: Jolley, Ryan
• Application Number:
  US12/537,566
• Publication Number:
  US 20100306029A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A system and method of using transaction data for a population of account holders, such as credit card holders, is described. A frequency distribution input variable (Frd) and average amount distribution input variable (Avd) are calculated for each account and each merchant category. The Frd and Avd, either alone or in conjunction with each other, are used to assign accounts to clusters as well as calculate factors for factor analysis. The assigned cluster and calculated factors for each account are both used for further processing, such for as selecting accounts to which advertising materials will be sent or determining a surrogate account for a control group.

Claims

1. A computer-implemented method of using transaction data for a population of account holders having accounts, the method comprising:

a) receiving a frequency distribution input variable (Frd) for each account in each merchant identifier based on the transaction data;

b) receiving an average amount distribution input variable (Avd) for each account in each merchant identifier based on the transaction data;

c) assigning each account to a statistical cluster using at least one of the frequency distribution input variable Frd and the average amount distribution input variable Avd;

d) calculating, using a processor, a factor for each account using at least one of the frequency distribution input variable Frd and the average amount distribution input variable Avd; and

e) performing further processing using the cluster and the factor.

2. The computer-implemented method of claim 1 wherein:

further processing comprises selecting an account, wherein the selected account is a surrogate account and selecting includes correlating two accounts based on the two accounts being assigned to the same cluster and based on factor analyses of factors associated with the two accounts.

3. The computer-implemented method of claim 1 wherein further processing comprises:

selecting an account using the cluster and the factor; and

sending an advertisement to the selected account.

4. The computer-implemented method of claim 1 wherein further processing includes predicting account holder demographic information selected from the group consisting of gender, income, and the presence of children.

5. The computer-implemented method of claim 1 further comprising:

normalizing the frequency distribution input variables (Frd'"'"'s) and average amount distribution input variables (Avd'"'"'s) to the transaction data for the population of account holders.

6. The computer-implemented method of claim 1 further comprising:

determining a diversity of purchases across merchant identifiers for each account based on the transaction data, wherein the assigning and calculating use the diversity of purchases.

7. The computer-implemented method of claim 1 further comprising:

gathering a percentage of transactions in a channel type for each account based on the transaction data.

8. The computer-implemented method of claim 1 further comprising:

receiving transaction data for the population of account holders, the data including a series of transactions for accounts, each transaction of the series of transactions associated with a merchant identifier.

9. The computer-implemented method of claim 8 wherein the merchant identifier is selected from the group consisting of a specific merchant identifier, a general merchant category class identifier, and a North American Industry Classification System (NAICS) code.

10. The computer-implemented method of claim 1 wherein steps a), b), c), d), and e) are performed in the order shown.

11. The computer-implemented method of claim 1 wherein steps a), b), c), d), and e) are performed using a processor.

12. The computer-implemented method of claim 1 wherein the creating the frequency distribution input variable (Frd) for each account uses the following equation:

Frd_a,MCC=(frq_acct_a,MCC−

tot_tran_cnt_a*dist_pop_MCC)÷

SQRT(tot_tran_cnt_a*dist_pop_MCC*(1−

dist_pop_MCC))

wherein;

Frd_a,MCC is the frequency distribution input variable for account a in merchant category MCC;

frq_acct_a,MCC is a total number of transactions for account a in merchant category MCC;

tot_tran_cnt_a is a total number of transactions for the account; and

dist_pop_MCC is a percent of transactions for the population at merchant category MCC.

13. The computer-implemented method of claim 1 wherein the creating the average amount distribution input variable (Avd) for each account uses the following equation:

Avd_a,MCC=(avg_acct_a,MCC−

avg_pop_MCC)÷

SQRT(avg_std/mcc_acct—

cnt_a,MCC)

wherein;

Avd_a,MCC is the average amount distribution input variable for account a in merchant category MCC;

avg_acct_a,MCC is an average amount spent by account a in merchant category MCC;

avg_pop_MCC is an average spent by the population at merchant category MCC;

avg_std is the standard deviation of the average amount spent for the population; and

mcc_acct_cnt_a,MCC is a total number of transactions for account a in merchant category MCC.

14. The computer-implemented method of claim 13 wherein the merchant category MCC is defined by a North American Industry Classification System (NAICS).

15. A machine-readable tangible medium embodying information indicative of instructions for using one or more machines to perform operations to use transaction data for a population of account holders having accounts, the instructions comprising:

a) receiving a frequency distribution input variable (Frd) for each account in each merchant identifier based on the transaction data;

b) receiving an average amount distribution input variable (Avd) for each account in each merchant identifier based on the transaction data;

c) assigning each account to a statistical cluster using at least one of the frequency distribution input variable Frd and the average amount distribution input variable Avd;

d) calculating, using a processor, a factor for each account using at least one of the frequency distribution input variable Frd and the average amount distribution input variable Avd; and

e) performing further processing of an account using the cluster and the factor.

16. The machine-readable medium of claim 15 wherein performing further processing includes:

selecting an account, wherein the selected account is a surrogate account and the selecting includes correlating two accounts based on the two accounts being assigned to the same cluster and based on factor analyses of the factors of the two accounts.

17. The machine-readable medium of claim 15 wherein performing further processing includes:

selecting an account; and

sending an advertisement to the selected account.

18. The machine-readable medium of claim 15 wherein further processing includes predicting account holder demographic information selected from the group consisting of gender, income, and the presence of children.

19. The machine-readable medium of claim 15 wherein the instructions further comprise:

normalizing the frequency distribution input variables (Frd'"'"'s) and average amount distribution input variables (Avd'"'"'s) to the transaction data for the population of account holders.

20. The machine-readable medium of claim 15 wherein the instructions further comprise:

determining a diversity of purchases across merchant identifiers for each account based on the transaction data, wherein the assigning and calculating use the diversity of purchases.