Method and apparatus for targeting best customers based on spend capacity

• US 20080221970A1
• Filed: 10/25/2007
• Published: 09/11/2008
• Est. Priority Date: 10/24/2005
• Status: Abandoned Application

• Current Assignee: Experian Marketing Solutions Incorporated (Experian plc)
• Original Assignee: Experian Marketing Solutions Incorporated (Experian plc)
• Inventors: Granger, Angela, Kornegay, Adam T., Megdal, Myles G.
• Application Number:
  US11/977,736
• Publication Number:
  US 20080221970A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. “Best customer” models can correlate SOW outputs with various customer groups for targeted marketing.

Claims

1. A method of targeted marketing, comprising:

(a) modeling consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

(b) determining spend capacities for existing customers using, for each existing customer, tradeline data of the customer, balance transfer data of the customer, and the model of consumer spending patterns; and

(c) identifying preferred customers based on spend capacity.

2. The method of claim 1, further comprising:

(d) identifying characteristics common to the preferred customers; and

(e) targeting consumers having the characteristics common to the preferred customers.

3. The method of claim 2, wherein the targeted consumers are prospective customers.

4. The method of claim 2, wherein the targeted consumers are existing customers.

5. The method of claim 1, further comprising:

(d) targeting customers identified as preferred customers.

6. The method of claim 1, further comprising:

(d) estimating a spend capacity for consumers using, for each consumer, tradeline data of the consumer, balance transfer data of the consumer, and the model of consumer spending patterns; and

(e) targeting consumers having spend capacities similar to spend capacities of the preferred consumers.

7. A method of targeted marketing, comprising:

(a) segmenting existing customers into categories;

(b) modeling characteristics of the existing customers using individual and aggregate data of the existing customers, including tradeline data, internal customer data, and consumer panel data;

(c) determining correlations between the categories and the characteristics of the existing customers; and

(d) targeting consumers having characteristics correlated to a particular category.

8. The method of claim 7, wherein the particular category represents preferred customers.

9. The method of claim 7, wherein the targeted consumers are prospective customers.

10. The method of claim 7, wherein the targeted consumers are existing customers.

11. The method of claim 7, wherein said step (d) comprises:

(i) estimating characteristics of individual consumers based on, for each consumer, tradeline data of the consumer, balance transfers of the consumer, and a model of consumer spending patterns;

(ii) determining a subset of consumers having characteristics similar to the characteristics correlated to a particular category; and

(iii) targeting the subset of consumers.

12. The method of claim 7, wherein consumers are targeted based on individual spend capacity.

13. An apparatus for targeted marketing, comprising:

a processor; and

a memory in communication with the processor, wherein the memory stores a plurality of processing instructions for directing the processor to;

model consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

determine spend capacities for existing customers using, for each existing customer, tradeline data of the customer, balance transfer data of the customer, and the model of consumer spending patterns; and

identify preferred customers based on spend capacity.

14. The apparatus of claim 13, wherein the processing instructions further direct the processor to:

identify characteristics common to the preferred customers; and

identify consumers having the characteristics common to the preferred customers.

15. The apparatus of claim 14, wherein the consumers are existing customers.

16. The apparatus of claim 14, wherein the consumers are potential customers.

17. The apparatus of claim 13, wherein the processing instructions further direct the processor to:

estimate a spend capacity for consumers using, for each consumer, tradeline data of the consumer, balance transfer data of the consumer, and the model of consumer spending patterns; and

identify consumers having spend capacities similar to spend capacities of the preferred consumers.

18. An apparatus for targeted marketing, comprising:

a processor; and

a memory in communication with the processor, wherein the memory stores a plurality of processing instructions for directing the processor to;

segment existing customers into categories;

model characteristics of the existing customers using individual and aggregate data of the existing customers, including tradeline data, internal customer data, and consumer panel data;

determine correlations between the categories and the characteristics of the existing customers; and

identify consumers having characteristics correlated to a particular category.

19. The apparatus of claim 18, wherein the particular category represents preferred customers.

20. The apparatus of claim 18, wherein the instructions for directing the processor to identify consumers having characteristics correlated to a particular category include instructions for directing the processor to:

estimate characteristics of individual consumers based on, for each consumer, tradeline data of the consumer, balance transfers of the consumer, and a model of consumer spending patterns; and

identify a subset of consumers having characteristics similar to the characteristics correlated to a particular category.

21. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to perform targeted marketing, the control logic comprising:

first computer readable program code means for causing the computer to model consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

second computer readable program code means for causing the computer to determine spend capacities for existing customers using, for each existing customer, tradeline data of the customer, balance transfer data of the customer, and the model of consumer spending patterns; and

third computer readable program code means for causing the computer to identify preferred customers based on spend capacity.

22. The computer program product of claim 21, the control logic further comprising:

fourth computer readable program code means for causing the computer to identify characteristics common to the preferred customers; and

fifth computer readable program code means for causing the computer to identify consumers having the characteristics common to the preferred customers.

23. The computer program product of claim 22, wherein the consumers are existing customers.

24. The computer program product of claim 22, wherein the consumers are prospective customers.

25. The computer program product of claim 21, wherein the control logic further comprises:

fourth computer readable program code means for causing the computer to estimate a spend capacity for consumers using, for each consumer, tradeline data of the consumer, balance transfer data of the consumer, and the model of consumer spending patterns; and

fifth computer readable program code means for causing the computer to identify consumers having spend capacities similar to spend capacities of the preferred consumers.

26. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to perform targeted marketing, the control logic comprising:

first computer readable program code means for causing the computer to segment existing customers into categories;

second computer readable program code means for causing the computer to model characteristics of the existing customers using individual and aggregate data of the existing customers, including tradeline data, internal customer data, and consumer panel data;

third computer readable program code means for causing the computer to determine correlations between the categories and the characteristics of the existing customers; and

fourth computer readable program code means for causing the computer to identify consumers having characteristics correlated to a particular category.

27. The computer program product of claim 26, wherein the particular category represents preferred customers.

28. The computer program product of claim 26, wherein the fourth computer readable program code means includes:

fifth computer readable program code means for causing the computer to estimate characteristics of individual consumers based on, for each consumer, tradeline data of the consumer, balance transfers of the consumer, and a model of consumer spending patterns; and

sixth computer readable program code means for causing the computer to identify a subset of consumers having characteristics similar to the characteristics correlated to a particular category.