Consumer household spend capacity

• US 10,360,575 B2
• Filed: 08/03/2017
• Issued: 07/23/2019
• Est. Priority Date: 10/29/2004
• Status: Active Grant

• Current Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Original Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Inventors: Chwast, Jeffrey A., Haggerty, Kathleen, Wang, Jing, Xu, Di W., Yuan, Chao M.
• Primary Examiner(s): Fleischer, Mark A
• Application Number:
  US15/668,543
• Publication Number:
  US 20170330210A1
• Time in Patent Office: 719 Days
• CPC Class Codes: G06Q 20/105 involving programming of a portable memory device, e.g. IC cards, "electronic purses"
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0234 Rebates after completed purchase
• CPC Class Codes: G06Q 30/0236 Incentive or reward received by requiring registration or ID from user
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer'"'"'s spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.

Claims

1. A method comprising:

extracting, by a computer-based system, spending data for a consumer from a transaction processing system internet website that processed the spending data for the consumer;

extracting, by the computer-based system, consumer panel data from consumer research companies'"'"' internet websites;

extracting, by the computer-based system, consumer tradeline data from consumer credit companies'"'"' internet websites;

modeling, by the computer-based system, consumer behavior and attributes including consumer panel data and consumer tradeline data, by using time series tradeline data and raw tradeline data;

refining, by the computer-based system, an accuracy of the modeling via continuous access to the spending data, the consumer panel data and the consumer tradeline data;

verifying, by the computer-based system and periodically on an ongoing basis, an applicability of the modeling to the consumer;

determining, by the computer-based system, a plurality of tradelines using point in time balance information;

associating, by the computer-based system, the plurality of tradelines with a household of the consumer;

calculating, by the computer-based system, a household size of wallet based on the plurality of tradelines;

identifying, by the computer-based system, a category for the consumer; and

targeting, by the computer-based system and based on the category, the consumer with at least one of a new product offer or a spend incentive.

2. The method of claim 1, wherein the extracting steps provide the continuous access to the spending data, the consumer panel data, and the consumer tradeline data.

3. The method of claim 1, wherein the extracting is performed by using an application programming interface (API) having the continuous access.

4. The method of claim 1, further comprising removing, by the computer-based system, tradelines associated with a supplemental consumer in the plurality of tradelines from consideration in calculating the household size of wallet of the consumer, to create a subset of tradelines.

5. The method of claim 4, wherein the removing is also based on comparing histories of tradelines.

6. The method of claim 4, wherein the removing is also based on comparing histories of tradelines within a time period.

7. The method of claim 4, wherein the removing comprises determining tradelines associated with the supplemental consumer based on comparing histories of tradelines.

8. The method of claim 4, wherein the tradelines associated with the supplemental consumer comprise duplicate tradelines.

9. The method of claim 4, wherein the removing is also based on comparing histories of tradelines.

10. The method of claim 9, wherein the histories of tradelines include at least one of account balance or transaction information.

11. The method of claim 4, wherein the removing results in unique tradelines being associated with the household.

12. The method of claim 4, wherein the removing results in different tradelines being associated with the household.

13. The method of claim 1, further comprising estimating, by the computer-based system, a spend capacity for each tradeline within a subset of tradelines.

14. The method of claim 1, wherein the calculating comprises summing spend capacities for each tradeline within a subset of tradelines.

15. The method of claim 1, further comprising associating the household size of wallet with the consumer.

16. The method of claim 1, further comprising targeting the consumer with the new product offer based on the household size of wallet.

17. The method of claim 1, further comprising targeting the consumer with the spend incentive for an existing product based on the household size of wallet.

18. A system, comprising:

a processor; and

a non-transitory memory communicating with the processor,

the non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising;

extracting, by the processor, spending data for a consumer from a transaction processing system internet website that processed the spending data for the consumer;

extracting, by the processor, consumer panel data from consumer research companies'"'"' internet websites;

extracting, by the processor, consumer tradeline data from consumer credit companies'"'"' internet websites;

modeling, by the processor, consumer behavior and attributes including consumer panel data and consumer tradeline data, by using time series tradeline data and raw tradeline data;

refining, by the processor, an accuracy of the modeling via continuous access to the spending data, the consumer panel data and the consumer tradeline data;

verifying, by the processor and periodically on an ongoing basis, an applicability of the modeling to the consumer;

determining, by the processor, a plurality of tradelines using point in time balance information;

associating, by the processor, the plurality of tradelines with a household of the consumer;

calculating, by the processor, a household size of wallet based on the plurality of tradelines;

identifying, by the processor, a category for the consumer; and

targeting, by the processor and based on the category, the consumer with at least one of a new product offer or a spend incentive.

19. A non-transitory computer readable storage medium bearing instructions, when executed by a processor, cause the processor to perform operations comprising:

extracting, by the processor, spending data for a consumer from a transaction processing system internet website that processed the spending data for the consumer;

extracting, by the processor, consumer panel data from consumer research companies'"'"' internet websites;

extracting, by the processor, consumer tradeline data from consumer credit companies'"'"' internet websites;

modeling, by the processor, consumer behavior and attributes including consumer panel data and consumer tradeline data, by using time series tradeline data and raw tradeline data;

refining, by the processor, an accuracy of the modeling via continuous access to the spending data, the consumer panel data and the consumer tradeline data;

verifying, by the processor and periodically on an ongoing basis, an applicability of the modeling to the consumer;

determining, by the processor, a plurality of tradelines using point in time balance information;

associating, by the processor, the plurality of tradelines with a household of the consumer;

calculating, by the processor, a household size of wallet based on the plurality of tradelines;

identifying, by the processor, a category for the consumer; and

targeting, by the processor and based on the category, the consumer with at least one of a new product offer or a spend incentive.