System, method, and computer program product for increasing inventory turnover using targeted consumer offers

• US 20070179836A1
• Filed: 08/10/2006
• Published: 08/02/2007
• Est. Priority Date: 08/10/2005
• Status: Active Grant

• Current Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Original Assignee: American Express Marketing & Development Corporation (American Express Company), American Express Travel Related Services Company, Inc. (American Express Company)
• Inventors: Juang, David, Rane, Rajendra R.
• Granted Patent: US 8,335,720 B2
• 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 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0259 based on store location
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

Consumer, merchant, and transactional data from a closed loop network and external sources may be leveraged to increase demand of a merchant'"'"'s inventory during normally low-demand periods. Extensive data mining is used to determine the excess merchant inventory and demand patterns at different times and different locations for merchants and groups of merchants. Similar data mining is used to analyze cardmember demand patterns to identify the cardmember preferences regarding when and where they which to purchase goods and/or services. Cardmembers may also be grouped based on their demand patterns. Using pricing as a lever, cardholders with specific preferences are targeted to shift the demand from peak periods and locations to non-peak periods and locations, and to increase the non-peak demand by location as well as time period. Higher precision may be obtained using product level transaction data from point-of-sale terminals used by merchants wherever applicable.

Claims

1. A method for targeted marketing of consumers, comprising:

collecting merchant data, consumer data, and transaction data;

identifying a period of excess inventory of a given merchant based on the merchant data;

identifying customers likely to respond to targeted marketing for the given merchant based on the consumer data and transaction data; and

sorting the identified customers based on the likelihood of the customers to respond to targeted marketing for the given merchant.

2. The method of claim 1, wherein the step of identifying a period of excess inventory comprises:

identifying dates and times that the given merchant posts the least revenue.

3. The method of claim 1, wherein the step of identifying a period of excess inventory comprises:

identifying demand patterns of merchants having a customer base similar to a customer base of the given merchant.

4. The method of claim 1, further comprising:

contacting the given merchant to offer targeted marketing opportunities.

5. The method of claim 1, further comprising:

integrating point of sale data from the given merchant into the merchant data.

6. The method of claim 1, wherein the step of identifying customers comprises:

determining customer spending patterns related to an industry of the given merchant.

7. The method of claim 6, wherein the step of identifying customers further comprises:

analyzing the customer spending patterns based on data from at least one of a competing merchant or a complementary merchant.

8. The method of claim 1, wherein the sorting step comprises:

sorting customers according to a concentric circles algorithm.

9. The method of claim 8, wherein the sorting step comprises:

identifying transactions for the given merchant over a given period of time to determine a subperiod;

summarizing the identified transactions to include the sum of the amount spent per identified customer and the number of transactions per identified customer for the subperiod;

determining demographic information and financial information for each identified customer;

determining physical distance from each identified customer to the given merchant; and

sorting the identified customers based on the transaction summary, demographic information, financial information, and physical distance for each identified customer.

10. The method of claim 9, wherein the sorting step further comprises:

further sorting the identified customers based on volume of transactions of each identified customer within low and high demand periods.

11. The method of claim 10, wherein the sorting step further comprises:

further sorting the identified customers based on at least one of;

transactions of the identified customers with competing merchants in the vicinity and direct marketing area of the given merchant;

transactions of the identified customers with all competing merchants;

transactions of the identified customers with merchants in a same industry category as the given merchant;

or transactions of the identified customers with merchants in complementary industries.

12. The method of claim 1, wherein the sorting step comprises:

sorting customers according to a weighted scoring mechanism.

13. The method of claim 12, wherein the sorting step comprises:

assigning a weight to each of;

a transaction amount per identified customer, a number of transactions per identified customer, and physical distance from each identified customer to the given merchant;

assigning a value for each identified customer to each of;

the transaction amount per identified customer, the number of transactions per identified customer, and the physical distance from each identified customer to the given merchant;

determining a score for each customer based on the assigned weights and values; and

sorting the identified customers based on the score for each identified customer.

14. A system for targeted marketing of consumers, comprising:

a processor; and

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

collect merchant data, consumer data, and transaction data;

identify a period of excess inventory of a given merchant based on the merchant data;

identify customers likely to respond to targeted marketing for the given merchant based on the consumer data and transaction data; and

sort the identified customers based on the likelihood of the customers to respond to targeted marketing for the given merchant.

15. The system of claim 14, wherein the instructions for directing the processor to identify a period of excess inventory comprise instructions for directing the processor to:

identify dates and times that the given merchant posts the least revenue.

16. The system of claim 14, wherein the instructions for directing the processor to identify a period of excess inventory comprise the instructions for directing the processor to:

identify demand patterns of merchants having a customer base similar to a customer base of the given merchant.

17. The system of claim 14, further comprising instructions for directing the processor to:

contact the given merchant to offer targeted marketing opportunities.

18. The system of claim 14, further comprising instructions for directing the processor to:

integrate point of sale data from the given merchant into the merchant data.

19. The system of claim 14, wherein the instructions for directing the processor to identify customers comprise instructions for directing the processor to:

determine customer spending patterns related to an industry of the given merchant.

20. The system of claim 19, wherein the instructions for directing the processor to identify customers further comprise instructions for directing the processor to:

analyze the customer spending patterns based on data from at least one of a competing merchant or a complementary merchant.

21. The system of claim 14, wherein the instructions for directing the processor to sort comprise instructions for directing the processor to:

sort customers according to a concentric circles algorithm.

22. The system of claim 21, wherein the instructions for directing the processor to sort comprise instructions for directing the processor to:

identify transactions for the given merchant over a given period of time to determine a subperiod;

summarize the identified transactions to include the sum of the amount spent per identified customer and the number of transactions per identified customer for the subperiod;

determine demographic information and financial information for each identified customer;

determine physical distance from each identified customer to the given merchant; and

sort the identified customers based on the transaction summary, demographic information, financial information, and physical distance for each identified customer.

23. The system of claim 22, wherein the instructions for directing the processor to sort further comprise instructions for directing the processor to:

further sort the identified customers based on volume of transactions of each identified customer within low and high demand periods.

24. The system of claim 23, wherein the instructions for directing the processor to sort further comprise instructions for directing the processor to:

further sort the identified customers based on at least one of;

transactions of the identified customers with competing merchants in the vicinity and direct marketing area of the given merchant;

transactions of the identified customers with all competing merchants;

transactions of the identified customers with merchants in a same industry category as the given merchant;

or transactions of the identified customers with merchants in complementary industries.

25. The system of claim 14, wherein the instructions for directing the processor to sort comprise instructions for directing the processor to:

sort customers according to a weighted scoring mechanism.

26. The system of claim 25, wherein the instructions for directing the processor to sort comprise instructions for directing the processor to:

assign a weight to each of;

a transaction amount per identified customer, a number of transactions per identified customer, and physical distance from each identified customer to the given merchant;

assign a value for each identified customer to each of;

the transaction amount per identified customer, the number of transactions per identified customer, and the physical distance from each identified customer to the given merchant;

determine a score for each customer based on the assigned weights and values; and

sort the identified customers based on the score for each identified customer.