System for individualized customer interaction

• US 7,945,473 B2
• Filed: 02/28/2005
• Issued: 05/17/2011
• Est. Priority Date: 02/27/2004
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Ghani, Rayid, Cumby, Chad M., Krema, Marko, Fano, Andrew E.
• Primary Examiner(s): Weiss; John G
• Assistant Examiner(s): Hamilton; Matthew L
• Application Number:
  US11/069,472
• Publication Number:
  US 20050189415A1
• Time in Patent Office: 2,269 Days
• Field of Search: 705/14, 705/26, 705/39, 705/10, 235/383
• US Class Current: 705/14.1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• 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 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0226 Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

A method and system for using individualized customer models when operating a retail establishment is provided. The individualized customer models may be generated using statistical analysis of transaction data for the customer, thereby generating sub-models and attributes tailored to customer. The individualized customer models may be used in any aspect of a retail establishment'"'"'s operations, ranging from supply chain management issues, inventory control, promotion planning (such as selecting parameters for a promotion or simulating results of a promotion), to customer interaction (such as providing a shopping list or providing individualized promotions).

Claims

1. A product promotion system comprising:

one or more computers; and

a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising;

determining an amount of time before at least one previous sale or promotion in which to examine a customer'"'"'s acquisition behavior, and an amount of time after the previous sale or promotion in which to examine the customer'"'"'s acquisition behavior, based on an individual replenishment rate of the customer for a particular product category;

generating a hoarding attribute for the customer based on examining the customer'"'"'s acquisition behavior with respect to the at least one previous sale or promotion, by determining a quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion, during the at least one previous sale or promotion, and over the amount of time after the at least one previous sale or promotion;

suggesting at least one product of interest to the customer;

determining whether to offer a promotion for the at least one product of interest to the customer based on the hoarding attribute; and

in response to a determination to offer the promotion to the customer, generating the promotion based on the at least one product of interest and the hoarding attribute, and transmitting the promotion.

2. The product promotion system of claim 1, further comprising determining a basket variability attribute comprising information indicative of variance of spending of the customer.

3. The product promotion system of claim 2,

where the basket variability attribute comprises a variance less than average variance indicating that the customer does not have a set amount of spending from one visit to a next visit;

and

where the operations further comprise selecting a promotion which increases margin for a grocery store.

4. The product promotion system of claim 2,

where the basket variability attribute comprises a variance less than average variance indicating that the customer does not have a set amount of spending from one visit to a next visit;

and

where the operations further comprise selecting a promotion which increases spending of the customer.

5. The product promotion system of claim 1, further comprising determining a sensitivity index indicative of a percentage of change in quantity of products of the product category acquired during a sale, over or under a quantity of products of the product category acquired when there is no sale.

6. The product promotion system of claim 1, further comprising determining a brand loyalty attribute indicative of loyalty of the customer to a brand related to the product of interest.

7. The product promotion system of claim 1, further comprising determining a wallet share attribute indicative of categories of products which are purchased by the customer above a predetermined level.

8. The product promotion system of claim 1, wherein the operations further comprise generating billing information of the product.

9. The product promotion system of claim 1, wherein the operations further comprise identifying the customer as a good hoarder or a bad hoarder based on comparing the quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion or the quantity of products of the particular product category purchased by the customer over the amount of time after the at least one previous sale or promotion, with the quantity of products of the particular product category purchased by the customer during the at least one sale or promotion.

10. The product promotion system of claim 9, wherein the operations further comprise:

determining, responsive to an identification of the customer as a bad hoarder, whether to offer a promotion to the customer by calculating an amount of revenue lost due to the promotion, and

determining whether the revenue lost outweighs benefits of providing the promotion.

11. The product promotion system of claims 9, wherein the customer is identified as a good hoarder or a bad hoarder by at least:

identifying the customer as a good hoarder when the quantity of the products of the particular product category purchased by the customer during the amount of time after the at least one previous sale or promotion is less than or equal to the quantity of the products of the particular product category purchased by the customer over amount of time before the at least one previous sale or promotion.

12. The product promotion system of claim 9, where, responsive to an identification of the customer as a bad hoarder, the operations further comprise calculating an amount of revenue lost due to the at least one previous sale or promotion and identifying benefits of offering the promotion.

13. The product promotion system of claim 12, where determining whether to offer the promotion comprises comparing the amount of revenue lost due to the at least one previous sale or promotion to the benefits of offering the promotion.

14. The product promotion system of claim 9, where identifying the customer as a good hoarder or a bad hoarder comprises:

comparing a pre-sale quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion to a post-sale quantity of products of the particular product category purchased by the customer over the amount of time after the at least one previous sale or promotion;

identifying the customer as a bad hoarder when the pre-sale quantity is greater than or equal to the post-sale quantity; and

identifying the customer as a good hoarder when the pre-sale quantity is less than the post-sale quantity.

26. The system of claim 1, wherein the amount of time before the previous sale or promotion and the amount of time after the previous sale or promotion are expressed as a quantity of replenishment cycles.

15. A computer-implemented method comprising:

determining an amount of time before at least one previous sale or promotion in which to examine a customer'"'"'s acquisition behavior, and an amount of time after the previous sale or promotion in which to examine the customer'"'"'s acquisition behavior, based on an individual replenishment rate of the customer for a particular product category;

generating a hoarding attribute for the customer based on examining the customer'"'"'s acquisition behavior with respect to the at least one previous sale or promotion, by determining a quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion, during the at least one previous sale or promotion and over the amount of time after the at least one previous sale or promotion;

suggesting at least one product of interest associated with the particular product category to the customer;

determining, by one or more computers, whether to offer a promotion for the at least one product of interest based on the hoarding attribute; and

responsive to a determination to offer the promotion to the customer;

generating the promotion based on the hoarding attribute and the at least one product of interest; and

transmitting, by one or more of the computers, the promotion to an output device associated with the customer.

16. The method of claim 15, where generating a hoarding attribute comprises identifying the customer as a good hoarder or a bad hoarder based on comparing the quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion or the quantity of products of the particular product category purchased by the customer over the amount of time after the at least one previous sale or promotion, with the quantity of products of the particular product category purchased by the customer during the at least one sale or promotion.

17. The method of claim 15, where identifying the customer as a good hoarder or a bad hoarder comprises:

identifying the customer as a good hoarder when the quantity of the products of the particular product category purchased by the customer during the at least one previous sale or promotion and over the amount of time after the at least one previous sale or promotion is greater than the quantity of the products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion, and

identifying the customer as a bad hoarder when the quantity of the products of the particular product category purchased by the customer during the at least one previous sale or promotion and during the period of time after the at least one previous sale or promotion is less than or equal to the quantity of the products of the particular product category purchased by the customer during the amount of time before the at least one previous sale or promotion.

18. The method of claim 15, further comprising, responsive to an identification of the customer as a bad hoarder, calculating an amount of revenue lost due to the at least one previous sale or promotion and identifying benefits of offering the promotion.

19. The method of claim 18, where, responsive to an identification of the customer as a bad hoarder, determining whether to offer the promotion further comprises comparing the amount of revenue lost due to the at least one previous sale or promotion to the benefits of offering the promotion.

20. The method of claim 15, where identifying the customer as a good hoarder or a bad hoarder comprises:

comparing a pre-sale quantity of the products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion to a post-sale quantity of the products of the particular product category purchased by the customer over the amount of time after the at least one previous sale or promotion;

identifying the customer as a bad hoarder when the pre-sale quantity is greater than or equal to the post-sale quantity; and

identifying the customer as a good hoarder when the pre-sale quantity is less than the post-sale quantity.

21. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

determining an amount of time before at least one previous sale or promotion in which to examine a customer'"'"'s acquisition behavior, and an amount of time after the previous sale or promotion in which to examine the customer'"'"'s acquisition behavior, based on an individual replenishment rate of the customer for a particular product category;

generating a hoarding attribute for the customer based on examining the customer'"'"'s acquisition behavior with respect to the at least one previous sale or promotion, by determining a quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion, during the at least one previous sale or promotion and over the amount of time after the at least one previous sale or promotion;

suggesting at least one product of interest associated with the particular product category to the customer;

determining, by one or more computers, whether to offer a promotion for the at least one product of interest based on the hoarding attribute; and

responsive to a determination to offer the promotion to the customer;

generating the promotion based on the hoarding attribute and the at least one product of interest; and

transmitting the promotion to the customer using an output device.

22. The computer storage medium of claim 21, where generating a hoarding attribute comprises identifying the customer as a good hoarder or a bad hoarder based on comparing the quantity of products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion or the quantity of products of the particular product category purchased by the customer over the amount of time after the at least one previous sale or promotion, with the quantity of products of the particular product category purchased by the customer during the at least one sale or promotion.

23. The computer storage medium of claim 21, where generating a hoarding attribute comprises

identifying the customer as a good hoarder when the quantity of products of the particular product category purchased by the customer during the at least one previous sale or promotion and over the amount of time after the at least one previous sale or promotion is greater than a quantity of the products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion, and

identifying the customer as a bad hoarder when the quantity of the products of the particular product category purchased by the customer during the at least one previous sale or promotion and over the amount of time after the at least one previous sale or promotion is less than or equal to the quantity of the products of the particular product category purchased by the customer over the amount of time before the at least one previous sale or promotion.

24. The computer storage medium of claim 21, further comprising, responsive to an identification of the customer as a bad hoarder, calculating an amount of revenue lost due to the at least one previous sale or promotion and identifying benefits of offering the promotion.

25. The computer storage medium of claim 24, where determining, responsive to an identification of the customer as a bad hoarder, whether to offer the promotion based is further on a comparison of the amount of revenue lost due to the at least one previous sale or promotion to the benefits of offering the promotion.