System for individualized customer interaction

• US 20050189415A1
• Filed: 02/28/2005
• Published: 09/01/2005
• 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, Fano, Andrew E., Cumby, Chad M., Krema, Marko
• Granted Patent: US 7,945,473 B2
• Time in Patent Office: Days
• US Class Current: 235/383
• 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. System for individualized communication for a customer comprising:

a customer model creation component configured to create at least a part of the customer model for the customer by statistically analyzing transaction data solely from the customer;

a customer identification component configured to determine the identity of the customer; and

customer communication component configured to access the customer model and the identity of the customer and to determine a content of a communication based on the at least a part of the customer model.

2. The system for individualized communication of claim 1, where the part of the customer model comprises a shopping list prediction sub-model statistically derived from grocery store transactions of the customer;

and

where the content of the communication comprises a predicted shopping list for the customer based on the shopping list prediction sub-model.

3. The system for individualized communication of claim 1, where the part of the customer model comprises a promotion attribute;

and

where the content of the communication comprises a promotion for a product determined based on the promotion attribute.

4. The system for individualized communication of claim 3, where the promotion attribute is selected from the group consisting of behavior analysis, brand loyalty, wallet share, price sensitivity, promotion sensitivity, product category substitution, and basket variability.

5. A shopping list prediction system comprising:

a shopping list computing device configured to communicate with a server;

a customer identification component associated with the shopping list computing device configured to identify a customer;

a shopping list prediction component configured to access a transaction database on the server containing at least one transactional data record associated with the customer, where the shopping list prediction component is configured to generate a proposed shopping list based on a statistical analysis of the transactional data record associated with the customer; and

a display component configured to display the proposed shopping list on the shopping list computing device.

6. The shopping list prediction system of claim 5, further comprising a customer model, the customer model comprising a shopping list sub-model being derived from customer data consisting of transaction data in the transaction database associated with the customer;

and

where the shopping list prediction component generates the proposed shopping list based on the shopping list sub-model.

7. The shopping list prediction system of claim 6, where the shopping list sub-model comprises at least one prediction for each of a plurality of product categories.

8. The shopping list prediction system of claim 7, where the shopping list sub-model comprises multiple predictions for each of the plurality of product categories;

and

where the shopping list prediction component generates the shopping list for the plurality of product categories by selecting at least one of the multiple predictions.

9. The shopping list prediction system of claim 8, where at least one prediction is a rule-based prediction methodology and at least one prediction is a machine learning methodology;

and

where the shopping list prediction component comprises a hybrid prediction component to determine a prediction based on the rule-based prediction methodology or the machine learning methodology in generating the shopping list for the plurality of products.

10. The shopping list prediction system of claim 5, where the customer identification component further identifies a current context;

and

where the shopping list prediction component generates a grocery shopping list based on the current context.

11. The shopping list prediction system of claim 10, where the current context is selected from the group consisting of day, date, and time.

12. The shopping list prediction system of claim 10, where the shopping list prediction component generates the proposed grocery shopping list solely based on the transactional data record associated with the customer and on the current context.

13. The shopping list prediction system of claim 6, where the customer model comprises promotion attributes, the promotion attributes being derived from customer data consisting of transaction data in the transaction database associated with the customer;

and

further comprising an individualized application component configured to generate at least one promotion associated with at least one product on the proposed shopping list, the promotion being based on at least one of the promotion attributes.

14. The shopping list prediction system of claim 13, where the promotion attributes are selected from the group consisting of behavior analysis, brand loyalty, wallet share, price sensitivity, promotion sensitivity, product category substitution, and basket variability.

15. The shopping list prediction system of claim 13, further comprising a billing component configured to generate billing information for the at least one promotion.

16. The shopping list prediction system of claim 15, where the billing information comprises a fee;

and

where the fee is based on at least one attribute from the customer model of the customer who received the at least one promotion.

17. The shopping list prediction system of claim 5, where the shopping list computing device comprises a shopping cart computing device.

18. The shopping list prediction system of claim 5, where the customer logins into the customer identification component at a current context;

and

where the proposed shopping list is predicted based on the current context.

19. The shopping list prediction system of claim 5, where the customer identification component comprises a mobile device selected from the group consisting of a touch screen and a bar code scanner.

where the proposed shopping list is predicted based on the current context.

20. The shopping list prediction system of claim 5, further comprising a customer locating component configured to locate the customer in an aisle of a retail establishment;

and

where the display displays product categories on the proposed shopping list which are located the aisle of the retail establishment.

21. A product promotion system comprising:

a customer identification component configured to identify a customer;

a customer model comprising a plurality of promotion attributes, the promotion attributes being derived from customer data consisting of transaction data associated with the customer;

a product prediction component configured to suggest at least one product of interest to the customer;

an output device; and

a promotion computing device in communication with the customer identification component and the product prediction component, the promotion computing device accessing the customer model, generating a promotion based on at least one of the promotion attributes and the product of interest to the customer, and transmitting the promotion to the output device.

22. The product promotion system of claim 21, where the promotion attributes are selected from the group consisting of behavior analysis, brand loyalty, wallet share, price sensitivity, promotion sensitivity, product category substitution, and basket variability.

23. The product promotion system of claim 21, where the promotion attribute comprises a hoarding attribute indicative of hoarding tendencies of the customer;

and

where the promotion computing device accesses the hoarding attribute of the customer in determining whether to provide a promotion for the product of interest.

24. The product promotion system of claim 23, where the hoarding attribute is indicative of hoarding by the customer;

and

where the promotion computing device withholds the promotion for the product of interest if the hoarding attribute indicates at least a predetermined level of hoarding by the customer.

25. The product promotion system of claim 21, where the promotion attribute comprises a basket variability attribute indicative of variance of spending of the customer;

and

where the promotion computing device accesses the basket variability attribute of the customer in determining what type of promotion for the product of interest.

26. The product promotion system of claim 25, where the basket variability attribute indicates a low variance;

and

where the promotion computing device selects a promotion which increases margin for a grocery store.

27. The product promotion system of claim 25, where the basket variability attribute indicates a high variance;

and

where the promotion computing device selects a promotion which increases spending of the consumer.

28. The product promotion system of claim 21, where the promotion attribute comprises a price sensitivity attribute indicative of sensitivity of the customer to price of the product of interest;

and

where the promotion computing device accesses the price sensitivity attribute of the customer in determining what type of promotion for the product of interest.

29. The product promotion system of claim 21, where the promotion attribute comprises a brand loyalty attribute indicative of loyalty of the customer to a brand related to the product of interest;

and

where the promotion computing device accesses the brand loyalty attribute of the customer in determining whether to offer a promotion to induce a brand switch to the product of interest.

30. The product promotion system of claim 21, where the promotion attribute comprises a wallet share attribute indicative of categories of products which are purchased by the customer above a predetermined level;

and

where the promotion computing device accesses the wallet share attribute of the customer in determining whether to offer a promotion for the product of interest.

31. The product promotion system of claim 21, where the product prediction component comprises a shopping list prediction component.

32. The product promotion system of claim 21, further comprising a billing component configured to generate billing information for the promotion.

33. The product promotion system of claim 32, where the billing information comprises a fee;

and

where the fee is based on at least one attribute from the customer model of the customer who received the at least one promotion.

34. The product promotion system of claim 32, where the billing information comprises a fee;

and

where the fee is based on goals of the promotion.

35. The product promotion system of claim 32, where the billing information comprises a fee;

and

where the fee is based on an outcome of the promotion.

36. The product promotion system of claim 35, where the outcome is selected from the group consisting of a brand switch, a brand extension, and a new trial.