Purchases method and system

• US 20080306808A1
• Filed: 06/08/2007
• Published: 12/11/2008
• Est. Priority Date: 06/08/2007
• Status: Active Grant

• Current Assignee: Conopco (Unilever PLC)
• Original Assignee: Conopco (Unilever PLC)
• Inventors: Li, Ming, Dias, Malcolm Benjamin, El-Deredy, Wael, Adjali, Iqbal, Sordo-Garcia, Carmen Maria, Lisboa, Paulo Jorge Gomes
• Granted Patent: US 7,983,926 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

In an automated method for providing personalised recommendations to a user, a global probabilistic purchase model based on prior interactions of a group of users of a system, is used in the generation of personalised recommendations for future purchases for a given user. Attributes of the given user are used to identify characteristics relating to the user'"'"'s personal purchasing, correction factors are calculated to update the output of the global probabilistic purchase model to personalise the recommendations for the user.

Claims

1. An automated method for providing personalised purchase recommendations to a user, the method comprising:

generating a global probabilistic purchase model based on the purchase transactions relating to a group of customers, the model providing recommendations for future purchases as a function of the group purchase transactions;

detecting attributes of the user so as to identify personal characteristics relating to the user'"'"'s behaviour; and

calculating at least one correction factor, based on the user'"'"'s personal attributes, to update the output of the global probabilistic purchase model to personalise the recommendations for future purchases for the user.

2. A method according to claim 1 wherein the attribute detecting step inputs data on the user'"'"'s prior purchases.

3. A method according to claim 2 wherein the data on each user'"'"'s prior purchases incorporates a calculation based on the number of previous visits made.

4. A method according to claim 1 wherein the attribute detecting step inputs data on user reports.

5. A method according to claim 4 wherein the data is derived from results of a purchasing questionnaire completed by the user.

6. A method according to claim 4 wherein the data is derived from results of an opinion survey completed by the user.

7. A method according to claim 1 wherein the attribute detecting step inputs behavioural pattern data relating to the users.

8. A method according to claim 1 wherein the attribute detecting step inputs psychological measures data relating to the users.

9. A method according to claim 7 wherein the behavioural pattern data or psychological measures data includes input representing interactions between the user and a device.

10. A method according to claim 1 comprising applying the correction factor to the global probabilistic purchase model to generate a personalised recommendation list for the user.

11. A method according to claim 1 comprising enhancing the results from the personalised recommendations to incorporate data on equivalent and/or similar recommendations based on prior user documentation.

12. A method according to claim 11 wherein the data on equivalent and/or similar recommendations comprise a cost-reduction factor.

13. A method according to claim 11 wherein the data on equivalent and/or similar recommendations comprise a quality-increase factor.

14. A method according to claim 1 comprising storing data representing the user attributes and/or the correction factor at a user unit.

15. A method according to claim 1, comprising storing data relating to the global probabilistic purchase model at a central server location accessible for operation with the at least one correction factor for personalisation of recommendations.

16. An automated system for providing personalised recommendations to a user, the system comprising:

a central unit comprising a global probabilistic purchase model based on data relating to a group of users of a system, the model providing recommendations for the future purchases as a function of the group purchase transactions;

a module to detect attributes of the user so as to identify characteristics relating to the user'"'"'s personal purchasing; and

means to calculate at least one correction factor, based on the user'"'"'s attributes, to update the output of the global probabilistic purchase model to personalise the recommendations for future purchases for the user.

17. A system according to claim 16 wherein the attribute module is operable to data on the user'"'"'s prior purchases.

18. A system according to claim 17 wherein the data on user'"'"'s prior purchases incorporate a calculation based on the number of previous visits made.

19. A system according to claim 16 wherein the attribute module is operable to input data on user reports.

20. A system according to claim 19 wherein the data is derived from results of a purchasing questionnaire completed by the user.

21. A system according to claim 19 wherein the data is derived from results of an opinion survey completed by the user.

22. A system according to claim 16 wherein the attribute module is operable to input behavioural pattern data relating to the user.

23. A system according to claim 16 wherein the attribute module is operable to input psychological measures data relating to the user.

24. A system according to claim 22 wherein the behavioural pattern data or psychological measures data includes input representing interactions between the user and a device.

25. A system according to claim 16 wherein the calculator means is operable to the correction factor to the global probabilistic purchase model to generate a personalised recommendation list for the user.

26. A system according to claim 16 wherein the results from the personalised recommendation incorporate data on equivalent and/or similar recommendations based on prior user documentation.

27. A system according to claim 26 wherein the data on equivalent and/or similar recommendations comprise a cost-reduction factor.

28. A system according to claim 26 wherein the data on equivalent and/or similar recommendations comprise a quality-increase factor.

29. A system according to claim 16 comprising means to store data representing the user attributes and/or the correction factor at a user unit.

30. A system according to claim 16, comprising means to store data relating to the global probabilistic behavioural model at a central server location accessible for operation with the at least one correction factor for personalisation of recommendations.