System and method for providing e-commerce consumer-based behavioral target marketing reports

• US 8,027,864 B2
• Filed: 11/21/2007
• Issued: 09/27/2011
• Est. Priority Date: 11/22/2006
• Status: Active Grant

• Current Assignee: Proclivity Media, Inc.
• Original Assignee: Proclivity Systems, Inc.
• Inventors: Gilbert, Sheldon
• Primary Examiner(s): DIXON, THOMAS A
• Application Number:
  US11/944,357
• Publication Number:
  US 20080162574A1
• Time in Patent Office: 1,406 Days
• Field of Search: 705/7.31
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• 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

Abstract

A system and methods which enable modeling of end consumer interests based on online activity and producing e-commerce reports is described. The method includes scoring and classifying interests and preferences of consumers in relation to various items being offered as function of time and utilizing such scores to predict purchasing activity and revenue yield for n-dimensional combinations of interest for generation of consumer lists for target marketing and merchandising. The method also includes converse modeling of the performance and behavioral profile of items offered as a function of consumer activity.

Claims

1. A computer-implemented method of producing a e-commerce report comprising:

deriving, using a computing apparatus, a multi-dimensional, multi-resolution, de-normalized interaction table;

populating, using the computing apparatus, the interaction table with a plurality of information, the plurality of information comprising;

information on products at varying resolutions, information on potential customers, and information on events at various times;

deriving e-commerce consumer and merchandise predictive behavioral models from the interaction table; and

deriving an e-commerce report from the models, wherein the report comprises projected revenue and is accessible to a user via the computing apparatus.

2. The method of claim 1, wherein the information on products at varying resolutions comprises information selected from a group consisting of:

a product attribute map, a product entity, a stock keeping unit attribute map and a stock keeping unit entity.

3. The method of claim 2, wherein the product attribute map comprises an attribute identification, and an attribute name and wherein the product entity comprises a product identification, an attribute identification and an attribute value.

4. The method of claim 1, wherein the information on potential customers comprises information selected from a group consisting of a customer attribute map and a customer entity.

5. The method of claim 4, wherein the customer attribute map comprises an attribute identification and a attribute name, and wherein the customer entity comprises a customer identification an attribute identification, and an attribute value.

6. The method of claim 1, wherein the information on events is derived from clickstream data.

7. The method of claim 1, wherein the information on events comprises information selected from a group consisting of a product view event, a cart insert event, and an order data event.

8. The method of claim 1, wherein the interaction table further comprises a date associated with an event.

9. A computing apparatus comprising:

a processor;

a memory;

a storage medium, wherein the storage medium contains processor executable instructions that, when executed by the processor configure the computing apparatus to;

derive a multi-dimensional, multi-resolution, de-normalized interaction table;

populate the interaction table with a plurality of information, the plurality of information comprising;

information on products at varying resolutions, information on potential customers, and information on events at various times;

derive e-commerce consumer and merchandise predictive behavioral models from the interaction table; and

derive an e-commerce report from the models, wherein the report comprises projected revenue.

10. The computing apparatus of claim 9, wherein the information on products at varying resolutions comprises information selected from a group consisting of:

a product attribute map, a product entity, a stock keeping unit attribute map and a stock keeping unit entity.

11. The computing apparatus of claim 10, wherein the product attribute map comprises an attribute identification, and an attribute name and wherein the product entity comprises a product identification, an attribute identification and an attribute value.

12. The computing apparatus of claim 9, wherein the information on potential customers comprises information selected from a group consisting of a customer attribute map and a customer entity.

13. The computing apparatus of claim 12, wherein the customer attribute map comprises an attribute identification and a attribute name, and wherein the customer entity comprises a customer identification an attribute identification, and an attribute value.

14. The computing apparatus of claim 9, wherein the information on events is derived from clickstream data.

15. The computing apparatus of claim 9, wherein the information on events comprises information selected from a group consisting of a product view event, a cart insert event, and an order data event.

16. The computing apparatus of claim 9, wherein the interaction table further comprises a date associated with an event.

17. A computer software product comprising:

a storage medium, wherein the storage medium contains processor executable instructions that, when executed by a processor configure a computing apparatus to;

derive a multi-dimensional, multi-resolution, de-normalized interaction table;

populate the interaction table with a plurality of information, the plurality of information comprising;

information on products at varying resolutions, information on potential customers, and information on events at various times;

derive e-commerce consumer and merchandise predictive behavioral models from the interaction table; and

derive an e-commerce report from the models, wherein the report comprises projected revenue.

18. The computer software product of claim 17, wherein the information on products at varying resolutions comprises information selected from a group consisting of:

a product attribute map, a product entity, a stock keeping unit attribute map and a stock keeping unit entity.

19. The computer software product of claim 18, wherein the product attribute map comprises an attribute identification, and an attribute name and wherein the product entity comprises a product identification, an attribute identification and an attribute value.

20. The computer software product of claim 17, wherein the information on potential customers comprises information selected from a group consisting of a customer attribute map and a customer entity.

21. The computer software product of claim 20, wherein the customer attribute map comprises an attribute identification and a attribute name, and wherein the customer entity comprises a customer identification, an attribute identification, and an attribute value.

22. The computer software product of claim 17, wherein the information on events is derived from clickstream data.

23. The computer software product of claim 17, wherein the information on events comprises information selected from a group consisting of a product view event, a cart insert event, and an order data event.

24. The computer software product of claim 17, wherein the interaction table further comprises a date associated with an event.

25. The computer software product of claim 17, wherein the storage medium is located on an apparatus on a network remote from the computing apparatus.