Analytical E-Commerce Processing System And Methods

• US 20080162268A1
• Filed: 11/21/2007
• Published: 07/03/2008
• Est. Priority Date: 11/22/2006
• Status: Active Grant

• Current Assignee: Proclivity Media, Inc.
• Original Assignee: Proclivity Media, Inc.
• Inventors: Gilbert, Sheldon
• Granted Patent: US 8,032,405 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 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. This Abstract is provided for the sole purpose of complying with the rules that allow a reader to quickly ascertain the subject matter of the disclosure contained herein. This Abstract is submitted with the explicit understanding that it will not be used to interpret or to limit the scope or the meaning of the claims.

Claims

1. A method of producing an e-commerce report comprising:

modeling an aggregate set of at least one affinity score from a plurality of information, the plurality of information comprising information selected from a group consisting of information on products at varying resolutions, information on potential customers, and information on events at various times;

calculating a buying probability from the at least one affinity score; and

producing an e-commerce report from the buying probability.

2. The method of claim 1, wherein modeling the aggregate set of at least one affinity score comprises:

generating a customer-product interaction score;

generating a customer-product recency score; and

generating a customer-product recent event classification.

3. The method of claim 1, wherein modeling the aggregate set of at least one affinity score comprises:

generating an aggregate customer-product interaction event-type classification;

generating an aggregate customer-product interaction recency classification; and

generating an aggregate customer-product interaction frequency classification.

4. The method of claim 1, further comprising generating a run-time set of affinity scores for a plurality of customers, wherein the generation of a run-time set of affinity scores comprises:

generating a customer-product interaction score for at least one of the plurality of customers and at least one of a plurality of products;

generating a customer-product recency score for the at least one of the plurality of customers and the at least one of a plurality of products; and

generating a customer-product recent event classification for the at least one of the plurality of customers and the at least one of the products.

5. The method of claim 3, further comprising generating a run-time set of affinity scores for a plurality of customers, wherein generating the run-time set comprises:

generating a run-time customer-product interaction event-type classification from the aggregate customer-product interaction event-type classification;

generating a run-time customer-product interaction recency classification from the aggregate customer-product interaction recency classification; and

generating a run-time customer-product interaction frequency classification from the aggregate customer-product interaction frequency classification.

6. The method of claim 1, wherein the calculating a buying probability comprises calculating a probability using a Hierarchical Bayesian calculation.

7. The method of claim 1, wherein the aggregate set of affinity scores comprises product-centric affinity scores that indicate a plurality of customer'"'"'s affinity for a product.

8. The method of claim 1, wherein the aggregate set of affinity scores comprises a customer-centric affinity scores that indicate customer'"'"'s affinity for a plurality of products.

9. A computing apparatus comprising:

a processor;

a memory communicating with the processor; and

a storage medium, communicating with the processor, the storage medium comprising a set of processor executable instructions that, when executed by the processor configure the computing apparatus to;

model an aggregate set of at least one affinity score from a plurality of information, the plurality of information comprising information selected from a group consisting of information on products at varying resolutions, information on potential customers, and information on events at various times;

calculate a buying probability from the at least one affinity score; and

produce an e-commerce report from the buying probability.

12. The computing apparatus of claim 9, wherein the configuration further comprises a configuration to generate a run-time set of affinity scores for a plurality of customers, wherein the configuration for generation of a run-time set of affinity scores comprises a configuration to:

generate a customer-product interaction score for at least one of the plurality of customers and at least one of a plurality of products;

generate a customer-product recency score for the at least one of the plurality of customers and the at least one of a plurality of products; and

generate a customer-product recent event classification for the at least one of the plurality of customers and the at least one of the products.

14. The computing apparatus of claim 9, wherein the configuration to calculate a buying probability comprises a configuration to calculate a probability using a Hierarchical Bayesian calculation.

15. The computing apparatus of claim 9, wherein the aggregate set of affinity scores comprises product-centric affinity scores that indicate a plurality of customer'"'"'s affinity for a product.

16. The computing apparatus of claim 9, wherein the aggregate set of affinity scores comprises a customer-centric affinity scores that indicate customer'"'"'s affinity for a plurality of products.

10. The computing apparatus 9, wherein the configuration for modeling the aggregate set of at least one affinity score comprises a configuration to:

generate a customer-product interaction score;

generate a customer-product recency score; and

generate a customer-product recent event classification.

11. The computing apparatus 9, wherein the configuration for modeling the aggregate set of at least one affinity score comprises a configuration to

generate an aggregate customer-product interaction event-type classification;

generate an aggregate customer-product interaction recency classification; and

generate an aggregate customer-product interaction frequency classification.

13. The computing apparatus of claim 11, wherein the configuration further comprises a configuration to generate a run-time set of affinity scores for a plurality of customers, wherein the configuration for generation of a run-time set of affinity scores comprises a configuration to:

generate a run-time customer-product interaction event-type classification from the aggregate customer-product interaction event-type classification;

generate a run-time customer-product interaction recency classification from the aggregate customer-product interaction recency classification; and

generate a run-time customer-product interaction frequency classification from the aggregate customer-product interaction frequency classification.

17. A computer software product comprising

a storage medium comprising a set of processor executable instructions that, when executed by a processor configure a computing apparatus to:

model an aggregate set of at least one affinity score from a plurality of information, the plurality of information comprising information selected from a group consisting of information on products at varying resolutions, information on potential customers, and information on events at various times;

calculate a buying probability from the at least one affinity score; and

produce an e-commerce report from the buying probability.

18. The computer software product of claim 17, wherein the configuration for modeling the aggregate set of at least one affinity score comprises a configuration to:

generate a customer-product interaction score;

generate a customer-product recency score; and

generate a customer-product recent event classification.

19. The computer software product of claim 17, wherein the configuration for modeling the aggregate set of at least one affinity score comprises a configuration to:

generate an aggregate customer-product interaction event-type classification;

generate an aggregate customer-product interaction recency classification; and

generate an aggregate customer-product interaction frequency classification.

20. The computer software product of claim 17, wherein the configuration further comprises a configuration to generate a run-time set of affinity scores for a plurality of customers, wherein the configuration for generation of a run-time set of affinity scores comprises a configuration to:

generate a customer-product interaction score for at least one of the plurality of customers and at least one of a plurality of products;

generate a customer-product recency score for the at least one of the plurality of customers and the at least one of a plurality of products; and

generate a customer-product recent event classification for the at least one of the plurality of customers and the at least one of the products.

21. The computer software product of claim 19, wherein the configuration further comprises a configuration to generate a run-time set of affinity scores for a plurality of customers, wherein the configuration for generation of a run-time set of affinity scores comprises a configuration to:

generate a run-time customer-product interaction event-type classification from the aggregate customer-product interaction event-type classification;

generate a run-time customer-product interaction recency classification from the aggregate customer-product interaction recency classification; and

generate a run-time customer-product interaction frequency classification from the aggregate customer-product interaction frequency classification.

22. The computer software product of claim 17, wherein the configuration to calculate a buying probability comprises a configuration to calculate a probability using a Hierarchical Bayesian calculation.

23. The computer software product of claim 17, wherein the aggregate set of affinity scores comprises product-centric affinity scores that indicate a plurality of customer'"'"'s affinity for a product.

24. The computer software product of claim 17, wherein the aggregate set of affinity scores comprises a customer-centric affinity scores that indicate customer'"'"'s affinity for a plurality of products.

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.