System and method for providing E-commerce consumer-based behavioral target marketing reports
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.
1. A computer-implemented method of producing an e-commerce report comprising:
providing a user interface on a computing apparatus, the user interface configured to allow a user to customize an e-commerce report;
providing a model in the form of an aggregate set of at least one affinity score from 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;
generating a run-time set of affinity scores by extrapolation from at least one of the aggregate set;
calculating a buying probability from at least one of the run-time set of affinity scores; and
producing an e-commerce report from the buying probability, wherein the report comprises projected revenues, and is accessible to the user via the computing apparatus.
2. The method of claim 1 , wherein the e-commerce report comprises a report selected from a group consisting of: an advertising campaign, a revenue forecast report, an inventory predication report, a supply chain report, a product pricing report, a product demand report, a customer-centric product affinity report, and a product-centric product affinity report.
3. The method of claim 1 , wherein the providing of the model, the calculation of the buying probability, and the production of the e-commerce report are accomplished by an analytical processing system, the system comprising:
a data collection module;
a predictive transaction module;
a schema management module;
a behavioral scoring module;
a customer and product intelligence module;
a recommendation engine; and
a report generation module.
4. The method of claim 3 , wherein the behavioral scoring module and customer and product intelligence modules generate the aggregate set of at least one affinity score by:
generating a customer-product interaction score;
generating a customer-product recency score; and
generating a customer-product recent event classification.
5. The method of claim 3 , wherein the behavioral scoring module and the customer and product intelligence modules generate the aggregate set of at least one affinity score by:
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.
6. The method of claim 3 , wherein the behavioral scoring module and customer and product intelligence modules generate a run-time set of affinity scores for a plurality of customers.
7. The method of claim 6 , 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.
8. The method of claim 6 wherein the aggregate set of affinity scores and the run-time set of affinity scores comprise scores selected from a group consisting of: product-centric scores that indicate a plurality of customer's affinity for a product, and 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, the storage medium comprising a set of processor executable instructions that, when executed by the processor configure the computing apparatus to:
provide a user interface, the user interface configured to allow a user to customize an e-commerce report;
provide a model in the form of an aggregate set of at least one affinity score from 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;
generate a run-time set of affinity scores by extrapolation from at least one of the aggregate set;
calculating a buying probability from at least one of the run-time set of affinity scores; and
produce an e-commerce report from the buying probability, wherein the report comprises projected revenues.
10. The computing apparatus of claim 9 , wherein the e-commerce report comprises a report selected from a group consisting of: an advertising campaign, a revenue forecast report, an inventory predication report, a supply chain report, and a product pricing report, a product demand report, a customer-centric product affinity report, and a product-centric product affinity report.
11. The computing apparatus of claim 9 , wherein the configuration for providing the model, the calculation of the buying probability, and the production of the e-commerce report are accomplished by an analytical processing system, the system comprising: a data collection module; a predictive transaction module; a schema management module; a behavioral scoring module; a customer and product intelligence module; a recommendation engine; and a report generation module.
12. The computing apparatus of claim 11 , wherein the behavioral scoring module and customer and product intelligence modules generate the aggregate set of at least one affinity score by: generating a customer-product interaction score; generating a customer-product recency score; and generating a customer-product recent event classification.
13. The computing apparatus of claim 11 , wherein the behavioral scoring module and the customer and product intelligence modules generate the aggregate set of at least one affinity score by:
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.
14. The computing apparatus of claim 11 , wherein the behavioral scoring module and customer and product intelligence modules generate a run-time set of affinity scores for a plurality of customers.
15. The computing apparatus of claim 14 , wherein the configuration to generate 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.
16. The computing apparatus of claim 11 , wherein the aggregate set of affinity scores and the run-time set of affinity scores comprise scores selected from a group consisting of: product-centric scores that indicate a plurality of customer's affinity for a product, and customer-centric affinity scores that indicate customer's affinity for a plurality of products.
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:
provide a user interface, the user interface configured to allow a user to customize an e-commerce report;
provide a model in the form of an aggregate set of at least one affinity score from 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;
generate a run-time set of affinity scores by extrapolation from at least one the aggregate set;
calculating a buying probability from at least one of the run-time set of affinity scores; and
produce an e-commerce report from the buying probability, wherein the report comprises projected revenues.
18. The computer software product of claim 17 , wherein the e-commerce report comprises a report selected from a group consisting of: an advertising campaign, a revenue forecast report, an inventory predication report, a supply chain report, and a product pricing report, a product demand report, a customer-centric product affinity report, and a product-centric product affinity report.
19. The computer software product of claim 17 , wherein the configuration for providing the model, the calculation of the buying probability, and the production of the e-commerce report are accomplished by an analytical processing system, the system comprising:
a data collection module;
a predictive transaction module;
a schema management module;
a behavioral scoring module;
a customer and product intelligence module;
a recommendation engine; and
a report generation module.
20. The computer software product of claim 19 , wherein the behavioral scoring module and customer and product intelligence modules generate the aggregate set of at least one affinity score by: generating a customer-product interaction score; generating a customer-product recency score; and generating a customer-product recent event classification.
21. The computer software product of claim 19 , wherein the behavioral scoring module and the customer and product intelligence modules generate the aggregate set of at least one affinity score by:
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.
22. The computer software product of claim 19 , wherein the behavioral scoring module and customer and product intelligence modules generate a run-time set of affinity scores for a plurality of customers.
23. The computer software product of claim 22 , wherein the configuration to generate 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.
24. The computer software product of claim 19 , wherein the aggregate set of affinity scores and the run-time set of affinity scores comprise scores selected from a group consisting of: product-centric scores that indicate a plurality of customer's affinity for a product, and 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.