METHOD AND SYSTEM FOR CREATING A PREDICTIVE MODEL FOR TARGETING WEB-PAGE TO A SURFER

• US 20110246406A1
• Filed: 06/10/2011
• Published: 10/06/2011
• Est. Priority Date: 07/25/2008
• Status: Active Grant

• Current Assignee: Amedesa, Ltd., LivePerson Incorporated
• Original Assignee: LivePerson Incorporated
• Inventors: Lahav, Shlomo, Ron, Ofer
• Granted Patent: US 8,762,313 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system and a method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects. Each web-page can include one or more links to be associated with content objects from the group. For each content object presented over a requested web-page, one or more predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content according to the preferences of the site owner. Each predicted model can be associated with a key-performance indicator (KPI). Further, a predictive model can reflect the number of times the surfer requested the web page during the surfer'"'"'s visit.

Claims

27. A non-transitory storage medium readable by a processor and storing instructions for execution by the processor, when executed by the processor, performs a method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects, the method comprising:

collecting information from a plurality of requests to the web-page that were responded by a web-page that included the object and collecting information on the results of presenting the object;

defining a plurality of predictive factors to be extracted from the collected information;

extracting, per each collected request, a value per each predictive factor;

defining one or more bins, wherein each bin is associated with a range of values of one or more predictive factors, and wherein the bins are defined based on their explained variance;

creating a bins table by defining per each collected request a value per each bin; and

processing the bins table with a predictive model engine configured to generate a predictive model based on data stored in the bins table.

1. A method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects, the method comprising:

collecting information from a plurality of requests to the web-page that were responded by a web-page that included the object and collecting information on the results of presenting the object;

defining a plurality of predictive factors to be extracted from the collected information;

extracting, per each collected request, a value per each predictive factor;

defining one or more bins, wherein each bin is associated with a range of values of one or more predictive factors, and wherein the bins are defined based on their explained variance;

creating a bins table by defining per each collected request a value per each bin; and

processing the bins table with a predictive model engine configured to generate a predictive model based on data stored in the bins table.

2. The method of claim 1, wherein the object is an image.

3. The method of claim 1, wherein the action of extracting, per each collected request, a value per each predictive factor further comprising converting non-ordinal values of predictive factor into ordinal values of predictive factor.

4. The method of claim 3, wherein the action of converting non-ordinal values of predictive factor into ordinal values is based on the distribution of the values.

5. The method of claim 1, wherein the action of converting the information into a plurality of predictive factors further comprises converting the configuration of web-pages that were transmitted in response to requests into one or more predictive factors.

6. The method of claim 1, wherein the prediction algorithm used by the predictive model engine to generate a predictive model is selected from a group consisting of:

logistic regression, linear regression and decision tree analysis.

7. The method of claim 1, wherein the collected information is embedded with the requests to the web pages.

8. The method of claim 7, wherein one or more cookies are embedded with the request and the information is collected also from the one or more cookies.

9. The method of claim 1, wherein the information comprises nominal values.

10. The method of claim 9, wherein the information represents one or more of:

a surfer'"'"'s IP address;

a receipt time for a request;

a communication application used by a surfer;

or

a URL that is associated with a request.

11. The method of claim 1, wherein the collected information further comprising a surfer'"'"'s past behavior.

12. The method of claim 11, wherein the collected information is time dependent.

13. The method of claim 11, wherein the collected information represents one or more of:

time of one or more previous visits by a surfer to a domain that includes a requested web-page;

time that a surfer requested a certain web-page;

the number of visits of a surfer in a domain;

or

the number of selections by a surfer of a certain object.

14. The method of claim 1, wherein the actions of defining one or more bins and processing the bins table with a predictive model engine configured to generate a predictive model include the use of properties'"'"' set selected from a group of properties'"'"' sets.

15. The method of claim 14, wherein the actions of defining one or more bins and processing the bins table with a predictive model engine configured to generate a predictive model are repeated per each properties'"'"' set in the group of properties'"'"' sets for creating a group of predictive models.

16. The method of claim 15, wherein an equivalent predictive model is generated from the group of predictive models.

17. The method of claim 1, further comprising:

validating the generated predictive model.

18. The method of claim 1, further comprising:

validating the created bins table.

19. The method of claim 1, wherein collecting information further comprising collecting information relating to one or more key-performance indicators (KPI).

20. The method of claim 19, wherein one of the KPI of an object is selecting the object.

21. The method of claim 19, wherein one of the KPI reflects revenue.

22. The method of claim 19, wherein the actions of defining one or more bins and processing the bins table with a predictive model engine configured to generate a predictive model are repeated per each KPI for creating a predictive model per each KPI.

23. The method of claim 22, further comprising:

validating a first generated predictive model based on a first KPI; and

utilizing the validated first predictive model in order to select objects to be presented in the requested web page.

24. The method of claim 23, further comprising

validating a second generated predictive model based on a second KPI, wherein the second KPI is more valuable than the first KPI;

and

utilizing the validated second predictive model in order to select objects to be presented in the following requested for the web page instead of utilizing the validated first predictive model.

25. The method of claim 1, wherein the action of collecting information from a plurality of requests to the web-page further comprises information related to a visit of a surfer in a web site that is related to the web page.

26. The method of claim 25, wherein the created predictive model reflects the number of times the surfer requested the web page during the surfer'"'"'s visit.

28. The non-transitory storage medium of claim 27, wherein the collected information is embedded with the requests to the web pages.

29. The non-transitory storage medium of claim 27, wherein one or more cookies are embedded with the request and the information is collected also from the one or more cookies.

30. The non-transitory storage medium of claim 27, wherein the instructions of defining one or more bins and processing the bins table with a predictive model engine configured to generate a predictive model include the use of properties'"'"' set selected from a group of properties'"'"' sets.

31. The non-transitory storage medium of claim 30, wherein the instruction of defining one or more bins and processing the bins table with a predictive model engine configured to generate a predictive model are repeated per each properties'"'"' set in the group of properties'"'"' sets for creating a group of predictive models.

32. The non-transitory storage medium of claim 31, wherein an equivalent predictive model is generated from the group of predictive models.

33. The non-transitory storage medium of claim 27, wherein collecting information further comprising collecting information relating to one or more key-performance indicators (KPI).

34. The non-transitory storage medium of claim 33, wherein one of the KPI of an object is selecting the object.

35. The non-transitory storage medium of claim 33, wherein the instructions of defining one or more bins and processing the bins table with a predictive model engine configured to generate a predictive model are repeated per each KPI for creating a predictive model per each KPI.

34-1. The non-transitory storage medium of claim 33, wherein one of the KPI of an object reflects revenue.