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

• US 20160055277A1
• Filed: 06/29/2015
• Published: 02/25/2016
• Est. Priority Date: 07/25/2008
• Status: Active Grant

• Current Assignee: LivePerson Incorporated
• Original Assignee: LivePerson Incorporated
• Inventors: LAHAV, Shlomo, RON, Ofer
• Granted Patent: US 9,396,295 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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

Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Claims

1-20. -20. (canceled)

21. A computer-implemented method, comprising:

generating, by a computing device, a predictive model for a web page of a website, wherein the predictive model is associated with a plurality of predictive model types, wherein one or more performance indicators correspond to a predictive model type, wherein performance indicators represent one or more benefits to a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein an object is associated with the predictive model;

selecting a first predictive model type of the predictive model, wherein the first predictive model type is selected based on a first performance indicator corresponding to the selected first predictive model type;

determining a configuration of the web page using the selected first predictive model type of the predictive model;

switching to a second predictive model type of the predictive model, wherein the second predictive model type is switched to based on a second performance indicator corresponding to the second predictive model type; and

determining an additional configuration of the web page using the second predictive model type of the predictive model.

22. The method of claim 21, further comprising:

gradually migrating over time from using the first type of the predictive model to using the second type of the predictive model.

23. The method of claim 21, further comprising:

determining the second predictive model type of the predictive model is ready for use to determine a configuration of the web page; and

switching to the second predictive model type of the predictive model when the second predictive model type is determined to be ready.

24. The method of claim 21, wherein performance indicators include a rate of clicking on an object, a rate of converting the object to a purchase, or a revenue rate generated by presenting a configuration of the web page.

25. The method of claim 21, wherein the first performance indicator includes a rate of clicking on the object associated with the predictive model, and wherein the second performance indicator includes a rate of converting the object to a purchase.

26. The method of claim 21, wherein the first performance indicator includes a rate of clicking on the object associated with the predictive model, and wherein the second performance indicator includes a revenue rate generated by presenting a configuration of the web page.

27. The method of claim 21, wherein the first performance indicator includes a rate of converting the object to a purchase, and wherein the second performance indicator includes a revenue rate generated by presenting a configuration of the web page.

28. The method of claim 21, further comprising:

receiving a request for the web page;

retrieving predictive information related to the request;

converting the predictive information into one or more predictive factors for an object presented with the web page;

defining a value for each of the one or more predictive factors; and

generating the predictive model for the object using the one or more predictive factors.

29. A system, comprising:

a processor; and

a non-transitory computer-readable storage medium containing instructions configured to cause the processor to perform operations including;

generating a predictive model for a web page of a website, wherein the predictive model is associated with a plurality of predictive model types, wherein one or more performance indicators correspond to a predictive model type, wherein performance indicators represent one or more benefits to a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein an object is associated with the predictive model;

selecting a first predictive model type of the predictive model, wherein the first predictive model type is selected based on a first performance indicator corresponding to the selected first predictive model type;

determining a configuration of the web page using the selected first predictive model type of the predictive model;

switching to a second predictive model type of the predictive model, wherein the second predictive model type is switched to based on a second performance indicator corresponding to the second predictive model type; and

determining an additional configuration of the web page using the second predictive model type of the predictive model.

30. The system of claim 29, further comprising:

gradually migrating over time from using the first type of the predictive model to using the second type of the predictive model.

31. The system of claim 29, further comprising instructions configured to cause the processor to perform operations including:

determining the second predictive model type of the predictive model is ready for use to determine a configuration of the web page; and

switching to the second predictive model type of the predictive model when the second predictive model type is determined to be ready.

32. The system of claim 29, wherein performance indicators include a rate of clicking on an object, a rate of converting the object to a purchase, or a revenue rate generated by presenting a configuration of the web page.

33. The system of claim 29, wherein the first performance indicator includes a rate of clicking on the object associated with the predictive model, and wherein the second performance indicator includes a rate of converting the object to a purchase.

34. A computer-program product, tangibly embodied in a non-transitory machine-readable medium, including instructions configured to cause a data processing apparatus to:

generate a predictive model for a web page of a website, wherein the predictive model is associated with a plurality of predictive model types, wherein one or more performance indicators correspond to a predictive model type, wherein performance indicators represent one or more benefits to a website, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein an object is associated with the predictive model;

select a first predictive model type of the predictive model, wherein the first predictive model type is selected based on a first performance indicator corresponding to the selected first predictive model type;

determine a configuration of the web page using the selected first predictive model type of the predictive model;

switch to a second predictive model type of the predictive model, wherein the second predictive model type is switched to based on a second performance indicator corresponding to the second predictive model type; and

determine an additional configuration of the web page using the second predictive model type of the predictive model.

35. The computer-program product of claim 34, further comprising instructions configured to cause the data processing apparatus to:

gradually migrate over time from using the first type of the predictive model to using the second type of the predictive model.

36. The computer-program product of claim 34, further comprising instructions configured to cause the data processing apparatus to:

determine the second predictive model type of the predictive model is ready for use to determine a configuration of the web page; and

switch to the second predictive model type of the predictive model when the second predictive model type is determined to be ready.

37. The computer-program product of claim 34, wherein performance indicators include a rate of clicking on an object, a rate of converting the object to a purchase, or a revenue rate generated by presenting a configuration of the web page.

38. The computer-program product of claim 34, wherein the first performance indicator includes a rate of clicking on the object associated with the predictive model, and wherein the second performance indicator includes a rate of converting the object to a purchase.

39. The computer-program product of claim 34, wherein the first performance indicator includes a rate of clicking on the object associated with the predictive model, and wherein the second performance indicator includes a revenue rate generated by presenting a configuration of the web page.

40. The computer-program product of claim 34, wherein the first performance indicator includes a rate of converting the object to a purchase, and wherein the second performance indicator includes a revenue rate generated by presenting a configuration of the web page.