LEARNING USER PURCHASE INTENT FROM USER-CENTRIC DATA

• US 20100114654A1
• Filed: 10/31/2008
• Published: 05/06/2010
• Est. Priority Date: 10/31/2008
• Status: Abandoned Application

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Zhou, Jing, Venkata, Satyanarayana Raju P., Li, Jiye, Lukose, Rajan
• Application Number:
  US12/263,176
• Publication Number:
  US 20100114654A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0256 User search

Abstract

A method of predicting user purchase intent from user-centric data includes applying a classification model to a user-centric clickstream, where the classification model predicting a likelihood of a future user purchase by a user within one or more product categories, and customizing content displayed to the user based on the likelihood of future user purchase. A system of predicting user purchase intent from user-centric data includes a computer programmed to record a user'"'"'s clickstream data as a user accesses a plurality of different websites. The computer is also loaded with a classification model configured to predict a likelihood of a future user purchase by the user within one or more product categories based on the clickstream data. A method of predicting user purchase intent from user-centric data includes, with a user'"'"'s own computer, recording user-centric clickstream data based on visits to a plurality of different websites; and storing a smart cooked based on the clickstream data on the user'"'"'s own computer.

Claims

1. A method of predicting user purchase intent from user-centric data comprises:

applying a classification model to a user-centric clickstream;

said classification model predicting a likelihood of a future user purchase by a user within one or more product categories; and

customizing content displayed to said user based on said likelihood of future user purchase.

2. The method of claim 1, compiling said user-centric clickstream with a user'"'"'s own computer.

3. The method of claim 2, further comprising recording said user-centric clickstream in a smart cookie on said user'"'"'s own computer, wherein said customizing content is performed using said data from said smart cookie.

4. The method of claim 1, further comprising generating said classification model, said classification model comprising a number of features that distinguish between buyers and non-buyers within a product category.

5. The method of claim 4, wherein generating said classification model comprises analyzing a training data set of user-centric data to generate said features.

6. The method of claim 5, further comprising analyzing said training data set to extract distinguishing search terms used by actual buyers which differentiate said actual buyers within a product category from non-buyers.

7. The method of claim 1, further comprising loading said classification model on a user'"'"'s own computer, said model obtaining said user'"'"'s clickstream data and analyzing said user'"'"'s clickstream data in real time on said user'"'"'s own machine.

8. The method of claim 1, further comprising observing actual purchase behavior of said user and updating said model based on said actual purchase behavior.

9. A system of predicting user purchase intent from user-centric data comprises:

a computer programmed to record a user'"'"'s clickstream data as a user accesses a plurality of different websites; and

said computer loaded with a classification model configured to predict a likelihood of a future user purchase by said user within one or more product categories based on said clickstream data.

10. The system of claim 9, further comprising an external server in communication with said computer and configured to customize content displayed to said user based on said likelihood of future user purchase.

11. The system of claim 9, wherein said computer records said user-centric clickstream data and likelihood of future user purchase in a smart cookie on said computer.

12. The system of claim 9, wherein said classification model comprising a number of features that distinguish between buyers and non-buyers within a product category.

13. A method of predicting user purchase intent from user-centric data comprises:

with a user'"'"'s own computer, recording user-centric clickstream data based on visits to a plurality of different websites; and

storing a smart cooked based on said clickstream data on said user'"'"'s own computer.

14. The method of claim 13, further comprising:

applying a classification model to said user-centric clickstream data;

said classification model predicting a likelihood of a future user purchase by a user within one or more product categories; and

recording said likelihood of future user purchase in said smart cookie.

15. The method of claim 13, further comprising selectively transmitting data from said smart cookie to websites accessed by said user'"'"'s computer, wherein said websites customize content served to said user based on said data from said smart cookie.