Clickstream analysis methods and systems

• US 8,095,589 B2
• Filed: 10/09/2002
• Issued: 01/10/2012
• Est. Priority Date: 03/07/2002
• Status: Active Grant

• Current Assignee: comScore, Inc.
• Original Assignee: Compete, Inc. (comScore, Inc.)
• Inventors: Singh, Man Jit, Cancel, David
• Primary Examiner(s): Nguyen, Tina
• Application Number:
  US10/267,978
• Publication Number:
  US 20030171977A1
• Time in Patent Office: 3,380 Days
• Field of Search: 709/201, 709/224, 705/10, 705/20, 705/22, 725/14
• US Class Current: 709/201
• CPC Class Codes: G06F 15/16 Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs coordinating program control therefor G06F9/52; in regulating and control system G05B
• CPC Class Codes: G06F 15/17 using an input/output type connection, e.g. channel, I/O port
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9566 URL specific, e.g. using aliases, detecting broken or misspelled links
• CPC Class Codes: G06Q 20/20 Point-of-sale [POS] network systems
• CPC Class Codes: G06Q 20/201 Price look-up processing, e.g. updating
• CPC Class Codes: G06Q 20/203 Inventory monitoring
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0633 Lists, e.g. purchase orders, compilation or processing
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/146 : Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Methods and systems are provided herein for the analysis of clickstream data of online users. The analysis methods and systems allow for the creation of new offline business methods based on online consumer behavior.

Claims

1. A method, comprising:

obtaining, by a processor, an input data set reflecting online actions of a plurality of online users, wherein the plurality of online users are selected according to a panel selection process based on a statistical process of analyzing demographic characteristics of the online users, wherein the input data set includes a plurality of timestamps associated with the clickstream activity;

analyzing, by the processor, the data set to identify characteristics of cross-shopping behavior of the users between different brands of products; and

modifying an off-line marketing promotion that includes an offline incentive for buying a particular brand of product based on the identified characteristics, wherein the offline promotion include a process for offline presentation of a media item, and wherein the statistical process comprises;

conducting a survey of user demographics in an initial panel of users,

determining a current statistic related to the user demographics in the initial panel of users,

comparing the current statistic to a statistic corresponding to a historical panel of users for updating the current statistic, and

selecting the plurality of online users in the current panel of users from the initial panel of users based on the updated current statistic and the user demographics.

2. A method of claim 1, wherein the input data set includes data related to price sensitivity of the plurality of online users, and the method comprises modifying an offline pricing process for an offline item.

3. A method of claim 1, wherein the input data set includes data related to an ordering of items for online user viewing, and the method comprises modifying an offline process related to product placement.

4. A method of claim 1, wherein the input data set includes data related to user buying behavior, and the method comprises modifying an offline process related to forecasting offline demand for a product.

5. A method of claim 1, wherein the input data set includes data related to actions of the plurality of users with respect to online content choices, and the method comprises modifying an offline process related to presenting media content offline.

6. A method, comprising:

obtaining, by a processor, an input data set reflecting clickstream activity of a plurality of online users, including data reflecting actions of the users with respect to online content choices, wherein the plurality of online users are selected according to a panel selection process based on a statistical process of analyzing demographic characteristics of the online users, and wherein the input data set includes a plurality of timestamps associated with the clickstream activity;

analyzing, by the processor, user behavior reflected by the clickstream activity;

drawing, by the processor, one or more inferences based on the user behavior; and

modifying one or more offline processes based on the inferences, wherein the offline processes include a process for offline presentation of a media item, wherein the statistical process comprises;

conducting a survey of user demographics in an initial panel of users,

determining a current statistic related to the user demographics in the initial panel of users,

comparing the current statistic to a statistic corresponding to a historical panel of users for updating the current statistic, and

selecting the plurality of online users in the current panel of users from the initial panel of users based on the updated current statistic and the user demographics.

7. The method of claim 6, wherein the offline item is one of:

an offline media product offering chosen based on the online content choices, and

an item having offline media content.

8. The method of claim 7, wherein the input data set includes data related to user buying behavior, and the offline processes include a process for forecasting offline demand for a product.

9. The method of claim 6, wherein the offline item is a catalog customized to reflect at least one demographic characteristic of the plurality of online users determined based on the online content choices of the users.

10. The method of claim 6, wherein the offline item is a media merchandise offered based on user online choices related to movie content.

11. A method, comprising:

obtaining, by a processor, an input data set reflecting clickstream activity of a plurality of online users, including data related to price sensitivity of the users determined from an online site of a first vendor, wherein the plurality of online users are selected according to a panel selection process based on a statistical process of analyzing demographic characteristics of the online users, and wherein the input data set includes a plurality of timestamps associated with the clickstream activity;

analyzing, by the processor, user behavior reflected by the clickstream activity;

drawing, by the processor, one or more inferences based on the user behavior; and

modifying one or more offline processes based on the inferences, wherein the offline processes include a pricing process for an offline item of a second vendor, wherein the offline processes include a process for offline presentation of a media item, and wherein the first vendor is different from the second vendor, wherein the statistical process comprises;

conducting a survey of user demographics in an initial panel of users,

determining a current statistic related to the user demographics in the initial panel of users,

comparing the current statistic to a statistic corresponding to a historical panel of users for updating the current statistic, and

selecting the plurality of online users in the current panel of users from the initial panel of users based on the updated current statistic and the user demographics.

12. The method of claim 11, wherein the price sensitivity relates to actions of the plurality of online users in response to a change in a pricing process of an online item.

13. A method, comprising:

obtaining, by a processor, an input data set reflecting clickstream activity of a plurality of online users, where the data set includes a plurality of timestamps associated with the clickstream activity, wherein the plurality of online users are selected according to a panel selection process based on a statistical process of analyzing demographic characteristics of the online users;

analyzing, by the processor, user behavior reflected by the clickstream activity;

drawing, by the processor, one or more inferences based on the user behavior; and

modifying one or more offline processes based on the inferences, wherein the offline processes include a process for offline presentation of a media item, wherein the statistical process comprises;

conducting a survey of user demographics in an initial panel of users,

determining a current statistic related to the user demographics in the initial panel of users,

comparing the current statistic to a statistic corresponding to a historical panel of users for updating the current statistic, and

selecting the plurality of online users in the current panel of users from the initial panel of users based on the updated current statistic and the user demographics.

14. The method of claim 13, wherein the clickstream activity includes a plurality of HTTP transactions and the timestamps include times of the respective HTTP transactions.

15. The method of claim 13, wherein at least one of the inferences relates to cross-shopping behavior of the plurality of users between different goods, and the offline processes include a process for marketing promotion.

16. The method of claim 15, wherein the marketing promotion is an offline incentive for buying a product, and the inference relates to cross-shopping behavior of the plurality of users between different brands of the product based on the offline incentive.

17. The method of claim 16, wherein the product comprises a brand of automobile, and the marketing promotion comprises an incentive for buying the brand of automobile.

18. The method of claim 13, wherein the input data set includes data related to price sensitivity of the plurality of online users, and the offline processes include a pricing process for an offline item.

19. The method of claim 18, wherein the price sensitivity of the online users is determined from an online site of a first vendor and relates to a pricing process for an offline item of a second vendor.

20. The method of claim 13, wherein the input data set includes data related to an ordering of items for online user viewing, and the offline processes include a process for product placement.

21. The method of claim 13, wherein the input data set includes data related to actions of the plurality of online users with respect to online content choices, and the offline processes include a process for presenting offline media content.

22. A method, comprising:

selecting, by a processor, a plurality of online users for a current panel of users according to a panel selection process, wherein the panel selection process selects the current panel of users based on a statistical process of analyzing demographic characteristics of the plurality of online users;

obtaining, by the processor, an input data set reflecting clickstream activity of the plurality of online users, wherein the input data set includes a plurality of timestamps associated with the clickstream activity;

analyzing, by the processor, user behavior reflected by the clickstream activity;

drawing, by the processor, one or more inferences based on the user behavior; and

modifying one or more offline processes based on the inferences, wherein the offline processes include a process for offline presentation of a media item, and wherein the statistical process comprises;

conducting a survey of user demographics in an initial panel of users,

determining a current statistic related to the user demographics in the initial panel of users,

comparing the current statistic to a statistic corresponding to a historical panel of users for updating the current statistic, and

selecting the plurality of online users in the current panel of users from the initial panel of users based on the updated current statistic and the user demographics.

23. The method of claim 22, wherein the panel selection process comprises selecting the plurality of online users from a larger set of online users in an initial panel of users.

24. The method of claim 22, wherein at least one of the inferences relates to cross-shopping behavior of the plurality of users between different goods, and the offline processes include a process for marketing promotion.

25. The method of claim 22, wherein the input data set includes data related to price sensitivity of the plurality of users in the user panel, and the offline processes include a pricing process for an item offline.

26. The method of claim 22, wherein the input data set includes data relates to an ordering of items for online user viewing, and the offline processes include a process for product placement.

27. The method of claim 22, wherein the input data set includes data related to user buying behavior, and the offline processes include a process for forecasting offline demand for a product.

28. The method of claim 22, wherein the input data set includes data related to actions of the plurality of users in the user panel with respect to online content choices, and the offline processes include a process for presenting media content offline.