Systems and methods for clickstream analysis to modify an off-line business process involving forecasting demand

• US 7,814,139 B2
• Filed: 10/24/2007
• Issued: 10/12/2010
• Est. Priority Date: 03/07/2002
• Status: Active Grant

• Current Assignee: comScore, Inc.
• Original Assignee: Complete Incorporated
• Inventors: Cancel, David, Singh, Man Jit
• Primary Examiner(s): NGUYEN, THUONG
• Application Number:
  US11/923,608
• Publication Number:
  US 20080183717A1
• Time in Patent Office: 1,084 Days
• Field of Search: 709/201, 709/219, 709/224, 709/225, 709/226, 705/15, 705/26
• 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. In embodiments, a third party is enabled to forecast offline demand for a good or service based on the online consumer behavior.

Claims

1. A method of processing data at a host computer, comprising:

receiving a data file at the host computer containing records of a plurality of HTTP (HyperText Transfer Protocol) transactions of a plurality of users, each of the HTTP transactions including at least one URL (Uniform Resource Locator);

converting the data file into a common file format at the host computer;

cleansing the data file in the common file format at the host computer by applying a plurality of URL rules to remove session identifiers while accounting for the use of proxies, thereby producing a cleansed data file containing at least one modified URL;

performing a panel selection process at the host computer, comprising;

conducting a periodic survey of a subset of Internet users to determine characteristics of an Internet community that includes the subset of Internet users, the characteristics including demographic characteristics;

selecting a panel of users from the subset of Internet users, the panel of users having a targeted combination of the demographic characteristics;

retaining the data file only when it contains a user identifier of a user in the panel of users;

performing a transformation process, comprising;

removing from the data file those URLs that do not contain the user identifier;

determining which URLs in the data file belong to a session of the user in the panel of users;

assigning a session identifier to the session;

creating a plurality of session data files, each of which contain the user identifier, a time stamp, the URLs in the data file that belong to the session, and the session identifier;

decomposing the URLs in each of the plurality of session data files so that the URLs in each of the plurality of session data files are decomposed URLs; and

hashing the decomposed URLs so that the decomposed URLs are hashed, decomposed URLs;

calculating a metric of user behavior at the host computer based upon the hashed, decomposed URLs;

merging the metric of user behavior into a file containing a plurality of metrics of online user behavior at the host computer; and

transmitting from the host computer to a remote machine the file containing the plurality of metrics of online user behavior, wherein the file containing the plurality of metrics of online user behavior enables a third party to forecast offline demand for a good or service based on the plurality of metrics of online user behavior.

2. The method of claim 1, wherein the good is a media content.

3. The method of claim 1, wherein forecasting comprises predicting a change in market share offline based on changes in a share of online users who research goods or services.

4. The method of claim 1, further comprising, enabling the third party to modify offline business planning in response to a demand forecast.

5. The method of claim 4, wherein the offline business planning relates to manufacturing and inventory needs.

6. The method of claim 4, wherein the offline business planning relates to allocating the correct amounts of inventory to a particular region.

7. A system, comprising:

a data collection facility on a host computer that receives a data file containing records of a plurality of HTTP (HyperText Transfer Protocol) transactions of a plurality of users, each of the HTTP transactions including at least one URL (Uniform Resource Locator), checks for integrity of the data file and the records, and converts the data file into a common file format;

a pre-calculation facility on the host computer that performs a cleansing method, a panel selection process, and a transformation process, wherein the cleansing method comprises;

cleansing the data file in the common file format by applying a plurality of URL rules to remove session identifiers while accounting for the use of proxies, thereby producing a cleansed data file containing at least one modified URL;

the panel selection process comprises;

conducting a periodic survey of a subset of Internet users to determine characteristics of an Internet community that includes the subset of Internet users, the characteristics including demographic characteristics;

selecting a panel of users from the subset of Internet users, the panel of users having a targeted combination of the demographic characteristics;

retaining the data file only when it contains a user identifier of a user in the panel of users;

the transformation process comprises;

removing from the data file those URLs that do not contain the user identifier;

determining which URLs in the data file belong to a session of the user in the panel of users;

assigning a session identifier to the session;

creating a plurality of session data files, each of which contain the user identifier, a time stamp, the URLs in the data file that belong to the session, and the session identifier;

decomposing the URLs in each of the plurality of session data files so that the URLs in each of the plurality of session data files are decomposed URLs; and

hashing the decomposed URLs so that the decomposed URLs are hashed, decomposed URLs; and

a calculation facility on the host computer that calculates a metric of online user behavior based upon the hashed, decomposed URLs, performs a post-calculation method, and transmits to a remote machine a file containing a plurality of metrics of online user behavior, wherein the file containing the plurality of metrics of online user behavior enables a third party to forecast offline demand for a good or service based on the plurality of metrics of online user behavior, and wherein

the post-calculation method comprises;

merging the metric of online user behavior into the file containing the plurality of metrics of online user behavior.

8. The system of claim 7, wherein the calculation facility comprises a cluster of machines configured to run parallel calculations on the plurality of session data files that are obtained from the pre-calculation facility.

9. The system of claim 7, wherein the data collection facility uses a rule that is generated by the host computer system.

10. The system of claim 9, wherein the good is a media content.

11. The system of claim 9, wherein forecasting comprises predicting a change in market share offline based on changes in a share of online users who research goods or services.

12. The system of claim 9, wherein the forecast enables the third party to modify offline business planning.

13. The system of claim 12, wherein the offline business planning relates to manufacturing and inventory needs.

14. The system of claim 12, wherein the offline business planning relates to allocating a correct amounts of inventory to a particular region.