RETAIL TIME TO EVENT SCORECARDS INCORPORATING CLICKSTREAM DATA

• US 20120109710A1
• Filed: 10/27/2010
• Published: 05/03/2012
• Est. Priority Date: 10/27/2010
• Status: Abandoned Application

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Rahman, Shafi Ur, Sowani, Amit Kiran, Agrawal, Rakhi
• Application Number:
  US12/913,185
• Publication Number:
  US 20120109710A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0253 During e-commerce, i.e. online transactions

Abstract

The current subject matter provides the ability to infer a richer customer profile using clickstream data obtained in connection with the traversal of a website by a customer. In some cases, this clickstream data is used in connection with in-store point of sale data and inputted into a Time to Event scorecard model in order to identify transactions (e.g., offerings, campaigns, etc.) to be initiated. Related apparatus, systems, techniques and articles are also described.

Claims

1. A method for implementation by one or more data processors comprising:

deriving one or more clickstream variables from recorded clickstream data, the recorded clickstream data characterizing a customer browsing through available products and services on a website;

inputting the derived clickstream variables into a Time to Event scorecard model to characterize a likelihood of the customer to undertake a future purchasing activity; and

initiating one or more transactions using output of the Time to Event scorecard model.

2. A method as in claim 1, further comprising:

computing website recency variables based on a time interval between visits by the customer to any web page; and

wherein the website recency variables are inputted into the Time to Event scorecard model.

3. A method as in claim 2, further comprising:

computing website frequency variables based a number of all web pages visited by the customer during a particular website visit; and

wherein the website frequency variables are inputted into the Time to Event scorecard model.

4. A method as in claim 3, further comprising:

computing in-store recency variables based on a time interval between purchases by customers of a particular product; and

wherein the in-store recency variables are inputted into the Time to Event scorecard model.

5. A method as in claim 4, further comprising:

computing in-store frequency variables based a number of all products purchased during a particular in-store visit; and

wherein the in-store frequency variables are inputted into the Time to Event scorecard model.

6. A method as in claim 5, further comprising:

aggregating the website frequency and recency variables at discretized time intervals.

7. A method as in claim 6, wherein the in-store purchase frequency and recency variables are discretized at the same time intervals as the website frequency and recency variables.

8. A method as in claim 7, further comprising:

processing the derived clickstream variables, website frequency and recency variables, in-store frequency and recency variables using a variable selection algorithm to optimize a likelihood of success of the transactions.

9. A method as in claim 1, further comprising:

accessing demographic data for the customer; and

wherein the demographic data is also inputted into the Time to Event scorecard model.

10. A method as in claim 1, wherein each product has a corresponding stock keeping unit (SKU), and wherein visit variables are created corresponding to each SKU, wherein the visit variables are used to generate a website line item for the SKU.

11. An article comprising a non-transitory storage medium embodying instructions which when executed by a data processor result in operations comprising:

recording clickstream data that characterizes a customer browsing through available products and services on a website;

deriving one or more clickstream variables from the recorded clickstream data;

inputting the derived clickstream variables into a Time to Event scorecard model to characterize a likelihood of the customer to undertake a future purchasing activity; and

initiating one or more transactions using output of the Time to Event scorecard model.

12. An article as in claim 11, wherein the operations further comprise:

computing website recency variables based on a time interval between visits by the customer to any web page; and

wherein the website recency variables are inputted into the Time to Event scorecard model.

13. An article as in claim 12, wherein the operations further comprise:

computing website frequency variables based a number of all web pages visited by the customer during a particular website visit; and

wherein the website frequency variables are inputted into the Time to Event scorecard model.

14. An article as in claim 13, wherein the operations further comprise:

computing in-store recency variables based on a time interval between purchases by customers of a particular product; and

wherein the in-store recency variables are inputted into the Time to Event scorecard model.

15. An article as in claim 14, wherein the operations further comprise:

computing in-store frequency variables based a number of all products purchased during a particular in-store visit; and

wherein the in-store frequency variables are inputted into the Time to Event scorecard model.

16. An article as in claim 15, wherein the operations further comprise:

aggregating the website frequency and recency variables at discretized time intervals.

17. An article as in claim 16, wherein the in-store purchase frequency and recency variables are discretized at the same time intervals as the website frequency and recency variables.

18. An article as in claim 17, wherein the operations further comprise:

processing the derived clickstream variables, website frequency and recency variables, in-store frequency and recency variables using a variable selection algorithm to optimize a likelihood of success of the transactions.

19. An article as in claim 18, wherein the operations further comprise:

accessing demographic data for the customer; and

wherein the demographic data is also inputted into the Time to Event scorecard model.

20. An article as in claim 11, wherein each product has a corresponding stock keeping unit (SKU), and wherein visit variables are created corresponding to each SKU, wherein the visit variables are used to generate a website line item for the SKU.