Method of predicting behavior of a customer at a future date and a data processing system readable medium

• US 20020165755A1
• Filed: 03/29/2001
• Published: 11/07/2002
• Est. Priority Date: 03/29/2001
• Status: Abandoned Application

• Current Assignee: Vignette Corporation (Open Text Corporation)
• Original Assignee: Vignette Corporation (Open Text Corporation)
• Inventors: Kitts, Brendan J.
• Application Number:
  US09/681,397
• Publication Number:
  US 20020165755A1
• Time in Patent Office: Days
• US Class Current: 705/10
• 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

Abstract

A computer-implemented method can be used to predict the behavior of a customer of a vendor at a future date. The method can comprise accessing data regarding the vendor'"'"'s customers and generating timeseries information for at least one of the vendor'"'"'s customers. The method can also comprise training a model to obtain weights. The training can be performed using at least some of timeseries information. The method can further comprise predicting the behavior of the first customer at the future date. The prediction can be performed using the weights in the model and at a frequency greater than monthly. A data processing system readable medium may include code having instructions to carry out the method

Claims

1. A computer-implemented method of predicting a behavior of a first customer of a vendor at a future date comprising:

accessing data regarding the vendor'"'"'s customers;

generating timeseries information for at least one of the vendor'"'"'s customers;

training a model to obtain weights, wherein training is performed using at least some of timeseries information; and

predicting the behavior of the first customer at the future date, wherein predicting is performed using the weights in the model and at a frequency greater than monthly.

2. The method of claim 1, wherein each of accessing and generating are performed at least daily.

3. The method of claim 1, wherein each of the accessing and generating are performed substantially in real time.

4. The method of claim 1, wherein the behavior includes likelihood of retention.

5. The method of claim 1, wherein the behavior includes future revenue.

6. The method of claim 1, wherein:

accessing is performed for a second customer; and

the method further comprises removing a datum for the second customer before training because the datum for the second customer exceeds an outlier limit.

7. The method of claim 6, wherein removing the datum for the second customer is performed after training.

8. The method of claim 1, wherein the model uses an approximator selected from a group consisting of a polynomial regression, a decision tree, and a spline.

9. A data processing system readable medium having code embodied therein, the code including instructions executable by a data processing system, wherein the instructions are configured to cause the data processing system to perform a method of predicting a behavior of a first customer of a vendor at a future date, the method comprising:

accessing data regarding the vendor'"'"'s customers;

generating timeseries information for at least one of the vendor'"'"'s customers;

training a model to obtain weights, wherein training is performed using at least some of timeseries information; and

predicting the behavior of the first customer at the future date, wherein predicting is performed using the weights in the model and at a frequency greater than monthly.

10. The data processing system readable medium of claim 9, wherein each of accessing and generating are performed at least daily.

11. The data processing system readable medium of claim 9, wherein each of the accessing and generating are performed substantially in real time.

12. The data processing system readable medium of claim 9, wherein the behavior includes likelihood of retention.

13. The data processing system readable medium of claim 9, wherein the behavior includes future revenue.

14. The data processing system readable medium of claim 9, wherein:

accessing is performed for a second customer; and

the method further comprises removing a datum for the second customer before training because the datum for the second customer exceeds an outlier limit.

15. The data processing system readable medium of claim 14, wherein removing the datum for the second customer is performed after training.

16. The data processing system readable medium of claim 9, wherein the model uses an approximator selected from a group consisting of a polynomial regression, a decision tree, and a spline.