Method of predicting a customer's business potential and a data processing system readable medium including code for the method

• US 20030009368A1
• Filed: 07/06/2001
• Published: 01/09/2003
• Est. Priority Date: 07/06/2001
• Status: Abandoned Application

• Current Assignee: Vignette Corporation (Open Text Corporation)
• Original Assignee: Vignette Corporation (Open Text Corporation)
• Inventors: Kitts, Brendan J.
• Application Number:
  US09/682,000
• Publication Number:
  US 20030009368A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

A method can be used to predict the purchasing potential of customers. In one embodiment, the prediction can be based in part on transactional data that is routinely collected by many businesses. An item preference model, a maximum spending model, a geographic model, and any combination of them can be used to make the prediction. The item preference model can be based on which items the customer prefers based on transactional data. The maximum spending model can use the daily maximum spending amount for a customer to determine potential. The geographic model may be based on distance or geographic indicate. Using any or all of the models, if the customer is spending below his or her predicted potential, he or she may be targeted for offers or other promotions.

Claims

1. A method of predicting a business potential for a first customer comprising:

accessing data regarding the first customer of a vendor; and

assigning a value for the business potential for the first customer, wherein the value is a function of at least a behavior for a group of other individuals in a population and is based at least in part on the data.

2. The method of claim 1, further comprising:

determining an individualized result and a group-wide result, wherein;

the individualized result includes a maximum amount spent by the first customer during a first transaction or over a first time period, wherein the maximum amount spent by the first customer is obtained from the data; and

the group-wide result includes a function of maximum amounts spent by other customers within a group of customers during a second transaction or over second time period; and

comparing the individualized result with the group-wide result.

3. The method of claim 1, further comprising:

determining an individualized result and a group-wide result, wherein;

the individualized result includes an individual preference score based on items purchased by the first customer, wherein the individual preference score is obtained from the data; and

the group-wide result includes a group-wide preference score based on items purchased by other customers within a group of customers; and

comparing the individualized result with the group-wide result.

4. The method of claim 1, further comprising using the data to determine an approximate distance between the first customer and a location of a vendor, wherein the distance is used in determining the value.

5. The method of claim 1, further comprising using the data to determine a geographic indicator, wherein the geographic indicator is used in determining the value.

6. The method of claim 1, further comprising:

collecting the data, wherein the data includes transactional data internal to the vendor; and

storing the data,

wherein the acts of collecting, storing, accessing, and assigning are performed by the vendor.

7. The method of claim 1, wherein the method takes a computational time that is substantially directly proportional to N or N*log(N), wherein N is a product of a number of customers and a number of items carried by the vendor or a site of the vendor.

8. The method of claim 1, wherein the value is determined by at least two of an item preference model, a maximum spending model, and a geographic model.

9. The method of claim 1, wherein the at least a behavior includes an average spending amount for a group of customers within the population.

10. 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:

accessing data regarding the first customer of a vendor; and

assigning a value for the business potential for the first customer, wherein the value is a function of at least a behavior for a group of other individuals in a population and is based at least in part on the data.

11. The data processing system readable medium of claim 10, wherein the method further comprises:

determining an individualized result and a group-wide result, wherein;

the individualized result includes a maximum amount spent by the first customer during a first transaction or a first time period, wherein the maximum amount spend by the first customer is obtained from the data; and

the group-wide result includes a function of maximum amounts spent by other customers within a group of customers during a second transaction or second time period; and

comparing the individualized result with the group-wide result.

12. The data processing system readable medium of claim 10, wherein the method further comprises:

determining an individualized result and a group-wide result, wherein;

the individualized result includes an individual preference score based on items purchased by the first customer, wherein the individual preference score is obtained from the data; and

the group-wide result includes group-wide preference score based on items purchased by other customers within a group of customers; and

comparing the individualized result with the group-wide result.

13. The data processing system readable medium of claim 10, wherein the method further comprises using the data to determine an approximate distance between the first customer and a location of a vendor, wherein the distance is used in determining the value.

14. The data processing system readable medium of claim 10, wherein the method further comprises using the data to determine a geographic indicator, wherein the geographic indicator is used in determining the value.

15. The data processing system readable medium of claim 10, wherein the method further comprises:

collecting the data, wherein the data includes transactional data internal to the vendor; and

storing the data,

wherein the acts of collecting, storing, accessing, and assigning are performed by the vendor.

16. The data processing system readable medium of claim 10, wherein the method takes a computational time that is substantially directly proportional to N or N*log(N), wherein N is a product of a number of customers and a number of items carried by the vendor or a site of the vendor.

17. The data processing system readable medium of claim 10, wherein the value is determined by at least two of an item preference model, a maximum spending model, and a geographic model.

18. The data processing system readable medium of claim 10, wherein the at least a behavior includes an average spending amount for a group of customers within the population.