METHOD AND APPARATUS FOR DETERMINING PROFITABILITY OF CUSTOMER GROUPS IDENTIFIED FROM A CONTINUOUS VIDEO STREAM

• US 20090083121A1
• Filed: 09/26/2007
• Published: 03/26/2009
• Est. Priority Date: 09/26/2007
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee
• Application Number:
  US11/861,966
• Publication Number:
  US 20090083121A1
• 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 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0212 Chance discounts or incentives

Abstract

A computer implemented method, apparatus, and computer usable program product for determining profitability of customer groups. The process parses event data to identify dynamic customer data, wherein the event data is derived from a continuous video stream captured at a retail facility. The process then combines the dynamic customer data with customer profile data to form dynamic customer profiles and analyzes the dynamic customer profiles to identify the customer groups. Thereafter, the process ranks the customer groups according to profitability of the customer groups.

Claims

1. A computer implemented method for determining profitability of customer groups, the computer implemented method comprising:

parsing event data to identify dynamic customer data, wherein the event data is derived from a continuous video stream captured at a retail facility;

combining the dynamic customer data with customer profile data to form dynamic customer profiles;

analyzing the dynamic customer profiles to identify the customer groups; and

ranking the customer groups according to profitability of the customer groups.

2. The computer implemented method of claim 1, wherein the dynamic customer data is at least one of a set of physical patterns of customer behavior and a set of observable characteristics of a customer.

3. The computer implemented method of claim 1, further comprising:

presenting marketing incentives to customers of a customer group based on a rank of the customer group.

4. The computer implemented method of claim 3, wherein the customers of the customer group are presented with preferential marketing incentives in response to the rank exceeding a threshold.

5. The computer implemented method of claim 1, wherein the ranking step further comprises:

assigning the customer groups a percentile score based on profitability.

6. The computer implemented method of claim 1, further comprising:

receiving the video data from a set of sensors associated with the retail facility; and

analyzing the video data to identify the event data, wherein analyzing the video data comprises generating metadata describing the dynamic customer data.

7. The computer implemented method of claim 6, wherein the set of sensors comprises a set of digital video cameras.

8. The computer implemented method of claim 1, wherein parsing the event data further comprises:

processing the event data using at least one of a statistical method, a data mining method, a causal model, a mathematical model, a marketing model, a behavioral model, a psychological model, a sociological model, or a simulation model.

9. A computer program product comprising:

a computer usable medium including computer usable program code for determining profitability of customer groups, the computer program product comprising;

computer usable program code for parsing event data to identify dynamic customer data, wherein the event data is derived from a continuous video stream captured at a retail facility;

computer usable program code for combining the dynamic customer data with customer profile data to form dynamic customer profiles;

computer usable program code for analyzing the dynamic customer profiles to identify the customer groups; and

computer usable program code for ranking the customer groups according to profitability of the customer groups.

10. The computer program product of claim 9, wherein the dynamic customer data is at least one of a set of physical patterns of customer behavior and a set of observable characteristics of a customer.

11. The computer program product of claim 9, further comprising:

computer usable program code for presenting marketing incentives to customers of a customer group based on a rank of the customer group.

12. The computer program product of claim 11, wherein the customers of the customer group are presented with preferential marketing incentives in response to the rank exceeding a threshold.

13. The computer program product of claim 9, wherein the computer usable program code for ranking the customer groups comprises:

computer usable program code for assigning the customer groups a percentile score based on profitability.

14. The computer program product of claim 9, further comprising:

computer usable program code for receiving the video data from a set of sensors associated with the retail facility; and

computer usable program code for analyzing the video data to identify the event data, wherein analyzing the video data comprises generating metadata describing the dynamic customer data.

15. The computer program product of claim 14, wherein the set of sensors comprises a set of digital video cameras.

16. The computer program product of claim 9, wherein the computer usable program code for parsing the event data further comprises:

computer usable program code for processing the event data using at least one of a statistical method, a data mining method, a causal model, a mathematical model, a marketing model, a behavioral model, a psychological model, a sociological model, or a simulation model.

17. A system for determining profitability of customer groups, the system comprising:

a set of sensors;

a database, wherein the database stores event data collected by the set of sensors; and

an analysis server, wherein the analysis server parses event data to identify dynamic customer data, wherein the event data is derived from a continuous video stream captured at a retail facility;

combines the dynamic customer data with customer profile data to form dynamic customer profiles;

analyzes the dynamic customer profiles to identify the customer groups; and

ranks the customer groups according to profitability of the customer groups.

18. The system of claim 17, further comprising:

a content server, wherein the content server presents marketing incentives to customers of a customer group based on a rank of the customer group.

19. The system of claim 18, wherein the content server presents customers of the customer group with preferential marketing incentives in response to the rank exceeding a threshold.

20. The system of claim 17, wherein the set of sensors comprises a set of digital video cameras.