Identifying customer behavioral types from a continuous video stream for use in optimizing loss leader merchandizing

• US 8,195,499 B2
• Filed: 09/26/2007
• Issued: 06/05/2012
• Est. Priority Date: 09/26/2007
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Angell, Robert Lee, Kraemer, James R.
• Primary Examiner(s): Boswell, Beth V
• Assistant Examiner(s): PORTER, WILLIAM ERNEST
• Application Number:
  US11/861,975
• Publication Number:
  US 20090083122A1
• Time in Patent Office: 1,714 Days
• Field of Search: 705/7.11, 705/7.12
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• 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 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0271 Personalized advertisement

Abstract

A computer implemented method, apparatus, and computer usable program product for optimizing loss leader merchandizing. The process parses event data to identify physical patterns of customer behavior, wherein the event data is derived from a continuous video stream captured at a retail facility, and associates the physical patterns of customer behavior with customer profile data to form dynamic customer profiles. The process then analyzes the dynamic customer profiles to identify types of customers and presents marketing incentives for a set of loss leader items to customers of a selected customer type, wherein the set of loss leader items are selected based on the dynamic profiles.

Claims

1. A computer implemented method for optimizing loss leader merchandizing, the computer implemented method comprising:

parsing, using one or more processors, first event data to identify first physical patterns of customer behavior, wherein the first event data is derived from a continuous video stream captured at a retail facility;

determining, through cross correlation, gaps in situation awareness, requiring installation of a new sensor different than the continuous video stream to increase situation awareness;

installing said new sensor different than the continuous video stream;

parsing, using one or more processors, second event data to identify second physical patterns of customer behavior, wherein the second event data is based on information taken by the new sensor;

cross correlating, using the one or more processors, the first event data and the second event data to form second physical patterns of customer behavior different than the first physical patterns of behavior;

associating, using the one or more processors, the second physical patterns of customer behavior with customer profile data to form dynamic customer profiles;

analyzing, using the one or more processors, the dynamic customer profiles to identify types of customers; and

presenting, on a display that receives data from the one or more processors, to customers of a selected customer type, marketing incentives for a set of loss leader items, wherein the set of loss leader items are selected based on the dynamic profiles, and wherein the set of loss leader items comprise retail items that are given away or sold at or below cost.

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

identifying, from the dynamic customer profiles, a set of complementary items for association with the set of loss leader items.

3. The computer implemented method of claim 1, wherein the set of loss leader items is at least one retail item selected to promote sale of a set of complementary items.

4. The computer implemented method of claim 1, wherein the patterns of customer behavior comprise a degree of interaction with at least one of the set of loss leader items and a set of complementary items.

5. The computer implemented method of claim 1, wherein the selected customer type is identified based on profitability.

6. The computer implemented method of claim 5, wherein customers of the selected customer type are provided with preferential marketing incentives.

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

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

analyzing the video data to identify the first event data, wherein analyzing the video data comprises generating metadata describing the first physical patterns of customer behavior.

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

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

processing the first 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.

10. A computer program product comprising:

a computer usable medium including computer usable program code for optimizing loss leader merchandizing, the computer program product comprising;

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

computer usable program code for determining, through cross correlation, gaps in situation awareness, requiring installation of a new sensor different than the continuous video stream to increase situation awareness;

computer usable program code for installing said new sensor different than the continuous video stream;

computer usable program code for associating the physical patterns of customer behavior with customer profile data to form dynamic customer profiles;

computer usable program code for analyzing the dynamic customer profiles to identify types of customers; and

computer usable program code for presenting, to customers of a selected customer type, marketing incentives for a set of loss leader items, wherein the set of loss leader items are selected based on the dynamic profiles, and wherein the set of loss leader items comprise retail items that are given away or sold at or below cost.

11. The computer program product of claim 10, further comprising:

computer usable program code for identifying, from the dynamic customer profiles, a set of complementary items for association with the set of loss leader items.

12. The computer program product of claim 10, wherein the set of loss leader items is at least one retail item selected to promote sale of a set of complementary items.

13. The computer program product of claim 10, wherein the patterns of customer behavior comprise a degree of interaction with at least one of the set of loss leader items and a set of complementary items.

14. The computer program product of claim 10, wherein the selected customer type is identified based on profitability.

15. The computer program product of claim 14, wherein customers of the selected customer type are provided with preferential marketing incentives.

16. The computer program product of claim 10, further comprising:

computer usable program code for receiving 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 physical patterns of customer behavior.

17. The computer program product of claim 16, wherein the set of sensors comprises a set of digital video cameras.

18. The computer program product of claim 10, 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.

19. A system for optimizing loss leader merchandizing, the system comprising:

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

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

determine, through cross correlation, gaps in situation awareness, requiring installation of a new sensor different than the continuous video stream to increase situation awareness;

install said new sensor different than the continuous video stream;

associates the physical patterns of customer behavior with customer profile data to form dynamic customer profiles;

analyzes the dynamic customer profiles to identify types of customers; and

presents, to customers of a selected customer type, marketing incentives for a set of loss leader items, wherein the set of loss leader items are selected based on the dynamic profiles, and wherein the set of loss leader items comprise retail items that are given away or sold at or below cost.

20. The system of claim 19, wherein the set of sensors comprises a set of digital video cameras.