Identifying customer behavioral types from a continuous video stream for use in optimizing loss leader merchandizing
First Claim
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.
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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.
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Citations
20 Claims
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1. A computer implemented method for optimizing loss leader merchandizing, the computer implemented method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer program product comprising:
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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. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A system for optimizing loss leader merchandizing, the system comprising:
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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. - View Dependent Claims (20)
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Specification