END CAP ANALYTIC MONITORING METHOD AND APPARATUS

• US 20130335572A1
• Filed: 07/01/2013
• Published: 12/19/2013
• Est. Priority Date: 09/03/2008
• Status: Active Grant

• Current Assignee: Target Brands Inc. (Target Corporation)
• Original Assignee: Target Brands Inc. (Target Corporation)
• Inventors: DePape, Neal, Fuhr, Jackie
• Granted Patent: US 9,838,649 B2
• Time in Patent Office: Days
• US Class Current: 348/150
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: H04N 7/18 Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

Methods and apparatus for monitoring and ranking end caps in a store include video monitoring one or more parameters of end caps, including dwell times, item removal, traffic monitored parameters to determine for example one or more of end cap efficiency, product desirability, and location desirability.

Claims

1-20. -20. (canceled)

21. A method for determining the effectiveness of product locations in a retail store, comprising the steps of:

directing a plurality of video cameras at predetermined locations in a retail store to collect video data relating to customer behavior including data relating to dwell times of customers at the predetermined locations;

determining for a first predetermined period of time an average dwell time of customers at the predetermined locations within the retail store using a plurality of video cameras directed at the predetermined locations, each of the predetermined locations having on display a product for sale;

ranking each of the predetermined locations based on a correlation between the average dwell time by customers at the predetermined locations and sales information for the product for sale at each of the predetermined locations;

changing the product for sale displayed at each of the predetermined locations at the expiration of the first predetermined period of time;

determining for a second predetermined period of time a second average dwell time of customers at the predetermined locations within the retail store using the plurality of video cameras directed at the predetermined locations;

ranking each of the predetermined locations based on a correlation between the second average dwell time by customers at each of the predetermined locations and sales information for the product for sale at each of the predetermined locations during the second predetermined period of time; and

comparing sales of the products at the predetermined locations during the first predetermined period of time to the sales of products at the predetermined locations during the second predetermined period of time to determine the effectiveness of the predetermined locations on sales of the products.

22. The method of claim 21, wherein ranking each of the predetermined locations further comprises determining a correlation between the average dwell time at each of the predetermined locations and a quantity of the product for sale removed from each of the predetermined locations.

23. The method of claim 21, and further comprising the step of using the video data to track a volume of customer traffic at each of the predetermined locations.

24. The method of claim 23, wherein determining an average dwell time further comprises determining for each of the predetermined locations a location average dwell time for each customer of a volume of customer traffic who dwells at the at least one of the predetermined locations for at least a certain period of time.

25. The method of claim 24, further comprising the step of:

determining whether the customer is viewing the product for sale at the predetermined location and identifying whether the customer is facing toward the product for sale at the predetermined location, and ignoring each customer not facing toward the product for sale in determining the location average dwell time.

26. The method of claim 21, wherein determining the average dwell time of customers at the predetermined locations within a store further comprises determining a dwell time for customers at a plurality of end caps of a retail store.

27. The method of claim 21, further comprising the steps of:

placing a first product for sale at a first one of said predetermined locations for said first predetermined period of time;

placing a second product for sale at said first one of said predetermined locations for said second predetermined period of time, wherein said first product is a competitive product to said second product; and

comparing sales of said first product with said second product during said first predetermined period of time and during said second predetermined period of time.

28. The method of claim 21, further comprising the steps of:

monitoring using the video cameras during each of the first and the second predetermined periods of time and collecting video data on a number of removals of the product for sale at each of the predetermined locations.

29. The method of claim 21, further comprising the steps of:

determining by using facial recognition software on the collected video data for each customer who dwells at at least one of the predetermined locations during each of the first predetermined period of time and the second predetermined period of time whether the customer is viewing the product for sale at the predetermined locations or is dwelling for a reason unrelated to the product for sale at the predetermined locations; and

ignoring each customer dwelling for a reason unrelated to the product for sale in determining the average dwell time.

30. The method of claim 21, further comprising the step of:

calculating a conversion factor for each of the predetermined locations during each of the first predetermined period of time and the second predetermined period of time based on the comparison of customers dwelling at each of the predetermined locations and sales of a product displayed at each of the predetermined locations.

37. The method of claim 30, further comprising the step of:

calculating a conversion factor for each of the plurality of end caps during each of the first predetermined period of time and the second predetermined period of time based on the comparison of customers dwelling at each of the plurality of end caps and sales of a product displayed at each of the plurality of end caps.

31. A method of determining the effectiveness of an end cap location in a retail store by monitoring video data, comprising the steps of:

monitoring and collecting video data at a plurality of end caps using at least one video camera, each of said end caps displaying a product for sale;

tracking with a video camera for a first predetermined period of time for each of the plurality of end caps a total number of guests who pass by each of the plurality of end caps;

determining using the video data for each of the plurality of end caps a first subset of the total number of guests who loiter at each of the plurality of end caps;

determining using the video data an average loiter time for each of the plurality of end caps;

ranking the plurality of end caps according to the average loiter time;

monitoring with at least one video camera and collecting video data for a number of removals of the product for sale at each of the plurality of end caps;

wherein tracking each first subset of the total number of guests who loiter at each of the plurality of end caps further comprises defining a loiter-time subset of each first subset, wherein each loiter-time subset is determined by averaging loiter times of those guests in each first subset who loiter for a period of time exceeding a predetermined loiter time;

determining the effectiveness of the end caps based on said first subset and each loiter-time subset by comparing sales of said product for sale displayed at each end cap;

changing the product for sale displayed at each of the plurality of the end caps;

tracking, for a subsequent second predetermined period of time equal in length to the first predetermined period of time, with a video camera for each of the plurality of end caps, a total number of guests who pass by each of the plurality of end caps during the second predetermined period of time;

tracking for each of the plurality of end caps a second subset of the total number of guests who loiter at each of the plurality of end caps during the second predetermined period of time;

determining a second average loiter time for each of the plurality of end caps;

ranking the plurality of end caps according to the second average loiter time;

monitoring a number of removals of the product for sale at each of the plurality of end caps during the second predetermined period of time;

wherein tracking each second subset of the total number of guests who loiter at each of the plurality of end caps during the second predetermined period of time further comprises defining a second loiter-time subset of each second subset, wherein each second loiter-time subset is determined by averaging loiter times of those guests in each second subset who loiter for a period of time exceeding a predetermined loiter time; and

comparing sales of the products at the end caps during the first predetermined period of time to the sales of products at the end caps during the second predetermined period of time to determine the effectiveness of the location of the end caps on sales of the products.

32. The method of claim 31, wherein determining a first average loiter time further comprises averaging the loiter times for each guest of the first subset.

33. The method of claim 31, wherein determining a first average loiter time further comprises:

ignoring those guests who are loitering at the plurality of end caps for a reason unrelated to the product for sale at the plurality of end caps in determining the first average loiter time.

34. The method of claim 31, further comprising the steps of:

placing a first product for sale at a first one of said plurality of end caps for said first predetermined period of time;

placing a second product for sale at said first one of said plurality of end caps for said second predetermined period of time, wherein said first product is a competitive product to said second product; and

comparing sales of said first product with said second product during said first predetermined period of time and during said second predetermined period of time.

35. The method of claim 31, further comprising the step of:

monitoring using the video cameras during each of the first and the second predetermined periods of time and collecting data on a number of removals of the product for sale at each of the plurality of end caps.

36. The method of claim 31, further comprising the steps of:

determining by using facial recognition software on the collected video data for each customer who dwells at at least one of the end caps during each of the first predetermined period of time and the second predetermined period of time whether the customer is viewing the product for sale at the end cap or is dwelling for a reason unrelated to the product for sale at the plurality of end caps; and

ignoring each customer dwelling for a reason unrelated to the product for sale.

38. A method of monitoring and analyzing video data for a retail store, comprising the steps of:

providing a plurality of video cameras directed at a plurality of particular locations;

providing a controller electrically coupled to the plurality of video cameras, the controller configured to determine an average dwell time of customers at the plurality of particular locations, and to rank each of the plurality of particular locations based on an average dwell time at each of the plurality of particular locations wherein the plurality of particular locations comprises a plurality of end caps and the controller is further configured to monitor, through the plurality of video cameras directed at the plurality of particular locations, a number of removals of each item from each of the plurality of particular locations, and a volume of customer traffic at each of the plurality of particular locations, and to include one or more of number of removals and volume of traffic in ranking each of the plurality of particular locations;

placing an advertisement into a local print media for the particular product for a second predetermined period of time wherein the second predetermined period of time begins after the first predetermined period of time has elapsed;

monitoring purchase data for a particular product at each of the plurality of particular locations for the second predetermined period of time; and

evaluating the effectiveness of the advertisement based on a first data set including one or more of the purchase data, number of removals, and volume of traffic recorded during the first predetermined period of time and comparing the first data set to a second data set including one or more of the purchase data, number of removals and volume of traffic recorded during the second predetermined period of time.

39. The method of claim 38, further comprising the steps of:

placing a first product for sale at a first one of said plurality of particular locations for said first predetermined period of time;

placing a second product for sale at said first one of said plurality of particular locations for said second predetermined period of time, wherein said first product is a competitive product to said second product; and

comparing purchase data of said first product with said second product during said first predetermined period of time and during said second predetermined period of time.

40. The method of claim 38, further comprising the steps of:

determining by using facial recognition software on the collected video data for each customer who dwells at at least one of the particular locations during each of the first predetermined period of time and the second predetermined period of time whether the customer is viewing the product for sale at the predetermined locations or is dwelling for a reason unrelated to the product for sale at the predetermined locations; and

,

ignoring each customer dwelling for a reason unrelated to the product for sale in determining the second average dwell time.