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Copyright © 2007 Indiana University Tools for Tracking Your Customers and Measuring Shopper Engagement Raymond R. Burke and Alex Leykin Kelley School of.

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Presentation on theme: "Copyright © 2007 Indiana University Tools for Tracking Your Customers and Measuring Shopper Engagement Raymond R. Burke and Alex Leykin Kelley School of."— Presentation transcript:

1 Copyright © 2007 Indiana University Tools for Tracking Your Customers and Measuring Shopper Engagement Raymond R. Burke and Alex Leykin Kelley School of Business Indiana University November 2, 2007 Copyright © 2007 Indiana University

2 How Do We Measure & Manage Shoppability? Survey Research Measure consumer perceptions of the shopping experience and diagnose problems with store, department, and category shoppability Observational Research Track shopper behavior, identify points of engagement and purchase obstacles, and then manipulate and measure response

3 Copyright © 2007 Indiana University Key Customer Touchpoints Store Entrance and Window Displays Lead Fixtures and Merchandising End-of-Aisle Displays High Volume / Margin Departments Customer Service Desk Checkout

4 Copyright © 2007 Indiana University Observational Measures Engagement: –Examination of signs, displays, circulars –Category dwell time –Salesperson contact –Product/package/display interaction Conversion: –Aisle penetration –Purchase conversion rate –Product price/margin (absence of incentive) –Shopping basket size –Returns

5 Copyright © 2007 Indiana University Benefits of Computer Tracking Breadth of Coverage: –Census of customers/items (e.g., for security, inventory) –24/7 tracking (time of day/crowding analysis) –Potential to track entire store (path analysis) –Scalable to multiple stores (benchmarking, experiments) Speed: –Real time data (e.g., for staffing, replenishment) Data Integration: –Link path, penetration, conversion data to consumer demographics, shopping basket, purchase history

6 Copyright © 2007 Indiana University Computer Tracking Solutions: Tracking Carts with Infrared/RFID Sensors

7 Copyright © 2007 Indiana University Computer Tracking Solutions: Tracking Carts with Infrared/RFID Sensors Limitations –Only applicable in retail stores using carts and/or baskets (e.g., grocery, mass retail) –Only tracks customers who choose to use carts/baskets, losing “fill-in” shoppers –Unable to track customers who leave carts. May overestimate perimeter traffic, dwell times –No measure of gaze direction or package interaction –No information on group size or behavior

8 Copyright © 2007 Indiana University Computer Tracking Solutions: Tracking Shoppers with Video Cameras Copyright © 2005 Burke and Sharma

9 Copyright © 2007 Indiana University Computer Tracking Solutions: Tracking Shoppers with Video Cameras Copyright © 2005 Burke and Sharma

10 Copyright © 2007 Indiana University Automatic Behavior Analysis Copyright © 2005 Burke and Sharma

11 Copyright © 2007 Indiana University Store Entry and Traffic Patterns Copyright © 2005 Burke and Sharma

12 Copyright © 2007 Indiana University Post Period Pre Period Aisle Penetration Copyright © 2005 Burke and Sharma

13 Copyright © 2007 Indiana University Category Dwell Time Copyright © 2005 Burke and Sharma

14 Copyright © 2007 Indiana University Computer Tracking Solutions: Tracking Shoppers with Video Cameras Limitations –Cameras have a limited field of view and work best in smaller stores (e.g., specialty retail stores, drug stores, convenience stores, banks) –Tracking entire customer path requires multiple cameras with overlapping views –Occlusions (e.g., shelving, signage, other customers) and shadows can interfere with tracking –Difficult to distinguish between employees and customers

15 Copyright © 2007 Indiana University Tracking - System Overview DetectionTrackingActivity Recognition The tracking system works in three steps:

16 Copyright © 2007 Indiana University Tracking – Background Subtraction Each background pixel is represented as a stack of values To decide if a new pixel is a part of the background, a lookup is performed through the full stack and if no matches are found the pixel is considered to be a “foreground pixel” codebook codeword

17 Copyright © 2007 Indiana University Tracking - Blobs The result of background subtraction is a binary bitmap Foreground regions corresponding to moving people are represented as blobs (in red)

18 Copyright © 2007 Indiana University Tracking – Camera Model Parallel lines and the heights of objects in the scene are used to determine the camera’s location and field-of-view The camera model permits the translation from world coordinates to image coordinates and back

19 Copyright © 2007 Indiana University Tracking – Detecting Heads The head is usually the least occluded part of the human body. Therefore, to reliably detect multiple people within one blob, we look at their head locations: 1.Estimate the height of each vertical line of the blob 2.Find a number of local maxima in the resulting histogram

20 Copyright © 2007 Indiana University Tracking – Detecting Heads (cont.)

21 Copyright © 2007 Indiana University Tracking – Probabilistic Modeling At each instant in time, the tracking system attempts to find the model of the scene which: –Best fits the current observation (what’s in the image) –Is consistent with the model from the last observation The system estimates the following parameters for each person: body width and height (cm) current location on the ground (X and Y) color histogram

22 Copyright © 2007 Indiana University Tracking – Sampling Dynamics To construct a new model, we randomly apply a number of “jump-diffuse” mutations to the old model Then the likelihood of the new model is evaluated Add body Delete body Move Change height Change width Change position Switch ID Jump Steps Diffuse Steps

23 Copyright © 2007 Indiana University Tracking - Results

24 Copyright © 2007 Indiana University Tracking Example: Camera View

25 Copyright © 2007 Indiana University Tracking Example: Store View

26 Copyright © 2007 Indiana University Insights from Observational Research Store Entry –Shoppers take time and space to adjust to the in-store environment –Identify “recognition points” where consumers slow down and start observing –Provide answers and solutions, including signs, circulars, baskets, cash/wrap

27 Copyright © 2007 Indiana University Insights (cont.) Traffic Flow –Identify dominant pathways through the store –Angle and direction of approach determines best position/orientation for signs and displays. –The greater the speed of approach, the shorter the message –Facilitate incoming access to destination products, outgoing access to impulse items

28 Copyright © 2007 Indiana University Insights (cont.) Penetration and Purchase Conversion –Low penetration categories may require additional navigational aids, new product displays, merchandising, and/or changes in store layout to improve traffic flow –Categories with low purchase conversion rates may indicate weaknesses in product assortment, pricing, or presentation

29 Copyright © 2007 Indiana University Store Penetration & Purchase Conversion Men’s Women’s

30 Copyright © 2007 Indiana University The Original Men’s Section

31 Copyright © 2007 Indiana University Men’s Style Center - Outfits

32 Copyright © 2007 Indiana University Men’s Style Center – Product Table

33 Copyright © 2007 Indiana University Making It Easier for Men to Shop Enhanced product display drives category traffic and sales: –85% increase in product fixture interaction –44% increase in unit sales –38% increase in dollar sales

34 Copyright © 2007 Indiana University Insights (cont.) Crowding –Provide sufficient aisle width for displays, carts, strollers, crowds –Reposition fixtures or product displays to eliminate bottlenecks –Avoid crowding in categories requiring extended decision times

35 Copyright © 2007 Indiana University Insights (cont.) Checkout –Measure queue lengths and waiting time to flag problems with line management, checkout process and customer service –Reduce waiting time by opening more lines, eliminating price checks, speeding up credit authorization, and employing self checkout

36 Copyright © 2007 Indiana University Source: Burke 2005

37 Copyright © 2007 Indiana University Source: Burke 2005

38 Copyright © 2007 Indiana University Challenges Creating the Digital Store Employee Identification Tracking Customer Groups Measuring Focus of Attention Recognizing Complex Behavior

39 Copyright © 2007 Indiana University Summary of Tracking Insights 1.Track customer path 2.Measure category penetration, dwell time, and conversion 3.Measure line queues and crowding 4.Cluster shoppers based on path similarity Evaluate store layout and product adjacencies Manage in-store communication, product assortment, and pricing Manage service levels, staffing Behavioral segmentation

40 Copyright © 2007 Indiana University Resources Questions? Indiana University’s Kelley School of Business


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