Copyright © 2007 Indiana University Tools for Tracking Your Customers and Measuring Shopper...

Copyright © 2007 Indiana University

Tools for Tracking Your Customers and Measuring

Shopper EngagementRaymond R. Burke and Alex Leykin

Kelley School of BusinessIndiana University

November 2, 2007

Copyright © 2007 Indiana University

Copyright © 2007 Indiana UniversityCopyright © 2007 Indiana University

How Do We Measure & Manage Shoppability?

• Survey ResearchMeasure consumer perceptions of the shopping experience and diagnose problems with store, department, and category shoppability

• Observational ResearchTrack shopper behavior, identify points of engagement and purchase obstacles, andthen manipulate and measure response


Key Customer Touchpoints

• Store Entrance and Window Displays• Lead Fixtures and Merchandising• End-of-Aisle Displays• High Volume / Margin Departments• Customer Service Desk• Checkout


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


Benefits of Computer TrackingBreadth 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


Computer Tracking Solutions:Tracking Carts with Infrared/RFID Sensors


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


Computer Tracking Solutions:Tracking Shoppers with Video Cameras

Copyright © 2005 Burke and Sharma


Computer Tracking Solutions: Tracking Shoppers with Video Cameras



Automatic Behavior Analysis



Incoming Store TrafficInitial Direction Distribution

Aisle26%

Aisle37%

Aisle113%

Checkout Area30%

MainAisle44%

Average Store Traffic by Hour of Day

0

20

40

60

80

100

120

140

12am

-1am

1am

-2am

2am

-3am

3am

-4am

4am

-5am

5am

-6am

6am

-7am

7am

-8am

8am

-9am

9am

-10a

m

10am

-11a

m

11am

-12p

m

12pm

-1pm

1pm

-2pm

2pm

-3pm

3pm

-4pm

4pm

-5pm

5pm

-6pm

6pm

-7pm

7pm

-8pm

8pm

-9pm

9pm

-10p

m

10pm

-11p

m

Total Store Traffic

500

550

600

650

700

750

800

850

900

950

3/4/

2005

3/6/

2005

3/8/

2005

3/10

/200

5

3/12

/200

5

3/14

/200

5

3/16

/200

5

3/18

/200

5

3/20

/200

5

3/22

/200

5

3/24

/200

5

3/26

/200

5

3/28

/200

5

3/30

/200

5

4/1/

2005

Collection Period 3/3/05 - 4/2/05

Store Entry and Traffic Patterns



Post Period

Pre Period

Aisle Penetration



Category Dwell Time



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


Tracking - System Overview

Detection Tracking Activity Recognition

The tracking system works in three steps:


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


Tracking - Blobs

• The result of background subtraction is a binary bitmap

• Foreground regions corresponding to moving people are represented as blobs (in red)


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


Tracking – Detecting HeadsThe 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


Tracking – Detecting Heads (cont.)


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


Tracking – Sampling DynamicsTo 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


Tracking - Results


Tracking Example: Camera View


Tracking Example: Store View


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


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


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


Store Penetration& PurchaseConversion

Men’s Women’s


The Original Men’s Section


Men’s Style Center - Outfits


Men’s Style Center – Product Table


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


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


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


"Black Friday" Boosts Store Traffic...

0

200

400

600

800

1000

1200

1400

1600

6-7AM 7-8AM 8-9AM 9-10AM 10-11AM

11-12PM

12-1PM 1-2PM 2-3PM 3-4PM 4-5PM 5-6PM 6-7PM 7-8PM 8-9PM 9-10PM

11/28/2003

11/21/2003

Source: Burke 2005Source: Burke 2005


… But Not Purchase Conversion Rate

0%

10%

20%

30%

40%

50%

60%

6-7AM 7-8AM 8-9AM 9-10AM 10-11AM

11-12PM

12-1PM 1-2PM 2-3PM 3-4PM 4-5PM 5-6PM 6-7PM 7-8PM 8-9PM 9-10PM

11/28/2003

11/21/2003

Source: Burke 2005Source: Burke 2005


Challenges

• Creating the Digital Store• Employee Identification• Tracking Customer Groups• Measuring Focus of Attention• Recognizing Complex Behavior


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


Resources

Questions?

[email protected]

Indiana University’s Kelley School of Business

www.kelley.iu.edu

Copyright © 2007 Indiana University Tools for Tracking Your Customers and Measuring Shopper...

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