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How to Effectively Use
Shopper Analysis in a
Retail Business
© CountBOX 2016
Retail Market
Overview
© CountBOX 2016
Physical Stores vs. Mobile & E-Commerce
© CountBOX 2016
Source: Mazedon Retail
© CountBOX 2016
Source: Mazedon Retail
Retail Evolution
What is Shopper
Analysis?
© CountBOX 2016
What is Shopper Analysis?
• Data collected and analyzed to create insights as to:
• Who are the shoppers?
• Where do they ...
Where does the data come from?
• Main data sources:
• Retailer
• Point of sale (POS) systems in bricks & mortar stores
• O...
What POS shopper data is collected?
• Purchase information
• Merchandise purchased in stores and online
• From single “dep...
Online Shopper
Analysis
© CountBOX 2016
Google Analytics
© CountBOX 2016
Google Analytics – Behavior
© CountBOX 2016
Google Analytics – Age
© CountBOX 2016
Google Analytics – Geography
© CountBOX 2016
Google Analytics – Device Used
© CountBOX 2016
Google Analytics – How Acquired
© CountBOX 2016
Google Analytics - Website
© CountBOX 2016
Summary: Online Shopper Analysis
• Google Analytics provides the following information on shoppers:
• Base demographics
• ...
In-Store Shopper
Analysis
© CountBOX 2016
In-Store Shopper Analysis
• Provides shopper and area analytics for retail stores/chains
• The data includes:
• Who the sh...
Unobtrusive In-Store Sensors
Door counting sensors:
Area counting sensor (WIFI)
Demographic sensor
In-Store Shoppers
Shoppers are key to retailers’
success …. do they know who
their customers are?
(Surveys cannot provide
...
In-Store Shopper Profiles
Using facial recognition technology
shopper profiles are created …
Shopper data is aggregated fo...
How Does It Work?
Images Reviewed For Accuracy
Sample Shopper Profile Analyses
Customer Loyalty
Using door sensors and
WIFI sensors within the
store or mall
• Calculates the
exact percentage of
loyal c...
Customer Service & Satisfaction
Using WIFI analyses queue
wait times are calculated
• Customer satisfaction is
directly re...
Customer Engagement
Area counting examines
Customer Engagement
• Exact percentage of
customers within areas of
stores or m...
Shopper Traffic by Week
© CountBOX 2016
Store Traffic by Day of Week
© CountBOX 2016
Store Traffic Distribution by Hour
© CountBOX 2016
Key Metrics With Retailer Data
When you combine retailer data with shopper analysis
data you create even more powerful mea...
Store Conversion Rate v. Traffic by Day
© CountBOX 2016
Assess Marketing Campaigns and Events
Lift in shopper traffic and sales for the campaign or event
• Shopper traffic analys...
Additional Shopper Analyses
• Benchmarking of key metrics
• Internal (company)
• Industry segment
• Measuring and modeling...
Summary: In-store Shopper Analysis
• In-store shopper analyses provide the retailer with data on:
• Who the customer is
• ...
Conclusions
• Shopper Analysis is invaluable in providing insights as to:
• Who customers are
• Where do they go
• What th...
Thank you!
Q & A
© CountBOX 2016
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How to Effectively Use Shopper Analysis in Retail Business

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Learn how you can build a complete shopper profile and analyze your customers using comprehensive suite of retail analytics tools to identify store traffic patterns, conversion rates, dwell times, demographic data and other key metrics.

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How to Effectively Use Shopper Analysis in Retail Business

  1. 1. How to Effectively Use Shopper Analysis in a Retail Business © CountBOX 2016
  2. 2. Retail Market Overview © CountBOX 2016
  3. 3. Physical Stores vs. Mobile & E-Commerce © CountBOX 2016 Source: Mazedon Retail
  4. 4. © CountBOX 2016 Source: Mazedon Retail Retail Evolution
  5. 5. What is Shopper Analysis? © CountBOX 2016
  6. 6. What is Shopper Analysis? • Data collected and analyzed to create insights as to: • Who are the shoppers? • Where do they go in stores? • What do they buy? • How do they buy the merchandise? • How long do they stay in the stores or the online store? • How often do they shop at the store? (Loyalty) • When do they shop during the week? • When do they shop during the days of the week? • What seasons do they shop the most? • When do they shop the least? © CountBOX 2016
  7. 7. Where does the data come from? • Main data sources: • Retailer • Point of sale (POS) systems in bricks & mortar stores • Online store transactional data • External data sources • Surveys • Online • In-mall • Observational data • Gathered in-store: 24/7/365 • Secondary data sources • Reports, third-party databases, etc. © CountBOX 2016
  8. 8. What POS shopper data is collected? • Purchase information • Merchandise purchased in stores and online • From single “department” • From more than one “department” • Size of transaction • Number of items purchased (UPT) • Dollars used in transaction (average receipt or dollars per transaction) • Type of transaction • Credit • Debit • Cash (in-store only) • Loyalty program usage © CountBOX 2016
  9. 9. Online Shopper Analysis © CountBOX 2016
  10. 10. Google Analytics © CountBOX 2016
  11. 11. Google Analytics – Behavior © CountBOX 2016
  12. 12. Google Analytics – Age © CountBOX 2016
  13. 13. Google Analytics – Geography © CountBOX 2016
  14. 14. Google Analytics – Device Used © CountBOX 2016
  15. 15. Google Analytics – How Acquired © CountBOX 2016
  16. 16. Google Analytics - Website © CountBOX 2016
  17. 17. Summary: Online Shopper Analysis • Google Analytics provides the following information on shoppers: • Base demographics • Shopper acquisition • Geographical dispersion of shoppers • Shopper movement through the online store • Pageviews • Bounce Rate, etc. • Analysis of online ad campaigns • Conversion rate of online ads © CountBOX 2016
  18. 18. In-Store Shopper Analysis © CountBOX 2016
  19. 19. In-Store Shopper Analysis • Provides shopper and area analytics for retail stores/chains • The data includes: • Who the shopper is: age, gender, ethnicity and mood • Where they go in the store or mall • Area analytics • What they do in the store • Shopper traffic distributions by time period • Dwell times • Occupancy • Store analytics • KPI’s • Labor • Marketing and advertising campaign analysis © CountBOX 2016
  20. 20. Unobtrusive In-Store Sensors Door counting sensors: Area counting sensor (WIFI) Demographic sensor
  21. 21. In-Store Shoppers Shoppers are key to retailers’ success …. do they know who their customers are? (Surveys cannot provide shopper profiles at the store or mall level.)
  22. 22. In-Store Shopper Profiles Using facial recognition technology shopper profiles are created … Shopper data is aggregated for composite shopper profiles that include: • gender, age, ethnicity and mood. The shopper profiles are reliable as they come from a census of the store’s shoppers
  23. 23. How Does It Work?
  24. 24. Images Reviewed For Accuracy
  25. 25. Sample Shopper Profile Analyses
  26. 26. Customer Loyalty Using door sensors and WIFI sensors within the store or mall • Calculates the exact percentage of loyal customers • Assessments made by time periods, promotions or events
  27. 27. Customer Service & Satisfaction Using WIFI analyses queue wait times are calculated • Customer satisfaction is directly related to wait times • Longer wait times = less satisfied customers • Compare average wait times to company standards
  28. 28. Customer Engagement Area counting examines Customer Engagement • Exact percentage of customers within areas of stores or malls • Heat maps: represent high and low traffic areas within a venue • Dwell time of customer in the store. • Longer dwell times = sales
  29. 29. Shopper Traffic by Week © CountBOX 2016
  30. 30. Store Traffic by Day of Week © CountBOX 2016
  31. 31. Store Traffic Distribution by Hour © CountBOX 2016
  32. 32. Key Metrics With Retailer Data When you combine retailer data with shopper analysis data you create even more powerful measures: • Conversion rate • Percentage of shoppers that make a purchase • Value of each shopper • Total sales divided by number of shoppers per time period • Shoppers to staff ratio • Number of shoppers per staff member on the sales floor • Staff optimization • Scheduling staff to shopper traffic based on staffing constraints • More scheduled during peak hours • Less scheduled during low traffic hours
  33. 33. Store Conversion Rate v. Traffic by Day © CountBOX 2016
  34. 34. Assess Marketing Campaigns and Events Lift in shopper traffic and sales for the campaign or event • Shopper traffic analyses • Percentage increase • Actual increase in the number of shoppers • Track staff to shopper ratio • Sales analyses • Percentage increase in sales • Actual increase in sales • Increase/decrease in sales per shoppers (shopper value) • Increase/decrease in conversion rate • Assess any potential “lost” sales through: • Staff to shopper ratios • Decrease in conversion rate Compare and contrast marketing campaigns and events: • Winners and losers • Assess any changes in shopper profiles that may occur during the campaign or event
  35. 35. Additional Shopper Analyses • Benchmarking of key metrics • Internal (company) • Industry segment • Measuring and modeling of environmental factors • Impact of weather and weather events • Industry trends • Seasonality
  36. 36. Summary: In-store Shopper Analysis • In-store shopper analyses provide the retailer with data on: • Who the customer is • When and where they shop in the store • How long they spend in line and in the store • KPI’s for store operations • Conversion rate • Shopper value • Shopper to staff ratio • Analyzing marketing campaigns and events © CountBOX 2016
  37. 37. Conclusions • Shopper Analysis is invaluable in providing insights as to: • Who customers are • Where do they go • What they do • Both online and in-store • This information is used to provide: • Key performance metrics to improve the business by focusing on the customer • To prove their relevance, physical stores need to embrace shopper analysis • Provide the right products at the right price in the right area with the right service levels • Shoppers and customers are assets © CountBOX 2016
  38. 38. Thank you! Q & A © CountBOX 2016

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