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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 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
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
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
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
• 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
In-Store Shopper
Analysis
© CountBOX 2016
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
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
shopper profiles at the store or
mall level.)
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
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 customers
• Assessments made
by time periods,
promotions or
events
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
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
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 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
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 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
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
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
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
Thank you!
Q & A
© CountBOX 2016

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How to Effectively Use Shopper Analysis in Retail Business

  • 1. How to Effectively Use Shopper Analysis in a Retail Business © CountBOX 2016
  • 3. Physical Stores vs. Mobile & E-Commerce © CountBOX 2016 Source: Mazedon Retail
  • 4. © CountBOX 2016 Source: Mazedon Retail Retail Evolution
  • 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. 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. 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
  • 11. Google Analytics – Behavior © CountBOX 2016
  • 12. Google Analytics – Age © CountBOX 2016
  • 13. Google Analytics – Geography © CountBOX 2016
  • 14. Google Analytics – Device Used © CountBOX 2016
  • 15. Google Analytics – How Acquired © CountBOX 2016
  • 16. Google Analytics - Website © CountBOX 2016
  • 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
  • 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. Unobtrusive In-Store Sensors Door counting sensors: Area counting sensor (WIFI) Demographic sensor
  • 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. 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. How Does It Work?
  • 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. 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. 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. Shopper Traffic by Week © CountBOX 2016
  • 30. Store Traffic by Day of Week © CountBOX 2016
  • 31. Store Traffic Distribution by Hour © CountBOX 2016
  • 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. Store Conversion Rate v. Traffic by Day © CountBOX 2016
  • 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. 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. 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. 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. Thank you! Q & A © CountBOX 2016