Retail Solutions
Stronger
Street Conversion
Retail operation re-defined with new insights
The Connected Store Data-driven insights
Immediate actions
Improved Store & Value Conversion
The Analytics
Advantage
Stronger
Street Conversion
Concept Design
Product Display
Communication Message
Improved Store & Value Conversion
Accurate, rich and detailed data
The raw data that Modcam provides in any format
Street Count
Store Count
Area Count
Dwell Time
Customer track
Traffic direction
Queue Length
Age
Gender
Attention time
ROI example
SOLUTION
Modcam services Patterns and Count were installed in departments within the store.
The staff and the managers could quickly get an overview of the areas with the most traffic (”hot zones”)
The staff and managers could also compare weekdays, specific hours and target groups.
POS data was combined with the traffic patterns in the department.
2. Minds of
innovation.
Successful
track record
Anders Laurin was
EVP at AXIS
Founded in 2013
by experts in
Devices Mangement
Compact Devices
Computer Vision
Peter Carlsson was
CPO & CSO TESLA
Bert Nordberg was
CEO of SONY MOBILE
is on BOARD of AXIS
Jan-Erik Solem was
AQUIRED by APPLE
Karl-Anders Johansson was
AQUIRED by BLACKBERRY &
AQUIRED by QUALCOMM
3. The Connected Store
Data-driven insights
Immediate actions
Typical
E-commerce
Click through
Attribution
A/B testing
…
Tradtitional
Brick & Mortar
Converstion rate
Point of Sale data
Retail operation
re-defined with
new insights
Modcam provides a sensor solution to track people and transforms
brick-and-mortar stores to data driven businesses.
6. Measure demographics and
Adapt Digital Signage
Measure zone conversion
and
Optimize Product Displays
Improved
Store &
Value
Conversion
Measure people flow
and
Optimize Store Layout
Measure dwelling customers and
Assign Staff
Measure que length and
Alert Staff
Understand shopper profile and
Improve self-checkout
7. Accurate, rich
and detailed
data
The raw data that Modcam provides in any format
Street Count
Store Count
Area Count
Dwell Time
Customer track
Traffic direction
Queue Length
Age
Gender
Attention time
8. Fully Flexible
To Your Needs
MOD.01
Sensor
MOD.Connect
Device Management Algorithms
Apps
Cloud
Data Cloud
Analytics
Web Experience
Cloud
Watson IoT & Bluemix
Analytics
Web Experience
Alt 1
Alt 2
11. Increased Street Conversion
Shop window re-design and messaging
based on data, A/B testing to identify
successful concepts
Increased Store Conversion
Shop window re-design and messaging
based on data, A/B testing to identify
successful concepts
Street
Traffic
Store
Traffic
Store Conversion
Basket
Size
Total Sales Sales Increase
Annual Sales
Increase
Annual Margin
Increase
Annual Solution
Cost ROI
Control 90 000 9 000 55% $44 $ 217 800
IMPROVED 90 000 9 900 56 % $44 $ 243 936
$26 136
+ 12%
+$313 632 +$ 94 089 - $ 15 800 495%
IMPROVED+ 90 000 10 800 57 % $44 $ 270 864
$53 064
+ 24%
+$636 768 +$ 191 030 - $ 15 800 1109%
ROI example
Active data-driven retail operation
12. Shop floor
layout
improvements
CASE STUDY #1
• PROBLEM
• Staff taken out of daily operation to conduct
shopper analysis.
• Shopper data analysis infrequent and sample
size small.
• Need better understanding of the customer
flow to actively improve and rearrange the
shop floor.
• Wish to work more actively with A/B testing:
Ability to measure conversion rate for product
families.
• RESULT
• 30% more ”back flow” traffic than anticipated. Product
display changed to be exposed in both directions,
which increased sales of by 8%
• Many customers made a shortcut past department B,
straight to department C. This path was closed and
department B increased sales by 6%.
• Detailed A/B testing gave new insights: Two different
displays were compared and one of them gave a
higher turnover: sales was 13% stronger.
• After 6 months there was a clear improvement in
results and turnover. The store manager was actively
using data to optimize layout and product display,
which led to an average revenue increase of 7%.
• SOLUTION
• Modcam services Patterns and Count were
installed in departments within the store.
• The staff and the managers could quickly get an
overview of the areas with the most traffic (”hot
zones”)
• The staff and managers could also compare
weekdays, specific hours and target groups.
• POS data was combined with the traffic patterns
in the department.