Retail TouchPoints's Retail Strategy & Planning Series session brought to you by Fujitsu #RSPS15

1. #RSPS15
#RSPS15
Retail
Touchpoints:
@RTouchPoints
Fujitsu:
@interstage
Keith
Swensen:
@swensonkeith
Debbie
Hauss:
@dhauss
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4. #RSPS15
Panelists
@swensonkeith
Keith
Swenson
VP
of
Research
and
Development
Fujitsu
North
America
MODERATOR:
Debbie
Hauss
Editor-­‐in-­‐Chief,
Retail
TouchPoints

5. 5 © 2015 FUJITSU
Connect. Challenge.
Inspire.
FUJITSU
Retail Solution
Engagement
Analytics
Keith Swenson
VP of R&D, Fujitsu America
September 21, 2015

6. 6 © 2015 FUJITSU
Fujitsu – global ICT provider
Fujitsu is a leading provider of
Information and communication
Technology (ICT) business solutions
for the global marketplace, offering a
full range of technology products,
solutions and services.
n Headquarters: Tokyo, Japan
n Established: 1935
n Net sales: US$ 46.2 billion
n No. 4 globally, No. 1 Japan
n 159,000 employees
n Supporting customers in more than 100
countries
n 5% R&D spend (US$ 2.2 billion)
n Research facilities: Japan, US, UK, Germany,
China, Singapore

7. 7 © 2015 FUJITSU
Fujitsu in Retail – at a glance
Revenue
$1.6b
Solutions
Retail R&D/Innovation
>$20m
Years
30
Global Retail
Team
8,000
Countries
52
Analytics
Omni-channel Self Service
ICT Services
Customers
>500

8. 8 © 2015 FUJITSU
Who we work with
EMEA Japan & Asia Americas
Oceania

9. 9 © 2015 FUJITSU
In-store Analytics

10. 10 © 2015 FUJITSU
Analytics in online retail is more advanced…
Unique Visitors
Engagement
Repeat Visitors
Purchase
n State of analytics is very mature in
the online world
n Each and every click, login and
purchase can be analyzed in real-
time
n Tools – Omniture, Web trends,
Google analytics
n Stores account for ~ 90% of
transactions but in-store analytics are
lagging far behind

11. 11 © 2015 FUJITSU
Customers are using mobile while shopping…
…and retailers are seeking to leverage mobile-driven analytics to identify
customers, track behaviour and tailor messaging and store offerings

12. 12 © 2015 FUJITSU
Opportunity to transform retail stores
After
Business Platform
Automated, Real-Time
Mobile Devices,
Digital, Context-Aware
Before
NETWORK ROLE
BUSINESS
INTELLIGENCE
CUSTOMER
ENGAGEMENT
Utility
Manual, Periodic
Face-to-Face, Print, Media
Advertising

13. 13 © 2015 FUJITSU
Retailers can transform the business with in-store analytics
• Presence and location detection
• Visibility (Wi-Fi, BLE)
• Easy Wi-Fi login, custom or social
• Zone-based, custom splash pages
• App-based mobile engagement
• Context-aware in-venue experience
Analytics
Detect Connect Engage

14. 14 © 2015 FUJITSU
Optimize staff allocation and scheduling
Understand customer visit patternsNEW
Optimize store floor layouts
Optimize number of checkouts & location
Manage in-store congestion in real-time
In-store Analytics can help the retailer to…

15. 15 © 2015 FUJITSU
Use Cases
How Anaytics is Changing Retail Operations and
Shopping Experience in the Store

16. 16 © 2015 FUJITSU
UC # 1 - Path to Purchase
n Store dashboard
shows critical
statistics about
in-store
customer
engagement

17. 17 © 2015 FUJITSU
UC # 1 - Path to Purchase
n Store dashboard shows
critical statistics about in-
store customer
engagement

18. 18 © 2015 FUJITSU
UC#2 - Customer Dwell Time vs. Sales
n Visualize traffic, dwell
time (by department),
and conversion rate
n Identify issues – long
dwell and low
conversion?
• Product Quality
• Price
• Packaging
• Visual Merchandising
• Location
Short dwell time &
high conversion rate
Problem Focus:
Long dwell time
& low conversion
rate

19. 19 © 2015 FUJITSU
Most frequent route
(20.5% of total shoppers took this path)
UC# 3 - Floor Plan Optimization
n Visualize the actual customer flow through the store
n Understand customer and staff positions, dwell time, behavior
Visualize typical routes
and the percentage of
the shoppers who take
these routes

20. 20 © 2015 FUJITSU
UC# 3 - Floor Plan Optimization
n Find the hot
spot or dead
zone, by
visualizing traffic
density in floor
map

21. 21 © 2015 FUJITSU
UC# 4 - Store Staff Optimization
Time of day
Threshold
CustomerCount
n Real time monitoring of customer traffic and staff behavior
n Alert supervisor when thresholds are approached
Visualization: Location of
Store Staff, customers and
behaviors
Predicted customer
arrival rate
Pre-emptive Alert to Shift Supervisor
Reactive Alert to Store Manager
Actual customer
arrivals
Real-Time Traffic Map
Call out
Stocking Break
Cashier
Supervisor

22. 22 © 2015 FUJITSU
UC# 4 - Store Staff Optimization
n Visualize store staff location and customer traffic in single view
n Visualize store staff location ratio in floor map
32.7%
29.2%
11.6% 13.4%
14.4% 9.5%
Store staff location VS customer traffic can tell
that whether resource is sufficient in peak time
Ratio of store staff location
In floor map

23. 23 © 2015 FUJITSU
And the possibilities are endless…
q Personalized offers via recommendation engine
q Benchmark store performance
§ Different stores
§ Compare department performance between different stores
q “Queue management” module
§ 1. Queue length, 2) Avg. wait time 3) No. of customers in
each queue
q Predict staffing needs
q Market with relevant promotional material/videos delivered on
phones
q Integrate with social media – Facebook, twitter
q Identifying customer demographics – customer segmentation
q Identify most loyal (repeat) customers to offer special incentives
q Identify number of people going to fitting rooms but not buying
anything
q Social Clientelling

24. 24 © 2015 FUJITSU
FUJITSU Retail Solution - Engagement
Analytics

25. 25 © 2015 FUJITSU
Fujitsu Retail Engagement Analytics
q Fujitsu Retail Engagement Analytics provides retailers with an
effective solution for understanding and analyzing shopper
behavior while they are present in the store.
q Key Features
§ Collect and aggregate live customer location data
§ Combine location data with sales data to provide
actionable insights
§ In-store traffic monitoring and alerting for better store
operations
§ Patented Automated Flow Discovery to visualize traffic
flows
§ Visualized analytics insights - dashboard/heat maps/
flow maps
§ Secure cloud hosting with global reach

26. 26 © 2015 FUJITSU
End-to-End Retail Analytics Solution
Technology and service integration
Customer Solution
Intelligence Layer
Data Handling Layer
Infrastructure Layer
Verticallyintegrated
WiFi Beacon Laser Camera
HA Datastore Data Aggregation Data Collection
Data Analytics
Business Process
Management
Real-time
Monitoring

27. 27 © 2015 FUJITSU
Delivery - Hosted on Trusted Fujitsu Cloud – S5
n Secure (accredited to
ISO27001)
n Available on demand
over the Internet
n On a cost-effective
pay-per-use basis
n Delivered via our
global network of data
centers -
• Japan, Australia, USA, Singapore,
UK and Germany
n Same service on
identical platforms
wherever you operate

28. 28 © 2015 FUJITSU
Customer Case Study

29. 29 © 2015 FUJITSU
Leading Fashion Clothing Retailer
q A multinational retail-clothing company, known
for its fashion clothing for men, women,
teenagers and children
q Fujitsu gathered and analyzed location and
sales data including the period of a store-wide
promotion

30. 30 © 2015 FUJITSU
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Repeat Customers
New Customer
Customer Visits - New vs. Repeat
Key Finding Potential Action
Loyal customers consist nearly half of the total traffic Loyalty program to reward repeat customers
Traffic drops significantly on March 19-20 Examine the cause of traffic drop
* This data is representative
data

31. 31 © 2015 FUJITSU
Shopper Traffic by Hour
0
200
400
600
800
1000
1200
10-11 am 11-12 pm 12-1 pm 1-2 pm 2-3 pm 3-4 pm 4-5 pm 5-6 pm 6-7 pm 7-8 pm 8-9 pm
Shoppers
Key Finding Potential Action
Traffic peaks around 1-2 pm and keep quite constant from 2-8 pm 1. Special promotion during the peak hour
2. Allocate more staff resources during the peak hours
* This data is representative
data

32. 32 © 2015 FUJITSU
Total Customer Visits by Department by Week
0
5000
10000
15000
20000
25000
Shoes Ladies Home Kids Cosmetics
Week1
Week2
Week3
Week4
Key Finding Potential Action
Traffic to cosmetic department almost doubled during week 4 Examine and analyse the promotion effectiveness
during week 4 at cosmetics department
* This data is representative
data

33. 33 © 2015 FUJITSU
Most Frequent Route
Key Finding Potential Action
Most frequent route is Front Door –> Ladies –>Out (20.5% of the
customers take this route)
1. Store layout improvement
2. Cross-sell potential on this route
3. More staff resource on this route
* This data is representative
data

34. 34 © 2015 FUJITSU
Sales Conversion/Dwell Time by Zone
Key Finding Potential Action
Mens department has the low dwell time but high conversion rate
Ladies department has the long dwell time but low conversion rate
Examine the reason behind this and design
corresponding strategy to boost the conversion rate
at Ladies department
* This data is representative
data
Short dwell time &
high conversion rate
Problem Focus:
Long dwell time
& low conversion
rate

35. 35 © 2015 FUJITSU
Traffic Density Across the Store
Key Finding Potential Action
“Hot spots” across the store layout Allocate more staff resources to the right spot at right time
* This data is representative
data

36. 36 © 2015 FUJITSU
Key Findings
q Identified most prominent routes taken by customers
§ Majority Visitors to one dept.- Floor plan optimization
§ Signage to encourage department visits
q Quantified the effectiveness of promotions
§ Marketing effectiveness
q Developed actionable intelligence for departments where
conversion rate was low
q Identified relationship between staff allocation and conversion
§ Staffing Optimization
q Identified most effective time periods to run promotions

37. 37 © 2015 FUJITSU

38. #RSPS15
Q
&
A
//
Panelists
@swensonkeith
Keith
Swenson
VP
of
Research
and
Development
Fujitsu
North
America
MODERATOR:
Debbie
Hauss
Editor-­‐in-­‐Chief,
Retail
TouchPoints

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