use in-store location data to create a better customer experience and more sales
TRANSCRIPT
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#RSPS15 Retail Touchpoints: @RTouchPoints
Fujitsu: @interstage Keith Swensen: @swensonkeith
Debbie Hauss: @dhauss
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Panelists
@swensonkeith
Keith Swenson VP of Research and Development Fujitsu North America
MODERATOR: Debbie Hauss Editor-‐in-‐Chief, Retail TouchPoints
5 © 2015 FUJITSU
Connect. Challenge. Inspire.
FUJITSU Retail Solution Engagement Analytics
Keith Swenson VP of R&D, Fujitsu America September 21, 2015
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 © 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 © 2015 FUJITSU
Who we work with
EMEA Japan & Asia Americas
Oceania
9 © 2015 FUJITSU
In-store Analytics
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 © 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 © 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 © 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 experiences
Analytics
Detect Connect Engage
14 © 2015 FUJITSU
Optimize staff allocation and scheduling
Understand customer visit patterns NEW
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 © 2015 FUJITSU
Use Cases How Anaytics is Changing Retail Operations and Shopping Experience in the Store
16 © 2015 FUJITSU
UC # 1 - Path to Purchase n Store dashboard
shows critical statistics about in-store customer engagement
17 © 2015 FUJITSU
UC # 1 - Path to Purchase n Store dashboard shows
critical statistics about in-store customer engagement
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 © 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 © 2015 FUJITSU
UC# 3 - Floor Plan Optimization n Find the hot
spot or dead zone, by visualizing traffic density in floor map
21 © 2015 FUJITSU
UC# 4 - Store Staff Optimization
Time of day
Threshold
Cus
tom
er C
ount
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 © 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 © 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 © 2015 FUJITSU
FUJITSU Retail Solution - Engagement Analytics
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 © 2015 FUJITSU
End-to-End Retail Analytics Solution
Technology and service integration
Customer Solution
Intelligence Layer
Data Handling Layer
Infrastructure Layer
Vert
ical
ly in
tegr
ated
WiFi Beacon Laser Camera
HA Datastore Data Aggregation Data Collection
Data Analytics Business Process
Management Real-time Monitoring
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 © 2015 FUJITSU
Customer Case Study
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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 2015 FUJITSU
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Q & A // Panelists
@swensonkeith
Keith Swenson VP of Research and Development Fujitsu North America
MODERATOR: Debbie Hauss Editor-‐in-‐Chief, Retail TouchPoints
#RSPS15
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