Use In-Store Location Data To Create A Better Customer Experience And More Sales

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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  

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