FUJITSU Retail Solution Retail Engagement Analytics

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1 FUJITSU Retail Solution Retail Engagement Analytics Cesare DeVecchi Country Representative Sales Retail Sector - Fujitsu Gianluca Ferrè Partner Executive - Cisco 0 Copyright 2016 FUJITSU

2 The BIG trends Mobile Smartphone is becoming the primary shopping tool Digitization Retailers no longer want bespoke (expensive) they want standard (simple and less expensive) Customer Experience Customers are expecting retailers to know, them, their shopping history and their shopping needs Channel Convergence Anything, Anywhere, Anytime shopping 1 Copyright 2016 FUJITSU

3 Digitization: The World is Becoming Digital Ubiquitous Connected Devices Automation Insight Analytics New Engagement Models Business Employees Consumers New Business Models Drive Additional Opportunity Improved Productivity and Employee Engagement Convenience and Better Experiences Connectivity + Insight = Opportunity The Network is the Foundation 2 Copyright 2016 FUJITSU

4 How Important is Customer Experience? In 2013 Consumers 66% Switched brands due to poor customer experience* Companies In 89% 2016 Of companies will compete based on customer experience (up from 36% in 2012)** 3 Copyright 2016 FUJITSU

5 In-store Analytics 4 Copyright 2016 FUJITSU

6 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 5 Copyright 2016 FUJITSU

7 Analytics in online retail is more advanced State of analytics is very mature in the online world Each and every click, login and purchase can be analyzed in real-time Tools Omniture, Web trends, Google analytics Stores account for ~ 90% of transactions but in-store analytics are lagging far behind Unique Visitors Engagement Repeat Visitors Purchase 6 Copyright 2016 FUJITSU

8 Opportunity to transform retail stores After Before Business Platform NETWORK ROLE BUSINESS INTELLIGENCE CUSTOMER ENGAGEMENT Utility Manual, Periodic Face-to-Face, Print, Media Advertising Automated, Real-Time Mobile Devices, Digital, Context-Aware 7 Copyright 2016 FUJITSU

9 FUJITSU Retail Solutions Retail Engagement Analytics 8 Copyright 2016 FUJITSU

10 What is Superior Customer Experience? What Do Customers Want? How Can You Deliver? Consistent Information across all Channels Effortless Service Experience at every Touchpoint Personalized Attention to Their Specific Needs 3 Buy 4 5 Create Omni-channel Experience Own Enhance In-store & Support Experience Predict Customer Behavior using Analytics & Insight 8 7 Consistent Information and Delightful Experience at Every Touchpoint Deliver Delightful Experiences and Effortless Transactions for Your Customer * Gartner Seven Best practices help EA to support a customer focused digital strategy, Marcus Blosch, Ed Thompson, Jul 2014

11 Fujitsu + Cisco Business Offers FUJITSU Retail Engagement Analytics How Can You Start? Virtual Expertise Create Omni-channel Experience Mobile Experience Enhance In-store & Support Experience Predict Customer Behavior using Analytics & Insight Intelligent Contact Center Intelligent Branch Location-Based Analytics Connected Analytics 10 Copyright 2016 FUJITSU

12 11 https://youtu.be/zsa6nu8d1uu Copyright 2016 FUJITSU

13 Fujitsu Retail Engagement Analytics Fujitsu Retail Engagement Analytics provides retailers with an effective solution for understanding and analyzing shopper behavior while they are present in the store: 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 Strategic partnership with Cisco 12 Copyright 2016 FUJITSU

14 What is the goal? To provide accurate insights into the customer behavior within the store Much more detail is available for online shopping With REA the retailer can: Optimize floor plans and staffing based on shopper location insights Respond in real-time to shopper traffic and address operational needs Analyze traffic trends and correlate them with sales (POS) data conversion rates Understand customer engagement (dwell time) among different departments and correlation with actual conversion Provide a personalized engaging experience to shoppers by integrating location data into existing mobile apps 13 Copyright 2016 FUJITSU

15 REA - What is it exactly? Store Fujitsu Retail Analytics Solution Dashboard Visualization ETL (Extract, Transform, Load) Business Process Manager Web Interface Customer Smartphones Access Points Wireless LAN Controller Collect, Transform Datastore / Analytics Engine Automated Flow Discovery Real-Time Monitoring Notification Text/ Data Warehouse Raw Data Analysis Aggregated Data Smartphones Tablets POS Data Location Data Fujitsu Secure Cloud (S5) 14 Copyright 2016 FUJITSU

16 Transform the business with in-store analytics Detect Connect Engage Presence and location detection Visibility (Wi-Fi, BLE, video) Easy Wi-Fi login, custom or social Zone-based, custom splash pages Analytics App-based mobile engagement Context-aware in-venue experiences 15 Copyright 2016 FUJITSU

17 The Need for Greater Location Accuracy Accurate Data Refresh Rate System Latency With Hyperlocation less than 1 meter With FastLocate 10 updates per minute With Cisco CMX 2-4 seconds Understand customer behavior Identify movement patterns Gain real-time insights 16 Copyright 2016 FUJITSU

18 Customer Count Turn Analytical Insights Into Action via BPM Real time monitoring of customer traffic and staff allocation Alert store manager and staff to initiate pre-emptive action Threshold Actual customer arrivals Time of day Predicted customer arrival rate Reactive Alert to Store Manager Pre-emptive Alert to Shift Supervisor Store Traffic Map Stocking Supervisor Cashier Call out Break Visualization: Location of Store Staff, customers and behaviors 17 Copyright 2016 FUJITSU

19 Retail Engagament Anaytics Applications Transforming Retail Operations and Shopping Experience in the Store 18 Copyright 2016 FUJITSU

20 Shopper Engagement Intelligence Dashboard shows critical statistics about in-store customer engagement: number of visitors, dwell time, traffic trends and conversion rate. 19 Copyright 2016 FUJITSU

21 Floor Plan Optimization Visualize the actual customer flow through the store Understand customer and staff positions, dwell time, behavior Visualize typical routes and the percentage of the shoppers who take these routes Most frequent route (20.5% of total shoppers took this path) 20 Copyright 2016 FUJITSU

22 Store Staff Optimization Visualize store staff location and customer traffic in single view Visualize store staff location ratio in floor map Store staff location VS customer traffic can tell whether resource is sufficient in peak time 11.6% 32.7% 13.4% Ratio of store staff location In floor map 29.2% 14.4% 9.5% 21 Copyright 2016 FUJITSU

23 Floor Plan and Staff Optimization Find the hot spot or dead zone, by visualizing traffic density in floor map Allocate more staff resources to the right spot at right time Traffic Density and Staff Allocation 22 Copyright 2016 FUJITSU

24 Why Retail Engagament Anaytics? 23 Copyright 2016 FUJITSU

25 Core Benefits for Retailers We can track key customer behavior in the store Number of people by store & zones Peak time in store/zone New compared to repeat visitors Common traffic patterns Where people spend time So retailers can Optimize floor layouts vs hot spots Optimize # checkouts & location Understand visit patterns Manage in-store congestion proactively Optimize staff allocation and scheduling 24 Copyright 2016 FUJITSU

26 And the possibilities are endless Personalized offers via recommendation engine Benchmark store performance Different stores Compare department performance between different stores Queue management 1. Queue length, 2) Avg. wait time 3) No. of customers in each queue Predict staffing needs Market with relevant promotional material/videos delivered on phones Integrate with social media Facebook, twitter Identifying customer demographics customer segmentation Identify most loyal (repeat) customers to offer special incentives Identify number of people going to fitting rooms but not buying anything Social Clientelling 25 Copyright 2016 FUJITSU

27 Customer Case Study 26 Copyright 2016 FUJITSU

28 Leading Fashion Clothing Retailer A multinational retail-clothing company, known for its fashion clothing for men, women, teenagers and children Fujitsu gathered and analyzed location and sales data including the period of a store-wide promotion 27 Copyright 2016 FUJITSU

29 Customer Visits - New vs. Repeat Key Finding Loyal customers consist nearly half of the total traffic Traffic drops significantly on March Potential Action Loyalty program to reward repeat customers Examine the cause of traffic drop Repeat Customers New Customer * This data is representative data 28 Copyright 2016 FUJITSU

30 Shopper Traffic by Hour 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 1200 Shoppers am 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 * This data is representative data 29 Copyright 2016 FUJITSU

31 Total Customer Visits by Department by Week Key Finding Traffic to cosmetic department almost doubled during week 4 Potential Action Examine and analyse the promotion effectiveness during week 4 at cosmetics department Week1 Week2 Week3 Week Shoes Ladies Home Kids Cosmetics * This data is representative data 30 Copyright 2016 FUJITSU

32 Sales Conversion/Dwell Time by Zone Key Finding Men s department has the low dwell time but high conversion rate Ladies department has the long dwell time but low conversion rate Potential Action Examine the reason behind this and design corresponding strategy to boost the conversion rate at Ladies department Short dwell time & high conversion rate Problem Focus: Long dwell time & low conversion rate * This data is representative data 31 Copyright 2016 FUJITSU

33 Traffic Density Across the Store Hot spots across the store layout Key Finding Potential Action Allocate more staff resources to the right spot at right time * This data is representative data 32 Copyright 2016 FUJITSU

34 Key Findings Identified most prominent routes taken by customers Majority Visitors to one dept.- Floor plan optimization Signage to encourage department visits Quantified the effectiveness of promotions Marketing effectiveness Developed actionable intelligence for departments where conversion rate was low Identified relationship between staff allocation and conversion Staffing Optimization Identified most effective time periods to run promotions 33 Copyright 2016 FUJITSU

35 So, How do you Differentiate? 34 Copyright 2016 FUJITSU

36 35 Copyright 2016 FUJITSU

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