Harnessing Data to Optimize and Personalize the In-Store Shopping Experience

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1 Harnessing Data to Optimize and Personalize the In-Store Shopping Experience People-tracking technology, including sensors, beacons, and video cameras, combined with mobile applications, can provide an endless supply of data on the behavior of shoppers in their stores. A modern BI platform can help you harness this data in combination with your customer data to optimize the in-store shopping experience, increase sales across all channels and make merchandising a data-driven process. Key Issues Datameer helped a large omnichannel retailer optimize their in-store customer experience to increase sales and speed purchases Getting the 360 view. With great confidence in their ability to track customer behavior online, a retailer s data blind spot is now in-store. Understanding the impact of the in-store experience is now paramount to getting a 360 view of customers and operations. Harnessing in-store data. Many retailers are experimenting with people-tracking technology to better understand what is happening in their stores.

2 Data-driven merchandising. Considered an art form, with in-store people tracking data, merchandising can now become a data- and results-driven process. Correlating tracking data with behavior. With data flowing from people-tracking technology, retailers have lots of data on customer behavior from different channels, but no way to bring it all together for simultaneous correlation and analysis the key to breakthrough insights. Limited potential insights. Retailers rely on many specialized analytical tools, each optimized for different data sources and set up to answer specific, predetermined questions. This limits potential insights considerably. The Opportunity In the past, merchandising was considered an art form, with no true way to measure the specific impact of merchandising decisions. And, as online sales grew, a new trend emerged where shoppers would do their physical research on products in-store and then purchase online at a later time. The advent of people tracking technology is allowing omni-channel retailers to better measure the impact of their merchandising efforts. This new people tracking data allows retailers to analyze the entire in-store customer experience. With this new data retailers can: Track the path of shoppers in their stores to determine the optimal merchandising and displays for various products Monitor what products and displays the customer sees to measure the impact on sales for that customer and overall, both in-store and online Link this information to loyalty programs and applications to ensure they stay top of mind with customers so the in-store experience is not wasted Big data analytics can turn this new data source into a major competitive advantage for retailers. The key is using data-driven insights to optimize in-store experiences that provide unique value and pleasure for consumers. Insights based on data from PAGE 2

3 websites, point-of-sale systems, mobile apps, supply chain systems, in-store sensors and cameras, and other sources can be used to improve in-store experiences, optimize online experiences, increase customer loyalty, drive up cross-selling, increase promotion effectiveness, and more. Using modern BI platforms, omni-channel retailers can: Test and quantify the impact of different marketing and merchandizing tactics on customer behavior and sales Use a customer s purchase and browsing history to identify needs and interests and then personalize in-store service for customers Monitor in-store customer behavior and drive timely offers to customers to incent in-store purchases or later online purchases, thereby keeping the purchase within the fold of the retailer Gathering Data for In-Store Customer Behavior New people-tracking technologies such as beacons, sensors, and cameras can capture customer activities inside the store. This will allow retailers to better understand their traffic patterns, empower in-store merchandising, schedule to demand, manage queues, and measure sales conversion. Retailers have perfected the ability to track customers in their online stores and use this data to their advantage to optimize purchase paths and maximize online sales. Wireless technology, sensors, cameras and mobile applications now allow retailers to capture similar data on their customers in a store. For example, sensors and/or video cameras can track customers in-store and capture their behavior and wireless hotspots can track loyalty program members with the retailer s mobile app to further identify specific customers and make real-time offers. While a number of retailers have started capturing in-store data from these new sources, in many cases, they have not found the right ways to harness it to their advantage. Here are some specific ways in which a modern BI platform can be used to not only harness this data, but also deliver the metrics the retailer needs to drive real business results and ROI. PAGE 3

4 Use Cases Cutting-edge retailers are taking big data analytics into exciting directions and Datameer provides the platform to make their plans happen. Here are a number of use cases a modern BI platform such as Datameer can enable to optimize the in-store shopping experience. Optimize In-Store Merchandising to Maximize Sales With big data analytics, merchandising can evolve from an art form to a data- and resultsdriven process. Big data analytics can generate key metrics that can help merchandizers understand what s working, what s not, and where customers spend time, as well as other in-store experience areas such as queues. Most importantly, big data analytics can add to very important aspects to these metrics: how purchases were influenced and why they were influenced. This allows merchandisers to make results-oriented choices that maximize business impact. Specific types of big data analytics retailers can perform include: Comprehensive understanding of store traffic and shoppers navigation of the store (paths) Identifying employee paths and interaction points to optimize the efforts of sales associates Measurement of the entire shopper journey, including new versus repeat customer, visit frequency and duration Correlating traffic patterns with purchases for various merchandizing options to gain insights into how each option impacted customer behavior Comparing product placement options and groupings to determine how to drive higher sales to optimize displays for the most popular customer paths The result is a data-driven methodology to help merchandisers and store planners most effectively influence the in-store customer experience and increase sales. PAGE 4

5 Deliver Offers to Loyalty Program Members Loyalty program members are among the most reliable customers with a high degree of wallet-share and strong repeat purchase rates. They are also very targetable via offers and in real-time through loyalty program mobile applications. Data generated by people-tracking technologies can be correlated with online activity and past purchase behavior by loyalty program members to influence purchases. Offers can be generated, both in real-time via beacons and mobile applications, and at future times via or when visiting online stores, to influence the loyalty program member to complete purchases on interested products. Specific types of big data analytics retailers can perform include: Combining loyalty program traffic in stores with customer shopping history and purchases to drive real-time, personalized offers to members while shopping in-store Tracking loyalty program shopper paths in comparison to other customers to optimize in-store loyalty program promotion placement Determining loyalty customer interest in specific products by in-store locations, correlated with in-store purchases that day/location to drive re-marketing Tracking the locations of customers via their loyalty program application can increase sales by enabling impulse purchases with real-time offers, and re-marketing programs that stimulate purchases after the in-store visit. PAGE 5

6 Datameer s Approach Using Datameer s comprehensive modern BI platform, you can aggregate, correlate and analyze all of your customer data from across channels and turn it into powerful insights. These insights can be used to optimize and personalize in-store merchandizing and customer experiences. With Datameer, you can: Answer deeper questions and discover insights: Datameer s self-service environment supports the entire analytic cycle and provides an agile, iterative method to discover detailed answers from your data. An easy- to-use interface and on-the-fly modeling empowers business analysts to work easily and without IT assistance. Answer more questions: Datameer helps your BI and analyst teams be more productive, perform a wider range of analytics more quickly, and become more responsive to the needs of the data-hungry business operations. Run complex analytics without data scientist skills: Datameer makes it vastly easier to perform any analysis from basic to more complex analytics. Now you can combine fluid data discovery with telematics, time series, graph, and other advanced analytics for deep insights in hours, rather than months, and without the need to find scarce, expensive data scientist skills. Combine path and behavior analytics: Datameer lets you quickly and easily combine location data with behavioral data to not only track what customers are doing in the store, but also understand the impact on customer behavior. Use more of your data: Datameer makes it easy to access and integrate more of your data in a variety of different formats structured and unstructured to get detailed answers to your difficult questions. Execute production-ready data pipelines: Analytic data pipelines are well governed and automated for specific uses, capturing the results to help you feed downstream business processes and applications. Datameer makes the entire analytic process easy and self-service, so business analysts the people who know the business and data best can do the analysis themselves. As a result, you re well positioned to harness your big data to innovate, compete, and grow your retail business quickly, and effectively. PAGE 6

7 Learn More Datameer s modern BI platform can help you apply big data analytics to your people tracking data in combination with all the other data you have on customers to turn in-store merchandising from an art form into a data- and result-driven process. It can also help increase revenue by driving the right incentives in-store and in re-marketing to customers. To learn more about how Datameer can help your business, please visit our website at FREE TRIAL datameer.com/free-trial T WIT LINKEDIN linkedin.com/company/datameer 2016 Datameer, Inc. All rights reserved. Datameer is a trademark of Datameer, Inc. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. Other names may be trademarks of their respective owners. SAN FRANCISCO 1550 Bryant Street, Suite 490 San Francisco, CA USA Tel: Fax: NEW YORK 9 East 19th Street, 5th floor New York, NY USA Tel: HALLE Datameer GmbH Große Ulrichstraße Halle (Saale), Germany Tel: SINGAPORE Datameer Singapore Pte Ltd Galaxis 1, Fusionopolis Place Singapore , Singapore Tel: HONG KONG 12/F International Commerce Centre, 1 Austin Road, West Kowloon, Hong Kong Tel: PAGE 7

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