Discovering Shopper Behavior Through New In-Store Metrics

Discovering Shopper Behavior Through New In-Store Metrics Phase I: In-Store Traffic Analytics September 2013 September 2013 2013 Center for Advancing Retail & Technology LLC 1

Report Structure Section I Section II Section III Section IV Section V Appendix Executive Summary Key Findings The Birdzi Mobile Analytics Solution Birdzi s Practical Application Conclusion & Next Steps Background Information Section I Executive Summary In April 2013, Birdzi, Inc. ( birds-eye ), in collaboration with CART, deployed a highly innovative, anonymous in-store shopper detection technology in two U.S. grocery retailers. This technology detects and follows the movement of Wi-Fi enabled personal mobile devices throughout a store environment to accumulate actionable data. Learning to understand real shopper behavior represents the latest front in the retail industry. Technologies such as the one presented here, helps independent retailers to remain competitive. Birdzi can be implemented to gather shopper data over a period of time as well as engage shoppers in real time during their visit, should they chose to participate. The solution is easy to deploy and scalable through a cloud-based centrally managed system that is capable of collecting data in real time. The research study has been divided into two phases. This paper (Phase I) focuses on the mobile analytics aspect, which can be used to adjust store operations through more effective scheduling or improve instore merchandising leading to an increased basket size. A subsequent paper will focus on Phase II, demonstrating Birdzi s potential as a shopper engagement tool. The aim of this case study is to showcase the benefits and relevance of mobile analytics technology in a retail setting. September 2013 2013 Center for Advancing Retail & Technology LLC 2

Section II Key Findings This section summarizes key characteristics of the Birdzi solution in addition to highlighting some of the high level findings of the in store research study. In-store Shopper Detection Through Birdzi The rise of the smartphone in all age categories has created an enormous opportunity for anonymous monitoring software and hardware that protects shopper identity while delivering insights into shopper behavior. Current smartphone ownership represents 61% in the U.S. and growing. Detection of unique mobile devices enables the measurement of return visit patterns, an indicator of shopper re-patronage. This holds promise as a substitute or additional measure of shopper loyalty. Rapidly deployed, cost-effective, nonintrusive and managed from the cloud. Birdzi offers many advantages over other means of shopper detection such as video tracking, which is capital intensive, may not protect the shopper s privacy and does not support shopper engagement. Timing, frequency and duration of store visits are reliably monitored and can be viewed in real-time to empower store managers to make better informed decisions on staffing levels, merchandise positioning or in-store displays. Research Findings Shopper traffic is correlated to point of sale (POS) transactions. Providing sufficient data is gathered over time, Birdzi average shopper traffic data can be used as a measure to predict sales transactions. Birdzi recorded twice the number of Frequent shoppers (>2 visits / month) compared to New or Repeat shoppers. In addition, the ratio between New, Repeat or Frequent shoppers is relatively constant throughout the week on a day-by-day basis. Weekday shopper traffic differs significantly from weekend shopper traffic. The average weekend increase for both stores was 35.7% for the recorded period. Weekend shopper traffic starts earlier, rises faster and peaks earlier, whereas weekday traffic increases gradually until it sees a peak between 4 and 6 PM. The recorded average shopper dwell time across both stores equaled 14:44 minutes. September 2013 2013 Center for Advancing Retail & Technology LLC 3

Section III The Birdzi Mobile Analytics Solution June 2013: Nielsen reports over 61% of mobile subscribers own smartphones (Nielsen, 2013). This figure increased by 10% within the last year alone. Birdzi is a technology that anonymously detects the location and path of Wi-Fi enabled mobile devices such as smartphones through its proprietary cloudbased indoor positioning technology. Small detectors are strategically placed within a given store environment to gather real time data and offer actionable insights. The goal is to empower supermarket operators to better understand their customers and learn to engage with them in a seamless and non-intrusive way. Birdzi offers the following features: Mobile analytics Enabled through the high rate of smartphone ownership among US citizens, Birdzi is able to collect information on trip timing, duration, dwell times at various in store locations. In addition, it can distinguish customers as New, Repeat or Frequent shoppers. Low latency Devices are detected and reported in near real time, ensuring that activity within the store can be actively and accurately monitored in real time. Customer privacy The shopper is protected. The data collected is anonymous and used in aggregates. It can only be tied to the MAC address of the individual device, not the shopper. Moreover, shoppers can choose to disable their Wi-Fi should they prefer not to participate. Scalability & ease of deployment The hardware installation is non-intrusive and rapidly achieved. The solution is instantly scalable through a network of detectors that provide data to a centrally managed system. Larger areas will simply require more detectors. Multi-store environments can be created and accessed via the cloud. Accuracy The high saturation of smartphone devices among all age groups in the U.S. ensures that Birdzi will be able to collect a significant amount of business intelligence. Accuracy is further enhanced by reducing outliers such as devices either owned by employees, or devices from bystanders who are close enough to be detected but do not enter the store. Birdzi represents a comprehensive mobile engagement solution for retailers that is low-cost, easy to deploy and offers real-time location analytics, while also giving retailers and brands, the ability to deliver highly targeted and relevant offers to customers through mobile channels. (Volker Hauf, Chief Architect, Birdzi) September 2013 2013 Center for Advancing Retail & Technology LLC 4

Section IV Birdzi s Practical Application In April 2013, Birdzi implemented their solution in two independently operated conventional supermarkets so as to showcase its mobile analytics platform. This section aims to explore some of the insights gained during this time and how they apply to a modern retail operation. The sample stores Store E and Store C are located in New England the Midwest respectively. The analysis below has been based on a timeframe starting 4/27/2013 and ending 7/12/2013 for both locations. Weekday versus Weekend traffic Looking at the shopper count data for the given time period, we instantly learn that there is a significant difference in traffic between weekdays and weekends. The average weekend shopper traffic increased by 35.7% compared to the average weekday shopper traffic for both stores. Store C experienced only an increase of 33%, compared to Store E s increase of 39%. Birdzi empowers a supermarket manager to see how shopper traffic affects their business. It provides the tools to make management decisions towards resource scheduling or shopper traffic management. In a multiple store scenario, a Birdzi user can look at factors that lead to weekend traffic increases. An explanation for the sharper increase in weekend traffic for Store E could be the shorter weekday operating hours, compared to Store C s 24-hour operation. Increasing operating hours slightly during the weekdays could convince certain shoppers to visit on a weekday rather than the weekend, thus easing the store s operation over the weekend. Such action could also simply result in more traffic overall. Figure 1: Store C shopper count between 7 AM and 8 PM! The 4 PM paradox Daily traffic counts suggest that the busiest time in the store occurs between 4 and 6 PM. Birdzi allows the user to separate intraday data and having already established above that weekday and weekend traffic differs, it is reasonable to assume that intraday shopper patterns do too. September 2013 2013 Center for Advancing Retail & Technology LLC 5

Referring to Figures 2 and 3 below, average weekend shopper traffic starts earlier, increases faster and lasts longer when compared to average weekday shopper traffic. Figure 2: Store C shopper traffic comparison! Figure 3: Store E shopper traffic comparison! Weekday Average! Weekend Average! Birdzi offers insights and an understanding into consumer patterns that would have otherwise gone unnoticed. Frequent vs. Repeat vs. New Shoppers Birdzi can distinguish between New, Repeat and Frequent shoppers. Data for Store E shows that there are no differences in terms of shopper traffic patterns as seen in Figure 4. It appears that the ratios between New, Repeat and Frequent are relatively constant. Store E could concentrate on enrolling New shoppers into a loyalty program on a Saturday as their traffic is at its peak and the chances of higher enrollment are given. Also, if the number of New shoppers decreases the store will realize negative shopper growth. 14! 12! 10! 8! 6! 4! 2! 0! Figure 4: New, Repeat and Frequent shopper comparison (Average Count Per Day)! Frequent! New! Repeat! September 2013 2013 Center for Advancing Retail & Technology LLC 6

Minutes 17:17! 15:50! 14:24! 12:58! 11:31! 10:05! Figure 5: Store E Dwell Times for Customer Types! Frequent! New! Repeat! Dwell times Store C data suggests that the average dwell time is 14:28 minutes with little difference between customer types or days referred to. Dwell times for Store E, however paint a different picture (Figure 5). New customers spend on average 90 seconds longer in the store, with peaks on Tuesdays, Thursdays and Fridays. A retailer can use this information to better cater to the needs of new shoppers who may not be as familiar with the store. Dwell times can also be viewed on a granular level, down to particular store sections using the heat-map functionality described below. This could help in arranging attractive displays for the New shopper category or aid in the placement of customer service employees. Heat-maps The Birdzi cloud computing interface allows a user to view shopper presence, dwell time or traffic given their own specific store layout. Users will be able to view how shopper traffic changes over time and identify crowded areas. Among other things, the heat-maps represent a great tool for determining locations for in-store displays or detecting bottlenecks or intra day periods with little traffic to restock shelves. Figure 6: Store C heat-map for Wednesday, 7/10/13 between 5 and 6PM During the time period in Figure 6, the shopper presence was largest in the following sections: the drugstore, the beer and wine section and the customer service desk. Such visual insights are invaluable to a store manager in terms of managing resources or determining where to place products. September 2013 2013 Center for Advancing Retail & Technology LLC 7

Birdzi is statistically relevant Monitoring shopper traffic via WiFi enabled mobile devices does not capture all the in-store traffic. However, the aggregate data gathered is sufficient to make relevant predictions, plans and forecasts. 2500! 2000! 1500! 1000! 500! 0! EOD Count Figure 8: Comparison of Regression output with real POS transaction data! Actual Transactions! Predicted Value! Running a linear regression between the total daily shopper count with the daily POS transaction count reveals the power of Birdzi data as a predictive tool. The R value equals 0.86, with an R 2 of 0.75 and a near to zero p- value. In laymen s terms this means that Birdzi shopper traffic data accounts for over 75% of the movement in the overall POS data and to that extent represents a tool for forecasting sales based on average shopper counts. Figure 7 graphically displays the actual POS transaction data and the predicted values from the regression for that same period. 80%! 70%! 60%! 50%! 40%! 30%! 20%! 10%! 0%! Figure 8: Shopper traffic and shopping trips based on Frequency of shopper! 1! 2! 3! 4! 5! 6+! Frequency Percentage of Consumers! of Trips Percentage of Shopping Trips! Shopper frequency analysis Detecting the frequency of visits, beyond that of the already described New, Repeat and Frequent shopper can be an important indicator of customer loyalty, but also profitability. For retailers lacking shopper data from loyalty programs, these mobile metrics provide important insight to the value of high-frequency repeat shoppers. An analysis of shopper frequency reveals that 17% of all detected shopping trips originate from 4.3% of customers. These most loyal shoppers visited Store C 5 or more times during the 6-week period of (4/27/13 6/07/13). Learning about these figures is critical to increasing the long-term profitability of a supermarket operation. Figure 8. shows that frequent customers are fewer, but cause more shopping trips which equals higher sales. These metrics are comparable to shopper data gathered via traditional loyalty programs. September 2013 2013 Center for Advancing Retail & Technology LLC 8

Section V Conclusion & Next Steps Birdzi delivers sophisticated shopper behavior analytics, which can be used to guide decisions for merchandising, service levels, promotions and space management. The system incorporates a complete package of relevant analytics tools, and a cloud-based secure dashboard element that enables practitioners to securely access relevant reports and analytics. The close relationship between shopper traffic and POS transactions increases Birdzi s validity as a predictive planning tool. The accumulation of long-term data will allow for predictive analytics to accurately plan store-level and shopper targeted merchandising as well as help guide staffing levels, by store, day, day-part or department. Furthermore, the in-store detectors allow for a very granular view of the store layout to enable a fine interpretation of data. Birdzi is affordable, rapidly deployable, easy to scale and cost effective, which highlights its appeal to both small and independent grocers. Among other things, future applications will include the linking of POS transaction data to dwell times to help detect and prove promotion performance for in-store displays. This will establish the inherent value of key promotion locations and empower the grocer to negotiate merchandise locations based upon performance facts. Next Steps Launching Phase II This will be marked by the launch of a mobile application that shoppers can choose to install to gain an enhanced shopping experience. Once participating, shoppers will receive push notifications specific to their in-store location in real time, allowing access to special offers or providing information relevant to their visit. These functionalities are enhanced through geo-fencing when outside of the given store. Birdzi will be able to provide retailers with powerful new tools to engage their shoppers with relevant and timely messaging. A second case study will highlight these capabilities and insights. September 2013 2013 Center for Advancing Retail & Technology LLC 9

Appendix Background Information: The National Grocers Association (NGA) and the Center for Advancing Retail & Technology (CART) have partnered to bring a unique service to the retail industry: The NGA Innovation Center, a network of live store environments in different markets across the U.S. available to research sponsors for testing merchandising concepts and piloting innovative solutions. CART partners are solution providers and academic institutions who serve and study the grocery industry. The NGA represents the retail and wholesale grocers that comprise the independent sector of the food distribution industry. For more information visit: http://nationalgrocers.org CART provides research and education focused on bringing innovative solutions and best practices to retailers. For more information visit: http://www.advancingretail.org The objective of this white paper series is to bring research, best practices and thought leadership to the grocery retail industry under CART/NGA collaboration. About Birdzi: Birdzi, Inc. (http://birdzi.com) ( birds-eye ) is an innovator in mobile-enabled shopper detection technology. The Birdzi solution anonymously detects the location and path of mobile devices like shopper s smartphones through the store, providing the retailer valuable insights to true shopper behavior. The Birdzi mobile app is a comprehensive mobile engagement platform, which enables retailers to understand, connect and deliver value to their shoppers at the point of purchase. Birdzi Inc. s proprietary cloud-based indoor positioning technology, location analytics and content delivery platform transform an indoor space such as a retail store into a location-aware, interactive environment. Birdzi's cloud-based platform provides actionable, real-time location analytics to retailers and brands at minimal cost, and, the ability to deliver highly targeted and relevant offers to customers in real-time thru mobile channels. September 2013 2013 Center for Advancing Retail & Technology LLC 10

This NGA/CART Thought Leadership Project was produced with essential cooperation from: www.leesmarket.com www.mycountymarket.com For press inquiries about the case study or to learn more about CART, please contact: Schuyler Hawkins Vice President, Research Engineering Center for Advancing Retail & Technology Phone: 1-877-712-2538 Email: info@advancingetail.org September 2013 2013 Center for Advancing Retail & Technology LLC 11

Sources: Nielsen, Mobile Majority: U.S. Smartphone Ownership Tops 60%, www.nielsen.com, accessed 7/24/2013 September 2013 2013 Center for Advancing Retail & Technology LLC 12