In-Store Consumer Analytics: A Key Technology for Brickand-Mortar Stores In-store consumer analytics technologies are providing storebased retailers with instruments to track and analyze shoppers behaviors to a level that is comparable to what has been available for years in e-commerce. In this report, we: 1) Define in-store retail consumer analytics and show how the adoption of the technology is important for retailers that aim to provide a tailored and frictionless in-store shopping experience. 2) Illustrate selected examples of in-store retail consumer analytics technologies that retailers can adopt to track and analyze consumer behavior in brick-and-mortar outlets. 3) Show how the in-store retail consumer analytics market is expected to see rapid growth in the future, as more and more retailers adopt the technology for their brick-andmortar operations. Deborah Weinswig Managing Director, FGRT deborahweinswig@fung1937.com US: 917.655.6790 HK: 852.6119.1779 CN: 86.186.1420.3016 1
Physical stores have traditionally been a data black hole for retailers. Introduction E-commerce has long gifted retailers an abundance of data on consumers visiting, browsing and buying behaviour. However, physical stores have traditionally been a data black hole for retailers, with limited robust or granular data on in-store visitors and shopper behaviour beyond traditional footfall measurement services. However, new in-store retail consumer analytics technologies are providing store-based retailers with ways to track and analyze shoppers behaviors to a level that is comparable to what is available in e-commerce. The ability to gather and analyze information about in-store consumer behavior enables retailers to offer a brick-and-mortar shopping experience that can compete with online shopping in terms of personalization and the reduction of friction points in the customer journey. In this report, we define in-store retail consumer analytics and showcase several examples of the technology that different tech firms have devised to track consumer behavior in-store. Finally, we show how the market for instore retail consumer analytics is expected to see rapid expansion in the future as more and more retailers embrace the technology. In-store consumer analytics technologies measure, track and analyze consumers activities in brick-and-mortar store environments. Hoxton Analytics uses cameras to count footfall and computer vision to capture consumers characteristics. Defining In-Store Consumer Analytics Technologies In-store consumer analytics technologies measure, track and analyze consumers activities in brick-and-mortar shopping environments both individual stores and shopping centers. Tracking occurs through sensors that gather information about where visitors go and how long they spend in the store or shopping center. Analysis can inform store strategies and be used to optimize processes such as store layout, marketing and sales operations. Moreover, the information can be used to understand the specific shopping patterns and preferences of individual consumers to provide a more targeted and personalized shopping experience. We identify two main categories of technologies used to gather consumer s data: 1) Counting technologies: Enable retailers to track in-store footfall, typically through sensor-based devices including video, thermal and laser. 2) Tracking technologies: Enable retailers to track the behavior of store visitors, typically by tracking devices such as smartphones. In-store retail consumer analytics deployed by retailers is often a combination of technologies, for example by deploying both sensors and cameras, or both Wi-Fi and Beacons. Below, we note several examples of the technologies that different tech firms have devised to track customers in-store. Sensors and Computer Vision: Hoxton Analytics, Amazon Go Hoxton Analytics uses cameras to count footfall and computer vision to capture consumers characteristics. Sensors and cameras are placed at ground level so that the information captured does not compromise people s privacy. By counting people walking into the store and observing 2
their shoes, the technology provides an accurate count of footfall traffic and consumer insight, such as visitors gender. A combination of sensors and computer vision is also deployed in Amazon s checkout-free physical store Amazon Go. Source: Hoxton Analytics A combination of sensors and computer vision is also deployed in Amazon s checkout-free physical store Amazon Go. The company has not disclosed much information about the technology, but it has noted that the store deploys computer vision, sensor fusion and deep learning, the same types of technologies used in self-driving cars. The technology detects which products the shopper picks up and which ones are returned to the shelves, and keeps track of them in the customer s virtual cart. Amazon Go opened to the public on January 22, 2018, in Seattle, following a year-long testing period that began in December 2016, during which time the store was accessible only to Amazon employees. Press reports following the opening of the store have emphasized the large number of cameras tracking customers and their behavior, implying that video monitoring, rather than shelf sensor technology, is the principal means through which Amazon Go tracks what items visitors have chosen. Source: Amazon.com 3
Wi-Fi and Bluetooth Beacon Analytics: Walkbase Finnish tech firm Walkbase is a provider of in-store retail consumer analytics technology that detects Wi-Fi and Bluetooth Beacon signals to track consumers in store. Wi-Fi analytics rely on the signal that Wi-Fi-enabled devices such as smartphones that visitors carry with them continually transmit to detect available networks. Wi-Fi analytics rely on the signal that Wi-Fi-enabled devices such as smartphones continually transmit to detect available networks. The signal enables sensors to detect the device and to gather information about the movement of the consumer browsing the store. This makes it possible for retailers adopting Wi-Fi analytics technology to analyze the consumer s visit in store and gather information such as where they went and where most of their time was spent. Source: Walkbase.com Nucleus Vision combines blockchain and deep learning with real-time proprietary Internet of Things (IoT) sensors. ION Sensors, Deep Learning and Blockchain Technology: Nucleus Vision Tech firm Nucleus Vision combines blockchain and deep learning with realtime proprietary Internet of Things (IoT) sensors to provide retailers with a technology that enables in-store customer identification and personalization. Retailers install Nucleus Vision IoT sensors, which identify customers as they enter the store and send data to the blockchain, where this piece of information and other customer data such as previous purchases and their browsing history and preferences on the retailer s website is anonymously stored. The company s deep learning platform gleans the data from the blockchain to create and inform each customer s profile, which can be accessed by store associates to enable them to provide a personalized shopping experience. Nucleus Vision aims to provide an easy-to-adopt technology to retailers, without relying on Wi-Fi, Bluetooth or facial recognition to operate, but solely on the company s proprietary sensors technology, deep learning platform and blockchain. 4
Source: Nucleus Vision Below, we show further examples of tracking and analytics technologies that retailers can deploy in brick-and-mortar environments. Figure 1. Other Selected Examples of In-Store Retail Consumer Analytics Technologies Technology Description Selected Providers Vision Analytics 3D Stereo Video Analytics Thermal Imaging Time of Flight The technology understands consumer behavior through the analysis of images by using artificial intelligence (AI) and deep learning software. The technology uses stereo sensors high-resolution cameras and processors for a threedimensional view to enhance the accuracy of the data gathered. The technology detects heat and is used to create heat-maps that depict the customer s behavior instore. Laser sensors are used to track shoppers movements by detecting the time of light between the sensor and the objects. Stanford Computer Vision Lab, Modcam, Seematics Brickstream, Xovis, Hella, ShopperTrak Irisys BEA Helma Source: behavioranalyticsretail.com/fgrt The market for in-store retail consumer analytics technologies is expected to grow rapidly in the future. Outlook: Rapid Growth Expected for In-Store Retail Consumer Analytics The market for in-store retail consumer analytics technologies is expected to grow rapidly in the future as more and more retailers and shoppingcenter owners adopt in-store tracking and analytics technology in their physical stores. In particular, we expect more retailers will want to close the gap between the insights they enjoy into consumer behaviour online and the view they have of consumer behaviour in their physical stores. 5
Real-estate firms will also likely seek to gain further insights into how consumers are using physical retail environments, and gain a more detailed view of who is visiting their shopping centers. The outlook for the video analytics market is one indicator of the growth prospects for the broader in-store retail consumer analytics category, given that smart cameras, software and services are used for in-store analytics. Revenue for video analytics applied to the retail industry is expected to grow at a compound annual rate (CAGR) of 18% globally during the period 2017 2022, according to market intelligence firm Tractica. Figure 2. Global Video Analytics Market Value Applied to the Retail Sector (USD Mil.) 522.4 650.1 796.1 956.3 1,102 1,215.1 2017E 2018E 2019E 2020E 2021E 2022E Video analytics include smart cameras, software and services. Source: Tractica Currently, no particular technology looks destined to prevail over others. Retailers and retail real-estate owners will likely continue to use a combination of different technologies that span IoT, Bluetooth, Beacon and video to track and analyze in-store consumer behavior. 6
Deborah Weinswig, CPA Managing Director FGRT New York: 917.655.6790 Hong Kong: 852.6119.1779 China: 86.186.1420.3016 deborahweinswig@fung1937.com Filippo Battaini Analyst Hong Kong: 2nd Floor, Hong Kong Spinners Industrial Building Phase 1&2 800 Cheung Sha Wan Road, Kowloon Hong Kong London: 242-246 Marylebone Road London, NW1 6JQ United Kingdom New York: 1359 Broadway, 18th Floor New York, NY 10018 FGRT.com 7