In-store consumer analytics technologies are providing store-based 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:
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 in-store 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 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:
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.
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 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. 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.
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—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.
Tech firm Nucleus Vision combines blockchain and deep learning with real-time 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.
Below, we show further examples of tracking and analytics technologies that retailers can deploy in brick-and-mortar environments.
The market for in-store retail consumer analytics technologies is expected to grow rapidly in the future as more and more retailers and shopping-center owners adopt in-store tracking and analytics technology in their physical stores.
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.
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.