In the past, the “internet of things” (IoT) referred mainly to the integration of non-computing technology and the world of digital media, through products such as Bluetooth-enabled toasters (seriously) or cars with sensors that track speed and acceleration. Measures of IoT devices tend to exclude smartphones, tablets, and computers from their count. However, given the role that smart devices can play in bringing offline “things” online, is this appropriate?
Euclid Analytics is leading the way in the retail space to bring the products on the shelves of stores into the digital age. The company “uploads” the customer’s experience of retail products in a physical brick-and-mortar store using a combination of smart phones, the existing wireless infrastructure, location sensors, and a precise understanding of the store layout. This gives the company the ability to determine the shopper’s exact location within the store and, therefore, the products with which he or she is interacting.
Nordstrom was one of the first stores implement this technology, starting with 17 retail locations. With this technology, they were able to map out customer pathways through the store, measure the length of time shoppers spend interacting with certain shelves or displays, and generate a heat map for which products customers interacted with the most. Retail stores previously relied primarily on point of sale data to gather information about the shopping experience and product effectiveness, but now the retailers can understand more holistically which products customers interact with, how long these interactions last, and whether these interactions result in a purchase.
Should this framework be considered a part of the IoT? Even though the sensors monitoring the “things,” the physical inventory of the store, are not contained within the products, the data still applies to each specific consumer good or device. It is akin to a holiday sweater looking back at the customer, observing what part of the store they came from, recording the amount of time spent sifting through the options, and finally taking note of whether this interaction led to a purchase.
Unfortunately for Nordstrom, this experiment in customer analytics ended before it could reach its full potential. When customers learned that their shopping habits and location were being monitored, it provoked a backlash of negative media and public opinion. Shortly after the experiment began, Nordstrom asked Euclid Analytics to withdraw the program from the stores. A spokesperson for Euclid called the incident “unfortunate,” and said, “Better data means a better shopping experience, but you don’t have sacrifice privacy to get there.”
Other companies are finding less intrusive means of pioneering the use of mobile applications to understand how consumers interact with specific retail products. For example, Shopkick is a mobile application that encourages shoppers to check in to participating stores, turn on wireless and location services, and scan potential purchases in exchange for gift card rewards. By leaving the tracking feature up to the consumer and incentivizing shoppers to use it, the companies are able to collect the same data as Euclid Analytics but without the discomfort of feeling “monitored.” This service is more transparent from the customer’s perspective, and the application has been adopted by stores like Macys, Sports Authority, and Old Navy.
Euclid’s business model has the potential to create a great amount of value by providing retailers with better shopping data and enabling them to more effectively get products into the hands of consumers. However, the company is struggling to capture this value because consumers aren’t incentivized to participate. Going forward, they should adopt an approach more similar to Shopkick, which delivers on this value proposition by giving shoppers a reason to play.
Mobile applications from companies like Euclid and Shopkick are generating meaningful data about the interface between consumers and retail products that helps store owners optimize things like store layout and design, product placement, and product selection. It is only a matter of time before all retailers are asking: “How can we bring our shelves online?”
 Nordrum, Amy. “Popular Internet of Things Forecast of 50 Billion Devices by 2020 Is Outdated.” IEEE Spectrum.
 Euclid Analytics. Accessed November 15, 2016. Website: http://euclidanalytics.com/products/. (745)s To Track in Store Customer Behavior.”Retail products product effectiveness, product placement, and product selection. Iwa
 Cohan, Peter. “How Nordstrom Uses WiFi To Spy On Shoppers.” Forbes.com. May 9, 2013.
 Dwoskin, Elizabeth and Greg Bensinger. “Tracking Technology Sheds Light on Shopper Habits.” The Wall Street Journal. Accessed at: http://www.wsj.com/articles/SB10001424052702303332904579230401030827722
 Shopkick Incorporated. Shopkick.com. Accessed at http://www.shopkick.com/how-it-works.
 Brandon, John. “5 Ways to Track in Store Customer Behavior.” CIO.com. Accessed at http://www.cio.com/article/2383681/retail/5-ways-to-track-in-store-customer-behavior.html.