Data collection powers IoT and retail analytics

Retail Writer

Much has been written about the impact that digital and mobile commerce have had on brick-and-mortar retailers in recent years, but ironically, physical retail experiences may actually benefit from the ongoing mobile and technical revolution. Enter retail analytics and its best friend, data extraction.

One of the biggest challenges for retailers has always been scaling great customer service, specifically how to better personalize in-store experiences. As a retail operation grows and attracts customers, it becomes more difficult to build individualized relationships and provide consistently remarkable experiences. Today, the solution is within closer reach than ever thanks to mobility, cloud services, the Internet of Things and cognitive computing.

Start by building methodology for data collection

One of the easiest ways for retailers to take advantage of this confluence of technologies is to create a "presence insights" practice. By installing small Wi-Fi beacons at strategic locations inside stores (in a grid, for instance, or in specific departments), retailers can automatically measure foot traffic patterns for each location by counting the number of nearby phones pinging the signal. The data from these automated interactions can be recorded, then measured, modeled and analyzed.

Armed with this information, retailers can map out which areas of stores attract the most and least shoppers and track where shoppers spend their time. One example of a retailer maximizing data collection is John Lewis, which, according to Retail Gazette, has committed to filling the in-store "black hole" of customer data using brand applications, virtual displays and QR codes. These collection methods will help retailers design better shopping spaces by continuously tracking customer behaviors and improving store layouts in a frictionless environment.

Incorporate intelligence into inventory

Pushing things a little further, inventory location information from smart price tags or hangers can be overlayed with sale and foot traffic data. The retailer can then gauge the impact that traffic has on the performance of specific items. For instance, is an area enjoying a lot of traffic but seeing proportionally less sales than a low-traffic area?

With this strategy, product placement can be tested and optimized with little manual effort. Even a 5 percent difference in product sales performance can make a huge difference for retailers, and using presence insights could help turn around a poorly performing product or department. In fact, as Wharton School Professor Morris Cohen explained in the Harvard Business Review, "it is now possible to link data generated by all product interactions (including orders, examinations and reviews by actual and potential customers) and transactions generated by suppliers and competitors who connect via Internet websites and cloud portals."

Leverage repeat customers and loyalty programs

Retailers can also use data extraction technology to inject customer relationship management data into the mix so the behaviors and traffic patterns of known customers (using a membership application, for instance) could be tracked or analyzed. Using the same technology, in-store displays might, for instance, learn that a customer has recently visited the same section of the store several times without making a purchase. This could signal that this customer likes an item but something is standing in the way of a purchase. If the system detects this opportunity, it can generate a special offer based on the customer's profile and place it on a store display the next time the customer visits. Alternatively, the special offer could be in the form of an email that prompts the customer to return to the store or purchase the item online.

Instead of offering store-wide promotions, retailers can target individual customers with the right offer at the right time. The benefit of this is two-fold. First, it would improve brand experience by making customers feel special. Second, it can help minimize profit erosion from overuse of scattershot sales that target everyone and no one.

Shopper personlization capabilities are now more possible than ever. Discover the potential of predictive analytics with IBM's Predictive Customer Intelligence for Retail.