Translating Customer Data Into Customer Satisfaction
By Patrick Blattner, chief product officer, iinside
When it comes to leveraging high-tech tools to analyze consumer behavior, improve customer experience and add to the bottom line, online retailers have led the way. Those days, however, are quickly coming to an end, as grocers begin applying cutting-edge location- and behavior-analytics to the in-store experience. Don’t expect online retailers to give up though. Last December, Amazon earned a patent for what they called “anticipatory shipping” to get products practically in their customers’ hands before they even know they want it. The key word is anticipate, and to do that Amazon will likely utilize every data point at their disposal including past purchases, product searches, cursor tracking, abandoned shopping carts, online paths, and more.
Until recently, grocers could only dream of having that type of predictive analysis. Now, like Amazon, grocers are also leveraging customer data in order to give shoppers exactly the products they want, precisely where they want them. Think of it as anticipatory merchandising, or the grocer version of Amazon’s “Customers who bought this item also bought…”
Grocers can now measure in-store customer behavior with incredible precision—down to the meter. Some of the available technologies, such as those from analytics firm iinside, passively capture location data from a variety of sources to give multiple layers of analysis. Another cutting-edge capability from iinside also deploys “tags” that can be cost-effectively embedded inside store-owned shopping carts, baskets and other assets and then analyzed. Regardless of the technology, information remains anonymized unless shoppers opt-in for store apps, promotions or loyalty programs.
Grocer tools can include detailed maps revealing shopping patterns, choke points, high-shopper volume areas, high-profit areas, underutilized square footage, and more. The impact this type of customer-behavior data and analysis can have on a grocer’s bottom line is immense, and it cuts across merchandising, product promotions, workforce deployment, customer wait time (queue management), marketing, and much more.
Anticipating Customers’ Desires: The Small Basket Example
Grocers have long known that small-basket shoppers are different than large cart shoppers. If nothing else, small-basket shoppers don’t typically buy as many products per visit, but that doesn’t mean they aren’t as profitable as they can make multiple trips over the course of a week and have a high perminute spend rate. Behavioral analytics gives grocers a completely new range of insights into when, where and how these shoppers traverse the store. For example, some stores have used the technology to reveal that small-basket shoppers tend to arrive just before breakfast and then again just before lunch. And, critically, it has also illuminated the distinct paths—and pit stops—they follow through the store.
Grocers are now using this data to experiment with relocating certain items along the basket-shopper circuit. Take, for example, pre-lunch basket shoppers who typically head straight to the deli, visit one more aisle, and then check out. Grocers can now optimize the shoppers’ path, placing key supplemental products along the route of these time-conscious shoppers. This could include condiments, sparkling water, fruits and nuts, and a wide range of other products. In the grocery business, these little improvements matter a lot. They enable customers to get in and out quickly, while making it easy for them to purchase desired products which add valuable dollars to each visit.
The benefits don’t stop there. Slow checkout lines, the final customer touch point, are a source of immense frustration for grocers. One location analysis at a regional grocer showed that 20 percent of customers were lane hopping because they were frustrated with the slow pace of checkout. Of those customers, 50 percent lane hopped more than once during a single visit. The behavior analysis also revealed that lane hopping didn’t save time, but actually added, on average, 2.15 minutes to a customer’s checkout time. For high-volume stores that can see 4,000 shoppers in a day, that translates to 27 extra hours of wait time each day, a total of 412 days of additional time wasted—and that’s just for the lane hoppers.
This same grocer was able to use that analysis to redeploy employees during peak small-basket periods to staff express checkout lanes. The result was a fully optimized store and a well-managed workforce providing a great customer experience. From the shoppers’ perspective, this unobtrusive analysis translated into easy navigation through stores with products placed right where they wanted them. Neither experience—the shoppers’ or the grocers’—would be possible without leveraging location data and behavior analysis.