A Beginners Guide to In-Store Analytics

Photo credit: Shutterstock_kikovic

In-store analytics is the latest buzz phrase making the rounds of retail company executive suites – and for good reason. Learn more here.

In-store analytics gives retail managers a real-time look at what a customer does when he or she walks into a store and what factors lead to what every manager wants before a customer walks out the doors – a sale. Unfortunately, too many retail businesses are giving short shrift to real-time analytics.

According to Forrester Consulting and RetailNext, “… shopper and retailer perceptions and expectations are not aligned, and … many retail stores lack the essential technology to measure and apply shopper data across all channels.”

The study notes that only one-third of retailers regularly measure conversion rates and “shopper and retailer perceptions and expectations are not aligned, and that many retail stores lack the essential technology to measure and apply shopper data across all channels.”

Despite the perception of retailer indifference toward in-house data analysis, technology experts say that while in-store analytics continues to evolve, the notion of having buyer behavioral data at one’s fingertips is a reality that no store manager should ignore.

“In-store analytics work for retailers because they open the black box of in-store shopper behavior,” says Jacob Suher, a marketing professor at Portland State University.

Given that more than 50 percent of purchases are unplanned, understanding the in-store path to purchase is a critical opportunity for retailers and brands to stimulate incremental sales and personalize services to customers, Suher explains. “The emergence of in-store analytics has been a boon for the burgeoning shopper marketing industry and likely will continue to grow as retailers embrace mobile shopping applications and in-store digital signage to provide relevant and customized offers to individual shoppers based on their in-store behavior,” he says.

“However, like all data-driven platforms, the adage garbage in, garbage out applies to in-store analytics,” Suher states. “Best-in-class applications will carefully monitor the accuracy of the incoming data as well as consumer response to data-driven tactics.”

Making informed decisions, especially in the critical area of customer service, has historically vexed retail industry managers, but in-store analytics are changing that equation, others say.

“In-store analytics can help business owners and managers make informed decisions at an unprecedented rate,” notes Mahir Abdi, CEO of Virtual Stacks Systems, in Lake Mary, Fla. “Now, we can see who came and left a store without buying anything, what people are looking at online, and so much more. 

Abdi points to customer support as an area with great potential for in-store analytics: “Why rely on a customer survey, with potentially insincere answers, when you can have the raw data of what was purchased, when, by whom and who they were helped by?” Abdi added, “With this information, it is easier to hone in on areas that need improvement rather than looking at one large, overwhelming picture.”

But to Abdi, in-store analytics cuts deeper than that just customer service.

“With data revolving around peak store traffic, volume of purchases and total amount spent on purchases, it is easier for a store to make informed decisions about operating hours, employee shifts and how to optimize the customer experience for those times,” he says.

The idea behind in-store analytics is to use data to make stores “must stop, must shop” destinations, says Daniel Malak, a spokesperson for Motionloft, a business sensor developer in San Francisco, California.

“We see real-time analytics helps retailers in several key ways,” he explains. These benefits include but are not limited to the following:

Store layout design. “The longer customers stay, the more they buy,” Malak states. “Knowing where people walk helps analysts create spaces to be more attractive and engaging.” 

Attribution modeling. Running a sales promo? How effective was the result? “Real-time analytics lets you see peak hours where traffic occurs,” Malak adds. “This can be correlated to marketing goals and helps optimize key performance indicators.”

Operations and staffing. Payroll costs can be high and staffing too many employees not only drains your bank account, but it can scare off customers if you have too many sales reps in your store trying to help patrons. “Knowing which times of day customers shop helps managers make smart part-time versus full-time hiring decisions,” he says.

Supply change management. Retail stores are changing, Malak says. “Big-box retailers are closing and trying to compete with online,” he notes. “Knowing what items sell most frequently helps planners buy more deeply into product categories that sell best. This reduces inventory holding costs and can increase margins through scale.”

The end game for in-house data analysis tools is simple: to immediately identify problem situations and make real-time adjustments, says Angela Megasko, CEO at Market Viewpoint, in Glenmoore, Pennsylvania.

“In-store analytics work for retailers because they can,” Megasko notes. “For instance, on a recent mystery shop, the mystery shopper found an employee eating McDonald’s on the floor behind a desk. Working with real-time responses, the manager can identify this person before the end of the work day and address the situation.”

“Traditionally this would be revealed in days or even weeks,” she adds. “At which time it is more difficult to ‘fix’ what went wrong.”

For retailers deploying in-store data analysis programs, the days of not receiving – and acting on information in a timely manner – are over. For those retailers who don’t take advantage of the new technology and the extensive data it provides, there’s a big risk of being left behind, wondering why there are fast-food wrappers on the floor and what can be done about it.

What Next?

Recent Articles

Leave a Reply

You must be Logged in to post comment.