What Retailers Need to Know About In-Store Analytics
In the hyper-competitive retail market, in-store analytics is gaining traction. And for obvious reasons: it helps retailers keep a close eye on their customers and provides valuable insights on store performance. Even better, all this data can be followed up with actions.
Traditional retail stores are not going to quietly cede their status to their online rivals any time soon. They are aggressively implementing analytics techniques to win audiences back from e-commerce competitors. To that end, in-store analytics is becoming an indispensable part of a brick-and-mortar retailer’s operating strategy. Its benefits can be seen in other areas too, including marketing, merchandising, and fraud prevention.
How Does In-Store Analytics Work?
In-store analytics is the process of finding meaningful insights from customer behavioral data. Retailers use smart carts with location beacons, pin-sized cameras installed near shelves, or the store’s Wi-Fi network to see how many shoppers entered the store, how they moved around once inside, and what key areas they visited. This process can even provide basic demographic data, like gender and age group.
Based on this digitally-collected information, in-store analytics can connect the dots between consumer, retail store, and buyer decisions.
What Retailers Want, and How In-Store Analytics Can Help
In order to compete with online stores, retailers need as much information about their customers as they can get. We’re not just talking about likes and dislikes. They want details on what displays are working, what products are flying off the shelves, and what could be causing store losses. And they want to understand and undercut the phenomenon of showrooming — when someone comes into the store to see something they want to purchase elsewhere, usually online.
This is where in-store analytics comes into play. It can help retailers with the following conundrums:
- Shopper or Consumer? In a store setting, people are either consumers or shoppers. Who went without purchasing anything and why? What did they do in the store, from entrance to exit? The old techniques of door counting and sale conversion can’t really answer these questions, much less give insight into buyer motivation.
How In-Store Analytics Can Help: In-store analytics can show retailers how customers behave, leading to an improved shopping experience and an optimized store layout. This can nudge people from being shoppers to consumers, and may even encourage them to spend more.
- What’s Missing from the Shopping Cart? Retailers know what customers purchase from their store. But they also need to know what customers didn’t purchase and why.
How In-Store Analytics Can Help: This technology can give detailed insights about the effectiveness of store displays, employee actions, and other factors that can be used to sway purchasing decisions.
- Getting From Data to Insight to Action. Running a successful retail environment means making lots of decisions based on lots of factors. Knowing the ROI on your decisions, from everyday operations to marketing, is important; even more important is moving from theoretical insight to positive action.
How In-Store Analytics Can Help: Store performance can be measured in real time for better decision making — even in on-the-fly situations. The technology behind in-store analytics can parse complex data sets into actionable options.
- Next-Gen Theft Prevention. Preventing theft is an ongoing challenge. Billions of dollars were lost in 2014 alone to shoplifting and employee theft.
How In-Store Analytics Can Help: In-store analytics can identify shoplifting behaviors and show which areas of the store are particularly theft-prone.
In-store analytics can help traditional retail operations understand consumer needs, improve employee efficiency, drive sales growth, and better appeal to customers. In the battle for consumer dollars, it’s one more way that traditional retailers can use technology to survive.