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In-Store Analytics: The Future of Retail

The balance of power when it comes to information has shifted from retailers to shoppers with the advent of e-tailing. Shoppers now have access to an infinite amount of competitive pricing information. Even if customers walk into a retail location, they may simply evaluate products in person, then purchase online from another retailer, or hustle down the street to a competitor who is offering a better deal.

Retailers need to be able to evaluate real-time consumer data in order to capture customer business more effectively. More importantly, they need access to in-store analytics capabilities to turn that data into actionable business information. According to the Forrester Consulting and RetailNext study “Real-Time Data Drives the Future of Retail,” consumer and retailer perceptions are not aligned, and many stores lack the technology to utilize shopper data across channels. The study also found that retailers struggle to measure customer behavior. Just 33 percent, for example, reported always measuring conversion rates.

Forrester believes the store of the future will be powered by real-time in-store analytics that can predict shopper behavior over the entire “shopping journey” across multiple channels.

This means more than just head counts and point of sale data. In-store analytics allows retailers to evaluate everything from the effectiveness of a display, apparel size selection, and store layout by tracking how customers interact with merchandise. Why did they try something on and not buy it? Are there areas of the store that customers simply don’t walk through? Is end-cap display placement affecting sales of nearby products?

Consumers shop with their mobile devices and expect to encounter sales associates who can use that same technology to help them find the right product at the right price. Those shoppers also want to experience a consistent sales experience and consistent pricing across channels.

Using analytics, retailers can evaluate traffic, conversion, fixture engagement, shopper paths, and other data, and use that information to rapidly adjust their marketing and in-store operations, as well as provide better data so that buyers and planners can make better decisions. The data can help stores evaluate why a particular item didn’t sell or help prepare for a potential out-of-stock situation.

Real-Time Data Fuels Analytics

Getting that data requires the integration of point-of-sale data, online channel data, information from in-store sensors and RFID systems, and data pulled from other mobile and online interactions. This investment in in-store analytics, combined with the ability to quickly share data across operational areas, can help retailers respond more quickly to sales trends, provide information that can be useful in vendor negotiations, and create more effective buy plans.

Analytics can also help address other data gaps in retail. When customers enter a store but don’t purchase anything, retailers gain zero data. Additional information from sales associates and sensor/RFID systems could help provide a better understanding of those shoppers. In-store analytics can provide information that will help improve product mix optimization, and gain a better return on investment in their data collection activities. Analytics can also improve the use of campaigns and promotional displays based on actual customer behavior.

By linking in-store mobility systems to customer data, sales associates and managers can respond more quickly to customer needs while they are still in the store, which can help increase conversions and turn shoppers into buyers. Stores can also improve staffing levels based on shopper volume, improve store layout, or co-locate products that are frequently purchased together. Getting shoppers into your store is only half the battle. Analytics can help you better understand the customers you’ve already attracted, keep them coming back, and encourage them to buy more from you, and do so more often.

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