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Location Analytics In Retail: Closing the Loop

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Retailers have been enriching spatial information with demographic and psychographic data to plan store locations for many years. However, prior to the era of mobile phones, that data tended to be static, refreshed only periodically, for example, by the census bureau. Spatial analyses are conducted to help get a sense of population trends within a geography, identify where the competition is, and understand traffic patterns, in order to find the best possible store locations.

But the fact is that once retailers get consumers into the store, what they do “inside the box ” has been of secondary importance. That’s why for a long time, the only technology a consumer was likely to see in a store was at the checkout stand; retailers just needed to track the transaction, and not how consumers interacted with the store.

It’s one of the great ironies of retailing in the 21st Century is that retailers can and often do analyze how consumers interact with the Brand in the digital space by tracking their paths to purchase to an incredible level of precision, and that’s made possible by all the digital “bread crumbs ” that consumers generate when they open emails, click on browsers and social media sites, and use their smart mobile devices. But how customers use the physical selling environment that retailers have the most experience with – the store – is still a big unknown for many retailers.

But that is changing, and changing fast. A 2014 Harvard Business Review article made the case: “Just as web analytics is an essential tool on the Web, location analytics will become a must-have for designing, managing, and measuring offline experiences. “[1] The article included several real-world examples of retailers using geo-location data gathered from consumer smart mobile devices:

Design. After analyzing traffic flows in their stores, a big box retailer realized that less than 10% of customers visiting their shoe department engaged with the self-service wall display where merchandise was stacked…

Marketing. A restaurant chain wanted to understand the whether or not sponsoring a local music festival had a measurable impact on customer visits. By capturing data on 15,000 visitors passing through the festival entrances and comparing it to customers who visited their restaurants two months prior to the festival and two weeks after, they concluded the festival resulted in 1,300 net new customer visits.

Operations. A grocery store chain used location analytics to understand customer wait times in various departments and check-out registers. This data … gave additional insight into (and justification for) staffing needs for each department throughout the day and optimal times to perform disruptive tasks such as restocking shelves or resetting displays.

Strategy. A regional clothing chain was concerned that opening an outlet store would cannibalize customers from its main stores. After analyzing the customer base visiting each store, they discovered that less than 2% of their main store customers visited their outlet…. “

Getting Creepy?

What those examples obscure is the difference between anonymous and non-anonymous data captured from consumer mobile devices. Our own recently published benchmark study on location analytics (Location Analytics In Retail: Turning New Data Into New Intelligence, January 10, 2018), highlighted the issue. We discovered that while retailers are excited about the potential benefits of geo-location data and analytics to offer a more personalized value proposition to consumers, they are unclear about some of the risks associated with using non-anonymous data.

Retailers are responding to the perceived threat of increasing consumer intolerance of an impersonal shopping experience, and that is the driving force behind the interest in location-based intelligence- to be able to target personalized value messages to consumers via their mobile devices. Regardless of performance, vertical, or size, retailers see that the greatest opportunity coming from location-based intelligence will be to close the loop between the digital and physical shopping experiences.

However, retailers are far less concerned about a possible consumer backlash to the “creepiness ” associated with geo-location tracking, or even whether or not the store is situated in the right location (the legacy use case that drove retailers to use location-based data in the first place). That’s worrisome; retailers’ naiveté about the risks and rewards associated with location-based data could create its own business challenge, kind of a “we have met the enemy and they are us ” problem. Interestingly, we found that the larger the retailer, the greater the concern there is about alienating consumers with highly personalized marketing schemes that use geo-location data.

Read The Report!

You can discover retailers’ attitudes about challenges and opportunities associated with using geo-location data to enable a better customer experience in RSR’s new benchmark on location analytics, Location Analytics In Retail: Turning New Data Into New Intelligence. The report, commissioned by esriand released on January 10, 2018, also uncovers some of the internal inhibitors standing in the way of adoption of new processes and technologies to take advantage of the new data, and which technologies are most valuable to retailers as they move forward.

We encourage you to read the report, or if you don’t have a lot of time, to check out the eBook version, The Case For Location-based Intelligence in Retail.


As always, RSR’s benchmark studies are free to everyone – so check it out!

[1] How Location Analytics Will Transform Retail, Tony Costa, HBR, March 12, 2014

Newsletter Articles January 23, 2018
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