Retailers allocate millions to location intelligence, which helps them understand who’s coming to their stores, whether customers are visiting competitors, and how their performance compares to regional and national benchmarks. But in order to achieve all of that, the intelligence has to be based on how people actually visited a store. It also has to remain reflective of the people who may visit in the near future. In other words, if a community’s behavior were to change dramatically from one week to the next or if the people who lived in a community were to stage a mass exodus—due to, say, a deadly airborne virus—a retailer’s insights into past foot traffic at one of its locations could become more misleading than instructive.
That precise situation played out on an unprecedented scale over the past 16 months. The core proposition of the more than $10 billion location intelligence industry, to help other businesses derive insights from the foot traffic at their brick-and-mortar locations and those of competitors, became both more fraught and more valuable. More fraught because one week’s audience was not necessarily reflective of the next week’s as twentysomethings fled urban areas and shoppers segregated based on risk tolerance and political ideology. More valuable because, with customer bases changing overnight and still evolving today, businesses have desperately needed up-to-date insights about their customers. As popular historical customer information sources such as Census data became less representative due to the dramatic changes afoot, up-to-date foot traffic data increased in value, providing a view into who was visiting stores in the previous week or two.
The composition of retailers’ audiences is “completely different today than it was three months ago,” said Rikard Bandebo, Chief Product Officer at Unacast. “As a result, they are having to find data sources that have a much shorter lag.”
How data becomes intelligence
The lag, or period of time from when location data is collected to when retailers and other companies are able to parse the insights it generates, is an issue because location data does not quickly translate from iPhone coordinates to useful business intelligence.
Location data originates from smartphone apps that provide GPS and allow users to opt-in so that the app understands where they are. Some prime examples with obvious geographic use cases are social, mapping, and weather apps. Location intelligence companies analyze their data in relation to other information, such as census data, to ensure their location datasets are representative of the broader population.
App publishers and companies that provide tools for app developers to collect location data from users. From there, the data makes its way to location data companies, which turn it into insights by aggregating it, enriching it with complementary sources, and comparing it to other data sets, such as census data. This allows location intelligence clients to understand foot traffic volume at a given location, what type of people make up that foot traffic, where else they are going, and how one location’s foot traffic matches up against visits to other locations.
“The real magic of what data science teams do is to turn [data] into something that’s meaningful and representative,” Bandebo said.
Once it’s been aggregated and summarized, location data helps companies such as retailers understand their audiences and make decisions about matters such as merchandising and service expansion. For example, one Unacast study leveraged location data to provide cross-retail insights for a hotel in Buffalo, New York. The study found that hotel visitors also frequented a grocery store, a Panera Bread, and a nearby office building. The frequent grocery store and Panera Bread visits might help the hotel determine that it is not offering enough culinary options, perhaps it could improve its café or build one so that its customers do not need to go to Panera or grocery-shop while staying at the hotel. The office building might tell the hotel what type of professionals are staying there, further strengthening its understanding of the services it should be offering or perhaps even the price points that would be most appropriate for its audience.
As retailers seek to harness the insights generated by location data, they need to consider the privacy of the customers from whom they are collecting that data. With anti-tracking moves by smartphone makers Google and especially Apple, location data is getting harder to collect without explicit consumer consent. In addition, data privacy laws by the European Union, California, and other US states are making businesses much more wary of collecting data they do not need, or data that could be traced back to an individual and make a company the subject of the next location intelligence exposé.
Many companies in the location data supply chain never see personally identifiable information, such as where a specific individual has moved around the world. Personally identifiable information is not only ethically and legally sensitive but also inessential to the broad, actionable insights buyers of location intelligence require.
Of course, that does not mean there is not bad behavior in the location data industry. Some apps that do not need location data to offer the user any sort of service collect it anyway. Some have unclear data collection policies. And, as the New York Times has pointed out, while device-level location data may not normally be used for nefarious purposes, its granularity does raise the question of what it might be used for in the hands of bad actors.
The increased attention to privacy in the press and among regulators has led companies to shore up the processes that ensure they are only collecting and using data with consent. It has also encouraged them to eschew the kind of information that could put them on the wrong side of emerging regulations.
“Firms have a reluctance to receive anything that is at the personal level,” Bandebo said. Unacast has been using the California Consumer Privacy Act as its standard for data privacy practices across the US, opting to follow California’s relatively strict guidelines in every state even though local laws vary and in many cases do not address data privacy. Other companies are doing the same, hoping to get ahead of inevitable legislation in other states.
The upshot is that for most companies when it comes to location data, the imperative is less to find out exactly where John Smith went on Friday night and rather to transform large amounts of individual device data into aggregate insights. This is where location intelligence companies come in, but the drawn-out process from data collection to aggregate insights is also why the industry has faced such tough challenges during the pandemic. Retailers have had to determine who among their providers furnishes up-to-date, useful insights—if they were savvy enough to realize their data needed to be updated in the first place. Meticulous companies verify the quality of their location intelligence by comparing it to other forms of insight at their disposal, such as census or point of sale data, Bandebo said.
Locating data’s value
For the retailers who figure it out, location intelligence serves a number of purposes. One is site selection. Retailers can determine what type of audiences lie in a given area, whether there’s a hole in the market for their services, and whether stores of theirs in similar demographic areas perform well, portending success in a new location.
Another use case is competitor intelligence. “Many brands have a good sense of what’s happening in their locations,” said Ethan Chernofsky, vice president of marketing at location intelligence company Placer.ai. “What we’re doing is giving them … a single lens on retail across the country.” In other words, what can be more powerful than seeing how their own stores are performing is getting a view into how rivals are doing, an edge that also helps executives develop benchmarks for individual franchisees’ success.
Finally, retailers may leverage location-based insights about their customers to update strategy for existing locations, adjusting merchandising or store layout. Say product X is performing especially well at store A, and store B’s audience is similar to store A’s. A company may encourage store B to follow the merchandising decisions that have led to store A’s success.
As retail tenants get more reliant on location intelligence, the property industry’s uses of it are expanding. For example, mall owners and commercial real estate funds are increasingly using location insights to assess where customers are going within malls and where specific outlets should be placed. In the brick-and-mortar world, this can lead to a spillover effect where visitors to one store patronize a neighbor. Location intelligence can maximize that potential, which is only getting more important as consumer spend shifts to digital channels and brick-and-mortar retailers struggle to lure customers in store.
Mall investors and operators are also using location insights to court promising prospects and identify current tenants that may need to break their leases, Bandebo said. In the long term, location data-driven analysis can help investors and developers decide which real estate projects to back in what places. In a sense, then, location technology represents the optimistic flipside to the oft-proclaimed retail apocalypse heralded by e-commerce. With consumer attention shifting to online channels, it is getting harder to capitalize on retail properties. But those properties, coupled with tech, also deliver insights about audiences and brand strategy that can buoy brands—online and offline.