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Geolocation Data Brings Web-Style Analytics to Bricks and Mortar

For anyone who doesn’t closely follow the retail sector (other than for, well, shopping) headlines over the past few weeks could seem contradictory. Macy’s announced that it would be closing 150 stores over the next 3 years. Meanwhile, Sephora announced it would open 150 new retail outlets in 2020. On the surface the stories seem to imply contradictory trends. One thing these two companies do have in common, however, is a shift in strategy: moving out of malls, in favor of more local and neighborhood stores. Shoppers’ behavior is changing, they explained, with malls going out of favor and customers preferring smaller locations, closer to home or on their commute. But then followed the news that Simon Property Group would be buying Taubman Centers Inc., for a significant premium to its valuation, implying that there is still plenty of opportunity for mall REITS.

Of course, each of these stories is more nuanced than the headlines suggest. Macy’s struggles and Sephora’s success are not just about location. Yet, location is one of the most critical decisions for any business. As Macy’s CEO highlighted on an investor call, the changes they announced reflect a bi-furcation, with A-rated malls thriving and lesser malls accelerating losses.

With so much at stake, retail firms of all sizes, and across all sectors, are looking for new and more precise ways to understand their opportunities for growth, and where they should pull back or rethink their strategy. Using data such as buying patterns is table-stakes for any retailer. Analyzing traffic data – measuring how many people are entering a specific building or store, is another, more sophisticated way to know your customers. Solutions using thermo-counters, feet counters or video from cameras have existed for many years. But imagine trying to do this for every building in the world, 24/7. A few years ago this was impossible. But, today, geolocation data has made this type of analysis possible.

Now we can see precise information on any location. Not just how many people have visited but where they live, where they have travelled from to get there, how long they usually stay, whether they are repeat visitors and even their interests. This is information that online retailers have been able to track for years. Geolocation technology is bringing web-style analytics to physical locations.

So how does it work? Geolocation firms analyze traffic data from smartphone apps around the world, including over 20 percent of smartphones in the US. While this number may sound small, it is highly representative as a sample of the population. By way of comparison, exit polls and other forms of market research typically capture approximately 0.01 percent of the population and are widely relied upon for their statistical accuracy.

Location technology is not new. It has been used by the finance sector since at least 2015 as a way to get fast insights into company performance, rather than having to wait for quarterly earnings. For financial analysts, this “alternative” data helps them derive meaningful insights into the companies they follow. For example, if they are covering a specific grocery chain, they can look at visitor numbers at multiple locations, compare this with historical traffic and competitor numbers, and use it as a benchmark to forecast quarterly revenues.

Geolocation data can tell us what is behind the ups and downs of companies that are in the news, and what is driving management decision-making. For Sephora, for example, it is possible to view foot traffic for stores that are located in malls, compared to those in non-mall locations to show the expected, steady downward trend. The graph below shows the average traffic a mall store has over a non-mall store, for the last 4 years. The trend is pretty clear: mall stores are losing their edge; for Sephora at least.

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In the case of Simon and Taubman, we see some interesting trends between the two property groups.. Florida has the largest foot traffic for Simon stores, as well as the greatest cross-traffic with Taubman stores, 27%. By contrast, California is the third largest market for Simon but has only 3% cross over with Taubman. This information may not provide clear-cut answers to the management team about strategic direction, but it does offer much deeper insights from which they can make decisions. Greater cross-traffic may mean cannibalization (and lost sales) for the newly combined group, or it may present opportunities to develop loyalty programs across locations and boost visitor numbers to both venues.

 A deeper analysis of the mall sector overall shows how much difference there is between A-rated malls and those that are ‘C’ and ‘D’ rated.

For the real estate sector, the ability to instantly visualize not just foot traffic, but also weather patterns and truck traffic to and from very specific locations is a game-changing tool. Brokers can offer detailed advice to their clients on site locations, immediately, and based on deep and reliable data. Investors can understand how visitor numbers have changed over time and in comparison with other locations around the country, even around the world. Business owners can make informed decisions about their sales and marketing approach at specific stores, based on demographic insights they have never previously had access to. All at the click of a button.

As this information becomes more widespread and easier to access, it is also important to have confidence in the data behind the screen. It is key to ensure that your data provider has stress-tested their data sets and accurately fenced all of their locations to cut out potential ‘noise’ that can make data inaccurate. For example, if a building has not been manually geofenced, then you may pick up traffic from pedestrians that are simply passing by—crossing at an intersection, or stopping briefly at an ATM. Geofencing is the process of creating a virtual fence around a real geographic area. This can be a store or a building, a mall or a city block, even a whole city. In this way we are able to select the mobile devices that are within the fence and provide analytics for these devices.

Though relatively new, geolocation applications are now mainstream and easily accessible. An important concern resulting from this growth is that of privacy. This is legitimate and it is essential that both the industry and regulators maintain high ethical standards and transparency for this new field. But limiting the data that can be collected may not be the right solution.  Regulations such as GDPR (General Data Protection Regulation) in Europe, and the CCPA (California Consumer Privacy Act) both aim to protect consumers by enacting legislation that requires greater transparency about how their cell-phone data is being used, and by whom. This is a positive development both for consumers and for data providers. Consent is key for maximizing the benefits of location data, not just for firms harvesting the data, but for consumers as well.

Geolocation data is, in many ways, bringing us full circle. The explosive growth in online retail was not just about convenience, it was also boosted by the ability for marketers to use web analytics to specifically target customers according to detailed information about their preferences and habits. Something that used to be impossible for traditional stores. But not any more. The optimists among us hope that this new wave of PropTech will help enable smarter urban planning, more efficient use of investment and faster change at underperforming locations to help turn them around. 

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