It’s one thing to know how many potential customers you have in a target area. It’s quite another to know if they really are your customers. For the past five years or so, analysts have been using mobile location data primarily to chart the number of pings created around specific locations. Even in this basic form, such data provides valuable insights for many industries by providing a good proxy for visitor or customer counts.
However, the value and usefulness of the data is limited without additional context around the device owner’s profiles. It’s the ability to differentiate between mobile devices by providing relevant customer profile-based counts that takes mobile location data to a new level.
Deeper analysis allows commercial real estate investors (developers, brokers, shopping center owners, retailers, restaurant operators and others) to gain an unprecedented understanding of how one real-estate location compares with another.
Among the applications for such technology are the ability to determine how far mobile devices are from a specific location, how much time a mobile device spends in one place compared with another, and the count of mobile device pings in one location compared with another.
When comparing mobile device counts at different locations, the question is whether totals alone are sufficient for investors to make crucial decisions, or if resources could be better deployed if the counts were supplemented with context about who owns the mobile devices.
In our experience, there is no question that a truly useful customer profile must accurately append household level attributes to the historical traffic data produced by a device. The benefits can be seen across multiple industries.
For example, many shopping center owners are seeking to develop the ideal tenant mix. By analyzing the mobile data generated around a property and further refining the information with household level psychographic and behavioral parameters, owners can make better decisions. In one location, there may be an opportunity to attract the retailer REI if enough mobile device pings in the target area are from people under age 50 who show a preference for skiing, camping, biking and other outdoor activities. It may make little sense to target Tiffany if a majority of pings are from 20-somethings with lower-than-average disposable incomes.
Houston Airport Systems changed the parameters of a current food and beverage tenant request after an analysis by Buxton added relevant customer profile inputs to mobile location data to provide management with a better understanding of the passenger mix at William P. Hobby Airport.
Also, the City of Fort Worth, Texas, wanted to know which types of events it should stage at a 14,000-seat multi-use arena being built as the new home of the Fort Worth Stock Show & Rodeo.
Using mobile device data with household level customer profile data, Buxton discovered that between 23% and 40% of those attending concerts at the American Airlines Center, located 35 miles to the east, live in the Fort Worth area. However, by looking beyond basic volume data, the analysis revealed that Fort Worth residents favor country and rock over hip-hop, dance, pop and indie bands. By adding household level insights, stadium management gained a better understanding of the genres that appeal to its real customers.
As mobile data use evolves, real estate investors should look beyond adding generalized inputs to location information, such as attaching simple averaged block group or ZIP code demographics. The average population of each of the more than 47,700 ZIP codes in the U.S. was almost 7,500, according to the 2010 census, meaning if you had 7,500 mobile devices from the same ZIP code, they would all have the exact same demographics. The same applies to the 211,267 census blocks with a typical population of 1,500.
While some start-ups might argue that analyzing to this general level protects privacy, a more likely explanation is that they lack the analytical capabilities, experience and resources needed to be as granular as possible, along with the investment in ultra-high security audits that’s necessary to ensure personal data is protected at all times.
Commercial real estate investors and operators who want to get the most out of mobile location technology need to rely on analysts with the track record, processing power, analytical capabilities and other resources that are required to create in-depth, relevant mobile device counts using household-level profiles.
When it comes to commercial real estate, the old adage, “location, location, location,” should be “customer, customer, customer.” That’s why, when it comes to mobile location data, high-quality context-based profiles trump quantity alone.