How Location Analytics Can Pull Commercial Real Estate out of the Darkness
Decision makers in the $15 trillion commercial real estate industry are forced to make huge decisions based on insufficient data. Surveys are expensive and slow. Other data sources are biased and limited, leaving professionals flying blind on choices worth tens of millions of dollars, or more.
Mobile location analytics, a recent entry into the field, is changing this picture dramatically by providing unprecedented visibility into consumer behavior. Location analytics collects data on the movement of individuals and presents it as an aggregate picture. Where do people shop? What days do they go to a specific restaurant and at what time does that restaurant see the most visitors? How did they get there and where did they visit afterward? This unique ability to visualize movement provides strong behavioral data that underlies performance.
By showing real-time and accurate insights into the movements to and from any place, this data can surface real trade areas, customer journeys, brand preferences, cross-shopping and much more. The change is hugely significant as it finally empowers the industry to make data-driven decisions on the questions that define success for their business. From acquisitions to leasing to marketing and operations, location analytics brings commercial real estate out of the dark.
Here are some examples of how location analytics drives success in commercial real estate.
Anyone who has spent time in the competitive commercial real estate industry knows many of the professionals rely on gut instinct to inform significant decisions. By leveraging location analytics, professionals can put proven rules and concepts behind their gut feelings to help make these concepts more tangible and widely applied.
One example of location analytics in action is the examination and identification of trade areas. Without the specificity provided by location analytics, companies are forced to take a generic approach to their trade area analysis. The basic assumption is made that an audience exists within a radius that extends a certain number of miles beyond the actual store site. However, analyzing true trade area data consistently shows that top retailers often miss major opportunities to expand their audience and drive more sales. A generic three-mile radius ignores critical physical and demographic factors that can determine where a property’s audience actually originates.
By looking at foot traffic patterns, a company can finally know – and not guess – where their audience comes from. This has huge implications for how they spend marketing budgets, where they build their next store, or even how they analyze their competitive landscape. Location data goes beyond guessing and allows professionals to create an accurate picture of their audience behavior.
A simplified analysis of the cost to buy and develop a shopping center estimates the investment at $25 million, on the low end of the spectrum. With this large financial stake, a developer should look to see major returns, as the cost of failing is significant. What is the health of the prospective center’s tenants? How do they rank compared to national and local benchmarks?
Obtaining objective data that can be applied to all cases is a huge asset. To begin, an investor can develop data-backed parameters on which factors drove success on previous sites. Is the proximity of a customer segment’s workplace the key to success for a specific location? Is the most important factor the mix of tenants, or the overall number of foot traffic nearby? Putting a number behind these components can help developers identify ideal properties and turn those properties into successful entities faster than ever before.
The ability to identify the high opportunity sites and apply effective strategies to them based on set performance data can be a defining trait in driving success.
