How Mobile Data is Taking The Risk Out Of Retail Site Selection - Lift361

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How Mobile Data is Taking The Risk Out Of Retail Site Selection

By Ed Higdon. Posted in Insight

March 22, 2018

When it comes to selecting the most optimal (and profitable) retail site location, there is an art and science to it. The decision making process should be supported by what the data is telling you. As we know, even big-box retailers make mistakes every now and then, which have proven to be an extremely costly mistake.

Fortunately, technology and big data have paved the way for mitigating risk in the site selection process. Gone are the days of relying on third-party data from real estate brokers trying to win your business or objective data that was hardly specific to your needs. Now, you can use objective, highly-targeted data to make the best site selection decisions for your new locations.

Data Beyond Demographics

Traditionally, retailers have collected residential, commercial and traffic data for a potential retail site, but thanks to mobile data, savvy retailers can now drill even deeper to understand a new potential market. Location-specific data can be leveraged to uncover:

  • Where potential customers are coming from
  • The types of customers likely to visit each location
  • Key areas in an existing shopping center that have the highest traffic

Leveraging this type of data, which can be gleaned from a user’s mobile data, can provide strong predictive indicators about the types of customers a location will draw, and whether those customers are truly aligned with a retailer’s targets.

A Closer Look At Where Customers Are Coming From

In a recent case, one regional retailer examined a new potential location in the greater Boston area. As you can see from the map below, the mobile data showed the center drew from very different parts of Boston. The red clusters represent where the highest volume of shoppers came from, orange the second highest, and yellow third highest.

Classifying Customer Types

Mobile data was also used to classify shoppers in the area into “mosaic clusters” based on important demographics that inform behaviors. People in a specific cluster often behave in the same ways. For example, they may shop at the same places, have the same number of children, drive the same types of cars, etc. By identifying these specific clusters of potential shoppers, retailers are better positioned to choose the most ideal new store locations.

The visitors to the shopping center under consideration in our example were very different. They drew in higher-income suburban residents, mature affluent couples as well as young singles and recent college graduates.

Uncovering Shopping Center “Hot Spots”

Thanks to the depth of mobile data, it is possible to drill down even further to examine where shoppers go once they are in a specific shopping center. These “hot spots” that attract the most people can be a key piece in determining the right location.

Based on the above image, you can see how it would make sense to choose a location close to 1, 2 or 3, respectively as these are the highest foot traffic areas.

Mobile Data Leaves Little To Chance

In the past, no matter how much research a retailer did before opening a new location, there was always significant risk involved. There just simply wasn’t enough data available to make accurate predictions about potential shoppers’ behaviors. However, thanks to the evolution of mobile data and rapid advancements in the ability to build predictive models, retailers are now able to eliminate much of the risk associated with new site selection. Furthermore, a trusted partner like Lift361 can help you navigate these waters to ensure you find the optimal site location.