How Location Data Improves Retail Site Location Analysis

Location Data Retail Sites

Choosing the location of a new retail site is the most important factor in the success or failure of the new store. The best offering in the wrong location is doomed to failure, while a superb location can often drive better-than-expected outcomes.

Selecting a new retail site location is critical to the success of the new store, but it is hardly a straightforward endeavor. Considerations must include the needs of the business (space, storage, displays, deliveries, parking), customer demographics, local population, foot traffic, accessibility, visibility and more. The wrong mix, or incorrectly weighting one item over another, can greatly impact results, and even experience cannot provide all the information needed for good decision-making. Even understanding these factors, site locations are often selected by valuing emotions over information, and expectations over analytics.

Incorporating location intelligence to the site selection process provides retailers with high-quality, relevant, and actionable insights that can help retailers make the optimal choice for new site locations. Location intelligence gives retailers information that relates directly to site selection, including:


Targeting your ideal customer means having a clear picture of who that customer is: age, gender, family status, etc. Location intelligence uses anonymized demographic data to create a picture of the individual customer, which is valuable to retailers in helping to build pictures of existing customers, which leads to targeting potential customers. And a clear representation of current and potential customers improves strategic audience targeting, which informs site location decisions.


The next level of consumer analytics, location data takes the demographic information above and adds a new layer: that of consumer habits. Location data takes raw data concerning where people are – such as foot traffic and visit frequency – and compiles it into information about how people live – their daily life and regular pursuits. This introduces valuable insight as to how people interact with brands and engage with products. For example, are people traveling from their home to a store location, and could sales potentially rise if a location closer to their neighborhood was opened? Do your customers stop by your location on the way home from the gym, and if that drives foot traffic, could a new location be near a gym as well?

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Retailers can gain an even deeper understanding of the customer with a further application of location data – one that reveals consumer interests. With verifiable, accurate data, a retailer can discover even more about their customers by learning their interests. An individual that attends a concert in the park has different interests than a person who goes to the park for a sporting event, or to use the playground. Understanding a customer’s interest adds the ‘why’, giving context to other types of data on demographics and customer habits.

Gravy Analytics builds a complete picture of the consumer by combining first-level demographic data with second-level habits, then adding additional information describing their interests. This information can inform a site location decision by providing reliable behavioral insights that can be used for audience targeting and predictive analytics.


Each level of data gathering and analysis (including demographics, habits, and interests) provides valuable information about your own existing and potential new customers. Imagine how valuable this analysis would be when applied to your competition’s customers. A picture of the competition’s customers that includes habits and interests can provide you with valuable insights that may remain undiscovered by other retailers, providing you with a competitive advantage.

Location intelligence is the sole method of learning how people spend their time. More than that, though, it is often more reliable than other types of data. First, it is available as a larger data set, often comprising millions of data points per day, allowing for large-scale analytics and consistent insights. Location data is also less biased than other forms of data – for example, that obtained by survey. On a survey, people tend to give idealized, subjective responses: location data shows what they actually do on a daily basis.

Using location data can help a retailer looking at a new location in many ways. With valuable insight into customer behaviors, activities, and pursuits, a site location can be evaluated in a new and different ways that inform predictive analytics and the expectations for success at the new location. Location intelligence can help a retailer choose the best location using a depth of analysis that is unavailable with other types of data, increasing the likelihood of success at the new location, improving sales, revenue, and ROI.

Real-World Location Intelligence

Gravy Real-World Location Intelligence Overview

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