• 08.25.14

How Fast Food Chains Pick Their Next Location

Starbucks and Wendy’s are relying on your location data to expand into new neighborhoods.

How Fast Food Chains Pick Their Next Location
[Image: Flickr user Little Larry]

When the global economic crisis hit in 2008, fast food companies were hit harder than expected. Although more people ate at places like McDonald’s and Domino’s Pizzas because they were cutting back expenses, even more Americans couldn’t afford to visit fast food joints at all. Plus, real estate values, the secret backbone of the fast food world, were in free-fall.


That last detail was crucial. Big food corporations spend much of their capital placing new locations where they think there will be growth, but suddenly those choices seemed much more risky. The land that sits under fast food outlets like Taco Bell and Wendy’s is typically owned by the parent companies. As the economic crisis hit the fast food industry, more and more fast food companies began adopting data-driven approaches to opening new locations, says Simon Thompson, commercial director at geoanalytics firm Esri.

At a recent Esri conference in San Diego, representatives from Starbucks Coffee, Chick-fil-A, and Wendy’s all spoke about how they use geographic information systems (GIS) to determine where to build new outlets. Thompson says that for fast food chains, comparing all sorts of data overlays which allow them to see auto traffic, consumer demographics, safety information, commercial mix, and other factors saves them significant money when deciding which properties to open up in.

Geographic and demographic data collected by Esri and competitors like MapInfo and a slew of smaller data brokers are increasingly used by fast food chains to determine where to open outlets.

Starbucks global market planning manager Patrick O’Hagan for instance, told conference attendees that Starbucks uses an in-house mapping and business intelligence platform called Atlas to determine where to open new locations. Atlas is used worldwide; for opening new branches in China for instance, O’Hagan’s team uses the platform to have local partners evaluate nearby retail clusters, public transportation stops, and neighborhood demographics. In an example he showed in Nanjing, Starbucks’ local representative used the platform to find a store location which had high potential foot traffic from several office buildings under construction–and then created a workflow which handled the permit and legal process for the new Starbucks’s opening.

This location data can also be used for unorthodox purposes. As the video below shows, Starbucks’s data-centric approach is good for more than just real estate purposes. They use demographic information regarding the number of local smartphone owners to determine which parts of southern states to target app-based discounts into. In Memphis, Starbucks used added weather overlays to predict when a heat wave would strike, and then timed a local Frappucino promotion to coincide with it. And for Starbucks’ ongoing effort to add beer and wine to store menus, they use Atlas to find locations with two criteria: high local spending patterns and a large number of wine away from home drinkers.

For chains, using GIS and other data-centric services follows simple logic: It helps the company save money, and prevents them from losing money by opening branches that will just underperform later.


John Crouse, the director of Wendy’s real estate services, told Co.Labs that because the fast food industry traditionally hasn’t used loyalty programs or branded credit cards, it’s been harder for fast food restaurants to obtain demographic information than other hospitality or retail sectors. But at Wendy’s, for example, Crouse and his coworkers use GIS platforms to help break down which blocks in an urban downtown will have high foot traffic and similar factors.

“We came up with our own urbanicity scheme,” he added. “One classification we created is called ‘downtown,’ which is places like downtown Dallas or Columbus which are less dense than the Loop in Chicago or downtown Manhattan–places where you can’t have freestanding locations but where behavior is different than bigger cities. We can see the differentiation, quantify it, and then do analytic work reflecting the similar environment.”

GIS platforms and data-based location planning are commonly used in the retail sector as well.