How one of the world’s largest restaurant brands makes smarter site selection decisions with Foursquare’s point-of-interest data

There’s a science to site selection – POI data takes the guesswork out.

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Whether you’re a first time business owner looking to open an independent coffee shop or a global brand expanding into a new market, choosing the right location is critical to your success.

There is a science to site selection, and location data can help take the guesswork out of finding the perfect spot to open a new venue by providing insight into everything from the local competitive landscape to local consumer behaviors and preferences.

Here’s a look at how market planners at Yum! Brands, one of the world’s largest restaurant companies, utilizes Foursquare’s Places data to make smarter real estate decisions at a global scale:

The challenge: Understanding the nuances of new markets

“As a large, rapidly growing organization, we require a global solution that supports the expansion of our brands and a partner that understands the complexities and realities of our international markets,” said Donuale Dean, Director of Digital and IT Strategy at Yum! Brands.

Hospitality brands expanding to new markets are continuously tasked with selecting the right location for their franchises. Yum! Brands, which operates KFC, Pizza Hut, Taco Bell and The Habit Burger restaurants, has over 45,000 restaurants in more than 140 countries and territories and 1.5 million employees globally, and is no stranger to the importance of site selection to impacting its bottom-line.

According to the company’s franchise disclosure documents, opening a new store location is a $1.2M to $2.6M decision, making it absolutely critical that they have the right insights at hand before opening a new restaurant. Location data is a powerful planning input, and Foursquare’s Places point of interest (POI) data provided a reliable layer of intelligence.

The solution: Depth and accuracy at a global scale create a powerful customer solution

Foursquare’s Places data represents 100M+ venues around the world and is rich with real-world context – incorporating 1B+ tips and recommendations for venues. These contextual attributes helped qualify the local landscape of interest for Yum! Brands, giving market planners a distinct understanding of nearby competitors and categories.

Yum! Brands Decision Sciences team ingested select POI data into their decision logic, scoring and ranking site choices based on several of Foursquare’s Places data attributes. The team built a whitespace visualization based on Foursquare Places data. This internal tool was provided to key market planners and offered visual, prescriptive insight into where certain trade areas were saturated with the competition or were ideally suited for a Yum! Brands chain.

The results: Making smarter, data-driven real estate decisions

Foursquare cleared a key hurdle for Yum! Brands’ expansion into emerging markets, allowing them to make real estate decisions based on data of nearby locations and building a faster method for site selection strategy. Using an AWS-based solution, and informed by the Decision Science team’s intelligent use of Foursquare data, Yum! Brands has greatly reduced the time it takes to discover, validate, and invest in new locations.

Using Foursquare’s data in combination with other tools like AWS makes the science of site selection easy.

A note on how to easily access, handle, and analyze Places data:

In addition to providing our POI data via our Places flat file and APIs, Foursquare provides requested data sets to companies like Yum! Brands via the AWS Data Exchange. Companies can download the latest Places data to their S3 bucket. Once the data is exported to S3, they can instantly push Foursquare’s Places data to downstream applications and services that power data analysis and visualization, making ingestion of Foursquare data low-code and technically efficient.

Want to learn more about FSQ/Places, or how to use POI data to your competitive advantage? Reach out using the form below.

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