As record layoffs hit corporate giants, fears of a global recession are surging. Faced with these uncertain times, quick-serve restaurants need to boost efficiency if they want to protect their bottom lines against the volatility of the market, inflation, and supply chain costs.
To do so, many will turn to location data. Quick-service restaurants can use location data to improve site selection, analyze rivals and boost their competitive advantage, and streamline marketing. Let’s dive into how location data can save quick-service restaurants money on each of these fronts while enabling them to grow—even during a downturn.
Leverage location intelligence to improve site selection
Location data can be used to identify the geographic position of a device, individual, or point of interest (POI). POI data comprises any physical site that could be of interest to individuals or companies, including brick-and-mortar stores and restaurants or landmarks. Mobility data is anonymized and aggregated data collected from smartphones, tablets, laptops, and other connected devices.
Quick-service restaurants specifically can benefit from using geospatial data for market research and expansion. Using POI and mobility data, quick-service restaurants can find locations that see significant foot traffic and fit target demographics while also evaluating the presence (and performance) of nearby competitors.
For example, Starbucks could plan to open three new locations in the Houston metropolitan area. The director of this expansion may want to focus on suburban neighborhoods with thriving shopper centers that have grown in residential popularity since the pandemic.
In this scenario, the director can use POI data to evaluate the presence of competitors in the neighborhood at large and in specific shopping centers that are under consideration for the expansion. They can also see the POIs their target demographics might frequent, such as office spaces, schools, university campuses, grocery stores, or gyms, to help them decide on the best potential sites for the expansion.
Develop global competitive intelligence
For quick-service restaurants with a global presence, market research can be even more challenging—and with costly ramifications. International expansion requires thorough competitive intelligence based on data that is both accurate and complete.
POI data comes into play as decision makers look to expand in markets around the world. In the Starbucks example, the chain could use POI data to find markets where competitors are scarce and residences and workplaces are dense, indicating limited competition and high foot traffic.
Then, when the time comes to choose sites for the new Starbucks locations within select markets, the growth team can use POI data to identify the specific neighborhoods and spots within those neighborhoods that are ripe for expansion.
Quick-service restaurants using location data to grow should be sure to access data that is extensive and collected from multiple data sources. The data should also be closely vetted by local sources for accuracy. Rigor in vetting data sources increases the likelihood that the locations hit their sales goals in the new market and prove profitable.
Streamline marketing with geospatial insights
For established quick-service restaurants locations, POI and mobility data drive geotargeting, geofencing, and geo-conquesting. These tactics can increase foot traffic and divert customers from the competition using strategically timed and delivered ads. Quick-service restaurants located within department stores or other POIs can also use beacons and proximity marketing to reach consumers who are already inside an identified space.
Specifically, mobility data empowers quick-service restaurants marketers to evaluate location performance not only in terms of sales, but in terms of overall foot traffic into, out of, and nearby the location. In the Starbucks example, a location within a regional grocery store chain could discover that sales are lower in the morning while overall foot traffic in the grocery store is much higher during that same time window.
The marketing manager for the region’s Starbucks chain sees this discrepancy and decides to use beacons and proximity marketing to reach morning grocery store shoppers. When a customer using the grocery store’s app enters the coffee aisle, they see a targeted ad for the Starbucks location within the store. The marketing manager also places print and digital advertisements in the aisle to encourage the customer to order a fresh coffee or breakfast from Starbucks before leaving the store.
Using geospatial data, the marketing manager also discovers a competing cafe nearby is capturing significant morning foot traffic. The grocery store and competing cafe are located near an office building. The marketing manager may then decide to try a geofencing and geo-conquesting campaign to capture business from the office building and competing cafe, respectively.
Find and make the most of location data
There are many ways to purchase and leverage location data, but quick-service restaurants must walk a fine line. Choosing the wrong site or implementing a poor geotargeting campaign can result in a tremendous amount of wasted spend and lost revenue.
At the same time, gaining quality insights from POI and mobility data can deliver results for any brick-and-mortar business. Most critically, quick-service restaurants should gather location data based on multiple data sources, strong verification techniques, and privacy-safe collection methods. This ensures that quick-service restaurants can use a highly accurate dataset in their decision making—and without violating consumer privacy.
Geoff Michener is the CEO and co-founder of the geospatial data company dataPlor, where he steers the company’s strategy as it aims to provide the world’s most comprehensive and accurate POI data. He previously co-founded the small business data company Prospectwise, which was sold in 2016, and was a nuclear counterterrorism contractor. Geoff hails from Colorado and is a proud Pine Ridge Reservation tribal member.