As quick-serve restaurants seek to boost the bottom line and get more efficient amid economic headwinds, many are turning to location data.
With point-of-interest (POI) data, quick-service restaurants are able to visualize their own physical presence across a geographic area, map the competition, and analyze future locations for expansion. Paired with mobility data, which offers insights about customer behavior, POI data can provide companies with an essential and dynamic source of information for strategic decision-making.
Those insights are particularly important when quick-service restaurants, like so many other companies, face the prospect of another economic downturn. In challenging financial times, it’s tempting to turn to free sources to identify market trends and get much-needed competitive intelligence. A popular example is store locator pages, where businesses can access a map of their competitor’s locations.
While this might seem like a valuable, cost-effective resource, in most cases the opposite is true: using store locator pages to inform site selection will do more harm than good. To better understand the risks and consequences, let’s unpack how quick-service restaurants are leveraging location insights to boost the bottom line, why store locator pages are poor indicators of market trends, and how to best find and use geospatial data.
How quick-service restaurants are using geospatial data
Location data is at the core of many quick-service restaurants’ business strategies. It’s a key source of information for site selection, enabling companies looking to expand locally or internationally to develop a holistic vision of a target area, region, or country. Quick-service restaurants also use it to map the competitive landscape by charting a competitor’s locations and highlighting the areas—for example, shopping centers, highways, or business parks—they target. Location data also empowers marketers, allowing geofencing and geotargeting to reach relevant customers and send them push notifications.
The usefulness of location data heavily depends on the nature of its sources. Companies often turn to open-source or third-party data for the information they need, since these sources are easy to access and look like a good way to save on data costs.
For example, imagine Nestlé’s Blue Bottle Coffee is looking to drive growth by expanding its locations in the Boston area. In particular, it wants to target the Allston and Brookline neighborhoods to take advantage of the multiple universities and startups in the area. One option is for Blue Bottle to analyze its major competitor, Starbucks, with data from store locator pages. This would hypothetically let it create a snapshot of Starbucks’ locations in the area, identify gaps in coverage, and determine what kinds of demographics Starbucks is aiming for based on nearby POIs (campuses, grocery stores, gyms, etc).
But relying on freely available data like store locator pages, while a seemingly efficient use of location data in a tough economy, can thwart companies hoping to accelerate growth.
Why store locator pages lead to incomplete insights
Blue Bottle’s hypothetical strategy relies on a single source of information from a single company, which can lead to serious problems down the road, including duplications and outdated information.
By contrast, dynamic geospatial data providers depend on multiple sources, frequent updates, and data enhancement to avoid these issues. To get similar data from store locator pages alone, Blue Bottle would have to piece together POIs from a whole range of individual pages, an inefficient and costly process.
One way store locator pages fall short is their inability to account for shuttered locations. Blue Bottle might see that there’s a Starbucks location near a Trader Joe’s that’s frequented by students and decide to build elsewhere—but, importantly, the location might have closed months ago, causing Blue Bottle to miss that ideal spot.
Another data point store locator pages often botch is hours. Perhaps Blue Bottle is considering staying open late to attract students looking for study spots. It turns to Starbucks’ locator page for information about their competitor’s hours, but it have no way of knowing whether the listed hours are current. Similarly, Blue Bottle may want to analyze its competitors’ practices during the frequent snowstorms in Boston, but Starbucks’ store locator data likely won’t allow it to track changes in store hours across time and season.
Overall, record verification across store locator pages tends to be non-existent or vague. To make informed decisions, quick-service restaurants like Blue Bottle rely heavily on insights about peak traffic hours and up-to-date changes in nearby POIs. Yet, without visibility into how store locator data is verified, Blue Bottle can’t be sure the open-source data it has is actionable.
How to find and use dynamic location data
Not all location data is created equal. When it comes from the right sources, it’s a difference maker for quick-service restaurants looking for an edge. Bad data, on the other hand, brings with it a host of costly problems and complications that can lead to poor decision-making.
While open-source data like that on store locator pages is free, it’s often riddled with errors and requires enhancement to be actionable and trustworthy. Third-party data avoids some of these pitfalls but doesn’t always provide truly global geospatial insights. If Blue Bottle decided to expand on its recent locations in Japan to challenge Starbucks overseas, for example, it would need location data at scale that it can trust. Yet most third-party international datasets are infamously error-prone and unreliable, with inaccuracies in up to 80 percent of the data.
That’s why it’s important for quick-service restaurants looking to capitalize on geospatial data to vet their sources for accuracy. How often is the data updated? Is the data provider an expert in the market the quick-service restaurant is considering? Does the provider specialize in the kind of location data the quick-service restaurant needs, be that mobility or POI data? In a challenging economic environment, quick-service restaurants can expand with confidence with location data—but only if it’s dynamic and accurate.
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.