How Food Trucks Can Leverage Location Data to Optimize Sales
Every day, in cities around the country, food trucks drive the streets looking for the best spots to set up shop. With an estimated 4,000 food trucks now operating in the U.S., the industry has entered the mainstream.
No longer just on the fringes, the food truck industry is slowly going the way of other legitimized business industries. That means local ordinances are being instituted, food truck owners are accepting outside funding, and they’re utilizing the same sorts of marketing strategies as larger, established hospitality businesses.
In an effort to create a set of best practices for food truck operators, the data science team at the location intelligence firm CARTO, formerly known as CartoDB, recently analyzed a month’s worth of anonymized transaction data for 10 food trucks in Manhattan and Brooklyn, New York. The goal? To uncover which factors food truck operators should consider in order to optimize revenue and location.
The topic itself has become more relevant in the food truck industry since the release of a report by Food Truck Nation, a project of the U.S. Chamber of Commerce Foundation, which noted the importance of location within a city.
Larger cities with sprawling suburbs can encompass multiple permitting jurisdictions, and truck owners are responsible for meeting the requirements in each of these jurisdictions. So an owner could be paying $300 for an operating permit in multiple jurisdiction as he or she searches for the best location to set up shop. It’s much better, then, for truck owners to pinpoint their best locations quickly, so they can get started selling and minimize their expenses.
“Food truck owners can increase sales with more accurate projection models that leverage location data from both traditional sources, like census and point of interest datasets, [coupled] with new streams, like transactional and foot traffic data,” explains Santiago Giraldo, an urban scientist at CARTO. “With more accurate revenue models, food truck owners have a better sense of site performance, which can also help stock each truck appropriately to reduce overhead costs.”
CARTO’s analysis found that the top factors to optimize food truck revenue in New York City were foot traffic, day of the week, and transaction frequency. Somewhat surprisingly, CARTO found that less important factors were total population and median rent prices. The company also found that food trucks averaged $11,000 in sales, but revenue patterns fluctuated based on hour and cart.
Utilizing a model that included location data streams, CARTO was able to make revenue predictions for food trucks in different areas around New York City. For example, the company found $5,234 in average revenue per week for trucks in the West Village, and $6,128 in average revenue per week for trucks in Corona Park, Queens.
Giraldo says the most important factor for food truck owners to consider as they work with location data is that they are building models with new data streams and derivative datasets. In the past, traditional sources, like the census or point of interest data, would serve as the basis for building models. However, these static sources aren’t able to monitor mobility trends and patterns in the same way that location data can.
“New data streams, like transactional data and foot traffic data, can provide more insights on what locations and at what times of the day certain sites are busiest,” Giraldo says. “Building models with this more precise information will make for more accurate sales forecasting.”
But the barriers to adoption of location data-driven strategies are significant. Regardless of industry, Giraldo says one of the main hurdles businesses must overcome is acquiring relevant location data to use in their strategies.
“Although more data exists today than ever before, this wealth of information is not readily accessible, and food truck owners are a perfect example,” Giraldo says.
Stephanie Miles is a senior editor at Street Fight.