With 2019 right around the corner, it’s time to start thinking about New Year’s resolutions. At the top of the list, it’s time for restaurant brands to embrace the future of big data and analytics and ditch the outdated sales forecasting techniques.
All businesses today are operating in a data-driven marketplace, and restaurants are no exception. Franchisors that are not leveraging the power of data may find that their competitors will “eat their lunch” so to speak, taking a bigger and bigger bite out of their market share. Operators can no longer choose locations based on gut instincts. Franchise groups need to make data-driven decisions that are backed by science.
The democratization of data means that very granular, accurate data is no longer available only to large corporations. It is now easily accessible – and affordable – for all brick and mortar franchise locations. Restaurants do have to be aware of where that data is coming from. One of the common missteps that people make with their data is pulling data from a hodge-podge of different data sources that may not be accurate or even all that current. Some of the classic examples of stale data include U.S. Census data and Department of Transportation traffic data.
Brands also need to ask themselves – are we using the “right” or most effective data? Location intelligence companies now provide deep market insights from pre-selected and vetted data partners across several core categories. All of these data sets roll into location intelligence platforms that can help to create more accurate sales forecasting models.
Data Pools Continue to Evolve
Traffic Volumes: Datasets can now break down traffic at any point in the U.S. to show how many cars are going by a certain address at any given hour or day of the week. For example, for restaurants that depend on breakfast customers, it is important to choose locations that are on the right side of those traffic flows during key morning hours.
Geofence: Users can create their own zones or “geofence” boundaries. For example, data sets can track where people are traveling two hours before entering and exiting a unit location, which helps users to identify marketing opportunities and potential cannibalization of existing stores.
Social Network Segmentation: Also known as geo-social, psychographics or sentiment data, this dataset collects, and aggregates keywords being used on social network platforms in certain geographical areas. This data set helps to create “heat maps” of interests in certain geographies or zip codes, such as identifying cookie lovers, families with small children or sports fans.
Store-Level Sales Data:
Measure the performance of restaurants through annualized sales, as well as indexed Market & National grades, A+ through F. Identify relationships between your locations and others – synergistic and competitive.
Performance Index: This data allows restaurant brands to benchmark themselves against other restaurants performance within a target trade area or zip code. Not only does this help to identify desirable markets for new locations, but it also can show how an existing store might be under-performing relative to its peers.
Internal Location Data: Multi-unit operators also have access to their own proprietary data. But are you collecting the right metrics? Research in the SiteZeus platform highlights the top site attributes that are most effective in forecasting sales performance for restaurants.
The top 10 site attributes for restaurants are:
*It is important to notice that location intelligence companies can now take hundreds of variables into account, so beginning to track your data is a big first step.
Data-Powered Technology Platforms
Data is put to work in location intelligence platforms to solve for a variety of issues including locating potential sites in new and existing markets – greenfield and infill growth, as well as conducting a data-driven analysis of store relocations, remodeling, and closures.
Location intelligence platforms simply put objective methodology behind key decisions. Who makes the best co-tenants for your brand? How does one location compare to another one down the block? How close to your competition do you want to be? Location intelligence technology like SiteZeus helps to answer those questions and remove the uncertainty so that operators can make more confident decisions about new and existing locations.
Keenan Baldwin is a fifth-generation Tampa native. Keenan along with his brother Hannibal Baldwin founded SiteZeus in 2013. SiteZeus’ location intelligence platform leverages artificial intelligence and machine learning to create fast, accurate and transparent predictive modeling. Multi-unit brands use the A.I.-powered technology to make confident, data-driven decisions to solve for infill expansion, greenfield growth, remodel analysis, relocation analysis and closure analysis.
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