Success in today’s competitive quick-service market requires companies to open more restaurants, faster and in the optimal locations. That’s all while trying to minimize sales cannibalization between restaurants and constantly evaluating which restaurants to close and which to renovate. For a franchise system to grow and improve its business, it needs to have control of the complicated relationship between sales performance and customers. By utilizing demographic data, together with research and modeling, quick-serves can develop a sound strategy for smart growth.

Understanding Your Customers

How well do you know your customers? The answer can be confusing because knowing the customers usually means different things to different departments—even within the same organization.

One of the best ways to know customers is to understand their preferences and purchasing behaviors. There are a variety of consumer research methods, both quantitative and qualitative, that offer answers and feedback from customers regarding likes and dislikes, purchasing patterns, satisfaction with merchandising, pricing, service issues, and competitive patronage. Quick-serves that learn the details of their entire customer base can make more informed decisions rather than relying simply on gut feelings.

As a first step toward franchising, chains can work with an analytics team to conduct primary and secondary research to develop the restaurant’s customer profile. This research helped capture a clear picture of the target customer.

Using Demographics, Research, and Modeling as Tools

The use of demographic (census data), psychographic, competitive and shopping center data, together with a brand’s own sales and site-related data enable operators to better understand the dynamics affecting unit-sales performance. In general, quick-serves have fairly-compact trade areas that are largely based on convenience. While residential-based sales typically provide the majority of profits for these units, the contribution of other sales (worker, shopping, and other generators) should not be overlooked.

Demographics and Research

By utilizing demographic information, along with sales data, brands can make more strategic decisions regarding real estate, operational, marketing, and merchandising questions. Areas to consider include:

  • Trade area size—Urban-suburban-rural are useful for managing cannibalization and store deployment.
  • Impact of distance to consumers on sales potential—This is especially important in franchising, as real estate and lease costs are primary considerations to franchisees. On the other hand, the benefit of prime locations should also be considered rather than just costs, as these may be more than offset in a prime location.
  • Percentage of sales from residential, worker, shopping, and other generators—As a franchise system or franchisee, it is important to know if the brand successfully operates in situations where one is seeking to deploy. For example, if a concept is not conducive to attracting working populations, then being located next to major office parks may not deliver the expected sales numbers.
  • Impact of competition—For most brands, the synergy from other restaurants far outweighs any negative impact from competition. This varies from category to category, but has generally held true in our research. There is, however, a certain tipping point where too much competition dampens the effect and understanding this relationship is key to exploiting it.


Site Modeling

Site modeling enables organizations to synthesize reality and determine the success of a certain location. The best models have a strong predictive capability, while being grounded in intuitive statistical relationships. By bringing together sales, demographics, psychographics, competition, and site characteristics, a quick-serve can use these predictive capabilities in a modeling environment.

In the end, the benefits of using site-selection tools (demographics, research, and models) are immense. From smaller companies that have a single unit to those that have thousands, all can benefit from the use of demographics and site-selection research. Poor real estate decisions can be lessened with an upfront investment in site or market knowledge. Avoiding bad real estate decisions only strengthens the long-term profitability of a company.

Brian V. Hill, restaurant practice leader for predictive analytics, joined Pitney Bowes MapInfo in 2006 with more than 15 years of experience in real estate site selection and retail operations.
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