Analytics and the Franchise Awards Process

People often ask me how I ended up in franchising, and the truth is that I’m not exactly sure — I feel like I just landed here and stayed because I love it. Prior to Franchise Foundry, I co-founded two technology companies, both with an analytics emphasis. The first was Sharp Analytics. At Sharp, we did some really cool work for companies like Procter & Gamble, Princess Cruise Lines, Sprint, and other well known brands. These companies came to us because they wanted to understand their customers better in an effort to attract more “best customers.” For example, we did a customer segmentation and analysis for a very high end skin care line. We overlaid purchase behavior with demographic information to identify what the best and worst customers looked like. We used that information to create a marketing campaign aimed at finding new customers that looked like the very best existing customers. And of course, we measured the results.  It was rewarding work because it produced results.

So when I ended up in franchising, it was natural to look at the awarding franchises through the same lens. What do the best franchisees look like? Is there just one profile, or are there several franchisee profiles we should be looking at? What messages will those candidates respond to? Where are they? How do I find them? And how can we systematize and measure the process of discovering new candidates and awarding new franchises?

When we kick off development for a new brand at Franchise Foundry, we bring this type of thinking to the table. We start with what the ideal franchisee looks like, and identify demographics that will help us find people like them. We develop messaging specific to each targeted segment and roll out measurable marketing based on our plan. We rarely get it exactly right the first time, which is why we measure everything we do. We want to know what works and what doesn’t so we can fine tune the process.

The result is that year over year, we have awarded more franchises per generated lead than the national average by a wide margin. And we plan to continue to fine tune the process, improving our conversion rates this year by tracking our efforts closely and throwing out the stuff that isn’t working and spending more time on the stuff that does.

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