How to Use Demographic Data for Site Selection

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In this video Candace Couture, director of franchise admissions at Planet Fitness, discusses her role in selling franchises and new franchise locations, and to approve new franchise locations for existing franchisees looking to open up new locations.

Planet Fitness has 536 locations currently; and is looking to open over 100 more this year. Right now they operate in 47 states right now. Candace has to tell whether or not an area is going to work demographically before a franchise location can be sold, so she uses the SCOUT software to run comprehensive demographic analysis, looking mainly at population in an area of 1-3-5 mile radius.

She also looks at highly Hispanic markets because they have worked very well for the company, so they look at race and ethnicity. Planet Fitness typically works well in a median level income market so they are making sure that the income levels aren't so high that the area wouldn't make sense.

SCOUT also interfaces with the Planet Fitness billing software which is very important because with the company's expansion across the country they are starting to go into markets and open up locations closer to each other so want to make sure they are not cannibalizing on each other.

And with the ability to interface with the Planet Fitness billing software, an employee can basically click on a star on the map of any location and populate the map with all Planet Fitness members so it's crucial for the company to have that data.

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1 Comment

  • user-pic John Mark Harras | October 10, 2012 1:43 PM | Reply
  • Interesting talk on demographics.

    At my former position I developed a methodology I called PSA Prime (PSA’) which was a blend of 1/3/5 census data rings and Voronoi diagrams using the closer boundary. PSA’3 had a quite good correlation with store performance and some distinct advantages.

    1. As a prospective site was analyzed a change in an existing stores’ PSA’3 would give an estimate of cannibalizing.

    2. Keeping the ring at a reasonable level was a good way to be reminded that people outside the range might initially be customers, but were subject to loss intra-company and to competitors.

    3. When the map was lit up with the PSA’ shapes areas available for development stood out.
    I have since developed penetration curves that estimate customer trial and retention rates based on distance from location, with some other factors affecting the curve.

    While we didn’t have the ability to use travel time analysis the basic issues are the same and I found population density to be a fair proxy for time estimates- assumption being that higher density populations travel less than lower. (In the specific case of our service) So an inner city PSA’ could be quite a bit smaller than a suburban and both sites could perform similarly.

    Good luck with your expansion and I hope you find some of these techniques helpful in the future!

    John Mark

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    This page contains a single entry by Courtney Hall published on April 29, 2015 8:43 PM.

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