When I started off on my own earlier this year I was in dire need of a cleanse. For years I had worked hard for an organization that I didn’t align with on any kind of a level when it came to community responsibility or participating in the industry outside the confines of its own business walls. I figured change is inevitable anyways so I decided to give it a push instead of waiting for it.
Since then, I presented my first research training session at one of the largest non-profit conferences in the country, have been forced to push my comfort zone in every direction, done pro bono volunteer work for numerous community organizations, and developed what I believe to be the first survey research training program in existence all while helping businesses professionally and taking care of my family. So, so long misery!
The giving back has made me feel whole again. I feel healthier, stronger and revitalized with energy and passion for what I am doing. I now have a day each week devoted to my own education and I am learning tons I never knew before. Just this last week I was presented with an interesting new perspective on segmentation studies. It all started with creating a short survey to help out a friend.
Segmentation 101 for the uninitiated: A segmentation study is one where we take a combination of demographic, behavioral and opinion data from a survey and conduct a few statistical tests (two-step or k-means cluster analysis) to come up with mathematical groupings. The groupings lead to a specific profile of the type of person that took each survey.
For example, if we see a relationship between gender, the times of year they like to shop and how they feel about say fishing we can then create an algorithm to place to come up with what type of fishing customer they are. So say for this example we find 3 distinct groups:
Group 1: Fellas buying fishing gear in the spring.
Group 2: Fellas buying fishing gear all year types (okay, this is me).
Group 3: Ladies who buy gear in the summer.
We can then label each of these prospects as their chosen segment and then look at trends in other fields like: Among spring fishermen, who is more likely to buy lures vs. bait? Who are buying salmon gear vs. bass gear? And what we normally do is look for any statistical differences between the segments to see what other insights we can extrapolate from the data as a whole. Now, the cool thing about segmentation is that it doesn’t end there. Then you take the same segmentation algorithm and program it into a spreadsheet. The spreadsheet can then be used to instantly classify their prospects into these categories. They then know who to target, at what time of year, and what those prospects would like to see. Powerful stuff that helps big businesses make a lot of money.
The perspective I gained the other day was how to use this business information for more good. That little survey turned into segmentation and possibly could help the non-profit world, which needs more tools like this. They need to know who they can count on for fundraising, what types of volunteers they have and are attracting and how to motivate them, and how to be sensitive to different audiences and their needs. The same application used for making money and building customer relationships can now be used to fund organizations just wanting to do good in this world. With non-profits getting more sophisticated everyday I’m hoping to take this idea and help bridge the gap a little. Okay, I also like to get paid for the business studies too. Something has to pay for all those lures.
I can’t wait to see what I find out next!
-Nate Laban
