In Part 3 of our 4-part Case Study on association Special Interest Groups (SIGs), we’ll continue our data-based analysis of 100 different SIGs to help our client better allocate association resources. (Click here to access Part 1 and Part 2 of this series).
After creating uniform data collection processes and evaluating each SIG using the same Key Performance Indicators, it was time to move on to Step 3 in our process: normalizing the data.
Step Three: Normalize The Data
Our client was an international association with tens of thousands of members and one hundred Special Interest Groups within that membership. Each of the SIGs had a different number of members who participated in the group to varying degrees.
As we examined the data, we noticed that some SIGs were very active, and would regularly attend association meetings and conferences. Others were less active, with members who only met twice per year or less.
Some SIGs were small in number but very active in terms of participation. Others had large memberships with only a small percentage of those members participating during events.
The differences in behavior were important to our data because they meant we could not compare one SIG to another without normalizing the data. Comparing a SIG with 25 people to a SIG with 200 people is like comparing apples to oranges. That’s why it was crucial to undergo a weighting process with the data we had in order to find common ground between each SIG.
This type of weighting process equalizes the data by finding intersections between different elements. For example, we looked at a SIG that had 200 members but only 10% of its membership were active participants. We then looked at a SIG with 50 members, 75% of whom were active members. Our weighting process used elements like membership numbers, participation, and per-member expenses to effectively compare “apples to apples,” even amongst wildly different SIGs.
It’s important to note that the data we collected, organized, and normalized covered a two-year time period, as it’s essential to base analysis on at least this amount of time if not longer to identify trends.
Part 4 of our series reveals how all of this data-driven work was put into motion and used to reallocate SIG funds and save our client money.
Food for Thought: Poached Lobster and Tomatoes
I ran across this recipe when I was doing some research on poaching. Growing up in the South, I got very good at frying things. As I grew older, I refrained – to save my life – from frying absolutely everything!
Instead, I learned balance. Sometimes I still fry some of my favorite Southern dishes, but I balance those out with fresh, healthy recipes like this one for poached lobster. It’s just like normalizing your data: you have to create an even playing field within your data so you’re not comparing apples to oranges (or, in my case, fried chicken to lobster!). Find those unifying qualities to balance (normalize) your data in order to work with it more strategically.
I was first drawn to this recipe by the photo they used – lobster has never looked so luscious! I chickened out on using a whole live lobster and dashed over to Whole Foods to get a couple of lobster tails instead. This change meant a lot less work in the kitchen, and was still very tasty (not to mention light and fresh!). It’ll make your brains fall out!
Are you investing in any part of your business where you’re NOT collecting data? Chances are you may be leaving money on the table. Contact Massa & Co. today to find out how you can boost profits, slash expenses, and grow your business through the power of data. Find out more by calling (312) 463-1050 or contacting us online.