It sounds simple, but for most associations, it’s not.
Think about all the data your association has at its fingertips: demographics of your members, conference registrations, product sales, vendor buying habits.
It’s a goldmine, right? But chances are, it’s untapped.
Data are crucial to associations’ decision making, so if an association has “dirty data” (vs. quality data), that’s a problem, said Elizabeth Engel, CEO of Spark Consulting, who recently co-authored a whitepaper with Peter Houstle, CEO of Mariner Management & Marketing, LLC, on evidence-based decision making.
“Much like a successful exercise program, a sustainable data quality management program must become a deeply ingrained institutional habit shared by every member of your team,” Engel said. “Achieving a clean, unified dataset that captures your key data points is a critical first step to implementing the type of evidence-based decision-making that allows you to most effectively allocate your limited resources to advance your mission.”
So where does an association start? Engel suggests answering three key questions:
1. What’s your association’s baseline? What is it trying to achieve? Where and how large is the gap between the two? The answers should be strategic, measurable goals, such as growing membership by 80 percent.
2. What drives success for your association? These are your Key Performance Indicators, the process-related metrics that determine how well your association is doing. So a KPI related to membership growth might be the retention rate.
3. Who are your customers and what do they need from your association? In other words, what do your members need to make membership so valuable that they’ll renew?
For example, think about your last conference. How does your association determine its success? Perhaps your event had the largest turnout in history, but what if several of those registrations were complimentary? Or what if your attendees’ buying needs didn’t match your vendors’ selling needs?
Simply put: When it comes to data, quality trumps quantity.
By themselves, data are just numbers. But inside those numbers are patterns and trends, which sometimes aren’t easy to spot. That’s why there’s a plethora of data visualization tools, i.e. graphs and charts, to help associations analyze data. Engel and Houstle list several examples in their whitepaper.
With such tools, associations can:
- Plot members by region and overlay income demographics from the U.S. Census
- Identify the most frequent sources of volunteers
- Spot trends in member participation
- Compare attendee profiles across event types
- Detect common exit points in website visits across various member demographics
Take the Entomological Society of America (yes, bugs). Students comprised 30 percent of its membership, and as such, the association had been focusing on recruiting and retaining students.
But upon analysis, ESA discovered a large membership drop off after graduation. After analyzing membership data, it concluded that focusing efforts on student retention wasn’t paying off. So ESA revamped its membership efforts to retain all members, especially regular professionals, who bring in more revenue.
ESA’s new membership model is just one example of effective data mining. The whitepaper lists several others, such as ASAE deciding to stop one of its print publications.
Tell us, how does your association use data?