What Member Scoring Taught Me (and How AI Makes It Better)

When I was leading membership at BIO and AdvaMed, I built a homegrown member scoring system to predict who would stay a member and who might walk away. It wasn’t fancy – just a simple scorecard that we updated manually. It worked, but it was mostly based on gut instinct.

We looked at:

• Were they on the board?

• Did they participate on committees?

• Were they a sponsor?

• Did they attend the annual conference or other events?

• How fast did they pay their dues?

Each action earned a weighted score. High scorers were safe. Medium scorers got attention. Low scorers? We flagged them early and reached out. More often than not, the score predicted what was coming.

But it was time-consuming. The data lived in different systems. Updating it quarterly was a slog. And it couldn’t adapt to behavior in real time.

Fast-forward to today—and AI changes everything.

What we did manually a decade ago, AI tools and smart CRMs can do automatically—daily, hourly, even instantly. You can track engagement across platforms, analyze trends, and trigger next steps without someone exporting a spreadsheet or writing a VLOOKUP formula.

The idea behind the scorecard hasn’t changed. But the execution has.

Here’s what AI-powered scoring can do better:

• Real-time updates: AI can analyze behavior—logins, clicks, event attendance—as it happens.

• Predictive insights: Based on patterns, the system can flag which members are at risk before they go quiet.

• Targeted outreach: Instead of one-size-fits-all campaigns, AI can segment messages based on score trends.

Back then, we’d manually check in with “at-risk” members. Today, your system can queue up a personalized email before your team even knows someone’s drifting.

And associations are already doing this.

This isn’t just theory. Organizations like Bear Analytics have taken engagement scoring to the next level – using real data to separate what feels important from what actually drives retention.

I talked with Joe Colangelo, Bear’s CEO & co-founder, about how associations can move beyond gut checks and toward data-driven decision-making when it comes to member retention.

At Bear Analytics, we focus beyond generic engagement scoring to hone in on the key touchpoints that actually move the needle. Not all engagement is created equal – we've found that certain activities have dramatically higher sensitivity scores when predicting retention. Conference attendance, certification program enrollment, and paid training participation can carry 5-10x more weight than passive activities like newsletter opens or website visits.

When a member goes dark across events or continuing education opportunities, that's a red flag that can have serious predictive power.  The value happens when you weight your scoring model correctly – giving premium activities the sensitivity they deserve while de-emphasizing vanity metrics that don't correlate with renewal behavior.

What I love about Bear’s approach is that it’s rooted in behavior. They're not chasing vanity metrics – they're focused on signals that actually correlate with renewal and retention.

Not sure where to start?

If your team is still using spreadsheets or gut instinct to track member engagement, try this:

1. List your five most predictive behaviors.

2. Assign a score to each.

3. Test it manually for a quarter.

4. Then explore platforms or AI tools that can automate it.

Even small improvements make a difference. A 5% lift in renewal can fund your next initiative.

Final thought:

We didn’t call it AI back then. But we were already trying to predict behavior, personalize outreach, and keep members engaged. AI just gives us better tools – and fewer headaches – to do it smarter and faster.