How to Use Data to Keep Members From Leaving

The Warning Signs Are Already in Your Data

Members do not leave suddenly. They disengage gradually. They stop attending events. They stop opening emails. They do not volunteer for the working bee. Then renewal time comes and they do not renew.

The signs were there for months. You just were not looking for them.

Four Signals That Predict Churn

Event attendance decline. A member who attended six events last year and zero this year is disengaging. This is the strongest predictor of non-renewal.

Email engagement drop. Open rates below 10% for a specific member over three months means they are not reading your communication. They may have mentally left already.

No interaction in 90 days. No event attendance. No email opens. No payments. No contact. This member is inactive in everything except their membership status.

Late renewal. A member who renewed immediately last year but has not responded to any reminder this year is on the fence.

What to Do With the Signals

The data tells you who is at risk. The intervention is human.

For attendance decline: A personal message from someone they know at the club. "We have not seen you at the last few events - hope everything is OK. The quiz night on the 15th would be great to see you at."

For email disengagement: Switch channels. A text message or phone call might reach someone who ignores email.

For 90-day inactivity: A welfare check, not a sales pitch. "Just checking in - we value your membership and want to make sure the club is working for you."

For late renewal: Ask directly. "Is there a reason you have not renewed? Is there something we could do better?" Sometimes the answer is practical - financial difficulty, changed circumstances. Sometimes it is feedback you need to hear.

Building a Retention Dashboard

In TidyHQ, you can pull these data points:

• Members who have not attended an event in 90+ days
• Members whose renewal is overdue
• New members who joined in the last 90 days (high risk if not engaged)
• Event attendance rates by month

Review this monthly at the committee meeting. Not as a data exercise - as an action list. "These 12 members have not attended anything this quarter. Who knows them? Who can reach out?"

The New Member Window

The first 90 days after joining are the highest-risk period. If a new member does not attend an event, connect with another member, or engage with the club in some way during this window, their probability of renewing drops below 40%.

Track new member engagement specifically. Set up a welcome sequence. Assign a buddy. Invite them personally to their first event. The investment of 30 minutes per new member in the first month pays off in years of membership.

The Retention Rate Calculation

Retention rate = (members who renewed) / (members due for renewal) x 100.

A healthy club has a retention rate above 75%. An excellent club is above 85%. Below 65% and you are losing members faster than you can replace them.

Calculate this annually. Track it year over year. If it is declining, the data will tell you why - you just need to look at the signals.

Numbers Over Feelings

Committees often discuss retention based on feelings. "I think we are doing well." "It feels like fewer people are coming to events." Feelings are unreliable.

Data is not. Run the numbers. Let them guide the conversation. The members who are about to leave are already telling you - through their behaviour, not their words.

References

• Australian Sports Commission - AusPlay participation data and resources on member engagement in community sport
• TidyHQ - Membership management platform with retention tracking, event attendance data, and engagement reporting
• Sport England - Active Lives research on participation trends and member retention in grassroots sport
• Harvard Business Review - Research on customer retention, churn prediction, and data-driven decision making