Case study · AI, data & growth
Predicting Partner Churn Before It Happened
I introduced churn and lifetime-value prediction so our partner happiness team could identify and support at-risk partners earlier.
The context
Reducing partner churn was a key ambition in our 2025 plan. The partner happiness team wanted to spot dissatisfaction earlier, rather than learning about it once a partner had already decided to stop working with us.
The challenge
I set out to help the team identify at-risk partners earlier, understand the factors behind that risk and take action before a partner left.
How I approached it
In autumn 2024, I connected data on industry, payment behaviour, lead volumes and return behaviour to a third-party churn prediction platform. Rather than launching it across the team at once, I introduced it in phases. Three team members first tested the predictions on a small group, comparing them with real-world outcomes and our existing reporting system.
Next, each team member applied the tool to 15 partners. We created feedback loops to validate predictions and tune the model. Before rolling it out to the full partner happiness team, I also defined the key performance measures—health score, active-to-on-hold rate, churn and lifetime value—and established a workflow for using the insights consistently.
What it delivered
The team gained a health score for each partner, giving them a practical way to prioritise attention before churn occurred. Because churn is a slow, lagging indicator, the structural effect takes time to show, but partners and industry verticals using lead qualification showed encouraging early signals.
Looking back
I learned that a prediction is only useful when a team can test it against real outcomes and act on it consistently. Starting with three team members, then expanding to 15 partners per person, gave us space to compare signals with existing reporting, build feedback loops and tune the model before a wider rollout.
I would take the same measured approach again, while staying patient about proof: churn is a slow, lagging indicator, so encouraging early signals are not the same as structural impact. The principle I carry forward is to pair predictive insight with clear measures and a practical workflow, so teams can act before a problem becomes a decision to leave.