Picture a mid-sized SaaS business in the edtech space—120 employees, $5M in annual recurring revenue (ARR). They were hemorrhaging 12% of their customers yearly, mostly small schools frustrated by a clunky onboarding process, costing them $600K in lost revenue.

Challenge:

In early 2024, churn spiked after a pricing tweak confused users, jumping monthly churn from 1% to 1.5%—a $75K hit in one quarter alone. The customer success team was swamped with complaints, and sales couldn’t land upsells amid the chaos. Growth was stalling, and panic was setting in.

Solution:

We deployed an AI-driven churn prediction tool that analyzed user data—like logins dropping off or key features going unused—and flagged at-risk accounts. For example, it caught schools skipping training modules. We then rolled out personalized retention campaigns: targeted emails with video tutorials and a 10% discount for annual renewals. The tool synced with their CRM (think HubSpot) in three weeks, despite some initial hiccups with messy legacy data.

Implementation:

• Pulled 18 months of user data to train the model.
• Tested predictions on a 50-account pilot, refining false positives (e.g., flagging active users by mistake).
• Trained the CS team in two days to act on alerts, though some reps grumbled about “another dashboard.”

Results:

Six months later, churn dropped from 12% to 7.2% annually—saving $240K. Upsells to engaged accounts added $160K in MRR, a 32% total boost. One hiccup: 10% of flagged accounts still churned, teaching us to prioritize faster follow-ups.

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