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Spotting churn
before the customer leaves
Level 1 · The Company
Meet heyData
Berlin-based compliance-tech company
All-in-one software for GDPR & data protection
Automates compliance workflows end to end
Rapidly expanding product portfolio
Customers across many industries
Level 2 · The Problem
Not accurate enough to act on
A risk score with low predictive accuracy
heyData's rule-based score applied the same fixed rules to every customer, and it wasn't accurate enough to flag who would actually churn.
Why it fell shortFixed thresholds
Set once, never adapted as the business grew.
No learning
The score never improved from what actually happened.
One size fits all
Every customer scored the same way, no matter how they behaved.
Level 3 · The Churn Score
Two scores, one customer list
Switch to the AI score and watch the list re-sort
Account risk dashboard
Schematic illustration, not actual customer data.
Churn score accuracy
–
Switch to see the accuracy gain
Accuracy doubled
Switch to the AI score to unlock the results
Level 4 · The Results
Twice as sharp at spotting churners
The Outcome
What runs automatically now
2×
accuracy in identifying top churners
Real-time
risk scores visible in the account dashboard for every customer
12 months
churn probability per customer, live in the dashboard
AI churn score
Replaces the old rule-based risk score.
Dashboard integration
Every score visible in the account dashboard.
Customer prioritisation
Account managers act on the riskiest accounts first.
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© Gemma Analytics · Case Study heyData