A SaaS with 200 clients couldn't detect who was about to cancel until it was too late. A dashboard with health scores and Slack alerts has cut monthly churn from 5% to 2.1%.
The company has a B2B SaaS product with 200 clients and a 3-person customer success team. Usage data sits in the app's database (PostgreSQL), billing in Stripe and satisfaction surveys in Typeform. The CEO spends 2 days a month building a cross-referenced report. When they spot a churn-risk client — because they've stopped using the product or given a low NPS — weeks of inactivity have already passed and it's too late to retain them. Monthly churn was 5%.
We built a unified dashboard connecting 3 data sources: PostgreSQL (logins, feature usage, access frequency), Stripe (MRR, churn, upgrades, failed payments) and Typeform (NPS per client). Each client gets an automatic health score from 0 to 100 based on activity, billing and satisfaction. When a client drops below threshold, a Slack alert fires to customer success with full context: 'Client X hasn't logged in for 14 days, main feature usage dropped 60%, last NPS was 6. Recommended action: retention call.' The team acts 3 weeks before cancellation.
Números concretos después de la implementación. Medidos sobre un periodo de 3 meses posterior al despliegue.
2.1%
monthly churn (was 5%)
3 wks
early churn risk detection
0
manual reports (was 2 days/month)
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Caso de uso basado en implementaciones reales. Los datos han sido adaptados para proteger la privacidad del cliente.
