churn-analysis

Calculate customer churn risk by extracting four signals—support tickets, communication history, usage data, and billing records—to compute a 0-100 risk score. This framework diagnoses churn drivers and prioritizes accounts for proactive retention outreach.

Stop subscription churn by generating targeted save plays from risk scorecard data. These plays include root-cause hypotheses, recommended actions, talk tracks for CS conversations, and escalation triggers to intervene before renewal dates.

Yes, churn analysis applies to both subscription and usage-based businesses. It focuses on churn spikes, renewal risk, and failed payments by gathering baseline data across support tickets, Slack history, usage logs, and billing records.

Generate a weekly risk scorecard by running churn analysis on your customer list. The output provides account-level analysis, total MRR at risk, trend data, and recommended focus areas to guide weekly retention planning.

Design interventions per churn driver including product fixes, CS outreach, cancel flows, dunning sequences, and win-back campaigns. Match each intervention to the specific signal—usage drop, failed payment, or support dissatisfaction—to maximize save rates.

Gather baseline data from support tickets, Slack communication history, product usage metrics, and billing records. This multi-signal intake establishes a churn risk profile required to accurately diagnose drivers and compute risk scores.