Customer success teams lack a systematic way to identify at-risk accounts and prioritize retention efforts. Manual health scoring is inconsistent, delayed, and misses early warning signals from product usage and support interactions. Without automated weekly refreshes, teams react to churn rather than prevent it.
This workflow ingests product telemetry, support data, and account metadata weekly to calculate or refresh a composite health score for each customer. It surfaces high-risk accounts to CS teams, enables data-driven prioritization, and creates an audit trail of score changes. The workflow reduces manual work, improves retention signal detection, and scales health monitoring across your entire customer base.
Trigger: Weekly schedule (e.g., Monday 8 AM UTC) or manual refresh on demand
Query product analytics platform (Amplitude, Mixpanel, or custom warehouse) for each customer: DAU/WAU, feature adoption rate, session count, and days since last login. Flag accounts with zero activity in past 14 days.
Pull support ticket data from Zendesk, Intercom, or Help Scout: ticket volume (past 30 days), average resolution time, CSAT/NPS score, and sentiment classification. High ticket volume + low CSAT indicates friction.
Retrieve from billing system (Stripe, Zuora, Chargebee): current MRR, payment failures in past 60 days, contract renewal date, and any dunning activity. Flag accounts with failed payments or upcoming renewal within 30 days.
Clean and standardize all signals (handle missing values, align timestamps, deduplicate accounts). Validate data quality; log any missing integrations or API failures for manual review.
Apply weighted scoring model: product engagement (40%), support health (30%), billing stability (20%), and account maturity (10%). Score range 0–100. Use rules-based thresholds or ML model trained on historical churn. Document weighting rationale for transparency.
Classify accounts into tiers: Green (75–100), Yellow (50–74), Red (0–49). Identify newly at-risk accounts (score drop >15 points week-over-week). Create separate lists for expansion opportunities (high engagement, low support) and churn risk (low engagement, high support friction).
Add account context: CS owner, last touch date, contract value, primary use case, and known issues. Include recent product updates or feature releases relevant to the account. Attach recommended next actions (e.g., 'Schedule QBR', 'Offer training', 'Escalate to support').
Write health scores and risk segments back to Salesforce, HubSpot, or Pipedrive. Update custom fields for score, risk tier, and last refresh timestamp. Sync to BI dashboard (Tableau, Looker, Metabase) for real-time visibility.
Send Slack notification to CS channel with newly Red-flagged accounts and top Yellow accounts. Include account name, score, primary risk driver, and CS owner. Link to CRM record and recommended action.
Create CSV or dashboard snapshot: total accounts by risk tier, week-over-week score movement, top risk drivers (e.g., 'No login in 14 days', 'High support volume'), and accounts with improving scores. Email to CS leadership and operations.
Record all score updates with timestamp, previous score, new score, and contributing signals. Store in audit table for compliance and historical analysis. Enable CS team to understand why a score changed.
CS ops or leadership reviews flagged anomalies: accounts with contradictory signals (e.g., high engagement but high support volume), data quality issues, or scores that don't match intuition. Adjust weights or investigate data gaps.
1. **Audit data sources**: Verify API access and data availability from product analytics, support, and billing systems. Document field mappings and data freshness expectations. 2. **Define scoring model**: Collaborate with CS leadership and data team to set weights for product engagement, support health, billing stability, and account maturity. Document rationale. Start with rules-based model; evolve to ML if churn data is available. 3. **Build data pipeline**: Use ETL tool (dbt, Fivetran) or custom script to extract, normalize, and combine signals weekly. Test with sample accounts first. Implement error handling and data quality checks. 4. **Configure CRM integration**: Map health score and risk tier to custom fields in Salesforce/HubSpot. Set up field-level permissions so only CS and ops can view/edit. 5. **Set up alerting**: Create Slack workflow or email template to notify CS team of Red-flagged accounts. Include account name, score, primary risk driver, CS owner, and link to CRM. 6. **Build dashboard**: Create BI dashboard showing accounts by risk tier, week-over-week trends, top risk drivers, and CS team performance. Share read-only access with leadership. 7. **Establish audit trail**: Log all score changes in database table with timestamp, previous/new score, and contributing signals. Enable CS team to query history. 8. **Pilot and validate**: Run workflow on subset of accounts for 2–4 weeks. Compare scores to CS team intuition and actual churn outcomes. Refine weights and thresholds. 9. **Train CS team**: Document how to interpret scores, access CRM fields, and use alerts. Explain recommended actions for each risk tier. Gather feedback and iterate. 10. **Monitor and iterate**: Track metrics (churn by score tier, response time, data quality). Quarterly review of model accuracy and weights. Adjust based on business outcomes.