This Langflow flow implements a multi-signal customer health scoring pipeline.

It begins by ingesting account data from multiple sources, such as billing/renewal systems (renewal date, payment status), support systems (ticket volume, time-to-resolution, sentiment), and product/interaction signals (usage trends, key feature adoption, meetings, notes). Normalization components standardize these inputs into a single schema so scoring is consistent across accounts.

A feature engineering layer derives health indicators: usage drop-offs, spikes in support tickets, negative sentiment patterns, overdue invoices, renewal proximity, and engagement gaps. The flow can weight factors differently by segment (SMB vs enterprise) or lifecycle stage.

A scoring stage calculates an overall health score (e.g., 0–100) and a churn risk label (low/medium/high), along with an explanation of which factors contributed most. The output includes a prioritized action plan—such as proactive outreach, onboarding refresh, escalation, billing remediation, or executive alignment.

Finally, structured output components generate a shareable scorecard that can be pasted into account notes, QBR decks, or task queues, preserving traceability and enabling consistent retention playbooks.

The flow can be extended to integrate with your full revenue stack. Add connectors to Salesforce/HubSpot, Stripe, Zendesk/Intercom, and product analytics tools to automate ingestion and scoring on a schedule. Store historical scorecards in a database to track trends and calibrate weights based on churn outcomes. You can also implement playbook automation: create tasks, send Slack alerts, or open escalation tickets when risk crosses thresholds. For higher accuracy, add segmentation-specific models, incorporate qualitative notes into risk drivers, and use feedback loops so outcomes (renewed/churned) improve scoring over time.