renewal-predictor

Renewal Predictor

Predict client renewal likelihood by computing a multi-dimensional Health Score and generating an actionable, evidence-backed risk assessment for every account.

Core Mission

For every account, answer three questions:

1. Will this client renew, and with what confidence?
2. What signals are driving that prediction?
3. What specific intervention should the team execute right now?

Contents

• references/dimension-rubrics.md -- 0-100 scoring criteria, data sources, red/green flags for all 7 dimensions
• references/confidence-and-scoring.md -- composite formula, thresholds, confidence calibration, missing-data rules, edge cases
• references/signals.md -- compound churn and expansion signals with severity tiers
• references/save-plays.md -- intervention templates by priority
• references/output-template.md -- exact structure for renewal-forecast.md
• references/data-templates.md -- CSV templates to request when data is missing

Health Score Model

The Health Score is a composite metric from 0 to 100, built from seven weighted dimensions. Score each dimension independently on a 0-100 scale using references/dimension-rubrics.md, then combine with these weights:

Dimension	Weight
Engagement Frequency	20%
Support Ticket Volume and Sentiment	15%
Feature Adoption	20%
NPS/CSAT Scores	10%
Billing History	10%
Stakeholder Continuity	10%
Usage Trends	15%

Map the composite score to a prediction category (80-100 Likely to Renew, 60-79 Neutral/Monitor, 40-59 At Risk, 0-39 Likely to Churn). See references/confidence-and-scoring.md for the formula, thresholds, and confidence rules.

Execution Protocol

1. Discover available data. Use Glob to find CSV, JSON, YAML, or XLSX files, CRM exports, meeting notes, or communication logs in the working directory. Use Grep for account names, metric patterns, and keywords like "churn," "cancel," "renew," "escalat," "competitor," "discount," "downgrade." If no data is found, request the templates in references/data-templates.md and stop.
2. Parse and normalize. For each account, extract values for as many of the seven dimensions as the data supports. Normalize every metric to the 0-100 scale in references/dimension-rubrics.md. Flag insufficient, missing, or ambiguous dimensions. Record the raw evidence behind each score.
3. Compute Health Scores. Score each dimension, apply the weights, compute the composite, map to a prediction category, and determine confidence per references/confidence-and-scoring.md (including the missing-data rules).
4. Detect signals. Scan for churn and expansion signals in references/signals.md. Cross-reference across dimensions for compound signals. Tag each with severity and triggering evidence.
5. Generate interventions. For At Risk or Likely to Churn accounts, identify the root cause, select the most impactful play from references/save-plays.md, and assign priority by ARR at risk, renewal proximity, and signal severity, with a clear owner, deadline, and success metric. For Likely to Renew accounts with expansion signals, suggest a specific upsell or cross-sell motion and its trigger.
6. Write the forecast. Generate renewal-forecast.md following references/output-template.md. Support every claim with evidence; make every recommendation actionable and specific.