Verdict: N/A Evidence: src/lib/churn-engine.ts: computeChurnScore() is a fully implemented weighted heuristic model across 6 dimensions: customer status (0–0.9), payment failure history (0–0.25), inactivity (0–0.35), usage trend (0–0.2), subscription lifecycle (-0.15 to +0.15), and renewal proximity. Signals array is populated per condition with impact/weight. detectUpsellOpportunities() inserts rows into upsellOpportunities on high growth or tenure triggers. src/lib/routers/churn.ts: listScores, getScore, refreshScore, getSummary, listUpsells, updateUpsellStatus, detectForCustomer, listCampaigns, markConverted, setCampaignStatus. src/db/churn-schema.ts: churnRiskScores, upsellOpportunities, winBackCampaigns all exist. File: N/A Recommendation: No action required. Real statistical model, well-structured.