{"id":1478,"slug":"retention-magnitude-vendor-recycled-quarantine","title":"Quarantine: \"5x-25x cheaper to retain,\" \"5pct retention → 25-95pct profit,\" \"80pct profits from 20pct customers,\" \"AI churn → 20-30pct retention improvement\" — vendor-recycled, untraced to primary","kind":"reference","scope":"business","status":"current","audiences":["kevin","smb-owner","candid-team"],"topics":["customer-retention"],"reference_body":"**Claim.** Four retention magnitudes circulate across vendor blogs (hashstudioz, expressanalytics, luthresearch) without traceable primary citation: acquisition costs 5-25× retention; a 5pct retention increase produces 25-95pct profit lift; 80pct of profits from 20pct of customers; AI-driven churn prediction produces 20-30pct retention improvement.\n\n**Source.** Documented vendor recirculation across the analytics blogosphere. The 25-95pct figure ultimately traces to Reichheld/Bain-era work; the 5-25× to a frequently-misattributed chain; primary sources not located in this research pass.\n\n**Confidence.** Vendor-recycled. Do NOT use without locating the primary source. *Capability* claims around retention are solid; these specific *magnitudes* are not.\n\n**Caveats.** The original Reichheld / Bain work likely had a defensible local claim that was then over-generalised; the chain from primary to vendor-blog has eroded the qualifying conditions. Until the primary work is located and re-read, any of these numbers in a draft should be replaced with the capability claim from [[informs-analytics-magazine-churn-binary-classification]] or dropped.\n\n**Implication / use.** Quarantine. Designated as next-pass research target ([[gaps-information-asymmetry-decision-edge-next-pass-research]]). Anchors [[rule-quarantine-recycled-retention-magnitudes]].","rationale_body":null,"metadata":null,"links":{"outgoing":[{"slug":"research-brief-information-asymmetry-decision-edge-june-2026","title":"Research notes (capture-layer): the affirmative, inward decision-edge case for data intelligence — information asymmetry applied to pricing, demand, risk, retention, targeting (June 2026)","kind":"research-notes","scope":"business","link_type":"depends-on"},{"slug":"informs-analytics-magazine-churn-binary-classification","title":"INFORMS Analytics Magazine — churn modelled as binary classification on RFM / engagement signals (logistic regression / decision trees / ensembles)","kind":"reference","scope":"business","link_type":"relates-to"}],"incoming":[{"slug":"rule-quarantine-recycled-retention-magnitudes","title":"Rule: quarantine the recycled retention magnitudes (5x-25x, 25-95pct, 80/20, 20-30pct AI churn) until primary sourced","kind":"rule","scope":"business","link_type":"depends-on"},{"slug":"rule-prefer-peer-reviewed-vetted-over-vendor-recycled-magnitudes","title":"Rule: prefer peer-reviewed / award-vetted magnitudes (Edelman, NBER, INFORMS) over vendor-recycled figures","kind":"rule","scope":"business","link_type":"depends-on"}]},"created_at":"2026-06-21T01:14:48.781Z","updated_at":"2026-06-21T01:14:48.781Z"}