---

name: ops-churn-risk-detector
description: Use when evaluating whether a specific user or account is at elevated risk of cancelling or downgrading. Combines login frequency, session depth, feature breadth, support ticket volume, NPS score, plan history, and explicit churn-signal events into a 0–100 risk score with tiered response triggers (CSM outreach at 70, discount offer at 90, cancel-prevention flow at 95).
license: MIT
metadata:
id: ops.churn-risk-detector
category: ops
jurisdictions: [multi]
priority: P1
intent: [churn, ops, retention, csm, predictive]
related: [ops-nps-collector-in-chat, ops-credit-burn-rate-watcher, onboarding-upgrade-prompt-when-credits-low]
source: Louis — HAQQ Legal AI (github.com/sboghossian/mini-claude-for-legal)
version: "1.0"

Ops — Churn Risk Detector

Purpose

Early identification of at-risk accounts gives the customer success team time to intervene before a cancellation decision is made. Churn in B2B legal AI is expensive to reverse — once a firm has moved their matters to a competitor, switching costs are high. This skill defines the predictive model inputs, the risk scoring logic, and the graduated response playbook.

Input signals

The model draws on the following signals, evaluated per user (for consumer/SMB) or per tenant/account (for firm/enterprise):

Signal	Weight	Churn indicator
Login frequency trend (last 30d vs prior 30d)	High	Declining login frequency
Messages-per-session trend (last 30d vs prior 30d)	High	Declining depth per session
Feature breadth (number of distinct features used)	Medium	Drift to single feature (over-reliance = fragility)
Support ticket volume (last 30d)	Medium	Rising ticket volume without resolution
NPS score (most recent)	High	Score ≤ 6 (detractor)
Plan downgrade history	High	Any downgrade in last 90 days
Time since last success event	High	>14 days since a completed draft, research, or matter milestone
Explicit churn-signal events	Critical	See table below

Explicit churn-signal events (highest weight)

These events are strong leading indicators and should immediately spike the risk score:

Event	Risk bump
User exports all their data ("Export all")	+25 points
User submits a delete-account inquiry	+30 points
User raises a billing dispute or chargeback	+20 points
User explicitly mentions a competitor by name	+15 points
User cancels a renewal reminder	+10 points

Risk score output

The model outputs:

{
  "userId": "<string>",
  "tenantId": "<string>",
  "riskScore": <0–100>,
  "topSignals": ["<signal description>", "..."],
  "suggestedAction": "<string>",
  "evaluatedAt": "<ISO timestamp>"
}

riskScore is a 0–100 integer where 0 = no risk and 100 = cancellation imminent.

topSignals lists the top 3 contributing signals in descending weight order.

suggestedAction is derived from the tiered playbook below.

Response playbook

Risk score	Action
0–50	No action. Monitor passively.
51–69	Flag in CSM dashboard. CSM may reach out at discretion.
70–89	CSM outreach required. Assign to account CSM; outreach within 48 hours. Goal: understand friction and remove blockers.
90–94	Discount or incentive offer. CSM calls with a concrete retention offer (discount, feature unlock, dedicated onboarding session).
95–100	Cancel-prevention flow. Escalate to senior CSM or account manager. Offer exit interview. Last-resort save: pause subscription instead of cancel.

Integration

• Scores are recalculated daily per user/tenant.
• Scores are surfaced in the ops dashboard under "At-Risk Accounts."
• Score changes of ±10 or more in a 24-hour window trigger a Slack notification to #csm-alerts.
• Pairs with [[ops-nps-collector-in-chat]]: a detractor NPS score (≤6) triggers an immediate score recomputation.
• Pairs with [[ops-credit-burn-rate-watcher]]: an account approaching credit exhaustion without upgrading is a moderate churn signal.

Model calibration

The risk model should be recalibrated quarterly against actual churn outcomes:

• Compare predicted scores (30 days prior to churn) against accounts that actually cancelled.
• Adjust signal weights based on which signals were most predictive.
• Track false positive rate (high-risk accounts that did not churn) to avoid over-triggering CSM workload.

Related skills

• [[ops-nps-collector-in-chat]] — provides the NPS detractor signal
• [[ops-credit-burn-rate-watcher]] — provides credit exhaustion signals
• [[onboarding-upgrade-prompt-when-credits-low]] — the in-product retention touchpoint for credit-low users