Question 1

Logo churn vs revenue churn, which should I measure?

Accepted Answer

Both. Logo churn (% of users lost) is easy and hits dashboards, but misses downgrades and upsells. Revenue churn (MRR lost) is what matters for sustainability. Example: 10% logo churn + 5% contraction = -12% ARR even if you signed 6 new customers. Measure both, act on revenue churn, celebrate when logo churn stays flat but revenue grows (negative churn = expansion revenue > churn).

Question 2

How do I build a churn prediction model?

Accepted Answer

Three steps: (1) Label historical users as churned/retained at 30/60/90-day windows; (2) Engineer leading indicators: login frequency last 7d, feature adoption score, NPS response, support tickets; (3) Train a classifier (logistic regression, XGBoost) on labeled data, evaluate precision/recall tradeoff (high precision = fewer false alarms, high recall = catch more at-risk). Deploy as a health score refreshed daily; flag users >0.7 churn probability for CS outreach.

Question 3

What's a leading indicator of churn?

Accepted Answer

Engagement drops are the strongest signals: 50%+ drop in login frequency, zero feature usage after onboarding, NPS <6, spike in support tickets about bugs/friction. Cohort retention curves show churn accelerates at Day 7-14 (product-market fit test) and Day 30-60 (renewal cliff for contracts). Segment by acquisition channel, organic users hold better; paid ads may have CAC-payback > LTV and inherent high churn.

Question 4

How do I calculate Kaplan-Meier survival curves?

Accepted Answer

Kaplan-Meier is a non-parametric method to estimate survival (retention) without assuming a distribution. For each cohort: count users at risk each day/week/month, count churned in that period, calculate (1 - churned/at_risk), multiply survival probabilities forward. Result = the iconic retention 'hockey stick' showing rapid early churn + plateauing. Python: `lifelines.KaplanMeierFitter()` or SQL window functions. Beats naive cohort curves when churn timing is irregular.

Question 5

Should I use automated outreach or CS intervention?

Accepted Answer

Automate at scale, personalize for high-value. Trigger email + in-app campaigns for low-engagement users (free tier, high churn risk, low LTV). Route top-quartile revenue-at-risk to CS teams for 1:1 calls. Example: 500 free users at risk → email drip; 20 enterprise accounts flagged → immediate CS outreach. Combine predictive score with LTV, $10k annual customer gets white-glove; $50/yr gets automated win-back.

Question 6

What metrics should I track to avoid vanity metrics?

Accepted Answer

Avoid: 'churn decreased 2%' without context. Measure: retention curves by cohort (new vs seasonal vs downgrades), revenue churn, net dollar retention (if you have upsells), time-to-churn distribution (Day 7 vs Day 90 dropoff), churn rates by segment/channel/plan. Report: '90-day retention improved 5% → +$50k MRR' not '5% improvement.' Pair with intervention metrics (% users reached, conversion to saved deal) to prove causation.

प्रदेश	ज्युनियर	मध्यम	सीनियर
USA	$92k	$130k	$180k
UK	£53k	£77k	£110k
EU	€58k	€82k	€115k
CANADA	C$98k	C$135k	C$185k

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