Install
openclaw skills install saas-churn-analysisSaaS Churn Analysis
SaaS churn and retention analysis: cohort-based churn rates, retention curves, revenue churn vs logo churn, at-risk customer identification, expansion vs contraction MRR, churn recovery playbooks, and net revenue retention (NRR) benchmarking. Produces investor-ready retention charts and actionable recovery plans. Use when: analyzing why customers are churning, building cohort retention tables, calculating NRR/GRR, identifying at-risk accounts before they cancel, or presenting retention data to investors/board. NOT for: executing churn recovery outreach (use CRM/email tools), real-time subscription billing changes (use billing platform APIs), general SaaS KPI dashboards (use saas-metrics-dashboard), or revenue forecasting without churn context (use startup-financial-model).
SaaS Churn Analysis Skill
Deep-dive churn and retention analysis for SaaS businesses. Build cohort tables, calculate NRR/GRR, identify at-risk accounts, and produce investor-ready retention metrics with actionable recovery playbooks.
When to Use This Skill
Trigger phrases:
- "Why are customers churning?"
- "What's our retention rate?"
- "Build a cohort analysis"
- "Show me net revenue retention"
- "Which accounts are at risk of canceling?"
- "Investor wants to see our logo churn"
- "What's our gross/net dollar retention?"
- "Analyze our expansion vs contraction MRR"
NOT for:
- Executing recovery outreach (emails, calls) — use CRM/email tools
- Billing changes, refunds, or cancellation processing — use billing platform
- General MRR tracking — use
saas-metrics-dashboardorsubscription-revenue-tracker - Revenue forecasting — use
startup-financial-model - Customer success management — use a CS platform skill
Core Churn Definitions
Logo Churn (Customer Churn)
Logo Churn Rate (monthly) = Customers Lost / Customers at Start of Period
Example:
Start of month: 200 customers
Canceled: 5
Logo churn rate: 5/200 = 2.5%
Revenue Churn
Gross Revenue Churn Rate = MRR Lost to Cancellations / MRR at Start of Period
Example:
Start MRR: $100,000
Churned MRR: $4,000 (from cancellations)
Gross churn: 4%
Net Revenue Retention (NRR / NDR)
NRR = (Beginning MRR + Expansion MRR - Contraction MRR - Churned MRR) / Beginning MRR × 100
Components:
+ Expansion MRR: upsells, upgrades, seat additions from existing customers
- Contraction MRR: downgrades, reduced seats
- Churned MRR: cancellations
Example:
Beginning MRR: $100,000
Expansion: +$8,000
Contraction: -$2,000
Churn: -$4,000
NRR = ($100,000 + $8,000 - $2,000 - $4,000) / $100,000 = 102%
NRR Benchmarks (SaaS industry):
| NRR | Signal |
|---|---|
| >120% | Elite (enterprise, product-led) |
| 110–120% | Strong — expansion > churn |
| 100–110% | Healthy |
| 90–100% | Adequate — watch churn trends |
| <90% | Red flag — structural problem |
Gross Revenue Retention (GRR)
GRR = (Beginning MRR - Contraction MRR - Churned MRR) / Beginning MRR × 100
(excludes expansion — pure retention, no upsell credit)
Healthy GRR benchmarks:
Enterprise SaaS: >90%
Mid-market: >85%
SMB SaaS: >75%
Cohort Analysis
Building a Cohort Retention Table
Track customers by their acquisition month and measure % remaining in each subsequent month:
import pandas as pd
from datetime import datetime
def build_cohort_table(subscriptions_df: pd.DataFrame) -> pd.DataFrame:
"""
Build a cohort retention table from subscription data.
Input DataFrame columns:
- customer_id: str
- signup_date: datetime
- cancel_date: datetime | None (None = still active)
Returns:
Pivot table: rows = cohort month, columns = months_since_signup,
values = retention percentage
"""
df = subscriptions_df.copy()
df['cohort_month'] = df['signup_date'].dt.to_period('M')
df['active_through'] = df['cancel_date'].fillna(pd.Timestamp.now())
rows = []
for cohort, group in df.groupby('cohort_month'):
cohort_size = len(group)
for month_offset in range(0, 25): # 0–24 months
cutoff = cohort.to_timestamp() + pd.DateOffset(months=month_offset)
active = group[group['active_through'] >= cutoff].shape[0]
retention = active / cohort_size * 100
rows.append({
'cohort': str(cohort),
'month': month_offset,
'cohort_size': cohort_size,
'active': active,
'retention_pct': round(retention, 1)
})
result = pd.DataFrame(rows)
pivot = result.pivot(index='cohort', columns='month', values='retention_pct')
return pivot
Example cohort table output:
Cohort | M0 | M1 | M3 | M6 | M12
-----------|-------|-------|-------|-------|------
2025-01 | 100% | 91% | 81% | 72% | 58%
2025-02 | 100% | 93% | 84% | 76% | —
2025-03 | 100% | 89% | 79% | — | —
2025-04 | 100% | 94% | — | — | —
Revenue Cohort (Dollar Retention)
Track MRR retained and expanded per cohort:
def revenue_cohort_table(mrr_events_df: pd.DataFrame) -> pd.DataFrame:
"""
Revenue cohort analysis tracking MRR per acquisition cohort.
Input DataFrame columns:
- customer_id: str
- event_date: datetime
- event_type: str # 'signup', 'expansion', 'contraction', 'churn'
- mrr_change: float
Returns:
Cohort revenue retention table (% of original MRR retained+expanded)
"""
# Group by signup cohort
signups = mrr_events_df[mrr_events_df['event_type'] == 'signup'].copy()
signups['cohort_month'] = signups['event_date'].dt.to_period('M')
# For each cohort, track MRR over time
# NRR by cohort = sum(all MRR changes for cohort customers) / initial MRR
pass
Churn Curve Analysis
Identify when in the customer lifecycle churn peaks:
Early churn (M1-M3): Onboarding failure, value not delivered
→ Diagnosis: activation rate, time-to-first-value, support tickets
Mid-term churn (M4-M12): Competitive displacement, budget cuts
→ Diagnosis: NPS trends, feature adoption, renewal engagement
Late churn (M12+): Strategic shifts, contract terms, enterprise competition
→ Diagnosis: executive sponsor changes, usage trends, renewal conversations
Churn by tenure bucket:
def churn_by_tenure(subscriptions_df: pd.DataFrame) -> dict:
"""Calculate churn rate for different tenure buckets."""
buckets = {
'0-3mo': (0, 90),
'3-6mo': (90, 180),
'6-12mo': (180, 365),
'12-24mo': (365, 730),
'24mo+': (730, float('inf'))
}
results = {}
for bucket_name, (min_days, max_days) in buckets.items():
mask = (
(subscriptions_df['tenure_days'] >= min_days) &
(subscriptions_df['tenure_days'] < max_days)
)
bucket_df = subscriptions_df[mask]
if len(bucket_df) == 0:
continue
churned = bucket_df[bucket_df['cancel_date'].notna()].shape[0]
results[bucket_name] = {
'total_customers': len(bucket_df),
'churned': churned,
'churn_rate_pct': round(churned / len(bucket_df) * 100, 1)
}
return results
At-Risk Customer Identification
Churn Risk Scoring
Score each active customer by leading indicators:
CHURN_RISK_WEIGHTS = {
'days_since_last_login': 0.25, # Usage drop
'feature_adoption_pct': -0.20, # Inverse: more features = lower risk
'support_tickets_30d': 0.15, # Escalations
'nps_score': -0.15, # Inverse: high NPS = lower risk
'days_to_renewal': -0.10, # Closer renewal = higher urgency
'billing_failures_90d': 0.15, # Payment issues
}
def churn_risk_score(customer: dict) -> float:
"""
Calculate 0-100 churn risk score for a customer.
Higher = more likely to churn.
Inputs:
customer: dict with keys matching CHURN_RISK_WEIGHTS
Returns:
Risk score 0-100 (>70 = high risk, 40-70 = medium, <40 = low)
"""
raw_score = 0
for factor, weight in CHURN_RISK_WEIGHTS.items():
if factor in customer:
# Normalize each factor to 0-100 scale first
normalized = normalize_factor(factor, customer[factor])
raw_score += normalized * weight
# Scale to 0-100
return max(0, min(100, raw_score * 100 + 50))
def get_at_risk_accounts(customers: list, threshold: float = 70.0) -> list:
"""Return customers with churn risk score above threshold, sorted by MRR."""
at_risk = [
{**c, 'risk_score': churn_risk_score(c)}
for c in customers
]
return sorted(
[c for c in at_risk if c['risk_score'] >= threshold],
key=lambda x: x.get('mrr', 0),
reverse=True # Highest MRR first — prioritize by revenue impact
)
Early Warning Signals
Usage-based signals (product telemetry):
🔴 High risk:
- No login in 14+ days (was weekly user)
- DAU/MAU ratio dropped >50% MoM
- Core feature not used in 30 days
- Below 20% feature adoption vs peers
🟡 Medium risk:
- Login frequency dropped >30% MoM
- Support ticket with "cancel" or "refund" keyword
- NPS score ≤ 6 (detractor)
- Seat count reduced
🟢 Healthy signals:
- Expanded seats or upgraded tier
- Used 3+ core features this month
- NPS ≥ 9 (promoter)
- Referred another customer
Financial signals:
🔴 High risk:
- Payment failure (retry in progress)
- Requested invoice-based payment shift (budget freeze)
- Contract not opened with 30 days to renewal
🟡 Medium risk:
- Asked about pricing alternatives
- Billing contact changed
- Discount request submitted
MRR Movement Analysis
MRR Bridge
Decompose monthly MRR change into components:
MRR Bridge: January → February
Beginning MRR: $100,000
+ New Business: +$8,500 (23 new customers × $370 avg)
+ Expansion: +$3,200 (upgrades + seat additions)
- Contraction: -$1,100 (downgrades + seat reductions)
- Churn: -$4,300 (11 cancellations × $390 avg)
= Ending MRR: $106,300
Net New MRR: +$6,300
MoM Growth: 6.3%
Python MRR bridge calculation:
from dataclasses import dataclass
@dataclass
class MRRBridge:
period: str
beginning_mrr: float
new_mrr: float # New customers
expansion_mrr: float # Upsells/upgrades
contraction_mrr: float # Downgrades (negative or positive — store as positive)
churned_mrr: float # Cancellations (store as positive)
@property
def ending_mrr(self) -> float:
return self.beginning_mrr + self.new_mrr + self.expansion_mrr - self.contraction_mrr - self.churned_mrr
@property
def net_new_mrr(self) -> float:
return self.ending_mrr - self.beginning_mrr
@property
def growth_rate_pct(self) -> float:
return self.net_new_mrr / self.beginning_mrr * 100 if self.beginning_mrr else 0
@property
def quick_ratio(self) -> float:
"""SaaS Quick Ratio = (New + Expansion) / (Contraction + Churn). >4 = healthy."""
numerator = self.new_mrr + self.expansion_mrr
denominator = self.contraction_mrr + self.churned_mrr
return numerator / denominator if denominator else float('inf')
def to_summary(self) -> str:
return (
f"MRR Bridge ({self.period})\n"
f" Beginning: ${self.beginning_mrr:,.0f}\n"
f" + New: ${self.new_mrr:,.0f}\n"
f" + Expansion: ${self.expansion_mrr:,.0f}\n"
f" - Contraction: ${self.contraction_mrr:,.0f}\n"
f" - Churn: ${self.churned_mrr:,.0f}\n"
f" = Ending: ${self.ending_mrr:,.0f}\n"
f" Growth: {self.growth_rate_pct:.1f}% | Quick Ratio: {self.quick_ratio:.1f}x"
)
SaaS Quick Ratio benchmarks:
| Quick Ratio | Signal |
|---|---|
| >4 | Elite growth efficiency |
| 2–4 | Healthy |
| 1–2 | Growing but inefficient — churn drag |
| <1 | Shrinking — churn exceeds new + expansion |
Churn Recovery Playbooks
Playbook 1: Early Churn (Month 1-3)
Root cause: Failed onboarding, didn't reach first value moment
Diagnosis questions:
□ Did they complete onboarding? (activation rate)
□ Did they use the core feature at least once? (activation event)
□ How long did it take to reach first value moment?
□ Did they get a human touchpoint in first 48 hours?
Recovery actions:
Day 1-7: Personal outreach from CSM — "What would make this a 10/10?"
Day 7-14: Offer 1:1 onboarding session + extend trial if applicable
Day 14-21: Share customer success story in their industry/use case
Day 21-30: Executive touchpoint if MRR > $500/mo
Playbook 2: Mid-Term Churn (Month 4-12)
Root cause: Value plateau, competitive evaluation, budget pressure
Diagnosis questions:
□ Usage trend: up, flat, or declining in last 60 days?
□ When did they last use the feature most tied to their stated goal?
□ Any support escalations or complaints in the last 90 days?
□ Have they been pitched by a competitor? (ask directly)
□ Is this a budget-driven decision or product-driven?
Recovery actions by root cause:
Budget:
→ Offer pause plan (90-day pause vs cancel)
→ Right-size to smaller plan vs lose them entirely
→ Annual prepay at 20% discount to lock in
Product gaps:
→ Roadmap call with PM — "here's what's coming"
→ Workaround documentation for their specific use case
→ Connect to power-user customer for peer validation
Competitor evaluation:
→ Direct competitive comparison matrix
→ Migration cost analysis (switching is expensive)
→ Win-back offer if they've already left (45-day re-engagement)
Playbook 3: Renewal-at-Risk (30-60 days to renewal)
Proactive renewal pipeline:
60 days out:
□ Usage review: send personalized "Your results with [Product]" email
□ Identify any open issues — resolve before renewal conversation
45 days out:
□ QBR or check-in call — confirm value, surface upsell opportunity
□ Flag to AE if NPS < 7 or usage declining
30 days out:
□ Renewal proposal sent — include current plan + upsell option
□ Executive sponsor confirmation (for accounts >$1k/mo)
14 days out:
□ Follow-up if no response — switch to phone
□ Escalate to manager if no reply
7 days out:
□ Final decision call — accept reduced terms if needed to retain
Output Formats
Investor-Ready Retention Summary
{
"period": "Q4 2025",
"generated_at": "2026-01-15",
"retention_metrics": {
"logo_churn_rate_monthly": 2.1,
"mrr_gross_churn_rate_monthly": 3.8,
"net_revenue_retention_pct": 108,
"gross_revenue_retention_pct": 96.2,
"quick_ratio": 3.2
},
"mrr_bridge": {
"beginning_mrr": 285000,
"new_mrr": 42000,
"expansion_mrr": 18500,
"contraction_mrr": 4200,
"churned_mrr": 10800,
"ending_mrr": 330500
},
"at_risk_pipeline": {
"high_risk_count": 8,
"high_risk_mrr_at_risk": 24600,
"medium_risk_count": 15,
"medium_risk_mrr_at_risk": 38200
},
"cohort_highlights": {
"best_cohort": { "month": "2025-03", "m12_retention": 74 },
"worst_cohort": { "month": "2025-08", "m3_retention": 71 },
"avg_m12_retention": 68.5
},
"benchmarks": {
"nrr_vs_industry": "above_median",
"grr_vs_industry": "top_quartile",
"logo_churn_vs_industry": "median"
}
}
CSV Export for Spreadsheets
cohort_retention_csv_template:
Cohort,Size,M1,M2,M3,M6,M9,M12,M18,M24
2025-01,45,91%,84%,81%,73%,67%,61%,55%,49%
2025-02,52,93%,87%,83%,—,—,—,—,—
...
Step-by-Step Workflow
Full Churn Audit
Step 1: Data collection
□ Customer list with signup date and cancel date (if churned)
□ MRR per customer per month (last 12 months)
□ Usage data: logins, feature events (from product analytics)
□ NPS scores if available
□ Cancellation reason codes (from offboarding flow)
Step 2: Calculate headline metrics
- Logo churn rate (monthly and annualized)
- Gross and net revenue retention
- Quick ratio
- Churn by tenure bucket
Step 3: Build cohort table
- M0–M12 retention by acquisition cohort
- Identify best and worst cohorts — find what's different
Step 4: MRR bridge (last 6 months)
- New vs expansion vs contraction vs churn
- Trend analysis: is churn improving or worsening?
Step 5: At-risk identification
- Score all active customers by churn risk signals
- Prioritize by MRR at risk (highest first)
- Output: top 10 at-risk accounts with reasons
Step 6: Root cause analysis
- What's driving churn? (onboarding failure, competition, budget, product gaps)
- Which segments have highest churn? (plan size, industry, use case, acquisition channel)
Step 7: Recommend playbook
- Match root cause to recovery playbook
- Estimate MRR at stake if intervention succeeds (recovery potential)
- Prioritize actions by expected ROI
Churn by Segment Analysis
Segment churn to find structural patterns:
def churn_by_segment(subscriptions_df: pd.DataFrame, segment_col: str) -> pd.DataFrame:
"""
Calculate churn rate by customer segment.
Args:
segment_col: column name to segment by (e.g., 'plan', 'industry', 'company_size')
Returns:
DataFrame with churn rate per segment, sorted by MRR impact
"""
results = []
for segment, group in subscriptions_df.groupby(segment_col):
total = len(group)
churned = group[group['cancel_date'].notna()].shape[0]
total_mrr = group['mrr'].sum()
churned_mrr = group[group['cancel_date'].notna()]['mrr'].sum()
results.append({
'segment': segment,
'total_customers': total,
'churned_customers': churned,
'logo_churn_pct': round(churned / total * 100, 1),
'total_mrr': total_mrr,
'churned_mrr': churned_mrr,
'mrr_churn_pct': round(churned_mrr / total_mrr * 100, 1) if total_mrr else 0
})
return pd.DataFrame(results).sort_values('churned_mrr', ascending=False)
Key segments to analyze:
- By plan tier (free trial → paid → enterprise)
- By acquisition channel (organic, paid, referral)
- By company size (SMB, mid-market, enterprise)
- By industry vertical
- By geographic region
- By sales rep / CSM (is one rep's book churning faster?)
Integration Points
saas-metrics-dashboard— Display NRR, GRR, and churn rate KPIs in dashboardkpi-alert-system— Trigger alerts when monthly churn exceeds thresholdstartup-financial-model— Feed churn rate assumptions into revenue forecastssubscription-revenue-tracker— MRR bridge data source for churn calculationscrypto-tax-agent— N/A (different domain)
Key Formulas Cheat Sheet
Logo Churn Rate (monthly) = Customers Lost / Customers at Start × 100
Annual Logo Churn = 1 - (1 - monthly_churn)^12 × 100
Gross Revenue Retention = (BOM MRR - Contraction - Churn) / BOM MRR × 100
Net Revenue Retention = (BOM MRR + Expansion - Contraction - Churn) / BOM MRR × 100
Quick Ratio = (New MRR + Expansion MRR) / (Contraction MRR + Churned MRR)
LTV (with churn) = ARPU / Monthly Churn Rate
Avg Customer Lifetime = 1 / Monthly Churn Rate (in months)
Rule of Thumb:
2% monthly logo churn = ~21% annual churn (B2B SMB benchmark)
0.5% monthly logo churn = ~6% annual churn (enterprise benchmark)
NRR >100% means you grow from existing base alone — key investor signal