Last quarter, your three biggest customers churned. Total surprise. Nobody saw it coming. That's the problem with tracking the wrong metrics—or worse, no metrics at all.

In customer success, retention metrics tell you if customers are staying, growing, or quietly heading for the door. They show if your efforts are working or if you're just busy. They reveal business health or hide growing problems.

But here's the thing: most teams track too much or too little. They drown in vanity metrics that look good in slides but don't drive decisions. Or they track nothing and wonder why customers keep leaving.

Great retention metrics answer the questions that actually matter. Are customers staying? Are they growing? Can we predict who's at risk? Are our interventions working? Where should we focus next?

The best CS teams don't just track metrics—they use them. They spot problems before they blow up. They prove ROI. They create accountability. When your metrics improve quarter over quarter, you're building a healthier, more sustainable business.

Here's how to measure what actually matters.

Core Retention Metrics

Customer Retention Rate (Logo Retention)

This one's straightforward. How many customers stick around?

Take your ending customer count, subtract new customers added during the period, divide by your starting count. Multiply by 100 for a percentage.

Example: You started Q1 with 100 customers. Added 20 new ones. Ended with 110. That's (110 - 20) / 100 = 90% retention rate.

What you're measuring: Customer loyalty at the account level. Who's staying, who's leaving.

The limitation? It treats all customers equally. Your $1K annual customer counts the same as your $100K customer. That's why you need the next metric.

Benchmarks to aim for:

  • Enterprise B2B: 90-95% annually
  • Mid-market: 85-90% annually
  • SMB: 70-85% annually (they churn more, it's just reality)

Revenue Retention Rate (Dollar Retention)

Now we're talking money. What percentage of your revenue stays?

Start with revenue from an existing cohort. Subtract what you lost from churn and downgrades. Don't include expansion yet—that's coming next. Divide by starting revenue.

Example: You had $1M ARR from last year's customers. Lost $100K to churn. That's $900K / $1M = 90% revenue retention.

This shows revenue stability. It accounts for customer size. Losing one enterprise customer hurts more than losing three small ones, and this metric reflects that.

Net Revenue Retention (NRR)

Here's the metric that every SaaS executive cares about. NRR includes expansion revenue—upsells, cross-sells, additional seats, higher tiers.

Take your starting revenue from existing customers. Subtract churn and downgrades. Add expansion. Divide by starting revenue.

Example: $1M starting ARR, lost $100K to churn, added $300K in expansion. That's ($1M - $100K + $300K) / $1M = 120% NRR.

When NRR exceeds 100%, you're growing from existing customers alone. You could stop acquiring new customers entirely (don't actually do this) and still grow revenue. That's the power of this metric.

What good looks like:

  • Best-in-class SaaS: 120-130%
  • Strong performance: 110-120%
  • Acceptable: 100-110%
  • Problem territory: Under 100%

Gross Revenue Retention (GRR)

This is NRR's more honest sibling. It shows how well you keep what you have, period. No expansion to hide behind.

Same starting revenue, subtract churn and downgrades, but don't add expansion. That's it.

Example: $1M starting, $100K churned. $900K / $1M = 90% GRR.

Why track both NRR and GRR? Because you can't cover up retention problems with upsells forever. If your GRR is 80% but NRR is 115%, you're papering over serious churn with aggressive expansion. Eventually, that catches up with you.

Solid benchmarks:

  • Best-in-class: Above 95%
  • Good: 90-95%
  • Acceptable: 85-90%
  • Concerning: Below 85%

Customer Lifetime Value (LTV)

This tells you what a customer is actually worth over their entire relationship with you.

The simplified version: Take average monthly revenue per customer, multiply by gross margin percentage, divide by monthly churn rate.

Example: $500 monthly revenue, 80% margin, 2% monthly churn. That's $500 × 0.80 / 0.02 = $20,000 LTV.

Here's the interesting part—small improvements in churn rate create massive LTV increases. Drop from 2% to 1.5% monthly churn and your LTV jumps from $20K to $26,667. That's a 33% increase in customer value from a half-point churn improvement.

This number determines how much you can spend acquiring customers and how much effort retention deserves. High LTV? Invest heavily in keeping customers. Low LTV? You've got a retention problem to solve.

Churn Metrics

Customer Churn Rate (Logo Churn)

The flip side of retention. How many customers are you losing?

Churned customers divided by starting customers. Simple math, painful implications.

Lost 10 customers out of 100? That's 10% churn.

Most teams calculate this monthly and annually. Monthly gives you faster feedback but more noise. Annual shows the true picture but problems can hide for months.

What to aim for:

  • Annual B2B SaaS: 5-15%
  • Monthly B2B SaaS: 0.5-2%
  • Enterprise: 5-10% annual (they're stickier)
  • SMB: 15-30% annual (higher churn is normal)

Revenue Churn Rate (Dollar Churn)

Same concept, different denominator. What percentage of revenue walked out the door?

Churned revenue divided by starting revenue.

Lost $50K from $1M starting revenue? 5% revenue churn.

Here's why this matters separately from logo churn: One large customer churning can hurt more than ten small ones. If your logo churn is 8% but revenue churn is 15%, you're losing your best customers. That's a red flag.

Voluntary vs. Involuntary Churn

Not all churn is the same. Voluntary churn means the customer actively decided to leave—unhappy, found a competitor, budget cuts, didn't see value. Involuntary churn means payment failure, expired credit cards, billing issues.

Track these separately. Why? Because the fixes are completely different.

Voluntary churn tells you about product-market fit, value delivery, relationship strength. Fix this with better onboarding, more engagement, clearer value demonstration, stronger relationships.

Involuntary churn tells you about payment infrastructure. Fix this with better dunning processes, payment retry logic, proactive billing communication.

We've seen companies with 12% total churn where 4% was involuntary. They fixed their billing system and immediately dropped to 8% churn. That's the easiest retention improvement you'll ever make.

Churn by Cohort

Group customers by when they joined—monthly or quarterly cohorts. Then track how each cohort retains over time.

This shows if you're getting better. If your 2024 cohorts retain at 95% after 12 months but 2023 cohorts only hit 88%, your improvements are working. If newer cohorts are worse, you've got systematic problems (worse product? different customer profile? worse onboarding?).

Visualize this as retention curves. Multiple lines, one per cohort, showing retention over months since acquisition. When the curves shift up and right, you're winning.

Time-to-Churn Analysis

When do customers typically leave? Average days from start to churn. Churn rate by lifecycle month. Critical risk periods.

Here's what we see constantly: Most companies have a "danger zone" between months 3-6. Customer finishes onboarding, feels good, then... nothing. No touchpoints. No value reinforcement. They drift. By month 6, they're gone.

Or there's the month-11 spike. Right before renewal, someone finally looks at usage, realizes they're not getting value, and cancels.

Find your danger zones. Then build intervention strategies specifically for those periods. If month 4 is deadly, create a dedicated month 3-6 engagement sequence.

Health and Risk Metrics

Customer Health Score Distribution

How is your portfolio doing overall? What percentage of customers fall into each health band?

A healthy distribution looks something like:

  • Green (healthy): 70-80%
  • Yellow (at-risk): 15-20%
  • Red (critical): 5-10%

But the distribution itself isn't the point. The trend is. If you're shifting left (more red, less green) month over month, you've got growing problems. If you're shifting right (more green, less red), your efforts are working.

At-Risk Customer Count and ARR

How much is at risk right now? Count it two ways: number of accounts and total ARR.

This is your executive metric. "We have $2.3M ARR at risk, representing 12% of our portfolio" creates urgency in a way that "23 at-risk accounts" doesn't.

Break this down by segment. Maybe your enterprise book is healthy but SMB is bleeding. That tells you exactly where to focus.

Save Rate and Saved ARR

When you identify at-risk customers and intervene, how often does it work?

Save rate formula: Saved accounts divided by total at-risk accounts.

Example: 40 accounts hit red status this quarter. You saved 25. Lost 15. That's a 62.5% save rate.

Track this multiple ways:

  • By risk level (yellow save rate vs. red save rate—red is harder)
  • By CSM (creates accountability, identifies coaching needs)
  • By churn reason (which issues can you actually save?)
  • By intervention type (what tactics work?)

A 60-70% save rate on identified at-risk accounts is good performance. Below 50%? Either your interventions aren't working or you're identifying risk too late.

Is the average health score across your portfolio going up or down? Are individual accounts improving or declining?

This is a leading indicator. Health scores drop before customers churn. If average health is trending down, churn is coming.

The goal isn't perfection. It's positive momentum. Average health score up month-over-month? Fewer sharp declines? You're headed in the right direction.

Risk Pipeline Forecast

Try to predict future churn based on what you see today.

Look at current at-risk accounts and apply your historical save rate. Look at customers moving from yellow to red to churned—what's the conversion rate at each stage? Factor in seasonal patterns. Consider upcoming renewals and their health scores.

This isn't perfect. But it helps you plan resources and set realistic expectations. If your pipeline shows $1.5M at risk with a 65% save rate, expect about $525K in churn this quarter. Plan for it. Staff for it. Don't be surprised by it.

Engagement and Activity Metrics

Product Usage and Adoption

Usage predicts retention. Low usage almost always leads to churn. High usage correlates strongly with renewal.

Key signals to watch:

  • Daily and monthly active users (DAU/MAU)
  • Feature adoption rates (especially core features)
  • Login frequency
  • Time spent in product
  • Key workflow completion rates

If a customer's usage tanks 50%, they're probably not renewing. That's your early warning signal—usually 60-90 days before they'd otherwise churn.

Customer Touchpoint Frequency

How often are you actually connecting with customers? CSM calls per month. Business reviews completed. Response rates to outreach. Engagement with content and resources. Event attendance.

Here's the reality: Engaged customers renew. Disengaged customers don't. If a customer stops responding to emails, skips QBRs, and ghosts your calls, they're already mentally gone.

Track response and engagement rates. Email open rates below 20%? Click rates under 5%? Call acceptance under 50%? That's a problem. Either your communication is irrelevant or the relationship is dead.

Business Review Completion Rate

For enterprise and mid-market accounts, what percentage of scheduled QBRs actually happen?

Target 90-95% completion rate. Anything less signals either disengaged customers or an overstretched CS team.

When customers consistently skip QBRs, that's a red flag. They're either not seeing value or actively avoiding the conversation. Neither is good.

NPS and CSAT Scores

Do customers actually like you?

Net Promoter Score asks "How likely are you to recommend us?" on a 0-10 scale. Calculate by subtracting percentage of detractors (0-6) from percentage of promoters (9-10).

Good B2B SaaS benchmark: 30-50 NPS

Customer Satisfaction typically uses a 1-5 scale. Target 4+ average, with over 80% rating you 4 or 5.

The correlation is clear: High NPS and CSAT strongly predict retention. Low scores predict churn. And the responses often tell you exactly why customers are leaving, giving you a roadmap for improvement.

Calculating Retention Metrics

Formula and Methodology Consistency

Here's the annoying part—your CFO, your board, and your CS team might all calculate retention differently. Someone counts significant downgrades as partial churn. Someone else doesn't. Someone includes trial conversions as "new customers." Someone else doesn't.

You need to pick one method and document it. Be specific:

  • Time period (monthly, quarterly, annual?)
  • Starting and ending points (calendar year? rolling 12 months?)
  • What counts as churn (cancellation? non-renewal? 80% downgrade?)
  • How you handle upgrades and downgrades
  • Treatment of pauses, hibernation, credits

Then stick with it. Consistency over time is more important than choosing the "perfect" formula.

Time Period Considerations

Monthly calculations are sensitive and noisy. You get fast feedback, but natural fluctuation can obscure trends. One big customer churning in January creates a spike that might not mean anything.

Quarterly calculations smooth out the noise while still providing reasonably fast feedback. Good for business reviews and trend analysis.

Annual calculations show the true retention picture. Less noise, better for benchmarking. But problems can hide for months before you see them.

Our recommendation? Track monthly for early warning signals. Review quarterly trends for decision-making. Report annually for benchmarking and strategic planning.

Cohort vs. Overall Calculations

Overall retention is simpler. All customers, regardless of when they joined. Easy to calculate and explain.

But it can hide important patterns. Maybe your 2024 cohorts are retaining beautifully at 94% while your 2023 cohorts are bleeding out at 82%. Overall retention of 88% looks merely okay and masks both the success and the problem.

Cohort retention groups customers by acquisition period. More complex, but far more insightful. You can see if retention is improving over time. If newer cohorts retain better, your improvements are working. If newer cohorts are worse, something changed for the worse (product? customer profile? onboarding?).

Track both. Use overall for simplicity. Use cohorts for insight.

Handling Edge Cases

Real-world scenarios that mess up your formulas:

Customer acquired and churned in same period: Exclude from retention calculation or track separately as "quick churn." Don't let them distort your numbers.

Significant downgrades: Count as partial churn. If a customer drops from $100K to $30K, that's $70K in lost revenue. Should affect your retention rate.

Upgrades: Count as expansion revenue, not new customers. Otherwise you're inflating your customer count and distorting retention.

Pauses and hibernation: Define a policy. After 60 days of pause? 90 days? Count as churned and welcome them back as a new customer if they return.

Acquisitions: If your customer gets acquired, the logo might disappear but revenue continues. Define how you count this.

Bankruptcy or company closure: Track separately as "unpreventable churn." You can't save a company that went out of business. Don't let it distort your save rate calculations.

Data Quality Requirements

Bad data produces meaningless metrics. You need accurate:

  • Customer start dates
  • Churn dates and reasons
  • Revenue amounts and changes over time
  • Customer status (active, churned, paused, at-risk)
  • Segment and cohort information

If your data is messy, fix that before you build fancy dashboards. A simple metric calculated with clean data beats a sophisticated metric built on garbage.

Segmentation and Analysis

Retention by Customer Segment

Your enterprise customers and SMB customers are completely different. They have different expectations, different retention patterns, different economics. Aggregate metrics hide these differences.

Analyze retention separately by:

  • Company size (enterprise vs. mid-market vs. SMB)
  • Industry and vertical
  • Geography and region
  • Annual contract value bands
  • Product tier or plan

Example: Overall 90% retention might hide 96% enterprise retention and 79% SMB retention. That's useful information. Maybe you should focus on enterprise and stop trying to make SMB work. Or maybe you need a different approach for SMB.

Retention by Cohort

We mentioned this earlier, but it's worth repeating: Group customers by when they joined. Track their retention curves over time.

This reveals:

  • Whether you're improving (newer cohorts retain better)
  • Seasonal acquisition patterns (Q4 customers churn faster because they were rushed deals)
  • Impact of product changes (retention improved after you launched Feature X)
  • Whether onboarding improvements are working (3-month retention up for recent cohorts)

Retention by Product or Plan

Different products and plans retain at different rates. Premium plans almost always retain better than basic plans. Your core product probably retains better than add-ons. Established products outperform newly launched ones.

Segment your retention analysis by product and plan. Then focus improvement efforts on whatever's underperforming.

If your $99/month plan retains at 70% but your $499/month plan retains at 92%, maybe you should push customers to upgrade earlier. Or maybe the $99 plan just attracts customers who aren't a good fit.

Retention by CSM or Team

This one's sensitive, but important. Track retention by individual CSM or CS team.

Benefits:

  • Identifies coaching opportunities (why is Sarah's retention 95% while Mike's is 82%?)
  • Recognizes top performers
  • Reveals best practices to replicate (what is Sarah doing differently?)
  • Creates accountability (retention is part of your job)

The caution: Adjust for portfolio mix. A CSM with all enterprise customers will naturally have better retention than a CSM with all SMB customers. Don't compare apples to oranges and then punish someone for the portfolio they inherited.

Geographic and Industry Analysis

Different markets and industries have different dynamics. Economic conditions vary by region. Industries face different challenges. Competitive landscapes differ. Regulatory environments create different pressures.

Retail might be getting hammered while healthcare is thriving. Europe might be struggling while North America is strong. Break down your retention data geographically and by industry to spot these patterns.

Then you can adjust strategies accordingly. Maybe you need different pricing for struggling industries. Maybe certain regions need more support. Maybe some verticals just aren't a good fit.

Benchmarking Retention

Industry Benchmarks by Sector

Here's where you stand compared to the market:

B2B SaaS (general):

  • Logo retention: 85-95% annually
  • Revenue retention: 90-95% annually
  • NRR: 105-120%
  • GRR: 90-95%

Enterprise Software:

  • Logo retention: 90-95% annually
  • NRR: 110-130%
  • GRR: 95-98%

SMB SaaS:

  • Logo retention: 70-85% annually (SMB churn is just higher, accept it)
  • NRR: 90-110%
  • GRR: 85-90%

Infrastructure and Platform SaaS:

  • Logo retention: 90-95% annually
  • NRR: 120-140% (expansion is huge here)
  • GRR: 95-98%

SaaS Retention Standards

World-class performance: NRR above 120%, GRR above 95%, logo retention above 90%. You're in the top tier. Keep doing what you're doing.

Good performance: NRR 110-120%, GRR 90-95%, logo retention 85-90%. You're solid. Room for improvement, but you're not bleeding.

Acceptable performance: NRR 100-110%, GRR 85-90%, logo retention 80-85%. You're keeping the lights on. But you need to improve.

Needs immediate attention: NRR under 100%, GRR under 85%, logo retention under 80%. You've got serious retention problems. Fix this before focusing on growth.

Internal Baseline and History

Industry benchmarks are useful, but your best comparison is yourself.

Track year-over-year improvement. Watch trend direction. Identify seasonal patterns. Measure the impact of initiatives.

If you're at 85% retention but you were at 80% last year and 75% the year before, you're improving. Keep going. If you're at 90% retention but you were at 95% two years ago, something is deteriorating. Figure out what changed.

Continuous improvement matters more than hitting some arbitrary industry benchmark. Progress is progress.

Segment-Specific Targets

Don't use the same target for every segment. Set different retention goals based on customer type:

Segment Logo Retention Target NRR Target GRR Target
Enterprise 95%+ 115-130% 95-98%
Mid-Market 88-93% 108-118% 90-95%
SMB 75-85% 95-110% 85-90%

This creates realistic expectations and focuses effort appropriately. Beating yourself up over 82% SMB retention is pointless if that's actually above industry average for your segment.

Competitive Comparison

Public SaaS companies report NRR in earnings calls and investor presentations. Best public companies hit 120-140% NRR. Good ones run 110-120%.

Private companies are less transparent, but industry reports, conferences, and peer groups share data. Get involved in CS communities and benchmark anonymously with similar companies.

Don't obsess over competitor metrics, but knowing where you stand helps calibrate expectations and identify improvement opportunities.

Leading vs. Lagging Indicators

Lagging Indicators

These tell you what already happened:

  • Churn rate last quarter
  • Retention rate last year
  • Revenue lost last month
  • Customers churned

These metrics are definitive, auditable, and clear. Your board wants to see them. Your CFO needs them for planning. They measure ultimate success.

But by the time you see a lagging indicator, it's too late to prevent it. The customer already left. The revenue is already gone.

Leading Indicators

These predict what's about to happen:

  • Health score declines
  • Usage decreases
  • Engagement drops
  • NPS and CSAT declines
  • Support ticket spikes
  • Missing QBRs
  • Payment failures

These give you early warning. They provide time to intervene. They're preventable.

The downside? They're less precise. Not every red health score becomes a churn. It's probabilistic, not certain.

Using Leading Indicators for Prediction

Build predictive models based on historical data:

  • Customers with health scores below 60 have 40% churn probability within 90 days
  • Usage drops of 50% or more increase churn risk by 3x
  • Customers who miss two consecutive QBRs churn at 65% rate

Then use these models to identify at-risk customers 60-90 days before they'd otherwise churn. That gives you time to intervene, diagnose the problem, and fix it.

Balancing Focus

Use lagging indicators to measure ultimate success and report to executives. Track them religiously. Hold yourself accountable.

But obsess over leading indicators in your daily work. They drive prioritization. They tell you who needs attention today. They prevent problems before they become churn.

The formula: Report on lagging indicators. Act on leading indicators.

Using Retention Metrics

Executive Reporting and Governance

Your monthly executive dashboard should include:

  • NRR and GRR trends (with targets and vs. prior period)
  • Churn rate and churned revenue (logo and dollar)
  • At-risk ARR and save rate
  • Health score distribution and trend
  • Impact of key initiatives

Make retention a board-level conversation. Not buried in some appendix. Front and center. Because retention drives valuation, growth efficiency, and business sustainability.

Team Goal Setting and Accountability

Set clear, measurable targets:

  • Company level: NRR target, GRR target, logo retention target
  • CS team: Retention rate target, save rate target, health score improvement target
  • Individual CSM: Portfolio retention target, at-risk reduction target, health improvement target

Then tie compensation to these outcomes. Retention can't be "nice to have." It has to be "must achieve." Align incentives with outcomes.

Customer Prioritization

Use metrics to allocate scarce resources:

  • High-risk, high-value customers get most attention (biggest impact)
  • Low-risk, high-value customers need engagement to stay healthy (maintain relationship)
  • High-risk, low-value customers get scaled intervention (email campaigns, webinars, not white-glove)
  • Low-risk, low-value customers go into tech-touch (automated)

Create a priority score: Risk level multiplied by account value equals priority. Sort by priority score. That's your focus list.

Program Effectiveness Evaluation

Measure whether your CS initiatives actually work:

  • New onboarding program: Compare retention of customers who went through it vs. those who didn't
  • Proactive engagement campaign: Retention of engaged customers vs. control group
  • Feature adoption push: Retention of users who adopted vs. those who didn't

This proves ROI. It shows what works. It tells you where to invest more and what to kill.

Stop funding programs that don't improve retention. Double down on programs that do.

Investment Decision Making

Your retention metrics tell you where to invest:

  • Low GRR? Invest in preventing churn (better onboarding, more CSMs, product improvements)
  • High GRR but low NRR? Invest in expansion (upsell playbooks, expansion CSMs, packaging changes)
  • Early churn (months 1-6)? Invest in onboarding and early adoption
  • Late churn (months 10-12)? Invest in long-term engagement and renewal management

Let the data guide your strategy. Don't guess where problems are. The metrics tell you.

Retention Analytics

Trend Analysis and Forecasting

Track your metrics over time, not just as snapshots:

  • Moving averages smooth out noise
  • Identify seasonal patterns (Q4 always higher? Post-conference bump?)
  • Trend lines show improving or declining trajectory
  • Forecasting projects future state based on current trends

Spot problems early. Set realistic goals. Plan resources accordingly.

Driver Analysis and Correlation

Figure out what actually drives retention:

  • Which features, when used, predict retention? (Customers who adopt Feature X within 30 days have 92% retention vs. 78% for those who don't)
  • Which behaviors predict churn? (Declining login frequency, support ticket spike, executive sponsor change)
  • Which CS activities improve retention? (Monthly check-ins, QBRs, executive engagement)
  • Which product changes impacted retention? (New UI rollout increased churn 5% for 60 days then stabilized)

This turns correlation into causation into action. You know what to do to improve retention because you know what drives it.

Cohort Retention Curves

Visualize retention over the customer lifetime:

  • X-axis shows months since acquisition (0, 3, 6, 9, 12, etc.)
  • Y-axis shows percentage of original cohort still active
  • Multiple lines represent different cohorts (Jan 2023, Apr 2023, Jul 2023, etc.)

This reveals:

  • Natural retention curve shape (where customers typically churn)
  • Critical drop-off periods (that 3-6 month danger zone)
  • Whether retention is improving (newer cohort lines higher than older ones)
  • Long-term retention ceiling (where the curve flattens)

Survival Analysis

This is a statistical technique for modeling time-to-churn:

  • Probability of surviving to each time period
  • Median lifetime of customers (50% churn by month X)
  • Factors that extend or shorten lifetime
  • Risk factors (what predicts faster churn?) and protective factors (what keeps customers longer?)

It's more advanced than simple retention rates, but powerful for understanding retention dynamics. Worth learning if you have enough data.

Predictive Modeling

Build machine learning models that predict churn probability:

  • Feed in usage data, engagement metrics, support tickets, firmographic data, product adoption
  • Output: Churn probability score for each customer
  • Identify most predictive features (usage is usually #1)
  • Validate model accuracy (how often is it right?)

This requires sufficient data—hundreds of churn events minimum for a reliable model. But if you have the data, predictive models dramatically improve early identification of at-risk customers.

Templates and Resources

Metric Definition Table

Metric Formula Calculation Period Target What It Measures
Customer Retention Rate (End Customers - New) / Start × 100% Annual 85-95% % customers who stay
Revenue Retention Rate Retained Revenue / Start Revenue × 100% Annual 90-95% % revenue retained
Net Revenue Retention (NRR) (Start - Churn + Expansion) / Start × 100% Annual >110% Revenue health with growth
Gross Revenue Retention (GRR) (Start - Churn) / Start × 100% Annual >90% Revenue retention without expansion
Customer Churn Rate Churned Customers / Start Customers × 100% Monthly/Annual <2% monthly, <15% annual % customers lost
Revenue Churn Rate Churned Revenue / Start Revenue × 100% Monthly/Annual <1% monthly, <10% annual % revenue lost
Customer LTV (Avg Monthly Revenue × Margin) / Monthly Churn N/A Maximize Total customer value
Save Rate Saved At-Risk / Total At-Risk × 100% Ongoing >60% Intervention effectiveness

Calculation Formulas

Net Revenue Retention (NRR)

Time Period: January 1, 2024 - December 31, 2024
Starting ARR (from 2023 customers only): $10,000,000
Churned ARR: $500,000
Contraction ARR (downgrades): $300,000
Expansion ARR (upsells/cross-sells): $2,000,000

NRR = (Starting ARR - Churned - Contraction + Expansion) / Starting ARR × 100%
NRR = ($10M - $500K - $300K + $2M) / $10M × 100%
NRR = $11.2M / $10M × 100%
NRR = 112%

Gross Revenue Retention (GRR)

Using same example:
GRR = (Starting ARR - Churned - Contraction) / Starting ARR × 100%
GRR = ($10M - $500K - $300K) / $10M × 100%
GRR = $9.2M / $10M × 100%
GRR = 92%

Customer Lifetime Value (LTV)

Average MRR per customer: $500
Gross margin: 80%
Monthly churn rate: 2%

LTV = (Avg MRR × Gross Margin) / Monthly Churn Rate
LTV = ($500 × 0.80) / 0.02
LTV = $400 / 0.02
LTV = $20,000

Benchmark Ranges

By Company Stage

Stage Logo Retention NRR GRR Notes
Early Stage (<$5M ARR) 75-85% 95-110% 85-90% Finding product-market fit
Growth Stage ($5-50M ARR) 85-90% 105-120% 90-95% Scaling operations
Scale Stage (>$50M ARR) 90-95% 110-130% 95-98% Optimized operations

By Customer Segment

Segment Logo Retention NRR GRR Annual Churn
Enterprise (>$100K ACV) 92-98% 115-135% 95-98% 2-8%
Mid-Market ($25-100K ACV) 85-92% 105-120% 90-95% 8-15%
SMB (<$25K ACV) 70-85% 90-110% 85-92% 15-30%

Dashboard Template

Executive Retention Dashboard (Monthly)

Headline Metrics

  • NRR: % (Target: >110%) ↑/↓ vs last month
  • GRR: % (Target: >90%) ↑/↓ vs last month
  • Logo Retention: % (Target: >85%) ↑/↓ vs last month
  • Monthly Churn Rate: % (Target: <2%) ↑/↓ vs last month

Risk Metrics

  • At-Risk ARR: $ (% of portfolio)
  • At-Risk Customers: accounts
  • Save Rate (Last 90 days): % (Target: >60%)
  • Avg Health Score: /100 (↑/↓ vs last month)

Engagement Metrics

  • Active Users: % of licenses
  • QBR Completion Rate: % (Target: >90%)
  • NPS: (Target: >40)
  • Avg Touchpoints per Customer: /month

Cohort Analysis

  • 2024 Cohort 12-Mo Retention: %
  • 2023 Cohort 12-Mo Retention: %
  • Improvement: percentage points

Segmentation | Segment | ARR | Logo Retention | NRR | At-Risk % | |---------|-----|---------------|-----|-----------| | Enterprise | $[X] | [X]% | [X]% | [X]% | | Mid-Market | $[X] | [X]% | [X]% | [X]% | | SMB | $[X] | [X]% | [X]% | [X]% |

Start with the basics. Track NRR and GRR. Add health scores. Layer in cohort analysis. Build from there.

Your metrics will reveal truth about customer satisfaction, product value, and business health. They'll predict problems before they blow up. They'll drive decisions and create accountability.

Measure what matters. Track trends religiously. Use data to drive improvement. That's how you build retention excellence that compounds over time.