Average SaaS Churn Rate in 2026: Full Benchmark Report
Get the definitive 2026 average SaaS churn rate benchmark report, segmented by industry, ARR, and region. See where you really stand.
Quick Answer
The 2026 median monthly churn rate for B2B SaaS sits at approximately 3.5%, split between 2.6% voluntary cancellations and 0.8-0.9% involuntary billing-related churn. Enterprise segments trend below 1% monthly while EdTech and consumer-adjacent verticals climb as high as 9.6%, making segmentation by ARR band, motion, and industry essential before drawing conclusions.
Introduction
Churn benchmarks get quoted constantly and interpreted poorly. A 5% monthly churn rate could signal serious retention failure for a $50M ARR enterprise platform or represent healthy performance for an early-stage PLG tool serving SMBs. The 2026 dataset compiled across 500+ private SaaS companies shows median monthly churn at 3.5% and median annual gross revenue churn near 12.5%, but those headline numbers hide the segmentation that actually matters for board reporting. Any operator using a single blended figure to justify retention investment is building strategy on the wrong reference point.
Key Takeaways:
Median 2026 B2B SaaS monthly churn is 3.5%, with roughly 25% of that figure driven by involuntary billing failures rather than active cancellations.
Churn benchmarks vary by more than 8 percentage points across verticals, ARR bands, and go-to-market motions, so peer comparison requires strict segmentation.
Revenue churn matters more than logo churn for growth-stage SaaS because net revenue retention above 110% can offset elevated customer churn.

The State of SaaS Churn Benchmarks in 2026
Churn measurement has matured, but reporting practices remain inconsistent enough that raw comparisons across companies still mislead more often than they inform. The 2026 landscape shows tighter enterprise retention, elevated SMB volatility, and a widening gap between PLG and sales-led motions.
Median Rates Across Company Size
Size explains more variance in churn than any other single dimension. Enterprise-focused SaaS companies benefit from procurement friction, multi-year contracts, and dedicated customer success teams, while SMB-focused products face constant discretionary spending pressure. Recent research covering 500+ SaaS companies confirms the split holds across every ARR tier.
SMB SaaS: Monthly churn typically lands between 3% and 7%, with billing failures contributing disproportionately.
Mid-market SaaS: Monthly churn settles between 1% and 3%, driven by longer sales cycles and higher switching costs.
Enterprise SaaS: Monthly churn falls below 1%, often reaching 0.5% or lower for accounts above $100K ACV.
PLG-native tools: Show bimodal distribution, with self-serve tiers churning at 5-8% and expansion accounts retaining above 95%.
Vertical and Motion Variation
Industry vertical creates the second-largest source of variance, and treating a horizontal median as a target ignores structural realities of the category. EdTech, consumer productivity, and low-ACV horizontal tools carry structurally higher churn because usage is discretionary and switching costs stay minimal. Vertical SaaS platforms embedded in operational workflows, such as healthcare scheduling or construction project management, routinely post monthly churn below 1.5% even at SMB price points. Understanding where your product sits on this spectrum is the prerequisite to any meaningful churn rate formula and benchmarks exercise.
Segmentation by industry against the 2026 dataset produces the following practical reference points for annual gross revenue churn.
Segment | Monthly Logo Churn | Annual Gross Revenue Churn | Typical NRR |
|---|---|---|---|
Enterprise Vertical SaaS | 0.4-0.8% | 5-8% | 115-125% |
Mid-market Horizontal SaaS | 1.5-2.5% | 10-14% | 105-115% |
SMB Horizontal SaaS | 4-6% | 18-25% | 95-105% |
PLG Self-Serve | 5-8% | 22-30% | 100-120% |
EdTech / Consumer SaaS | 6-9.6% | 25-40% | 85-100% |
The most important takeaway from this table is that annual gross revenue churn above 20% is not automatically a red flag if net revenue retention comfortably clears 110%. Expansion revenue changes the interpretation of every gross churn number, which is why board-facing retention dashboards should always report both figures side by side.

Benchmarking by Stage, Geography, and Churn Type
Company stage and geography shift the definition of healthy churn as decisively as vertical does. Applying enterprise benchmarks to a Series A startup or US benchmarks to an APAC operator produces conclusions that miss the actual retention picture.
ARR Band and Company Stage Effects
Churn rates drop sharply as companies move through the $1M to $10M ARR range, then continue tightening more gradually through $50M and beyond. Analysis of churn by ARR band shows median annual revenue churn of 12.5% for private B2B SaaS overall, but Series B startups typically post rates 30-50% higher than Series D peers due to smaller customer bases, less mature onboarding, and thinner customer success coverage. For early-stage teams, this means comparing against blended medians will consistently make performance look worse than it actually is. A more honest benchmark uses stage-matched cohorts and layers in retention analytics metrics that account for cohort maturation curves rather than single-point snapshots.
Geography introduces another layer. US SaaS companies benefit from mature buying committees and higher willingness to pay, translating to APAC market retention benchmarks that typically show 1.5-2x higher SMB churn than comparable US cohorts. European mid-market SaaS sits between the two, closer to US patterns but with longer collection cycles that inflate involuntary churn by 15-25%. TrackRaptor's coverage of regional analytics practices explores these dynamics in more depth for teams operating cross-border.
Voluntary vs Involuntary Breakdown
The 2026 data draws a sharp line between churn types, and treating them as a single number obscures the highest-leverage retention interventions. Involuntary churn from failed payments, expired cards, and billing errors accounts for roughly 25% of total churn across the median SaaS company, meaning a company reporting 3.5% monthly churn is losing nearly a full percentage point to fixable payment infrastructure. Voluntary cancellations require product, pricing, or value interventions; involuntary churn requires company size benchmarks paired with dunning workflows, card updater services, and retry logic that most teams underinvest in. The calculate churn rate formula should always be applied separately to each type before drawing operational conclusions.
Revenue churn versus customer churn deserves the same disciplined split. A SaaS product losing 5% of logos but only 2% of revenue is retaining its high-value accounts, which is a fundamentally different problem than uniform bleed. Growth-stage boards increasingly prioritize revenue churn and NRR over logo churn because expansion economics dominate long-term valuation, and the churn rate formula guide covers the calculation differences that trip up finance teams reporting these numbers for the first time.

Conclusion
Benchmarking churn against a single median figure produces bad decisions and worse board conversations. The 2026 data supports a clear operational discipline: segment by ARR band, motion, vertical, and geography before drawing any peer comparison, then split every headline number into voluntary versus involuntary and logo versus revenue components. Teams building retention strategy off blended medians will consistently misallocate investment between product improvements, pricing changes, and billing infrastructure. Reference the SaaS churn calculation methodology TrackRaptor publishes alongside this benchmark set to ensure your internal numbers are directly comparable to the 2026 dataset. Accurate segmentation is the difference between defending performance and misreading it.
Ready to build retention dashboards that compare apples to apples across cohorts and segments? Explore TrackRaptor's analytics and tracking coverage for practitioner-grade guidance on operationalizing these benchmarks inside your stack.
Frequently Asked Questions (FAQs)
What is a good SaaS churn rate by industry?
A good churn rate depends entirely on segment, but enterprise vertical SaaS should target below 1% monthly, mid-market horizontal SaaS around 1.5-2.5%, and SMB or PLG tools 4-6% while maintaining NRR above 105%.
How do you calculate churn rate for SaaS products?
Divide the number of customers or revenue lost during a period by the total at the start of that period, then report logo churn and revenue churn separately to capture both count and value dynamics.
Why is my SaaS churn rate increasing?
Rising churn typically stems from one of four causes: weakening product-market fit in a specific segment, pricing misalignment after a change, deteriorating onboarding quality, or unaddressed involuntary billing failures that compound month over month.
What is the difference between gross and net revenue churn?
Gross revenue churn measures only lost revenue from cancellations and downgrades, while net revenue churn subtracts expansion revenue from that loss, producing a figure that can go negative when upsell exceeds attrition.
Are churn benchmarks different for PLG companies?
Yes, PLG companies show bimodal churn patterns with self-serve tiers churning at 5-8% monthly while product-qualified expansion accounts retain above 95%, requiring separate benchmarks for each user tier.
What are the best churn benchmarks for early-stage SaaS?
Series A and Series B startups should benchmark against stage-matched cohorts rather than blended medians, expecting 30-50% higher churn than Series D peers due to smaller customer bases and less mature retention infrastructure.
Revenue churn vs customer churn: which matters more?
Revenue churn matters more for growth-stage SaaS because expansion economics dominate valuation, but customer churn remains critical for early-stage teams still validating repeatable retention across their target segment.
About the Author
Ryan Thompson is a Cybersecurity and Application Security Expert who writes on secure software development, cloud security, compliance, and risk management. His work focuses on how measurement discipline and infrastructure reliability intersect with retention economics in modern SaaS environments, giving operators a grounded view of the systems behind the numbers.
