# Benchmarking SaaS analytics: what good looks like
Your dashboard says DAU/MAU (daily active users over monthly active users) is 22%. Is that good? A product manager at a project-management SaaS company once presented that exact number to her board as a win. The board member who used to run growth at Slack said nothing, pulled up a public benchmark deck, and pointed out that 22% is roughly where Slack was before it became a daily habit tool, and well below what a "sticky" collaboration product should show. The meeting changed direction in five minutes. A number without a benchmark is just a number.
This lesson is about building that comparison muscle: knowing where to find credible external benchmarks, what "good" looks like across the metrics that matter, and how to avoid being fooled by numbers that look healthy in isolation.
A SaaS (Software as a Service) company generates dozens of usage metrics daily. Almost none of them mean anything without a reference point. Three reasons:
You don't need a paid research subscription to get directionally reliable numbers. Useful free or largely-free sources as of 2025-2026:
A good starting free resource for orienting yourself: OpenView's SaaS Benchmarks report, updated annually.
Caveat: most of these are self-reported, survey-based, and skew toward venture-backed, US-headquartered companies. European benchmarks are thinner. Treat every number as directional, not precise.
DAU/MAU (daily active users divided by monthly active users) measures stickiness, how often a monthly user actually shows up daily.
Worked example:
A SaaS tool has 50,000 monthly active users and 9,000 daily active users on an average day.
DAU/MAU = 9,000 / 50,000 = 0.18, or 18%.
Rough, widely-cited benchmark ranges (estimates, vary by source and year):
So that 22% from the opening scene needs a category check. For a daily-collaboration tool, it's mediocre. For a weekly-cadence CRMCRMCustomer Relationship Management: software and strategy to manage and analyse customer interactions throughout their lifecycle.Voir la définition complète →, it's actually good.
Feature adoption rate (percentage of active users who use a specific feature within a period) gets misused constantly because companies compare it to nothing, or to an arbitrary internal target with no external grounding.
Practical benchmarking approach:
1. Segment by feature type. Core workflow features (the reason someone bought the product) should see 60-80%+ adoption among active users. Secondary features (integrations, advanced reporting) often sit at 10-30%, and that's normal, not a failure.
2. Track adoption curves over time since feature launch, not just a snapshot. A feature at 15% adoption after one week can be healthy; the same number after 18 months signals a problem.
3. Compare against your own historical launches before reaching for external benchmarks. External data on feature-specific adoption is scarce and rarely comparable across products.
Support-ticket-per-user (or per-account) ratios tell you about product friction and, indirectly, about data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → in your own tracking.
Rough estimate ranges cited by customer support benchmarking firms (e.g., Zendesk's public benchmark data, Intercom's reports): mature B2B SaaS products often see 0.1 to 0.3 tickets per user per month as a reasonable band, though this varies enormously by product complexity. A ticket rate that's unusually low isn't automatically good news: it sometimes means users are silently churning rather than complaining, or that your ticket-logging integration is broken and undercounting.
This is the data-quality trap: a "good-looking" metric can be a governance failure in disguise. Before trusting any ratio, check:
Before comparing your numbers to any external dataset, verify:
1. Definition match: is "active user" defined the same way (login vs. core action)?
2. Time window match: daily/monthly/rolling 28-day, not calendar month vs rolling window
3. Denominator consistency: total registered users vs. paying users vs. licensed seats
4. Data source lineage: product analytics tool (Amplitude, Mixpanel) vs. billing system vs. CRM
5. Sampling: full population or sampled? Bot/test-account filtering applied?Skipping this checklist is the single most common cause of a benchmarking exercise producing a false conclusion.
Vérification des acquis
1. A payroll SaaS product shows a DAU/MAU of 8%, much lower than a messaging app's 40%. What is the most likely explanation?
2. Why did the board member's benchmark comparison change the meeting's direction in the anecdote?
3. Two SaaS companies both report 'monthly active users' of 100,000, but one defines 'active' as a login and the other as completing a core action (e.g., closing a deal). What is the main risk of comparing these two numbers directly?
4. Select ALL correct answers about why a raw SaaS metric can be misleading without external context.
Sélectionnez toutes les réponses correctes.
5. Select ALL correct answers about how to responsibly use external SaaS benchmarks (like OpenView's report) when evaluating a company's metrics.
Sélectionnez toutes les réponses correctes.
While this lesson avoids general financial ratios, one usage-linked metric sits squarely in the data domain because it's built from product and billing data, not accounting statements: Net Dollar RetentionNet Dollar RetentionNet Revenue Retention measures the percentage of recurring revenue retained and grown from existing customers over a period, including upsell and expansion, net of downgrades and churn.Voir la définition complète → (NDRNDRNet Revenue Retention measures the percentage of recurring revenue retained and grown from existing customers over a period, including upsell and expansion, net of downgrades and churn.Voir la définition complète →), the percentage of recurring revenue retained from existing customers after upgrades, downgrades, and churn, excluding new customers.
Public benchmark estimates (2024-2025 figures, treat as approximate):
This metric depends entirely on clean usage-to-billing data mapping, another reason data lineagedata lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète → (tracking where a number originated and how it was transformed) matters as much as the number itself.
🎬 [VIDEO: "SaaS Metrics that Matter" - youtube.com/@saastr - SaaStr's channel regularly features founder and investor talks breaking down DAU/MAU, NDRNDRNet Revenue Retention measures the percentage of recurring revenue retained and grown from existing customers over a period, including upsell and expansion, net of downgrades and churn.Voir la définition complète →, and retention benchmarks with real company examples]
Most widely-cited SaaS benchmark datasets (OpenView, KeyBanc, Bessemer) are heavily US-weighted. European SaaS benchmarking is comparatively sparse. Sources like SaaStock and reports from European VC firms (Point Nine's SaaS surveys, for instance) offer partial coverage but with smaller sample sizes. When benchmarking a European SaaS company, flag this gap explicitly rather than silently applying US norms, adoption cadence and support expectations can differ by market and language complexity.