DAU/MAU Ratio Calculator

What DAU/MAU Stickiness Actually Measures

The dau mau ratio calculator scores one of the most durable engagement signals in product analytics: the stickiness ratio. Divide Daily Active Users by Monthly Active Users and you get the share of monthly users who return on any given day. A 40% stickiness means the average MAU visits 12 of 30 days; a 20% stickiness means 6 of 30. The stickiness ratio calculator makes that math explicit and then plots your score against a category-adjusted benchmark distribution — because 20% is "broken" for a daily consumer app and "top-quartile" for a weekly-intent infrastructure tool.

Stickiness works as a leading indicator because it measures behavior, not sentiment. NPS surveys and CSAT scores capture what users say; DAU/MAU captures what they do. Every product lead who has written a monthly board deck knows: the stickiness slide is the #1 way to tell a durable engagement story without cherry-picking metrics.

The DAU/MAU Ratio Formula in Plain English

The dau mau formula calculator uses the simplest math in product analytics: stickiness = DAU ÷ MAU. Both are counts of unique users — DAU over a single day, MAU over the last 30. The stickiness ratio formula comes with three footnotes that trip up most founders. First, the two windows must overlap: if you pull DAU from today and MAU from last month, the ratio is meaningless. Second, "active" has to mean the same thing for both. If DAU counts any session but MAU counts only activated users, you are conflating two cohorts. Third, do not confuse DAU with MAU/30; those are structurally different numbers and produce very different stickiness values.

How to Calculate Product Stickiness from Your Analytics

How to calculate product stickiness step-by-step from Amplitude, Mixpanel, PostHog, or Heap:

1. Define "active" consistently — session start, meaningful event, or unique login. Pick one and stick with it.
2. Pull DAU for the most recent full day (yesterday, not today — today is always mid-day noise).
3. Pull MAU for the trailing 30 days ending on that same day.
4. Divide. Express as a percentage. This is your stickiness.
5. Optionally pull WAU (trailing 7 days) to unlock the habit-formation signal via the DAU/WAU/MAU triangle.

This is the dau mau ratio calculator workflow used in every Series A data room. The 5-minute version gives you a directional number; the full version adds WAU, cohort breakdowns, and an L28 approximation.

Category-Adjusted Benchmarks (B2B SaaS vs Consumer vs Infra)

The product stickiness metric does not have a single threshold. What is a good dau mau ratio depends entirely on what kind of product you are shipping. Calibrated from Amplitude 2024 Product Benchmarks, Foundation Capital Consumer Social 2023, and OpenView 2024 SaaS data, the distributions are:

• Consumer social: p50 42%, p90 65%
• Creator tools: p50 32%, p90 55%
• Prosumer: p50 27%, p90 50%
• Marketplace: p50 25%, p90 45%
• B2B SaaS: p50 22%, p90 45%
• Infra / devtool: p50 13%, p90 30%

Use the category you actually are, not the one you aspire to. A 25% stickiness as a consumer app is broken. The same 25% as a B2B SaaS is healthy. The same 25% as an infra tool is top-quartile. The dau mau benchmark saas vs consumer disconnect is the single biggest source of bad engagement decisions in SaaS.

DAU/MAU for B2B SaaS — Why 20–30% Is the Median

The dau mau for b2b saas median sits around 22% because most B2B products serve weekly-intent workflows. A CRM gets meaningful traffic when sales reps are logging calls — typically 3–5 days per week. A dashboarding tool gets weekly usage during reporting cycles. A RevOps platform gets monthly usage during board prep. A 22% stickiness means the average MAU visits ~7 of 30 days, which is exactly the pattern you would expect from a weekly-cadence workflow with occasional power users who visit more. Anything above 32% stickiness for B2B SaaS is truly exceptional — think Slack, Notion, Figma, Linear — products that have crossed into daily-habit territory.

DAU/MAU for Consumer Apps — What Slack and Facebook Actually Report

Dau mau slack facebook numbers are the anchors most founders cite. Slack has publicly reported stickiness around 50%, meaning the average monthly user visits 15 of 30 days. Facebook has reported 50–65% depending on region and year. These are the high-water marks for consumer-scale social engagement. Snapchat, Instagram, and TikTok sit in the 55–70% range. The dau mau consumer app benchmark for a healthy but not elite consumer product sits at 35–45%. If your consumer app is below 30% stickiness, you are in pivot territory — consumer attention is zero-sum and sub-30% means you are losing to a competitor or to the phone's lock screen.

The DAU/WAU/MAU Triangle — What It Reveals

The dau wau mau calculator unlocks a second layer of diagnosis. Stickiness alone cannot distinguish between two very different engagement stories. Case A: 12% stickiness because the weekly footprint is huge but weekly-to-daily conversion is weak (WAU/MAU = 60%, DAU/WAU = 20%). Case B: 12% stickiness because the weekly footprint is small but weekly visitors come daily (WAU/MAU = 20%, DAU/WAU = 60%). Those are two completely different products with the same stickiness ratio. Case A needs a habit loop (push notifications, daily triggers). Case B needs a reach problem fixed (activation, marketing). The triangle reveals which.

Amplitude's L7 and L28 Metrics — How They Relate to Stickiness

Amplitude popularized the l7 l28 retention calculator concept as an alternative to aggregate stickiness. L7 counts users active 7 of the last 7 days — "fully sticky" users. L28 counts users active 7 or more of the last 28 days — "meaningfully sticky" users. The amplitude dau mau benchmark "highly sticky" threshold is L28 ≥ 35%. L-metrics are cohort-event-level and can't be computed from aggregate DAU/MAU without empirical curve-fits. This tool uses Amplitude's own published curve data to approximate: L28 ≈ stickiness × (0.55 + 0.35 × (WAU/MAU − stickiness)). The approximation is directional — measure exactly in Amplitude or Mixpanel when you have event data.

Stickiness vs Retention — Disentangling the Two Stories

Stickiness measures how often a user returns during their active month. Retention measures whether they return at all N days later. A product can have high stickiness and low retention (users love you for a month, then churn) or low stickiness and high retention (users return monthly but never daily). Both tell different stories. The engagement ratio calculator treats stickiness as a leading indicator of retention via a sigmoid calibration: 10% stickiness ≈ 22% 90d retention, 25% ≈ 38%, 40% ≈ 55%, 55% ≈ 68%, 70% ≈ 73% (plateau). Above 70% stickiness you are in habit territory and retention maxes out — further gains come from widening the top of funnel, not from deepening the habit.

How Stickiness Predicts 90-Day Retention and Revenue

High-stickiness products compound because engaged users refer peers, expand seats, and resist churn. The revenue correlation is category-dependent: consumer social monetizes stickiness at ~1.2× (ads on daily impressions), B2B SaaS at ~0.7× (seat expansion scales with team adoption), infra at ~0.4× (usage-based revenue correlates with workload, not visits). A 10-point stickiness lift in consumer apps is typically worth 10–15% ARPU lift; in B2B SaaS it is 5–8%; in infra it is 2–4%. Do not expect the same revenue reward everywhere — but do expect the churn reduction to show up regardless of category, because churn is a pure function of habit loss.

Frequency Fit — When High Stickiness Is a Red Flag

A high DAU/MAU is not always good news. If you positioned a product as a weekly-cadence workflow and users are coming daily, you may have built an over-engineered habit loop — incentivizing daily visits that do not create value. A 60% stickiness for a weekly-reporting tool means users are checking their dashboards daily for small changes, which is attention-burning without proportional value. Conversely, a daily-intent app with 18% stickiness has an activation and habit problem, not a value problem. This tool's Frequency Fit diagnostic cross-checks your stated target use with actual stickiness and flags misalignment in either direction.

How VCs Read DAU/MAU During Diligence

In consumer and prosumer diligence, DAU/MAU is one of the first three metrics an investor will ask for (alongside MAU growth rate and K-factor). For consumer investors, 40%+ stickiness is table stakes for a Series B, 50%+ is required for a competitive round. In B2B SaaS diligence, DAU/MAU matters less absolutely but matters hugely for PLG positioning — a self-serve SaaS claiming PLG with <20% stickiness is a red flag. VCs also read the L28 approximation as a consumer-style durability score, and the WAU triangle as a test of whether you actually understand your own engagement geometry. The dau mau for indie apps question usually comes up when the founder is pre-fundraise and wants a defensible engagement story — this tool produces the artifact.

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