What's a Good MAU Growth Rate? MoM Benchmarks & Model
There is no single good MAU growth rate. The most-cited yardstick is Paul Graham’s Startup = Growth: 5 to 7% per week (~20 to 30% a month) for a startup that’s working. He calls 10% a week exceptional. A product with hundreds of thousands of MAU cannot sustain 30% a month.
MAU growth rate is an output, not an input. Your retention, new-user inflow and viral loops produce it, run forward month after month.
I’ve seen good teams waste a quarter chasing a growth-rate target directly. So the question is “what rate do my current retention and acquisition support, and is that the rate I want?”
What MAU growth rate measures
MAU counts the distinct people who did something meaningful in your product in a given month. MAU growth rate (month-over-month) is how much that number changed versus the previous month, as a percentage.
Formula: MoM growth rate = (MAU this month − MAU last month) ÷ MAU last month.
With 80,000 MAU last month and 88,000 this month, that’s (88,000 − 80,000) ÷ 80,000 = 10% month-over-month growth.
Why there’s no single “good” number
For most metrics on this site we can at least give you an industry range (retention, LTV:CAC, K-factor). For MAU growth rate even the range misleads. The same percentage means opposite things depending on three variables a benchmark can’t see:
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Your absolute size. 30% MoM on 2,000 MAU is 600 net new users, a good month for a seed-stage product. 30% MoM on 2,000,000 MAU is 600,000 net new users every month, compounding, and essentially no product sustains that.
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Your motion and market. A consumer app riding a viral moment can post numbers a low-frequency B2B tool will never see. B2B compounds more slowly and more durably. (See B2B SaaS growth benchmarks and Consumer app benchmarks.)
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Whether the growth is durable. A product can post 25% MoM growth for two quarters by pouring acquisition into a leaky bucket. Then those cohorts churn out the back and the number collapses. Only the cohort structure underneath the rate tells you which one you’re looking at.
The model: a stable base plus new cohorts
The benchmark tool models MAU growth as a cohort-based forward simulation with two pools of users. It needs two inputs about your current state plus two levers.
The two inputs (where the chart starts):
- N0, new active users last month. The users who activated for the first time last month.
- A0, MAU last month. Your total monthly active users last month.
From those, the model splits your MAU into two pools:
- Stable base =
A0 − N0. Your long-tenured users, the ones who already survived the early retention funnel, churn at a low “mature” rate. - New cohorts = each month’s fresh arrivals. They retain at your month-1 rate, and the survivors graduate into the stable base the following month.
The two levers that drive everything forward:
- M1 retention: what fraction of each new cohort survives to the next month.
- K-factor: how many additional new users each new user generates (your viral coefficient).
Each month the simulation is:
stableBase[t] = stableBase[t-1] × matureRetention + newUsers[t-1] × M1
newUsers[t] = N0 + K × newUsers[t-1]where matureRetention = 1 − (1 − M1) × 0.10. Long-tenured users churn roughly 10× less than fresh ones, because they have already proven the product fits their job.
The 0.10 factor is a deliberate simplification in the tool’s growth model (docs/growth-model.md), not a measured industry constant. It encodes the shape of retention curves flattening with tenure.
Your MAU growth rate is what happens to stableBase + newUsers when you run that forward.
What the levers do
Take a product with N0 = 5,000 new active users and A0 = 85,000 MAU last month, so a stable base of 80,000. The same starting point produces very different growth depending only on the two levers:
| M1 retention | K-factor | What happens to MAU |
|---|---|---|
| 100% | 0 | ~+5% MoM (a flat +5,000 users/month). Nobody leaves, all new users graduate in. |
| 62% | 0 | ~+0% MoM. Surviving new cohorts almost exactly offset mature-base churn. This is the break-even line for these inputs. |
| 55% | 0 | ~−1% MoM, just below break-even: the base leaks faster than the cohorts replace it. |
| 55% | 0.6 | ~+3% MoM and rising. The same 55% retention, but viral inflow now feeds the base faster than it decays. |
| 30% | 0 | ~−5% MoM. Far too few new users survive to replace mature churn; the base is in steady decline. |
| 80% | 1.0 | ~+12% MoM and accelerating. High retention plus exponentially growing inflow (K = 1 means new users keep compounding). |
Source: cohort growth model, docs/growth-model.md (the model behind the tool’s growth chart).
Look at the 30%-retention row. You can post positive MAU growth for a while and still be on that line, if your N0 is rising fast enough from paid acquisition to mask it. You only see it when you split base from new cohorts.
How to read your MAU growth rate honestly
1. Decompose it into retained vs. new. Net growth = (new users this month) − (users who churned). A healthy 10% can be 12% inflow minus 2% churn (durable) or 35% inflow minus 25% churn.
2. Find your break-even M1. At one specific monthly retention rate, surviving new cohorts exactly offset mature-base churn. Below it you shrink unless acquisition keeps rising. Above it you compound. Where it sits depends on the ratio of your N0 to your base size.
3. Read it against retention, not in isolation. Retention sets your sustainable growth ceiling.
The ranges that drive your rate
You can’t benchmark MAU growth directly, but you can benchmark the inputs that produce it. These ship in the tool, each with its source or its status:
| Input | B2B SaaS | Consumer | Why it caps your growth |
|---|---|---|---|
| Day-7 retention | 4 to 20% (estimate) | 8 to 15% | Early habit formation: the first real “did it stick?” check. |
| Day-90 retention | 2.5 to 15.6% | 1 to 4% | Long-term value; the plateau is your durable base. |
| K-factor | 0.1 to 0.3 | 0.3 to 0.7 | Free inflow: each user bringing more users. B2B virality is weak, and that’s normal. |
Sources: Amplitude B2B Technology Product Benchmarks (B2B Day 90, median to 90th percentile; B2B Day 7 is an estimate); Adjust, AppsFlyer, UXCam (consumer retention); Reforge, Andrew Chen (K-factor). B2B figures are cohort retention (Amplitude for Day 90; Day 1 and Day 7 are estimates). Pendo’s returning-user rates (50 to 70% Day 1, 40 to 60% Day 7, 25 to 35% at 90 days) measure something else and are not comparable.
How to move it
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Fix retention before you buy more inflow. Raising M1 retention grows the new-cohort survivors, and it lifts the mature-retention rate of your whole base. Retention is the cheapest, most durable lever you have. (Where the curve flattens is also the signature of product-market fit; see What is product-market fit.)
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Raise K-factor where the product can support it, and watch cycle time. If K is below 1, new users converge to a ceiling of
N0 ÷ (1 − K). Nudging K from 0.2 to 0.4 raises that ceiling. The full framework (multiple loops, cycle time, worked examples) is in Understanding K-factor in product growth. -
Increase N0, but only once the bucket holds. New-user inflow is the obvious lever. It also flatters a broken product the longest.
See where your numbers land
Watch your own stable base and new cohorts run forward for 12 months. The free benchmark tool takes your N0 (new active users last month) and A0 (MAU last month), plus your retention and K-factor. It charts the resulting MAU trajectory against the B2B and Consumer ranges.
Live sliders show how much a few points of retention or a higher K change the curve.
→ Run your numbers at benchmark.scilla.studio
Frequently asked questions
See where your numbers actually land
Plot your retention, CAC payback, LTV:CAC and K-factor against the B2B and Consumer bands, and find out whether a good-looking number is real or sitting on a leaky retention curve.
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