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Short answer: there isn't one number, and anyone who hands you a clean "good MoM growth rate" without asking your stage and motion is guessing. As a rough orientation, early-stage products chasing product-market fit often aim for double-digit month-over-month MAU growth. The most-cited yardstick comes from Paul Graham's essay Startup = Growth: 5 to 7% per week (~20 to 30% a month) is a good rate for a startup that's working, with 10% weekly being exceptional. But for a product with hundreds of thousands of MAU, sustaining 30% a month is near-impossible and not the right target.
Here's the part most "good growth rate" articles skip, and the thing this whole page is built around: MAU growth rate is an output, not an input. It's what your retention, your new-user inflow, and any viral loops produce when you run them forward month after month. You don't really set a growth-rate target and hit it; you set the inputs and the rate falls out. So the honest question is less "what rate should I hit?" and more "what rate do my current retention and acquisition actually support, and is that the rate I want?" Honestly, I think chasing a growth-rate target directly is one of the most common ways I've seen good teams waste a quarter, because you can only move it by moving the things underneath it.
Let me show you why, because once you see the mechanics, the benchmark question mostly answers itself.
So what is MAU growth rate, exactly? MAU is your count of monthly active users, 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.
So if you had 80,000 MAU last month and 88,000 this month, that's (88,000 − 80,000) ÷ 80,000 = 10% month-over-month growth. Simple arithmetic. The trap is treating that 10% as a single lever you can pull, when it's really the visible surface of two very different things happening underneath: people staying, and people arriving.
A benchmark answers exactly one question well ("is my number unusual?") and zero others. For most metrics on this site we can at least give you an industry band (retention, LTV:CAC, K-factor). For MAU growth rate, even the band is misleading, because the same percentage means opposite things depending on three variables a benchmark can't see:
Your absolute size. 30% MoM on 2,000 MAU is 600 new net users, a good month for a seed-stage product. 30% MoM on 2,000,000 MAU is 600,000 net new users every single month, compounding, which essentially no product sustains. Growth rate and growth math diverge violently as you scale. A "slowing" rate at scale can still be a far bigger absolute business than your hypergrowth year.
Your motion and market. A consumer app riding a viral moment can post numbers a deliberate, low-frequency B2B tool will never see, and shouldn't try to. Consumer acquisition is faster and virality runs higher; B2B compounds more slowly and more durably. (See B2B SaaS growth benchmarks and Consumer app benchmarks; they're genuinely different machines.)
Whether the growth is durable. This is the big one. A product can post 25% MoM growth for two quarters by pouring acquisition into a leaky bucket, then collapse when those cohorts churn out the back. The rate looked identical to healthy growth right up until it didn't. The rate alone can't tell you which one you're looking at; only the cohort structure underneath it can.
So instead of inventing a number, let's look at the machine that produces the number. That's where "good" actually lives.
The benchmark tool models MAU growth the way you should think about it, as a cohort-based forward simulation with two pools of users, not one blended number. This is the same model that drives the growth chart in the tool, and it only needs two inputs about your current state plus two levers.
The two inputs (where the chart starts):
From those, the model splits your MAU into two pools:
A0 − N0. Your long-tenured users, the ones who already survived the early retention funnel. They churn at a low "mature" rate.The two levers that drive everything forward:
The simulation each month is just:
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've already proven the product fits their job. The 0.10 factor (the "10× less" assumption) is a deliberate model simplification in the tool's growth model (docs/growth-model.md), not a measured industry constant. It encodes the well-established shape of retention curves flattening with tenure, but treat the exact multiple as a modelling choice, not a hard number.
Your MAU growth rate is simply what happens to stableBase + newUsers when you run that forward. That's the whole secret: growth rate is the arithmetic of retention and inflow compounding, never a setting you dial in directly.
Take a product with N0 = 5,000 new active users and A0 = 85,000 MAU last month, so a stable base of 80,000. Here's how the same starting point produces wildly 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. The topline goes up; the base is quietly rotting. That's the leaky bucket, and it's invisible in the growth-rate number alone. It's only visible when you split base from new cohorts, which is exactly why the rate works as a scoreboard and never as the strategy itself.
Three checks turn the number from a vanity readout into a real diagnostic.
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 (a furnace eating cash). Same headline, opposite health. If you only track the net number, you can't tell which one you're running.
2. Find your break-even M1. There's a specific monthly retention rate where surviving new cohorts exactly offset mature-base churn: below it you shrink without ever-increasing acquisition, above it you compound. Where it sits depends on the ratio of your N0 to your base size. Knowing your break-even tells you whether your growth is structurally self-sustaining or rented from your ad budget.
3. Read it against retention, not in isolation. This is the same rule that governs every metric on this site: no single number tells the truth alone. Your sustainable growth ceiling is set by retention. Which means the bands that do have sources are the ones worth benchmarking.
You can't sensibly benchmark MAU growth directly, but you absolutely can benchmark the inputs that produce it. These ship in the tool with sources:
| Input | B2B SaaS | Consumer | Why it caps your growth |
|---|---|---|---|
| Day-7 retention | 40 to 60% | 8 to 15% | Early habit formation: the first real "did it stick?" check. |
| Day-90 retention | 25 to 35% | 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: Pendo Product Benchmarks, Amplitude, Mixpanel (retention); Adjust, AppsFlyer, UXCam (consumer retention); Reforge, Andrew Chen (K-factor).
The takeaway: a B2B product with 50% Day-7 retention and a K-factor of 0.2 has a very different sustainable growth ceiling than a consumer app with 12% Day-7 and a K of 0.5, and comparing their MAU growth rates head-to-head is meaningless. Benchmark the inputs; let the rate be the consequence.
Because growth rate is an output, you move it by moving the inputs, and they're not equally powerful.
Fix retention before you buy more inflow. Raising M1 retention does two things at once: it grows the new-cohort survivors and lifts the mature-retention rate of your whole base (matureRetention = 1 − (1 − M1) × 0.10). It compounds on both pools. Pouring acquisition into a product people don't keep just raises N0 while the base leaks, the most expensive way to fake growth there is. 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.)
Raise K-factor where the product can support it, and watch cycle time. If K is below 1, new users converge to N0 ÷ (1 − K), a ceiling. Nudging K from 0.2 to 0.4 raises that ceiling. But don't chase a number your product's nature won't support: B2B virality lives at 0.1 to 0.3 for a reason, and cycle time matters as much as the coefficient. 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 and the one everyone reaches for first. It's also the one that flatters a broken product the longest. Earn the right to scale acquisition by fixing retention first; otherwise you're just raising the temperature on the furnace.
Here's what I actually think after watching plenty of these charts: the teams that win the growth-rate game mostly stop playing it directly. They obsess over retention and the new-cohort survival rate, and they treat the MoM number as a thermometer rather than a thermostat.
Reading about the model is one thing; watching your own stable base and new cohorts run forward for 12 months is where it clicks. The free benchmark tool takes your N0 (new active users last month) and A0 (MAU last month), plus your retention and K-factor, and charts the resulting MAU trajectory against the B2B and Consumer bands, with live sliders so you can see exactly how much a few points of retention or a higher K change the curve. It won't tell you your growth rate is "good." It'll show you whether it's durable, which is the question that actually matters.
→ Run your numbers at benchmark.scilla.studio
What is a good month-over-month MAU growth rate? There's no single right number; it depends on your size, motion, and whether the growth is durable. As orientation, early-stage products often target double-digit MoM growth, and the most-cited yardstick, Paul Graham's Startup = Growth, puts a good rate at 5 to 7% per week (~20 to 30% a month) for a startup that's working. But sustaining that gets structurally harder as your base grows, and the same rate can be healthy or a leaky bucket depending on retention.
How do you calculate MAU growth rate?
MoM growth rate = (MAU this month − MAU last month) ÷ MAU last month. For 80,000 → 88,000 MAU, that's 10%. The number is easy; the hard part is decomposing it into how much is retained users vs. new arrivals, because that's what tells you if the growth is durable.
Why is my MAU growing but the business feels weak?
Almost certainly a retention problem masked by acquisition. You can post positive MAU growth while your stable base is shrinking, if new-user inflow (N0) is rising fast enough to cover the churn. Split your MAU into stable base (A0 − N0) and new cohorts and check whether the base is actually growing; see Retention rate benchmarks.
Does MAU growth rate slow down as you get bigger? Yes, almost always, and that's normal, not failure. The same absolute number of net new users is a huge percentage of a small base and a tiny percentage of a large one. Judge a mature product on absolute net adds and retention durability, not on matching its early-stage percentage growth.
Is retention or acquisition more important for MAU growth? Retention, by a distance. Raising monthly retention grows your new-cohort survivors and slows the churn of your entire mature base, so it compounds on both pools. Acquisition only raises inflow, and on a product with weak retention, that just fills a leaking bucket faster. Fix retention first, then scale acquisition.
