✍️ Essay
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Free
Intermediate
Why we picked it
This is the essay that reframes the whole question. Instead of asking whether your DAU/MAU is high enough, it tells you to look at the histogram of how many days per month people actually use the product, and it says plainly that not every product should have a daily-use curve. For a low-frequency product, that is the honest starting point: match the metric to the real shape of usage instead of forcing a daily lens that will always look like failure.
From
Andreessen Horowitz (a16z)
by Li Jin and Andrew Chen
- DAU/MAU is a single blunt number that hides the variance in how often your users return; the power user curve shows the full distribution of active days per month.
- A flat or right-skewed curve is not automatically bad. Some product categories (professional, investment, and other infrequent-use tools) are healthy at low daily engagement.
- Pick a frequency lens that fits how people naturally get value, then track whether a real core segment keeps coming back at that cadence.
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a16z.com →
📄 Article
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Free
Beginner
Why we picked it
Most active-user counts quietly measure app opens, which is exactly the dishonesty this question is trying to avoid. This piece is blunt that opening an app or logging in does not count, and that every product has to name the specific value action that means a user actually got what they came for. It also walks through when DAU, WAU, or MAU is the right window based on your natural usage rhythm, which is the second half of doing this honestly.
From
Mixpanel blog
by Mixpanel
- Define active by a meaningful value action, not a raw open or login; name the specific event that signals the user got value.
- Match the window to natural frequency: daily-use products lean on DAU, asynchronous and content or scheduling tools often read truer on WAU, and genuinely occasional tools on MAU.
- A weekly cadence is the honest middle for a low-frequency product, because measuring it daily invents false churn every weekend and measuring it monthly loses too much resolution.
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mixpanel.com →
📖 Book
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Paid
Intermediate
Why we picked it
The reason people track the wrong active-user number is usually that they copied a metric from a different kind of business. This book is the canonical fix: it maps which engagement and stickiness metric actually fits each business model (ecommerce, SaaS, mobile app, media, user-generated content, marketplace) so you are not measuring a low-frequency product with a social-app yardstick. Read the chapter for your model, then define your active-user rule to match.
From
O'Reilly Media
by Alistair Croll and Benjamin Yoskovitz
- Different business models call for different core metrics; there is no universal definition of active, so borrow the one that fits your model.
- The One Metric That Matters idea forces you to pick the single number that reflects real value at your current stage instead of a vanity dashboard.
- Its stickiness and engagement sections give you a language for defining an honest active-user event rather than counting logins.
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oreilly.com →