Activity Metrics
Activity Metrics are numbers that measure how often and how intensively people actually use a product. They show activity, but on their own say nothing about whether a company is making money from it.
Anyone running an app or a website wants to know whether it’s being used. To find out, they count: How many people logged in today? How long did they stay? How many messages did they write? Such numbers are called Activity Metrics. They don’t measure how many people once downloaded a product, but how many actually use it. The difference is bigger than it sounds: a program can have millions of downloads and still be nearly empty on a daily basis.
Why investors stare at these numbers
Many tech companies earn little or no money in the beginning. Their value lies in the hope that many users will later turn into revenue. As long as that revenue is missing, investors need a substitute measure. Activity metrics are that substitute. They’re the early-warning system: when daily usage shrinks, revenue usually follows a few quarters later.
That’s why such numbers appear in almost every quarterly report of major platforms. Meta regularly reports its daily active people, streaming services their subscriber counts, AI providers their weekly active users. A single disappointing figure can move the stock price by several percent in a single day. The number itself changes nothing about the business, but it changes expectations.
That’s exactly where the danger lies. Companies often decide for themselves what counts as “active.” Does simply opening the app count? Or only a genuine action? Whoever chooses the definition generously can make their growth look better without technically lying. A critical look therefore always checks how a metric is defined, not just how high it is.
From DAU to Retention: the common metrics
The best-known figures are DAU and MAU. DAU stands for Daily Active Users, meaning the number of people who were active on a given day. MAU means the same for a month. Dividing DAU by MAU gives you the so-called stickiness. A value of 0.5 means: an average monthly user opens the product on about half of all days.
Often more important than the raw number is retention. It measures how many of the new users are still around after a week or after 30 days. An app that gains 10,000 people daily and loses 9,500 is barely growing. The comparison is a bucket with a hole: pouring more in only helps if it doesn’t all run out the bottom.
This data is collected automatically. Every action in an app can trigger a small event that is sent to a server and stored. Analytics programs calculate the metrics from millions of such events. Because this captures the behavior of individual people, data privacy is a constant concern here.
Where you encounter activity figures in everyday life
They are most visible in business news. When a report states that an AI chatbot has 800 million weekly active users, that’s an Activity Metric. Sentences like “user numbers are stagnating” or “growth is slowing” almost always refer to such metrics as well.
But you also encounter them directly. Your phone’s screen time display is an activity metric about yourself. Streak counters in learning apps or weekly stats in games work on the same principle. They’re deliberately designed to motivate continued use.
A common mistake is to automatically equate high activity with success. Usage is not revenue. A service with many active users can still be running at a loss, because every request costs computing power. For AI products, that’s currently the normal case. Activity figures show whether there’s interest. Whether that turns into a sustainable business is a question they don’t answer.