Conversion rate
Conversion rate is the percentage of users who complete a specific target action out of everyone who had the chance to. That action can be anything you care about — registering, finishing a tutorial, making a first purchase, subscribing. It is the most general efficiency metric in product analytics: any time you can define a “before” group and a “did the thing” group, you can measure conversion.
How it is calculated
The definition is a simple ratio, but defining the two sets carefully is everything:
conversion rate = users who completed the action / users eligible to complete it
The denominator is the trap. “Of all visitors” and “of all users who reached the checkout” give very different numbers for the same purchase, so the eligible population must be stated explicitly. Conversion is most powerful when chained into a funnel: install → register → tutorial complete → first purchase, where each step has its own rate and the product of all step rates is the end-to-end conversion. That chaining is what turns a single percentage into a map of where users drop off.
Why it matters
Conversion rate is where you find the cheapest growth. Lifting a key step from 20% to 25% flows through every step after it, often beating the cost of buying more top-of-funnel traffic. It directly shapes the economics of acquisition: a higher purchase-conversion rate raises blended ARPU and improves your LTV-to-CAC ratio without spending another cent on ads. And because it isolates one step, a falling conversion rate pinpoints exactly where a redesign, a paywall or a bug is costing you, which a top-line number never could.
In games and apps
Teams track conversion at every meaningful gate: tutorial completion, day-1 to day-2 progression, free-to-payer conversion, and offer purchase rates during live-ops events. Payer conversion is especially watched because, together with ARPPU, it decomposes monetization into “how many pay” and “how much they pay.” Conversion is almost always segmented — by channel, country, platform and cohort — because an average rate hides the segment that is quietly failing.
In Keentics
Keentics computes conversion between any two events you define, across any population, straight from raw event data — no fixed funnel templates required. You can build multi-step funnels, see the drop-off at each gate, and segment conversion by channel, country and cohort. Pair it with user path analysis to discover the routes high-converting users actually take, and the conversion funnel glossary entry for the chained view.
Related: Active users · ARPPU · ARPU · Attribution