Attribution

Attribution is the practice of assigning credit for a conversion to the marketing touchpoints that preceded it. When a user sees an ad, clicks a link, installs, and later pays, attribution decides which channel earns the credit. It is the bridge between money spent on user acquisition and the revenue those users generate — without it, you cannot tell which campaigns are working.

How it is calculated

Attribution depends on a model that distributes credit across the touchpoints in a user’s path:

First-touch: all credit to the first interaction — good for understanding discovery.
Last-touch: all credit to the final interaction before converting — the most common default.
Linear (multi-touch): credit split evenly across every touchpoint, acknowledging that several channels contributed.

For mobile games and apps, attribution also relies on install postbacks — the signals an attribution provider or platform sends back when a user who clicked an ad installs and later converts. These postbacks are what let you connect an anonymous ad click to in-app behavior, with privacy frameworks (like aggregated or modeled signals) increasingly shaping what is measurable.

Why it matters

Attribution is what makes paid growth accountable. Once conversions are tied to the campaigns that drove them, you can compute ROAS (return on ad spend):

ROAS = revenue attributed to a campaign / spend on that campaign

Combined with lifetime value, attribution lets you set ROAS targets at a given age — day-7 or day-30 ROAS — and scale only the channels that clear them. Choosing the wrong model can systematically over- or under-credit a channel, so teams pick a model deliberately and keep it consistent across reporting.

In games and apps

UA (user acquisition) teams live in attribution. They compare channels not on installs but on the downstream value of the cohorts each channel delivers — a channel with cheap installs but poor retention loses to a pricier one that brings payers. Because game revenue is dominated by whales, attribution is paired with cohort analysis so that a single high-value cohort is not mistaken for a broadly profitable channel.

In Keentics

Keentics ties attributed acquisition data to the raw event and revenue stream those users produce, so ROAS is computed against real downstream behavior rather than installs alone. You can break revenue and retention down by channel and campaign and follow each cohort forward. See the game analytics feature, related LTV analysis, and the pricing page for details.

Related: Active users · ARPPU · ARPU · CAC