Retention rate
Retention rate is the percentage of users who return to your product over time after they first show up. It answers the single most important question in product analytics: of the people who arrived, how many actually came back? A high acquisition number means nothing if users churn the same day, so retention is the metric that separates real growth from a leaky bucket.
How it is calculated
Retention is almost always measured as N-day retention against a starting cohort. Take everyone who installed or signed up on a given day — that is your day-0 cohort — then count how many were active again exactly N days later.
Day-N retention = (cohort users active on day N) / (total cohort size)
The three values teams quote most are day-1 (next-day) retention, day-7 retention and day-30 retention. Plotting day-0 through day-30 gives you a retention curve: it usually drops steeply in the first few days, then flattens into a “retained core.” The shape of that curve — how fast it falls and where it levels off — tells you more than any single number.
There are two common conventions: classic (active on exactly day N) and rolling/range (active at any point in a window). Mixing them up is the most frequent reason two tools disagree, so pick one and stay consistent.
Why it matters
Retention compounds. A product that keeps 40% of users to day 30 instead of 20% does not grow twice as fast — it grows on a fundamentally different trajectory, because every cohort stacks on top of a larger retained base. For games, day-1 retention is a fast read on whether the first session delivers; day-30 retention predicts long-term revenue and is tightly linked to lifetime value. Weak retention also makes paid acquisition unprofitable no matter how cheap your installs are.
In games and apps
In practice you rarely look at one global curve. You break retention down by acquisition channel, app version, country and platform, because a build that retains fine on iOS can quietly collapse on a specific Android device. You also tie retention to behavior: did users who finished the tutorial retain better? Did reaching level 5 on day 1 change the day-7 curve? That is where retention stops being a vanity chart and starts driving roadmap decisions.
In Keentics
Keentics computes N-day retention directly on your raw event stream, so the curve reflects real activity with no sampling. You can define what “active” and “returned” mean per product, slice any cohort by dimension, and compare curves side by side. Retention sits naturally alongside funnel analysis and cohort analysis — see the full retention analysis feature for how it fits the rest of your reporting, and the pricing page for tiers.
Related: Active users · ARPPU · ARPU · Attribution