App LTV & UA Payback Calculator
Does a paid install actually pay back? Enter your cost per install, day-30 retention, and ARPDAU. This models app lifetime value, the LTV-to-CPI ratio, how many days to payback, and the break-even retention your user acquisition needs to survive.
An install only matters if the value it returns clears what it cost. This calculator fits a retention curve to your day-30 number, sums the active days a cohort stays over a year, and multiplies by ARPDAU to estimate app LTV — then compares it to CPI to show payback, the LTV-to-CPI ratio, and the smallest day-30 retention that breaks even. You can’t buy your way out of a leaky retention curve; this shows you where the line is.
App LTV and UA payback inputs and result
| Day | Retention | Cumulative rev / install | vs CPI |
|---|
Illustrative model · RGM analysis. The retention curve is a power-law fit to a single day-30 point, and monetization is held flat at ARPDAU. Real LTV uses your measured cohort curve and monetization ramp — treat this as a planning estimate, not a forecast.
How to use this calculator
- Enter your blended cost per install.Use total UA spend divided by installs across channels, not your cheapest channel on its best day. That is the number an install has to repay.
- Set day-30 retention honestly.Pull it from your cohort report — the share of a given install cohort still opening the app at day 30. This one number shapes the entire lifetime curve.
- Enter ARPDAU.Average revenue per daily active user per day, blending ad revenue and in-app purchases. If you only have monthly ARPU, divide by ~30 as a rough start.
- Choose your target payback window.Most UA teams want an install to repay within 90–180 days. The verdict turns on whether the model clears your window.
- Read the verdict, then export.Check LTV, the LTV-to-CPI ratio, payback days, and break-even retention. Copy a share link, download the CSV, or print a one-page PDF for your UA plan.
RGM Expert Says
We open this before approving any paid-UA scale-up, in the meeting where someone points at a low CPI and says “installs are cheap, let’s pour it on.” Cheap installs into a leaky retention curve are the most expensive mistake in mobile growth — you pay to rent users who never come back. The tool turns the argument into a number: at this CPI, this retention, and this ARPDAU, an install pays back in this many days, or it never does.
The single most important input is day-30 retention, because it sets the shape of everything downstream. A move from 18% to 24% doesn’t just add a few points — it lifts every future day the cohort stays active, and the LTV moves far more than the same effort spent chasing a lower CPI. That is why we almost always fix onboarding and the day-1 experience before we scale acquisition. A great UA team cannot outrun a broken first session.
Use the break-even retention readout as a target, not a trophy. If your break-even D30 is 30% and you’re at 22%, that gap is your product-and-lifecycle roadmap, quantified. When the verdict comes back “underwater,” the honest moves are to lift retention, raise ARPDAU, or lower CPI — not to spend faster. This model is deliberately simple so it fits on one screen; we build the real, cohort-measured version on your data.
How it works
You can’t judge an install by its price — only by what it returns. So we work forward from retention. First we fit a power-law decay curve to your single day-30 retention point, which is a standard shape for app retention:
Then we sum the active user-days a cohort accumulates over a one-year horizon, and multiply by ARPDAU to get lifetime value per install:
Payback is the first day the running cumulative revenue per install clears CPI. The break-even day-30 retention is the value that makes app LTV exactly equal CPI at your ARPDAU, solved numerically by binary search:
- D30 — day-30 retention as a fraction (0.22 for 22%). k — the decay exponent implied by D30. ARPDAU — average revenue per daily active user per day.
- LTV : CPI — app LTV divided by cost per install. Above ~3× is healthy, 1–3× is thin, below 1× loses money over the horizon.
- Payback — days until cumulative revenue per install equals CPI. Shorter payback means less capital financing growth.
The power-law retention shape is a common empirical approximation for mobile apps; fitting it to one day-30 point and holding monetization flat is RGM’s own simplifying model for fast planning. A production model uses the full measured curve and a monetization ramp.
The install is the start, not the win
Mobile retention is brutally honest. A widely cited 2015 analysis of 125M+ Android devices found the average app kept about 29% of users at day 1, 17% at day 7, and under 10% at day 30 — roughly 90% gone within a month (Quettra / Andrew Chen, 2015). Roughly one in four apps is opened only once (Localytics, 2017). When that’s the baseline, installs poured into a leaky curve just rent users who leave.
Retention drives LTV more than CPI does. Because lifetime value compounds across every day a cohort stays, a few points of day-30 retention can outweigh a large cut in install price. That is why the highest-ROI work is often onboarding and the day-1 experience — apps that lead with value instead of a sign-up wall cut one-and-done use from about 25% to 13% (Localytics, 2017), and early push notifications can lift retention materially (Airship, 2023).
This is where a payback model stops being a spreadsheet and starts being a decision. If break-even retention sits above where you are, no amount of paid scale fixes the economics — the honest moves are to lift retention, raise ARPDAU, or lower CPI. If you already clear it comfortably, scale the winning cohorts. Either way, you decide on the loop, not the install count. See the full picture on the app marketing field guide.
Retention anchors to sanity-check your inputs
Retention and monetization vary enormously by category, platform, and year, so treat these as directional anchors, not targets. Date every figure and build your real numbers on your own cohorts.
| Anchor | Typical value | Use it as |
|---|---|---|
| Average app, day-30 retention (2015 Android) | ~9.6% | Low baseline |
| Average app, day-1 retention (2015 Android) | ~29% | Curve anchor |
| Strong consumer app, day-30 retention | ~25–40% | Healthy target band |
| Top-decile / habitual app, day-30 | ~50%+ | Best-in-class ceiling |
| Apps used only once | ~24% | The one-and-done tax |
What the app-growth field says
“Retention is the single most important thing for growth.”
The value of a user is set long before you monetize them — it’s set by whether they come back.
In mobile UA, you don’t buy installs — you buy the retained, monetizing users those installs contain.