Why use a mobile app ad cost calculator?

CPI connects your acquisition budget with the number of installs you can buy. Cohort receipts connect those installs with monetization over time. Looking at both helps you test whether a campaign can recover its ad spend within your chosen horizon.

This calculator projects one cohort from your inputs. It does not pull live campaign data, estimate industry benchmarks, or predict changes in user quality as spend scales. Replace the example values with observations from the same platform, geography, acquisition channel and cohort period. Keep the currency consistent.

How subscription receipts and LTV are modeled

The install-to-paid probability is install-to-trial rate multiplied by trial-to-paid rate. Each successful payer makes a first payment after your entered trial or payment delay, then renews every 30 days. Before renewal number n, the expected surviving share is (1 − monthly churn) raised to n. The commission entered is deducted from every modeled payment.

Receipts per paid subscriber at a horizon equal the sum of those expected payments through that day. Multiply this value by the install-to-paid probability to get receipts per install. A subscriber value must not be divided directly by install CPI: users who never subscribe still incur acquisition cost.

For example, a 40% install-to-trial rate and 50% trial-to-paid rate produce a 20% paid probability. At a $10 price and 20% commission, with first payment on Day 7 and zero churn, D30 receipts are $8 per payer and $1.60 per install. At a $2 CPI, D30 ROAS is 0.80x. The next expected payment arrives on Day 37, so modeled ad payback occurs on Day 37.

These are finite-horizon values, sometimes called D30, D90 or D365 LTV. Zero churn means all modeled renewals survive through D365; it does not imply an infinite value or an arbitrary subscriber lifetime. The model uses 30-day billing periods, not calendar months, annual plans or weekly plans. It assumes every future payer starts their trial on install day and shares the same payment delay.

Gaming, ad revenue and IAP cohort modeling

ARPDAU is revenue per daily active user, including nonpayers in the denominator. Divide daily ad or IAP revenue by the same day’s active users. Enter ad ARPDAU as publisher receipts after ad network fees, and IAP ARPDAU before the store commission you enter. Applying commission again to already net IAP would understate receipts.

The model includes Day 0 revenue at 100% active. It linearly interpolates daily active fractions between your D1, D7 and D30 retention anchors. After Day 30, retention is D30 retention × (1 − entered daily decline) raised to the number of days since Day 30. The daily decline is an editable assumption, not a benchmark or a measured forecast.

Daily receipts equal modeled active fraction × ARPDAU. Hybrid mode splits gross ARPDAU by your IAP share, deducts commission from the IAP portion, and leaves publisher ad receipts unchanged. IAP Only applies commission to the entire IAP amount. Ad Revenue mode applies no store commission.

D1 must be at least D7, and D7 at least D30. This simplified declining curve does not support reactivation or rising retention. Test plausible tail assumptions against your observed later cohorts. ARPDAU is held constant across cohort age; an app with strongly changing monetization by age needs a richer model.

Understanding CPI, ROAS and ad-spend payback

CPI equals cohort ad spend divided by attributed installs. For a positive spend, modeled net receipts ROAS equals receipts per install at the selected horizon divided by CPI. D30, D90 and D365 include Day 0 and the named final day. They describe receipts earned by this acquisition cohort over time, not a month’s total company revenue.

Break-even CPI at a selected horizon equals modeled receipts per install through that horizon. This is break-even against acquisition spend only. Receipts minus ad spend excludes refunds, taxes, additional payment fees, creative production, hosting, salaries and other operating costs; it is not net profit. Return after ads divided by ad spend is shown for the same limited cost basis.

Ad-spend payback is the first day through D365 when cumulative modeled receipts per install reach CPI. If that never happens, the tool says ‘Not within D365’; it does not claim payback will occur later. Subscription payments are discrete, so payback can occur only on a modeled charge day. Store payout delays and cash settlement timing are excluded.

Compare scenarios before changing a campaign

The comparison holds cohort budget, conversion, retention, commission and monetization constant, and changes only CPI. Lower CPI buys more installs at the same budget in this scenario. Real changes in creative, targeting or bidding can also change user quality, so validate the assumptions after making a campaign change.

Use the daily CSV to inspect subscription charges, daily IAP and ad receipts, cumulative receipts per install, and current and comparison cohort receipts. It includes all days through D365 and the inputs used, even when the headline reporting horizon is D30 or D90. A zero spend produces zero modeled installs and cohort receipts; cohort ROAS and payback are undefined and shown as N/A. A zero CPI cannot determine installs from a budget and is rejected.

Ways to improve acquisition economics

Creative tests, targeting changes and onboarding improvements can affect both CPI and downstream quality. Evaluate those changes using comparable cohorts, consistent attribution windows and the same receipt horizon. An improvement in install volume alone does not establish stronger economics.

For subscription apps, test trial conversion, payment timing and renewal retention. For games, examine both retention and the monetization effect of ad placements or IAP offers. Increasing monetization can change retention, so enter the combined observed effect instead of assuming one metric moves in isolation.

Comparing platforms and store commissions

Run separate scenarios for each acquisition platform, operating system, country or campaign using your own observed CPI and cohort data. There is no universal platform cost or profitable ROAS threshold that applies to every app.

Store commissions depend on the store, product, program eligibility and applicable commercial terms. Enter the effective rate that applies to your scenario; the calculator does not assign a universal Apple or Google rate. It holds that rate constant through D365. Commission changes by renewal, refunds, discounts and territory-specific tax treatment require adjustments outside this model.