Cannibalisation is real but measurable
Rewarded video usually adds revenue without hurting purchases; interstitials at high frequency reliably suppress them. Run a holdout on ad load for two weeks, compare payer rate between groups, and put the measured number into the cannibalisation input instead of guessing.
Where the mix usually lands
The right answer differs by genre and payer depth.
- • Hyper-casual: ads carry nearly everything; IAP is a remove-ads SKU.
- • Hybrid casual: 50–75% ads, with rewarded video as the bridge.
- • Mid-core and RPG: IAP dominates; heavy ads actively damage the whale economy.
- • Utility and subscription apps: ads mostly exist to convert free users to paid.
Test the load, don't set it once
Ad load is the fastest-moving lever you own. Change it in 10–15% steps, hold for a full retention cycle, and judge on net ARPDAU rather than ad ARPDAU — otherwise you'll optimise straight into churn.
Related guides
Long-form playbooks on the same topic, written by the RevenueLab editorial team.
Website Ad Revenue in 2026: AdSense RPM, Direct Deals, and What Actually Pays
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Read the guideYouTube RPM by Niche in 2026: What Creators Actually Earn per 1,000 Views
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Read the guideYouTube Shorts Monetization in 2026: How the Ad-Revenue Pool Actually Works
How the Shorts revenue-share pool is calculated, what RPMs creators are actually seeing, and where Shorts fit alongside long-form for serious channel revenue.
Read the guideFAQ
How do I measure cannibalisation properly?
Split users into ad-load cohorts and compare payer rate and ARPDAU over at least 14 days. The difference in IAP per extra daily impression is your input here.
Does rewarded video cannibalise IAP?
Far less than interstitials, and it can lift purchases by exposing users to premium currency. Model it with a low cannibalisation rate and a separate eCPM.
Should store fees be 15% or 30%?
Depends on program eligibility, subscription duration, and jurisdiction. Use the effective blended rate from your last payout statement.
Why include a retention penalty?
Aggressive ad loads shrink tomorrow's DAU. Without that term, the model will always tell you to add more ads.
How this calculator is built
Independently maintained
Written by Sam Doshi and the RevenueLab editorial team. We don't sell the data feeds this tool is built on.
Sourced from primary data
Benchmarks come from public AdSense / Stripe / IRS disclosures and reader-submitted data — never third-party "$X per view" claims. Full methodology.
Last editorial review
Reviewed on a rolling quarterly cycle. Dated reviews are published on the methodology record for each calculator.
Editorial standards
See our editorial policy and disclaimer. Results are estimates, not advice.