How concentrated is in-app spend?
№ 079 · IAP revenue concentration
Definition
MetricShare of in-app purchase revenue from the top X% of spenders (Lorenz view)
Unit% of IAP revenue by spender percentile
Pareto curve of paying users by spend. Top 1-5% (whales) often drive 50%+ of revenue. Build by sorting payers by trailing 12-month spend. Tells you whether to design for whales or breadth. Example: top 1% of payers = 38% of IAP revenue (heavy whale tilt).
- IAP
- In-app purchase. Revenue from purchases made inside the app, net of store commission.
Benchmarks
| Bottom 30% | Median | Top 30% |
|---|---|---|
| Top 1% above 35-45% of monthly revenue, with a countable number of accounts carrying the P&L and payer conversion flat or falling | Top 10% of spenders around 60-70% of IAP revenue; top 1% around 29-30%; whale segments of 1-2% of players carrying ~50% in gacha-style economies | Healthiest mixes: top 10% of spenders below ~50% of revenue, with a mid-spender band contributing 30-40% and payer share of users growing |
Mobile IAP spender distributions, gaming-skewed datasets, 2013-2024. The card reads as a shape: how steep the Lorenz curve is and which way steepness is trending, not a single number. · Confidence set low despite six sources: the user-level splits are dated (2013-2016 for the most-cited figures) and gaming-dominated, while the recent Tier 1 data (Adapty, RevenueCat) measures app-level concentration, a different cut included here only as context. MIDiA 2024 is the freshest user-level datapoint. Build internal percentile tracking rather than steering to these numbers.
Category split omitted: No two independent recent Tier 1-2 sources publish user-level IAP concentration split by app category; the available splits are dated single-dataset gaming studies.
When it looks bad
The Lorenz curve hugging the axis and steepening month over month: top-1% revenue share rising while payer conversion of the base falls, so fewer accounts carry more of the P&L.
Top 1% of payers at 45% of monthly IAP revenue, roughly 200 accounts carrying a $400K month, and payer share of MAU down from 3.5% to 2.9%.
What to do about it
- Track top-decile revenue share monthly and put churn-risk flags on the top 100 spenders; a lapse in that group should trigger human outreach within 48 hours, since the datasets put 60-78% of spend in the top band (Tapjoy, MIDiA).
- Build the mid-spender band with recurring mid-price offers (season passes, bundles between the entry SKU and the top SKU); mid-tier spenders contribute 30-40% of revenue in economies that give them a ladder.
- Stress-test dependence: model monthly revenue with the top 50 spenders removed and hold cost plans against that scenario, because concentration risk is a solvency question, not a chart aesthetic.
- Widen payer breadth with a low-price first-purchase offer; with 60-70% of users never paying in typical F2P economies, one point of extra payer conversion at the base de-risks the mix more than any whale program.
Sources
- MIDiA Research 78% of mobile app revenue concentrated in 26% of app buyers, mostly in-app purchases midiaresearch.com ↗
- AdWeek (Tapjoy data) Top 10% of spenders drive 70% of IAP revenue and 59% of total revenue; whale median ARPPU $335/month (dated, flagged) adweek.com ↗
- Game Developer (Everyplay survey) Top 1% of spenders ~29% of mobile game revenue; top 10% ~66% (dated, flagged) gamedeveloper.com ↗
- arXiv (Chen, Guitart, Perianez et al., Silicon Studio data) Whales, ~2% of players, may provide up to 50% of total game revenue arxiv.org ↗
- Adapty App-level concentration context: top 10% of Lifestyle apps capture 97.9% of category revenue (different cut, spend concentrates across apps as well as within them) adapty.io ↗
- RevenueCat App-level concentration context: top 10% of apps grew 306% while the median grew 5.3% revenuecat.com ↗