MODELED CASESYNTHETIC DATA · NOT A CLIENT RESULT

App yield reset:
+27% revenue index
with retention held.

A coherent 30-day scenario for a freemium utility app, built to show the operating method and reporting standard before a client-approved case study is available.

1.8Mmodeled MAU56 / 44Android / iOS mix63%Tier-1 traffic
ARPDAU$0.048 → $0.061+27.1%
Fill rate81% → 89%+8pp
p75 latency790ms → 648ms−18.0%
D7 retention24.6% → 24.2%−0.4pp
THE MODELED BRIEF

More yield,
one hard guardrail.

The modeled app begins with a waterfall-heavy mediation setup, uneven bidder coverage, high interstitial frequency and incomplete app-ads.txt entries. The commercial target is a 20%+ ARPDAU improvement while keeping D7 retention decline within 1 percentage point.

Category
Freemium utility
Markets
US, UK, CA, AU + Western Europe
Baseline window
28 days
Test window
30 days
30-DAY MODEL

Compounding, not a spike.

The curve assumes changes are staged weekly so product and revenue effects remain attributable.

100Baseline
106Week 1
113Week 2
121Week 3
127Week 4
FULL DATASET

Every signal,
same timeline.

PeriodRevenue indexARPDAUFillp75 latencyD7 retention
Baseline100$0.04881%790ms24.6%
Week 1106$0.05183%756ms24.5%
Week 2113$0.05485%711ms24.4%
Week 3121$0.05888%673ms24.3%
Week 4127$0.06189%648ms24.2%
WHAT CHANGED

Five controlled moves.

  1. 01Remove low-value SDK paths and expand in-app bidding coverage.
  2. 02Introduce rewarded inventory at intentional value-exchange moments.
  3. 03Cap interstitials by session depth and time since last exposure.
  4. 04Split floors and demand decisions by geo, OS and format.
  5. 05Repair app-ads.txt and consent-state routing before scaling.
DISCLOSURE + METHOD

Useful because the assumptions are explicit.

This is synthetic data created by UpscaleLab to demonstrate a realistic measurement framework. It is not derived from a named or anonymous client and must not be read as a performance guarantee.

The model holds traffic volume and geo mix broadly stable, uses a 28-day baseline, stages changes over four weeks and applies a retention guardrail. Actual outcomes depend on category, demand access, consent rates, seasonality, audience, implementation quality and existing stack maturity.

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