Revenue lost to fake installs and attribution fraud in mobile game user acquisition
Definition
Mobile game studios pay for fake installs, click spamming, SDK spoofing and bot-driven traffic that never becomes real players, because fraud is not correctly detected in their UA and in‑app event validation workflow. This inflates reported acquisition volumes while delivering little or no paying users, causing direct media spend waste and distorted ROAS calculations.
Key Findings
- Financial Impact: $1M–$10M per year for mid-to-large mobile gaming advertisers (industry-wide: billions annually)
- Frequency: Daily
- Root Cause: Fraud detection for installs and in‑app events is weak or delayed, allowing ad networks and fraud rings to generate fake installs (device farms, emulators, bots), fake clicks (click spamming) and spoofed SDK traffic that pass as legitimate conversions; many studios rely on last-click attribution and limited anomaly checks, so fraudulent traffic is billed but never clawed back.[8]
Why This Matters
This pain point represents a significant opportunity for B2B solutions targeting Mobile Gaming Apps.
Affected Stakeholders
User Acquisition Manager, Marketing Director, CFO/Finance Controller, Fraud/Data Analyst, Ad Network Account Manager
Deep Analysis (Premium)
Financial Impact
$1.5M-$6M annually in media spend waste targeting fake family account profiles; lower true CAC than reported • $1M-$10M annually directly lost to fake traffic payments; additional opportunity cost of capital; potential audit/tax implications if fraud undiscovered • $1M-$4M annually in misallocated budget to fraudulent family account campaigns; inaccurate segment ROI calculations
Current Workarounds
Custom scripts and manual Excel logs to simulate and filter fraudulent installs in test environments. • Export impression/click logs to CSV; filter by flagged networks; manual IP reputation lookup in blocklists; spreadsheet-based anomaly detection; Slack disputes with ad networks • Manual audit of invoice line items vs. reported installs; spot-checking campaign UTMs in data warehouse; requesting detailed reports from multiple attribution vendors; Excel-based ROI recalculation
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Methodology & Sources
Data collected via OSINT from regulatory filings, industry audits, and verified case studies.
Related Business Risks
Player churn from false-positive fraud blocks and cumbersome verification
Unrecovered chargebacks and card testing on in‑app payments
Excessive manual review and investigation workload for payment and exploit fraud
Refunds, chargebacks and compensation from undetected bonus abuse and exploit schemes
Delayed cash realization due to conservative holds and slow payout verification
Fraud traffic, bots and exploiters consuming platform capacity and analyst attention
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