Most app teams don't realise they're spending 5-10% of their entire marketing budget on fraudulent installs until they audit their raw data. A recent r/Affiliatemarketing discussion put this cost under the spotlight: Click Flooding on AppsFlyer or Singular MMP?, and the numbers hold up.
Community Spotlight
This post was inspired by a discussion on Reddit: Click Flooding on AppsFlyer or Singular MMP?
Posted by an Anonymous Community Member in r/Affiliatemarketing
Click flooding (or click spamming) is one of the most pervasive forms of mobile ad fraud. Bad actors send millions of fake clicks on behalf of real users. When one of those users organically installs the app later, the fraudulent network claims credit for the install, stealing organic conversions and ruining your attribution data.
Spotting Click Flooding in Your Dashboard
Several commenters pointed out that relying solely on top-line dashboard metrics masks the problem. You need to look at specific behavioural anomalies:
Massive Click-to-Install Ratios: If a network generates 500,000 clicks but only 50 installs, they are likely spraying clicks to catch organic traffic.
Flat Time-to-Install (TTI) Distributions: Real campaigns show a spike in installs shortly after a click. Click flooding shows an even, flat distribution of installs extending up to 7 or 14 days after the click.
Extremely Low Conversion Rates: High volume with sub-0.1% CVR is a classic red flag.
Many teams discover too late that their MMP locks raw data exports or advanced fraud prevention behind enterprise tiers, making it impossible to audit these TTI distributions without upgrading their contract.
Tech Explainer:
Time-to-Install (TTI) is the duration between an ad click and the first app open. Deterministic attribution models use TTI to validate intent. If the TTI curve is completely flat over a 7-day attribution window, the clicks are almost certainly fabricated.
How a Modern MMP Handles This
A modern MMP would unify deep linking and attribution in a single platform, with fraud detection built into the core infrastructure rather than sold as an add-on. It would automatically flag anomalous TTI distributions, block suspicious IP ranges, and provide unrestricted CSV exports so growth teams can audit their traffic independently.
Linkrunner, for instance, does exactly this. Fraud detection - including click spam and device-farm protection - is included at every tier. Because data exports are open and unrestricted, teams can pull their own logs to verify network quality at any time. The SDK setup naturally supports these protections; check the Linkrunner introduction documentation to see how it works under the hood.
Protecting Your UA Budget
To audit your current setup:
Export a raw click and install report for the past 30 days.
Chart the Time-to-Install distribution by network.
Pause any affiliate or programmatic source showing a flat curve or excessive click volumes.
The original thread raised a valid point about the prevalence of fraud in affiliate networks. Here's the actionable version: your measurement tool should actively protect your budget, not charge you extra to do so.
If you're evaluating your attribution setup after reading this, Linkrunner offers 25,000 free attributed installs to test with, no commitment required. Request a demo
