Rebuilding trust in mobile attribution - A guide to ad fraud
Are you wasting budget on fraudulent campaigns?
Attribution fraud is quietly draining millions of dollars from mobile marketers every year. While the mobile advertising industry relies on attribution data to guide budget decisions, some demand-side platforms (DSPs) are manipulating this system to win unearned credit for app installs, re-engagements, and conversions.
Today’s fraudulent tactics exploit mobile measurement partner (MMP) setups to mislead advertisers and inflate reported performance for programmatic app marketing campaigns.
In this guide, you’ll learn about the most common attribution fraud tactics, how to spot if you’re falling victim to them, and what you should be asking your DSP partners.
Attribution fraud may be your biggest blind spot
“The most powerful element in advertising is the truth.”
William Bernbach, co-founder of Doyle Dane Bernbach (DDB)
Without due diligence, advertisers run the risk of shifting their budgets to DSPs who report questionable or inflated numbers. This can lead to misguided investments across different marketing channels and a tendency to overlook legitimate, respected partners who deliver real results.
You can, however, protect your budgets by understanding the methods used to manipulate attribution, identifying key metrics that demonstrate true in-app user engagement, and selecting DSPs that prioritize transparency and performance integrity.
Remerge is committed to leveling the attribution playing field. By highlighting industry-wide manipulation and advocating for attribution fairness, we’re helping marketers to invest smarter and realize the full potential of their programmatic campaigns.
The hidden cost of attribution fraud on UA and retargeting
Attribution credits the right ad partner for driving installs and in-app conversions like purchases, sign-ups, or other specific actions. However, in the programmatic space, it can be manipulated.
It's not just a technical issue either, it’s a financial one. In Q1 2024, 23% of programmatic mobile in-app traffic was classified as invalid traffic (IVT), resulting globally in an estimated $1.4 billion of wasted ad spend. This means nearly one in four ad interactions in the programmatic in-app ecosystem may be inaccurate or entirely fraudulent.
Some DSPs use tactics like click flooding, gesture hijacking, and click spoofing to steal attribution for conversions they were not responsible for. Not only do these practices affect user install reporting, they distort lower funnel metrics such as return on ad spend (ROAS) and lifetime value (LTV).
According to MMA Global, up to 50% of paid installs are fraudulent, which is bad for user acquisition (UA) but also re-engagement activities. The potential knock-on effect is that the same DSPs running those UA campaigns could be taking credit for in-app events they didn’t influence, thus inflating performance to secure more budget from their clients.
The result: mobile marketers invest in dishonest programmatic ad platforms instead of high-integrity partners. Consquently, budget optimization becomes skewed, while CPIs and ROAS industry benchmarks become unreliable.
Attribution scams and how to spot them
Below are the most common practices you should know about, and how to spot them before they drain your mobile marketing budget.
Forced clicks
Mobile ads that are designed to trigger clicks without user intent. This includes using hidden clickable zones, auto-click mechanisms, or missing close buttons.
How to detect:
- Manually review creatives or request UX demos with your DSP partner
- Compare CTRs across DSPs and look for unusually high CTRs with poor post-click performance (e.g., low installs, high bounce rates)
🚩 The red flag: High CTR, low post-click conversion
Gesture Hijacking
Legitimate gestures like swipes or taps are misinterpreted as ad clicks to inflate in-app engagement metrics.
How to detect:
- Compare click logs with user session recordings
- Look for click increases with no corresponding installs or in-app event
🚩 The red flag: Click spikes, no funnel progression, or low in-app engagement
Click spoofing/flooding
A DSP sends fake clicks server-to-server to an MMP, often without winning an auction or showing a real impression, using device IDs or fingerprint data (device characteristics like IP address and OS used for identification). These spoofed clicks are often timed before a likely conversion and may flood the MMP to steal attribution.
How to detect:
- Look for very short or very long click-to-conversion times, plus excessive clicks for a single user
- Monitor excessive click volumes per user and attribution 'flip-flopping' between DSPs (constant attribution switching within short timeframes)
🚩 The red flag: Unusually fast or slow click-to-conversion times
Device ID reuse (Subtype of click spoofing)
A single device ID (IDFA/IDFV) is linked to multiple device models or IPs, suggesting reuse or synthetic traffic generation.
How to detect:
- Review MMP rejection or deduplication logs
- Check for device IDs associated with numerous device types or IP addresses
🚩 The red flag: Reused IDs across logs or IDs flagged by MMP
ID Hijacking via Postbacks (Subtype of click spoofing)
Known device IDs are injected into tracking links to steal credit for organic or competitor installs. This often abuses full postback permissions in MMP setups.
How to detect:
- DSP resists sharing publisher transparency or source app info
- Temporarily disable full event postbacks and check for performance consistency
🚩 The red flag: Performance drops when postbacks are limited
Blackbox reporting
A DSP fails to provide basic setup details, such as campaign names, publisher lists, and tracking logic.
How to detect:
- Request publisher-level transparency
- Evaluate whether the DSP can explain targeting logic and setup parameters
🚩 The red flag: DSP is unwilling or unable to share information upon request
Your attribution fraud checklist
You can reduce your exposure to fraudulent activity by adopting a few practical habits in managing, monitoring, and assessing DSP performance. These best practices are designed to help identify potential red flags early, before attribution or budget is lost to bad traffic.
Benchmark metrics across partners
Compare performance data like CPI, engagement, and retention across all of your DSP partners. Outliers often stand out. If one partner consistently performs far above the rest without an apparent reason, it may warrant deeper inspection.
Temporarily turn off postbacks
Try disabling event postbacks to specific partners and observe the impact. If performance drops dramatically for only one DSP, this partner may be relying on attribution signals rather than genuine engagement.
Use your MMP’s fraud reporting tools
Check your fraud dashboard or rejection logs from your MMP (like Adjust or Appsflyer). These tools can show whether traffic from certain DSPs is being flagged or rejected, and why. This can help identify systemic issues with specific traffic sources.
Ask how DSPs handle users with tracking disabled
Limit Ad Tracking (LAT) users should not have persistent identifiers like IDFAs. Ask each DSP how they treat devices with tracking turned off. Reusing or recycling device IDs across LAT traffic is a serious red flag (see attribution hacks and how to spot them).
Monitor for attribution hijacking
Watch for cases where a DSP gets credit for conversions just before they happen, even when impressions or clicks are questionable. Tracking click-to-conversion time and comparing patterns across partners can help surface this behaviour (see Attribution hacks and how to spot them).
Look beyond your core KPIs
Consider tracking click-to-install time (CTIT) to detect spoofing or flooding, install-to-event ratios to confirm whether installs lead to meaningful in-app actions, and retention/session depth to measure lasting engagement. Combined with your core KPIs like ROAS and LTV, these metrics provide a clearer picture of campaign quality, ensuring budgets are allocated to real users, not fraudulent activity.
Why Remerge leads with performance-based transparency
At Remerge, we focus on metrics that matter: real in-app engagement and clean conversion data. We do this through:
- Transparent reporting
- Customized dashboards
- Campaign experimentation tools
- Uplift testing
- A data science team to interpret data
