AdjustNew in Conversion Rules: More control over post-install validation
Summary
The article details significant updates to Conversion Rules, a fraud prevention system that now covers post-install events like sessions, events, and ad revenue, not just installs. Key additions include Flow Checks, which validate event timing, sequence, and required parameters (e.g., user_id or transaction_id) to prevent duplicate or invalid triggers. Source Checks enable server-to-server (S2S) event validation with IP allowlisting, ensuring data only from trusted servers.
Suspicious ad revenue filtering allows granular controls by country and source, plus test mode. Parameter Rules enforce presence of SDK parameters (e.g., product_id) for installs or post-install activity, flagging missing ones as unverified or untrusted. These features reduce fraudulent activity, clean reporting, and help optimize UA and monetization.
Actionable takeaways: use Flow Checks to limit event spam; set up S2S IP allowlisting for security; filter suspicious ad revenue; and apply Parameter Rules to validate critical data points. The updates improve data quality and fraud detection without requiring new SDKs.
Analyst Note
The article signals a maturation in mobile ad fraud prevention: moving beyond attribution-level checks to granular post-install validation. For UA teams already grappling with SKAdNetwork and privacy-driven data gaps, this is a timely reminder that fraud risk doesn't stop at the install. The addition of flow checks and parameter rules addresses a common pain point—repeated, anomalous events that distort ROAS and LTV models.
Monetization strategists will note the suspicious ad revenue filtering, which plugs a blind spot in in-app ad fraud detection. The source check for S2S events is particularly relevant as server-to-server integrations proliferate under privacy constraints, offering a way to enforce data integrity without SDK dependencies. What's notable here is the emphasis on configurable logic: teams can now tailor validation rules to their specific app behavior, reducing false positives while catching sophisticated invalid activity.
This reflects an industry-wide shift from binary accept/reject to a spectrum of trust levels (unverified, untrusted). For ad ops professionals, the key implication is that data quality control is becoming a continuous, customizable process rather than a set-it-and-forget-it filter. However, the onus is on teams to invest time in rule configuration and testing—otherwise, these capabilities risk being underutilized.
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