A thread on r/AskMarketing last week asked a deceptively simple question: which MMP is more accurate, AppsFlyer or Adjust? The answers ranged from timezone mismatches to lookback windows. Most were half-right. Here's the full picture.
Community Spotlight
This post was inspired by a discussion on Reddit: MMP: Appsflyer v. Adjust accuracy?
Posted by an Anonymous Community Member in r/AskMarketing
Accuracy in mobile attribution is a misunderstood concept. MMPs do not inherently "miss" installs; they simply apply a strict set of rules to the data they receive. When a growth marketer compares Meta's dashboard to AppsFlyer, or Google Play Console to Adjust, they will always see different numbers.
The Anatomy of a Discrepancy
As several commenters pointed out, determining "accuracy" requires understanding the methodology of the tool.
Self-Attributing Networks (SANs): Meta and Google will claim any install where a user saw or clicked an ad within their window. An MMP will deduplicate these claims, assigning credit only to the last touch. Your MMP will always report fewer installs than the combined network dashboards.
Timezone and Date Definitions: Ad networks often report conversions on the day the click occurred. MMPs report conversions on the day the install occurred.
Store Analytics: Apple App Store Connect and Google Play report total downloads, but lack the context of where the user came from. They also handle re-downloads and device reinstalls differently than an MMP.
Many teams discover too late that their legacy MMP locks raw data exports behind premium tiers, making it impossible to audit these rules and understand why an install was attributed to a specific source.
Tech Explainer:
An Attribution Window (or Lookback Window) is the period of time an MMP will wait after a click to credit an install to that click. If a user clicks an ad on Monday but installs the app 8 days later (outside a standard 7-day window), the MMP will record it as an Organic install.
How a Modern MMP Handles This
A modern MMP would unify deep linking and attribution, providing complete transparency into its matching logic. It would offer fully customisable attribution windows, straightforward timezone configurations, and unrestricted access to raw data logs so data science teams can independently verify the accuracy of the attribution waterfall.
Linkrunner, for instance, does exactly this. It provides deterministic-first attribution and gives teams total access to their raw data via CSV, APIs, and webhooks at every pricing tier. By operating without hidden fees or export restrictions, Linkrunner ensures that teams can audit their discrepancies down to the user level. You can see the full data structure in the Linkrunner documentation.
Managing Discrepancies
Standardise your lookback windows (e.g., 7-day click, 1-day view) across your MMP and ad networks.
Align all dashboards to the same reporting timezone (usually UTC).
Expect and accept a baseline 10-15% variance between platforms.
The original thread raised a valid point about the stress of misaligned data. Here's the actionable version: accuracy isn't about matching numbers perfectly; it's about having transparent rules.
Teams that want to validate these patterns in their own data can get started with Linkrunner's free tier and see results within 24 hours. Learn more
