A thread on r/GrowthHacking last week asked a deceptively simple question: how much do you actually trust your marketing data? The answers ranged from total cynicism to reliance on custom backend scripts. The thread surfaced several practical takes regarding the gap between reported Return on Ad Spend (ROAS) and actual bank deposits, but the topic deserves a deeper look because most teams are operating on inflated metrics.

Community Spotlight

This post was inspired by a discussion on Reddit: How much do you actually trust your marketing data?
Posted by an Anonymous Community Member in r/GrowthHacking

The Illusion of Accurate Dashboards

When Meta reports an install and Google claims the same install, the marketer sees two successful campaigns. The finance team sees one user. Several commenters pointed out that Self-Attributed Networks (SANs) will inherently claim credit for any user who interacted with their platform, regardless of whether that interaction actually drove the conversion.
This inflation is compounded by the lack of transparency in legacy Mobile Measurement Partners (MMPs). Many teams discover too late that their MMP charges separately for fraud detection, restricts raw data exports, or locks multi-touch attribution behind enterprise tiers. This leaves marketers dependent on aggregated reports they cannot verify.

Why Data Integrity Fails

To understand why your dashboard lies, you must understand the mechanics of mobile attribution.

• View-Through Dominance: An ad is shown on a screen, the user scrolls past, and later searches for the app organically. The network claims the install. If your MMP doesn't enforce strict view-through windows, your organic baseline disappears.

• Click Spam: Fraudulent publishers flood the ecosystem with fake clicks. When an organic user installs the app, the fraudster's click happens to be the last touchpoint. They steal the organic attribution.

• Data Silos: When your deep linking tool and your attribution tool are separate, the data never matches perfectly. A user clicks an influencer link, goes to the App Store, and the attribution is lost in the handover.

How a Modern MMP Handles This

A modern MMP would unify deep linking and attribution in a single SDK, route users to the correct in-app destination even through deferred installs, and charge a flat per-install rate with no hidden fees. Linkrunner, for instance, does exactly this, with SDK integration typically completed in 2 to 4 hours.
Instead of fighting disjointed systems, a modern platform provides deterministic measurement out of the box. Linkrunner includes click spam, bot, and device-farm detection at every pricing tier, ensuring you only pay for genuine engagement and trust your ROAS.
Furthermore, a modern platform ensures pricing transparency. For example, Linkrunner offers tiered pricing that starts at $0.012 per install and includes full data exports without export fees. This is a stark contrast to legacy setups that can cost Rs3 to 8 lakh per month at scale.

Regaining Trust in Measurement

The original thread raised a valid point about the skepticism surrounding marketing data. The solution is not to build your own attribution engine from scratch. The solution is transparent, verifiable measurement.
Teams that want to validate these patterns in their own data can get started with Linkrunner's free tier, which includes 25,000 one-time free attributed installs, and see results within 24 hours. Learn more