Why Traditional Attribution Is Unreliable

Traditional marketing attribution methods face significant challenges that make them increasingly unreliable. This page explains why ad platform attribution and UTM-based tracking often fail to provide accurate measurement of ad performance.

Why Can Ad Platform Attribution Based on Cookies Be Unreliable?

Ad platform attribution (like Google Ads, Facebook Ads, etc.) faces several specific challenges:

Different Attribution Logic and Scope: Different ad platforms and even different tactics within the same platform can use different attribution methodologies, making it difficult to compare performance across channels and tactics. Click-through attribution often misses view-through conversions that are incrementally driven by ads, while view-through attribution can capture conversions that would have happened anyway, creating a different set of measurement challenges.

Platform Over-Claiming Credit: Platforms compete for credit, often leading to duplicated conversion counts. Different ad touchpoints can coexist in a customer's conversion journey; however, without seeing the complete picture, each platform claims full credit, leading to inflated total attributed conversions that can sometimes be even larger than actual total conversions from first-party source of truth.

Non-Incremental Credit Claims: Neither click-through nor view-through conversions account for incrementality. Branded search is a prime example—users searching for your brand name were likely already going to convert. Some tactics like Google Performance Max and Meta's Advantage+ can chase after prospects who are already at the bottom of the conversion journey, driving "conversions" that are not incremental.

View-Through Attribution Issues: Top-of-funnel channels like YouTube and Connected TV (CTV) rely heavily on view-through conversions. Users see ads on those platforms and later convert through search or other paths. During this journey, there can be device switches and cookie churn, making it hard to associate these touches with the conversions they initiated.

Why Can UTM-Based Tracking in Google Analytics or Other Web Analytics Tools Be Unreliable?

UTM-based tracking in web analytics tools often serves as the foundation for Multi-touch attribution solutions in the market, however it faces similar challenges:

Consent and Privacy Restrictions: Users must consent to tracking under GDPR/CCPA, and many opt out. Without consent, UTM parameters may be set but conversions aren't tracked.

Cookie Churn and Short Attribution Paths: Users frequently clear cookies, breaking attribution chains and making paths very short. For B2B SaaS companies or advertisers whose most conversions happen on desktop, seeing ads on mobile or personal devices and converting on desktop or company devices breaks the attribution chain entirely. This prevents stitching together touchpoints across the full customer journey.

Click-Through Requirement: UTM tracking only works when users actually click. This means channels like YouTube and Connected TV (CTV) are severely undercounted since users rarely click through from these platforms, despite the fact that these ads may lead users to search and convert later. Other channels that generate view-through conversions in addition to click-through conversions can be undercounted as well. For B2B SaaS or verticals with few impulsive click-through conversions, this is more of a issue.

Multi-Touch Becomes Last-Touch: Because of the short tracked customer journey and click-through requirement reasons mentioned above, even when configured for "multi-touch" attribution, last touches (which are often search-focused) are often overly represented in conversion paths, while mid and higher funnel touches get undercounted, leading to marketing decisions optimized toward search.

Is Traditional Attribution Useless?

Not at all. While unreliable for measuring true incrementality or absolute contribution, traditional attribution provides granular, day-to-day signals that can still be used to make relative comparison locally within a channel and tactic. The key is to calibrate these signals using incrementality measurement like MMM and Incrementality Testing.

Learn how Maxma combines the granularity of attribution with the accuracy of incrementality in our Holistic Attribution System.