Before and After Attribution: Real Dashboard Comparisons
An example last-click dashboard for one month, the budget conclusion it invites, and why most marketing dashboards report correlations, not causation.
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By Joris van Huët, Founder & CEOUpdated 2 min read
This is Your Dashboard. This is Your Dashboard on Causality.
You can't fix a problem you can't see. The fundamental issue with most marketing dashboards (Google Analytics, Shopify, ad platforms) is that they are showing you a distorted view of reality. They report on correlations, not causation.
Before: The Last-Click Attribution View
Imagine this is your typical marketing dashboard for a month of data:
Source / Medium | Conversions | Revenue | ROAS Facebook / Retargeting | 800 | €120,000 | 8.0x Google / Branded Search | 650 | €97,500 | 13.0x Google / Shopping | 400 | €60,000 | 4.0x TikTok / Prospecting | 150 | €22,500 | 1.5x
The Obvious (and Wrong) Conclusion: "We should spend more on Branded Search and Facebook Retargeting! They are our best-performing channels. TikTok is barely breaking even; we should consider cutting it."
This is the logical conclusion based on the data presented. It is also the conclusion that leads to stagnating growth and wasted ad spend.
From Misleading to Actionable
The "Before" dashboard encourages you to invest in channels that capture existing demand.
Every channel gets a data-health score from 0 to 100 that grades the read, not the channel, and a next step: raise, cut or hold, or fix tagging first.
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Related Resources
Causality Engine vs. Ruler Analytics: Which Is Worth It?
Best Google Analytics Attribution Alternative for Shopify ecommerce in 2026
Best Data Driven Attribution Alternative for Shopify ecommerce in 2026
Frequently asked questions
Are the 'Before' numbers wrong?
They are not 'wrong' in that they accurately report what happened (e.g., which ad was clicked last). They are misleading because they fail to account for causality. They tell you what happened, but not *why* it happened or what would have occurred without the ad.How can I trust your numbers over Google Analytics?
Every number in the read checks against two files, your GA4 export and the read, recomputed in your browser.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Keep reading
Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- DashboardsDashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
- Data Driven AttributionData-Driven Attribution uses machine learning to analyze customer touchpoints and assign conversion credit. It determines the true impact of each marketing channel.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- RetargetingRetargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.