Agency vs In House Attribution Numbers: Who Is Right
Agencies and in-house teams often present competing attribution reports. The reasons: different models and assumptions, data sources of varying quality, and incentives to highlight certain channels.
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By Joris van Huët, Founder & CEOUpdated 1 min read
The Attribution Conflict Between Agencies and In-House Teams
Marketing agencies and internal teams often present competing attribution reports. This conflict arises due to:
Different attribution models and assumptions.
Varying data sources and quality.
Incentives to highlight certain channels.
These discrepancies impair decision making and erode trust.
Why a Unified Attribution Model Matters
Brands need a single source of truth to:
Resolve conflicts objectively.
Align stakeholders around accurate performance data.
Refine marketing spend effectively.
Next Steps
Check our pricing and resources for details. Sign up at causalityengine.ai/start.
See the Wikipedia marketing attribution page for more concepts.
Related Resources
Causality Engine vs. Measured: Incrementality Testing Compared
Last Click vs. Data-Driven Attribution: Which Should You Use?
Attribution Model Comparison Template: Side by Side Analysis
Frequently asked questions
Why do agency and in-house attribution numbers differ?
Due to different models, data sources, and potential biases in reporting.How does Causality Engine unify attribution?
Causality Engine uses causal inference on the GA4 history you already have to estimate what each channel caused.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- ClickClick is the action a user takes to interact with a digital advertisement, redirecting them to a website or landing page. Clicks are a fundamental metric for measuring ad engagement and a primary input for click-based attribution models.
- IncrementalityIncrementality is the part of an outcome, such as orders or revenue, that a marketing activity caused: the extra results compared with what would have happened without it. It is a causal effect, not the number of conversions credited to a channel.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.