How To Compare Attribution Models
Compare attribution models by evaluating accuracy, incrementality measurement, and channel interaction handling using Bayesian inference.
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By Joris van Huët, Founder & CEOUpdated 1 min read
Why Compare Attribution Models?
Different attribution models assign credit in varying ways, affecting budget decisions. Comparing models reveals biases and helps select the approach that best reflects true marketing impact.
Common Attribution Models
Last-click: All credit to final touchpoint.
First-click: All credit to first touchpoint.
Linear: Equal credit to all touchpoints.
Time decay: More credit to recent touchpoints.
Markov chain: Probabilistic removal effects.
Bayesian causal inference: Estimates incremental impact accounting for channel interactions and uncertainty.
Step 1: Gather Data
Collect conversion paths with timestamps, touchpoints, and channel identifiers.
Step 2: Apply Multiple Attribution Models
Use software to assign credit using different models.
Step 3: Evaluate Incrementality
Assess which model best captures true incremental conversions by comparing predicted lift against experimental or holdout data if available.
Step 4: Analyze Channel Interaction Effects
Examine how models handle overlapping channels and cannibalization. Bayesian inference explicitly models these effects.
Step 5: Consider Business Context
Select models aligned with your marketing goals, data quality, and resource constraints.
Our causal approach offers superior incrementality estimation versus rule-based models.
Related Resources
Frequently asked questions
Are rule-based models useful?
They can offer quick heuristics but often misattribute credit, leading to suboptimal budget decisions.How does Markov chain attribution differ?
It uses removal effects to estimate channel importance but does not directly measure incrementality.Can I combine models?
Yes, hybrid approaches can provide complementary insights but require careful interpretation.Does Causality Engine support model comparison?
Causality Engine reads one file, the Attribution paths export from GA4, and shows what each channel caused next to what last-click gave it.
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.
- Bayesian InferenceBayesian Inference updates the probability of a hypothesis based on new evidence. It refines marketing attribution by incorporating prior beliefs about channel effectiveness.
- 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.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- 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.
- Interaction EffectAn Interaction Effect occurs when one variable's effect on an outcome depends on another variable's level.
- TouchpointTouchpoint is any interaction a customer has with a brand throughout their journey. In marketing attribution, each touchpoint is a data signal to understand marketing impact.