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Analytics

What is Linear Attribution?

TL;DR

An Attribution Model distributing conversion credit equally across all touchpoints. If a customer interacted with your brand through four channels before converting, each receives 25% credit. Linear attribution acknowledges that multiple touchpoints contribute to conversions, more realistic than First-Click Attribution or Last-Click Attribution for complex journeys. However, treating all touchpoints equally is also unrealistic; the ad that created initial awareness and the landing page that closed the deal probably aren't equally valuable. Linear works as a simple compromise when you want to value the full funnel but lack sophisticated analysis tools. For most businesses, Data-Driven Attribution provides better insights if your volume supports it.

Frequently Asked Questions About Linear Attribution

Is linear attribution better than first/last-click?

It's more realistic for complex journeys since it acknowledges multiple touchpoints matter. But treating all touchpoints equally is still oversimplified. Linear is a reasonable compromise when you can't use data-driven attribution.

When should I use linear attribution?

When you want simple multi-touch attribution and don't have enough conversion volume for data-driven models. It's better than single-touch models for understanding the full customer journey.

What's the problem with linear attribution?

It assumes all touchpoints are equally valuable, which is rarely true. A casual first impression isn't worth the same as the final convincing ad. Data-driven attribution weights touchpoints based on actual impact.

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