Definition
Attribution Modeling
Attribution modeling is the process of determining which marketing touchpoints get credit for a conversion when a customer interacts with multiple channels before buying.
A customer might see a paid social ad, later click a search ad, and finally convert after an email — attribution modeling decides how much credit each touchpoint gets. Common models include last-click (all credit to the final touchpoint), first-click, and multi-touch models that distribute credit across the whole journey. Choosing the wrong model can make some channels look far more or less effective than they actually are.
Attribution Modeling in practice
A customer sees your Instagram ad on Monday, searches your brand on Wednesday and clicks a Google ad, then buys on Friday after an email reminder. Last-click attribution gives all the credit to the email, a linear model splits it equally three ways, and a position-based model gives most of the credit to the Instagram ad and the email that started and finished the journey.
Related terms
Common questions about Attribution Modeling
What are the common attribution models?
Last-click gives all credit to the final touchpoint, first-click to the first, linear splits it evenly, time-decay favours recent touches, and data-driven models use your own conversion data to assign credit.
Which attribution model should I use?
For most businesses with enough conversion volume, a data-driven model is the most realistic. With low volume, compare last-click with a position-based model rather than trusting any single view.
Why do different ad platforms report different numbers?
Each platform counts conversions using its own attribution window and rules, and often takes credit for the same sale. That's why totals across Google, Meta and TikTok usually add up to more than your actual sales.
Is data-driven attribution better?
Often, if you have enough conversions. Data-driven models assign credit based on your actual conversion paths, but they need a meaningful number of conversions to be reliable, so smaller accounts may do better with a simpler model.
