Term
Attribution Models
Attribution models are rules by which the value of a conversion is distributed across the touchpoints involved in a customer journey. They range from simple models such as last-click to data-driven approaches based on machine learning.
Attribution Models — explained in detail
Attribution models determine how the success of a conversion, such as a purchase or an inquiry, is distributed across the various touchpoints a person passed through beforehand. Since users rarely arrive through a single channel but often pass through several ads, searches, and visits, an attribution model answers the question of which of these steps is credited with what share of the success.
Classically, several model types are distinguished. In last-click, the last touchpoint before the conversion receives the entire value; in first-click, the first does. Linear models distribute the value evenly across all touchpoints. Time-decay models weight later contacts more heavily, position-based models emphasize the first and last contact. Data-driven models, finally, use machine learning and compare successful with unsuccessful paths in order to derive the contribution of each touchpoint from the data itself.
The development in the advertising environment should be noted. In Google Ads, the rule-based models first-click, linear, time-decay, and position-based were removed, so that in practice only data-driven attribution and last-click remain available there. The data-driven variant is the default and recommendation because it estimates the distribution based on actual paths.
No model provides an objective truth. Each makes assumptions and emphasizes different aspects. The choice influences which channels appear valuable and should fit the goals and data. The quality of the underlying data is also decisive.
Example / Practical context
A person first sees a display ad, later clicks on a search ad, and finally buys after a direct visit to the website. Under the last-click model, the direct visit receives the entire value, while the previous steps get nothing. A linear model would distribute the value in equal parts, a data-driven model would weight it based on many similar paths. Depending on the model, a different channel appears particularly valuable.
Distinction from similar terms
Attribution models are the umbrella term; last-click attribution is one of these models, namely the simplest, which credits the last touchpoint with the full value.
They also differ from measurement technology: conversion tracking records that conversions occur and through which paths; the attribution model then decides how the value is distributed across the recorded touchpoints. A completely different logic is followed by marketing mix modeling, which estimates not individual user paths but aggregated channel effects.
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Marketing Mix Modeling
Marketing mix modeling (MMM) is a statistical method that estimates the contribution of individual marketing channels and factors to business results. It uses aggregated data without user tracking and is seeing a renaissance driven by privacy and AI.
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