And why the last-click model is no longer relevant.

Marketers in the field of e-commerce and marketing widely discuss multi-touch attribution (MTA), yet the number of companies actually using MTA for conversion attribution remains low. There are various reasons for this; the marketing department bases its results on the last-click model or the required resources to switch to MTA are simply not available. This blog will discuss why conversion attribution via the MTA model is important, what a good MTA algorithm looks like and how it can be applied.

Why is Multi-touch attribution relevant?

One of the best analogies to highlight the importance of MTA is that of a bank robbery. Robbing a bank requires a lot of work by multiple robbers; without a planner, the robbery won’t take place and without brute force the money won’t be handed over. Without someone driving the getaway car, the robbers can’t escape. It would be odd to hand over the entire loot to the driver, simply because he was the last robber to perform a task. This is, in essence, what the last-click model does to touchpoints in a customer journey. The attributed value, however, does not mean the touchpoint really was responsible for the conversion. The other two robbers were, after all, instrumental to the robbery, but receive no credit. Results obtained using the last-click method are therefore a misrepresentation of the path to a conversion.

A good attribution algorithm does assign to each touchpoint the value it contributed to the conversion. Read how we carry out MTA here.

What does a good attribution algorithm look like?

An MTA algorithm needs the following four properties to be considered a ‘good’ algorithm: fairness, data driven, interpretability and economic efficiency. An algorithm for which these four conditions hold is the Shapley algorithm, derived from game theory. This algorithm allocates value to a touchpoint based on its ability to increase the probability of a conversion taking place, given it’s included in the customer journey. This makes it fair. It is also data-driven, as it takes all previous customer journey data into account. The algorithm is based on A/B tests, making it interpretable. Economic efficiency also holds, as the results provide a complete overview of the (monetary) success of a campaign per media channel. All in all, a good attribution algorithm helps optimize marketing and media inputs by predicting outcomes of campaigns based on statistics and machine learning techniques.

How should it be implemented?

Configuring and maintaining an MTA algorithm requires a number of different resources. Collect all relevant data periodically in an engagement system. It should be placed on a server where it can be processed and analysed. The MTA algorithm will run on this server. Developers and Data Scientists can configure the algorithm to work with the existing data structure. Continuously check the data for errors and visualize it in a dashboard (see our blog on data quality). Finally, it is important to install a dedicated team and maintain a long-term focus within the organisation. Isolated analyses often fail to lead to the collection of structural insights and changes in the decision-making process.


MTA is the only model that assigns value to touchpoints in the customer journey according to its contribution to the conversion. There is a standardized approach to MTA, with a classification system of what can be considered a good algorithm. Implementing MTA requires resources and dedication, but will provide you with invaluable insights and the ability to make data-driven decisions. Conversion attribution using an MTA algorithm is always preferable to basing decisions on last-click models.