Why We Prefer Markov Models to Last Click for Attribution Modelling

You have access to holistic raw data about the consumer interaction journey. But what attribution model are you going to use to base your decisions Prefer Markov Models on the significance of each marketing channel? There are a number of different options. You can go In addition, for “first-click wins”, where you place all the value on successful conversion into the first online interaction. You can go for “last-click wins”, where the value is placed on the last online interaction before conversion. E  ven allocation”, and give equal credit to every step from the beginning to the end.

These models are heuristic

Or many other approaches. These models are heuristic; they use a specific, straightforward approach in understanding the complex marketing cycle. Which accelerates analysis at the expense of accuracy. Here at Windsor.ai, we drill deep into data Belgium Mobile Number List ensuring that the best insights can be extracted from the information available to you. Our experience has led us to believe that Markov models are the best option from those available for attribution modelling. A Markov model is a probabilistic model, which focuses on specific calculations of the chance that an interaction in one channel will transition to a different state, such as a conversion.

 

All other marketing channels

Let’s compare last click to Markov models and then explain why we prefer Markov models for attribution modelling. What is a last click model? In a last click model, all the credit for a conversion goes to the marketing channel the customer interacted with BQB Directory directly before making a purchase. All other marketing channels are given zero credit. Let’s take the example of a web hosting company, that we’ll call Brand X. They have a number of marketing channels: an active Twitter account.