Building a data-driven channel attribution model using Markov chains

In the rapidly evolving world of digital marketing, you should be keeping a reliable finger on the data-pulse of your customers’ journeys. This will allow you to consistently choose the most effective ways of spending your precious advertising budget and maximizing ROI. In order to make sense of the data however, you need to be aware of the models shaping the way it is presented to you. One of the central aims of anyone in digital marketing should be the capacity to monitor with precision the effectiveness of investments, in particular regarding which channels lead to conversions. In the interest of creating data-driven models that allow us to optimize our clients’ needs, we at comtogether are constantly exploring new ways of burrowing into the numbers to extract useful insights and novel perspectives on what exactly is going on within our digital marketing portfolios.

Using Markov chains for optimal channel attribution

Among a number of modeling possibilities for multi-channel attribution, we have Markov chains. Markov chains are a powerful tool we can use to extract precise probabilities from a series of sequential events. The realm of applicability is enormous and has long fueled research in fields as far apart as predictive inventory management, innovation of airport queuing, and the design of search-engine page-ranking algorithms. Within the context of digital marketing, performing a Markovian analysis upon a dataset of customer journeys and their conversion paths allows us to put weighted values on the effectiveness of each channel, particularly as relates to the desired outcome, a conversion. We do this in two stages: first by calculating the transition probabilities for each channel and, second, by removing channels from the journeys in order to calculate the impact of their absence and, therefore, their relative importance to the conversion. Make sense? Let’s have a look at this in a little more detail.