Markov Chain Attribution
The Markov Chain attribution model is a mathematical approach used to analyze the impact of various marketing channels on customer conversions. It models the customer journey as a sequence of states (representing different channels) with transition probabilities between them, derived from your own historical interaction data. By considering the entire path, rather than just the first or last touchpoint, the model provides a dynamic and comprehensive view of how different channels contribute to conversions. This allows marketers to understand channel synergy, and to optimize their strategies based on the relative importance of each channel in driving customer actions.
This mathematical method of attributing scores to engagements with a customer or entity can be used in many circumstances far beyond the digital realm. It can be used for Omnichannel resource allocation, call centre effectiveness of agents, sales Reps performance, etc. As long as you have many touchpoints for a potential customer, prior to them doing a desired outcome, we can utilize the Markov Chain attribution.
Let’s explain the method in simple terms:
1. Building the Transition Matrix:
- Think of the transition matrix as showing the likelihood of moving from one particular channel to another distinct channel. For instance, if a lot of customers move from ‘Paid Search’ to ‘Organic Search’, this will reflect a higher probability for this transition.
2. Identifying States:
- The journey can end in two ways: the customer either makes a purchase (conversion), or finally leaves the site without converting (null). These are called “absorbing states” because once a customer reaches one of these, they don’t move to another channel.
3. Breaking Down the Matrix:
- We split the matrix table into two parts:
- One part shows how customers move between the various channels.
- The other part shows how customers move from a channel to either making a purchase, or leaving.
4. Calculating the Impact of Each Channel:
- Imagine we create a helper table for the Transition Metrix, that shows how many times customers pass through each channel before converting or leaving.
- Using this helper table, we can calculate the probability of a customer converting after visiting a specific channel.
5. Attribution Scores:
- The attribution score for each channel tells us how likely it is that the channel will lead to a conversion. In other words, it shows the impact or importance of each channel in leading to a purchase or conversion.
- Higher scores mean the channel plays a more significant role in driving conversions.
In Summary
The process uses customer journey data to create a model that tracks movements between channels. By analyzing these movements, we determine how influential each channel is in leading to conversions. The final attribution scores help marketers understand which channels are most effective and should be focused on in their strategies.
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