Traditional attribution models estimate the contribution of each channel to conversion. While helpful, these models usually treat every customer the same—ignoring important context like user region, segment, or behavior over time.

In this article, we describe an approach that builds one Markov model for each feature (e.g. region, segment, time to conversion), and combines them into a final, context-aware attribution score.