Much has been said regarding the benefits of multi-touch or algorithmic attribution models to understanding your customers’ conversion paths, but running analyses merely looking at some numbers in a table doesn’t quite inspire insight in the same way that a well-constructed visualization can. So, in this post, I’m going to give you two great ways to construct and visualize customer data sequences at scale so that you can expose how your customers move and interact with your brand. We’ll also get a glimpse into which sequences are working for your customers and which aren’t. Before we begin, you should probably get caught up if you haven’t read any of my previous posts: How to Setup sparklyr: An R Interface for Apache Spark How to Setup Adobe Analytics Data Feeds For the sake of this post, I’m going to visualize customer movement through various marketing channels – but you don’t have to use marketing channels. This approach can readily work for page names, site sections, app screens, search keywords, or even product views by merely swapping the “campaign” dimension in my examples for any variable of interest. It’s also not strictly required to use a conversion metric, but I think it makes the resulting visualizations more meaningful and actionable.