There are two ways that I typically analyze Online / Retail Dynamic data.
If the client is looking for purchase-specific information, then I create a table similar to the one outlined here. Each order is "tagged", if you will, by the channels that were attached to the order. Look at the first purchase. This customer bought in a store. Each column that follows outlines a yes/no indicator for what that customer did in the 30 days leading up to that purchase. In this case, the customer clicked through an email campaign, visited the site via natural search, visited the site via social media, visited the core website, and engaged with the retail app. You can imagine the columns that would be valuable to your business - just tag each order with the channels that impacted that order in the thirty days prior to a purchase, and start analyzing!
I tend to use a different approach in most projects. Specifically, I analyze customer behavior across a full year. The information, across a year, is so much richer and more revealing than the information tied to a specific visit and/or purchase. We learn that customers have interesting, consistent, reliable behavior that is masked by noise in individual visits ... but becomes easy to see on an annual basis.
Tomorrow, I'll talk about the variables I use, on an annual basis.