From this table, some quick insights we have:
This is only one user so the data isn’t too reliable, so now that you have an understanding of the data, it is easy to aggregate up to millions of sessions and users and show this in Indicative:
The UX designer and Product manager can work on this to maximize conversions where possible.
In a previous section we talked through how to do a standard user stitch that relies on a user identifying themselves. Snowplow was built as a tool to capture many user identifiers so it is well suited to that task.
However, in some cases a user will just refuse to identify themselves, browsing your site in blissful anonymity. For example, you might have multiple people browsing your site with the same browser (a family, office or school) where not all of them identify themselves in every session: how would the analysts on your data team separate their behavior?
Let’s steer clear of Machine Learning as a one stop solution for now though. What can you do to build on the user stitch of the previous post?
Assuming each family member identifies themselves at some point, you have some sessions where you have a high confidence that you know who they are. You can start assigning probabilities to future sessions on the same device based on behavior.
You can build a simple ranking of frequent behavior using the event level data. Then, when a session starts and the user doesn’t identify themselves you can guess who they are.
This is a very simplified example just meant to illustrate what you can do with access to rich, event level data. The model that you build will be specific to your business and will be designed after thorough exploration of this rich dataset.
Read part 5 next: What can we do with the data when we’re well established?