Funnel analysis is a key part of ecommerce analytics, and being able to understand how your visitors move through your cart and checkout process (and where they leave) is a key part of optimizing any website.
Google Analytics has some good functionality in this area, letting you see how many users are moving through your funnels for a given date range, as well as the excellent Goal Flow visualization that lets you see where visitors are coming from and going to around your funnel pages.
However, there are a number of types of analysis that are simply not possible within Google Analytics itself.
Introducing the advanced funnel query in Analytics Canvas.
Analytics Canvas lets you configure a funnel using an easy graphical interface, and then automatically creates all the API calls needed to give you the insight you need.
This new query lets you define funnels using events, custom variables, goals and pages. The result is a much more powerful way to understand how your visitors are interacting with your funnel and to have much more sophisticated funnels.
Here is an example of a series of funnel steps defined using the new feature:
Once you define the funnel, you get all the details for each step in a clear dataset with the following values:
Whats more, you can specify the period and frequency that you want to run the analysis, meaning you can analyze your funnel performance as it evolves over time.
Why would you want to do this? Because if you are making changes to your checkout process, or signup pages etc., then you want to know when the exit rate of a funnel step changes FAST, so you can fix any issues.
We’re looking forward to seeing the more sophisticated analysis that serious ecommerce sites will be able to do with this new functionality- give it a try on your funnel and discover the optimization you are missing.
This article was written by James Standen
James is the founder of nModal Solutions, the creator of the Analytics Canvas tools. nModal's vision is to bring an entire new class of visual, flexible tools to web analytics and social media analysis. You can find him on Google+.