Consumer buying behavior has been fascinating & Google Analytics is becoming superior every day for such purposes. As usual this blog post is one of the efforts trying to find better ways to create interesting advance segments within Google Analytics.
We generally start looking at any web analytics project from the perspective of web analytics framework that we have developed. So obviously we will start with a business question or hypothesis that we can test using google analytics data.
The business question/hypothesis that that we were aiming to check was
“whether those visitors who are more engaged, ends up buying more. If they do, how much more worth they become.”
We used Time on site per visit as primary metric representing engagement and created multiple advance segments for different time ranges as below.
- Visit which lasted between 0 to 100 seconds
- Visit which lasted between 100-199 seconds
- Visit which lasted between 200-299 seconds
- Visit which lasted between 300-300 seconds
- Visit which lasted more than 400 seconds
The image below shows how you can create advance segment for different time range using brand new Google Analytics V5. Each advance segment will become one line item of frequency distribution of website conversion rate and other metrics with frequency as time on site (in seconds) that user is staying on website.
Similarly you can create different Google Analytics Advance Segments in the same lines. Once you create them you don’t need to go any far to generate actionable analysis.
Once you check all the advance segments & apply to reports it will result into a frequency distribution against several metrics. I recommend you use it with e-commerce conversion rate & Average order value for this purpose to start with. Here is what your e-commerce dashboard in Google analytics would look like.
These are actual results of a client e-commerce application & it is clear that if we can have tools, application information on the website that increases engagement, the likelyhood of conversion increases.
An inference from this could be if my visitor spends more than 200 seconds of time (close to 3 minutes) he is twice (or more) as much likely to convert than other. Moreover, its not just to convert, its likely that he would buy more items resulting in higher revenue per transaction.
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