Mode walks you through the reasoning behind the query, what each portion accomplishes, how you can tweak it to suit your needs, and the kinds of further analysis you can add on top of it. Here’s the query to make it happen, but read Mode Analytics’ blog post for more information. To define sessions with raw SQL, a great query and explanation comes from Mode Analytics. Segment recommends checking out Looker’s approach here. With Looker, for example, you can take advantage of their persistent derived tables and LookML modeling language to layer sessionization on top of your Segment SQL data.
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How to define user sessions using SQLĮach of Segment’s SQL partners allow you to define sessions based on your specific business needs. Segment leaves session interpretation to SQL partners, which let you design how you measure sessions based on how customers use your product.įor more on why Segment doesn’t collect session data at the API level, check out a blog post here. And since Segment’s API is about collecting raw, factual data, there’s no API for collecting sessions. They’re stories Segment builds around the data to understand how customers actually use the product in their day-to-day lives. Sessions aren’t fundamental facts about the user experience. Segment’s API does not impose any restrictions on your data with regard to user sessions. That query returns a table like this: date tracks where event = 'completed_order' group by date Select date ( sent_at ) as date, sum ( case when item = 'shirt' then 1 else 0 end ) as shirts, sum ( case when item = 'pants' then 1 else 0 end ) as pants from initech.
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