- Tags:: 🗞️Articles , Product analytics and sequence analytics, Reflect on OKRs and prepare next ones, Data team vision and mission
- Author:: Timo Dechau
- Link:: substack.tinofechau.com
- Source date:: 2023-02-28
- Finished date:: 2023-08-21
Not sure I see what the problem is. A minor inconvenience over order:
And funnily, the same thing happened later when I struggled to find a proper data model for product event data in a data warehouse. And people did not understand why a star schema was not really working for me (40 fact tables for each event type - join hells, you get it). Because their focus was on classic BI and marketing reporting.
The main difference he sees in Product Analytics is that it is mostly exploratory vs. traditional BI or marketing analytics, which is mostly descriptive.
And the goal is:
determining which product usage leads to maximum customer lifetime value is the main objective.
Product usage consists of:
- a sequence of events (results of user or system actions) of a long period of time (weeks, months, years) conducted by one identified user.
- properties for these events (dimensional data) at the time of the event.
- properties for the user (dimensional data) that can change over time (quite often not slowly).
Only by this do we end up with millions of combinations we would need to analyze (or parameterize if we want a model doing the job for us).
Product analytics and sequence analytics and a counter argument to Digital Analyst Is Dead. Long Live Digital Analyst!.
This also makes the technical requirements so different. There is a reason why there is still dominant usage of product analytics platforms, even when there is a data warehouse and all other reporting happens based on that data. Because it is mostly exploration with event sequences, and dimensional breakdowns, it would need a crazy SQL hacker to achieve that by writing SQL in a reasonable time.
It is surprising that, for him, Google Analytics is not product analytics, but you get to do sequence analysis there (although I understand you have better tools for this).
Good luck if you try to tackle it with your existing analytics (like Google Analytics) or BI setup (Tableau, Looker,..). I can already guarantee you that you will fail.
Then start with defining, tracking, and analyzing the core product usage funnel. Then set up your core retention curves. Once this is in place, the real expedition can start.