@@timodechau Your welcome! I am starting to use Mixpanel for a new project and your videos helped me a lot. I come from the BI world, mostly using Power BI. How would you face a project like the one you just showed using a BI tool instead of a tool like Mixpanel or Amplitude? Does it make sense to use another tool for reporting and visualization? It is not that I don't like Mixpanel, but it lacks in the board side.
Any BI tool would do if you only want to report on the metrics and how they change over time. But if you want to understand the correlation between specific user behavior and subscription revenue you need a tool like Mixpanel.
@@timodechau I am starting to get that feeling! Have you ever experimented with tree-based machine learning models to predict metrics, like MRR, only to find that the most significant connections are in the lower branches of the metric tree? For example, by using Shap values and models like Random Forest or XGBoost to uncover deeper insights.
I can't believe we have already reached our final episode :( . I am learning a lot from this series Timo. Thank you very much for your content!
Thanks! Let me know what you would like to see in the next video.
@@timodechau Your welcome! I am starting to use Mixpanel for a new project and your videos helped me a lot. I come from the BI world, mostly using Power BI. How would you face a project like the one you just showed using a BI tool instead of a tool like Mixpanel or Amplitude? Does it make sense to use another tool for reporting and visualization?
It is not that I don't like Mixpanel, but it lacks in the board side.
Any BI tool would do if you only want to report on the metrics and how they change over time.
But if you want to understand the correlation between specific user behavior and subscription revenue you need a tool like Mixpanel.
@@timodechau I am starting to get that feeling! Have you ever experimented with tree-based machine learning models to predict metrics, like MRR, only to find that the most significant connections are in the lower branches of the metric tree? For example, by using Shap values and models like Random Forest or XGBoost to uncover deeper insights.
No - but this sounds extremely interesting. Did you do something like this?