[idrism]

This is interesting. If you can deliver on this, I'm guessing you can deliver on a lot more. Figuring out what warrants an alert is a non-trivial problem, and it's in the same problem space as answering other business questions like "what is our true organic traffic".

Also, on metric drops I'm interested not just in the alerts but also in the narrowing down of what is causing the drop. For example, the first question we always ask is "could marketing blend be causing this". I imagine your ML can figure that out. You could also point out where to look, like "iOS 13 is fine, but there is a severe drop in conversion for iOS 12" or "Conversion dropped for app version 13.2 on Android".

Great stuff! I'd love to see if it works!

[tixocloud]

Funny, Actually I know a startup in Edinburgh that has figured out the “true organic traffic” and they’ve used ML to fix the data for marketing attribution model.

[zhangwins]

Was this ML attribution model output explainable / deterministic? I've seen some really complicated marketing attribution models in the past and hear it was something of a never-ending battle to understand and arrive at the "right" model.

[tixocloud]

I believe it is explainable as I didn’t hear anything fancy about the model being built. It’s been tested and proven to cut marketing spend quite a bit while delivering the same results. A patent has also been filed.

You are spot on that sometimes we just overcomplicate models and sometimes it’s best to go with something explainable and deterministic but less accurate as opposed to more accuracy but complicated.

[zhangwins]

Thanks! We'll rolling out slowly (kinda Superhuman onboarding style back in their old days) so definitely hope to get in touch with you soon :)

Also re: narrowing down what's causing the drop, that's definitely on the roadmap. We know teams have playbooks of things to check when they know something looks wrong, so we should be able to productize & automate this