It’s a separate team from central data, focusing specifically on solving problems for the GTM team. I had a similar (IC) role on a similar RevOps data at Smartsheet, which also reported up into the CRO org.

Analytics within the domain is really compelling, with tight alignment to leadership.

No: "Don't worry GTM team, we'll build all the data models and dashboards for your team, just tell us your requirements!"

Yes: "Hey GTM team, your system data is loaded into Snowflake nightly, here's a dbt Github repo for transformations, go ahead and use it as you need."

Great thread idea!

Same! It’s not good

phenomenal metaphor

Ironically, if you repeatedly kick your customers on the shins and don’t see much change in churn … you get a great understanding of your market position

And the latter is the way to go! Iteration is crucial, and I also rarely see teams set up churn models with iteration in mind.

Right, NPS numbers correlate to churn, folks don’t stop and say “wow that’s great, we can predict churn accurately now!”

The immediate response is “what caused NPS to drop and how do we improve it?”

I agree they say they want to know predictors. I just haven’t met many GTM leaders that want to just stop at predictors.

They then want to know what levers are available such that they materially change the predictors. The model can’t tell you how to move NPS, the model only says NPS predicts churn

E.g. “active usage is correlated with churn” is correct in SaaS… but that doesn’t tell you anything useful around *how to move active usage*.

Which usually requires product intervention since active usage is largely driven by how good your PMF is.

This was really intense when it came through 😱

All the excuses in the world to stay inside and fire up some rounds of rocket league