What should you do with this information though? Should the salespeople focus on the customers that are most likely to convert? Or should the salespeople give minimal attention to those, and focus most on the ones less likely, but not 0 probability, to convert.

I think the most important outputs of this are understanding the factors involved in conversion to tune business processes, not necessarily using the outputs of the model to target specific users.

I worked previously at a company that was trying to do a predictive analytic, ML on top of Salesforce (to qualify and score leads, generate new promising leads, etc). The task was extremely difficult and from what I saw people were getting some marginal improvements from ML itself. Most companies at the end were interested in basic (but quite hard things to do right) - cleaning, dedupoing, enriching, generating more leads based on ICP, etc.

Not sure if in this case it's something different but my take from the past experience it's hard since data is noisy + closing the deals depends on a human as well and these tools don't take that into account usually.

I like your blog on how you use EDA, but I'm not sure I'm getting the Machine Learning piece. It would be nice if you guys went into more details, but I appreciate how you were able to tie together different data sources and walked ppl through the analysis!

In Shakespeare's Macbeth, the witches prophecise to him that he will become King. Spoiler alert, he does, by killing the previous King in order to make the prophecy come true.

Which sales leads to focus on? We were in a unique position to answer this because we connect data from marketing tools like MailChimp with CRMs like Pipedrive, plus we track web visits.

Why does the X-axis on the marketing email count graph to up to 60? Is this where we've ended up, sending 60 emails to the same lead? Ouch.