[rukuu001]

Yeah, the salespeople avoid spending time on leads unlikely to close.

They're all operating on this kind of information already (like the basic stuff - is this person a decision-maker, do they have budget etc etc).

I take this seriously because I've seen consultants making money in this space already (advising on leads unlikely to close).

[roenxi]

I think I agree, and we could make exactly that observation about syntax highlighting. It isn't like syntax highlighting is telling me things I already know, but it is a free win for my productivity while coding.

Making something that is already obvious cheaper to discover can help the people who need to act on it.

[jross225]

But then this has the impact of reenforcing model predictions (unless controlled for). This lead won't convert based on the model, so I won't spend time with them or give them any preferential pricing, so the lead doesn't close (and the converse is true).

This isn't a pure ML problem, and without "treatment" data I'm not quite sure how the blog is adjusting for customer propensity towards an outcome :/

[6stringmerc]

Maybe it’s like the nuance in music - knowing the notes not to play. I think the art is in weighing the variables as a human because they can change also based on competition in the room. Having a career ongoing in sales support I’ve seen first hand how erratic the decision trees can be for private or public organizations. While the general setup is similar “do business with X with Y” the ingredients can differ widely to get to the sale. ML might tell you to send a holiday gift but I bet the human has a better idea of what kind of gift to send than ML.