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.
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).
Making something that is already obvious cheaper to discover can help the people who need to act on it.
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 :/
Also, explore the BANT leads conversation ideas. Adapt it to your industry.
It almost always makes sense to not waste time on "dead" leads. Best to start there.
You'll probably get the highest ROI when you focus on the leads that are somewhat likely to convert (ie. you'll influence those sitting on the fence).
But if you're short of people and have lots of high-quality leads, you'll probably want to focus on the leads most likely to convert.
Back at my previous company (fast-growing SaaS), we ran an AB test where leads were split into six groups: half received sales touches and half didn't, and there were three lead score groups in both (low, medium, high probability to close). Working with the medium group gave the biggest uplift.
Anecdotally, I’ve seen the same thing in a B2C context. The uplift in the highest probability group was so bad that we would leave those leads alone completely, even though the marginal cost of an email or sms is basically 0 as a % of revenue from a successful conversion.