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Using machine learning to predict the leads that close

outfunnel.com

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Re: Using machine learning to predict the leads that close

#21

Post by BiteCode_dev »

kjellsbells wrote: post #7
shcheklein wrote: post #5

Earlier quoted context omitted.

I hope though there will be an ML product that can do a decent improvement in this space. Even small improvement there brings significant ROI.

I spent years using a tool called Clari that applied analytics to Salesforce and Office (email) to predict deal activity. When it worked, it was really good. But it had a fatal flaw: relying on Salesforce meant relying on the data that sales people input. And I quickly learned that sales people hate reporting tools, update them only under duress, and generally fill the database with crap. Perhaps it's different when…

Every people hate reporting tools.

Spending time and energy on describing the job you've done yesterday is taken from the job you are doing now. Not only it is unfulfilling work, but it's also often not taken in consideration by highers up that will likely assume your regular job should remain unaffected by reporting.

Then you get the feeling on being spied upon, nevery good for trust or moral.

And finally, you know that some actions you take will be judged by the people reading the reports. Unfortunately said judgment as ha chance to be unfavorable yet unfair, because the person making it might not have the context, personality, or knowledge required for a fair one.

However, workers have little chance to gain anything from favorable reports. So the asymmetry is just very much against the person filling the reports, who is paying the price for it.

What do you expect?

Re: Using machine learning to predict the leads that close

#22

Post by capn_duck »

malshe wrote: post #6
CarbonCycles wrote: post #3

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!

I am guessing they used logistic regression because they could show the lead win probability as a function of emails opened. It's quite easy to do with logistic regression.

nah its ml bro

Re: Using machine learning to predict the leads that close

#23

Post by roenxi »

rukuu001 wrote: post #11
unlikelymordant wrote: post #8

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…

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).

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.

Re: Using machine learning to predict the leads that close

#25

Post by tdeck »

spoonjim wrote: post #16
unlikelymordant wrote: post #8

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…

This is not like medical triage where some people will just recover on their own... there are vanishingly few customers who are going to buy without a focused sales effort. Your time is best spent on those most likely to convert because even "most likely to convert" in B2B sales is not someone coming to your office and pounding your desk and saying TELL ME WHAT I NEED TO DO FOR YOU TO LET ME BUY THIS

Many B2B companies kidna bring this on themselves by hiding pricing and product information behind a sales wall, forcing you to do a little dance with them to get basic information without seeming too committed.

Re: Using machine learning to predict the leads that close

#27

Post by jross225 »

rukuu001 wrote: post #11
unlikelymordant wrote: post #8

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…

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).

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 :/

Re: Using machine learning to predict the leads that close

#29

Post by tdeck »

standrews wrote: post #26
tdeck wrote: post #24

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.

It's someone opening or clicking emails 60 times (can happen!), not sending 60 marketing emails.

With Gmail and the like you only get to know about the first open.

I wonder if this is actually caused by the email being forwarded around to many people.

Re: Using machine learning to predict the leads that close

#30

Post by tl_donson »

standrews wrote: post #19
unlikelymordant wrote: post #8

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…

The answer here, as always, is "it depends". 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…

“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.

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