The Ledger
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Mehta: Long-term historical B2B data models often yield useless results
“So you look at a whole bunch of data over five years, and the customer says, I've got five years worth of data, right? But in those five years, they got bought by a private equity firm, They introduced new products. They fired their CEO. They redid their prici…”
Mehta: Driving analytics adoption requires taking humans out of the execution loop
“At the end of the day, you've got to figure out how you take the humans out of it, to be honest. It's a lot of, like, how do you make it more automated?”
Mehta: Enterprise focus early forces startups to build sophisticated products
“We went after larger customers early on, and that's allowed us, it forces you to build a bigger product, right? There's pros and cons to it, by the way. There's lots of companies that get killed by going after larger customers, but by, for us, we built a very …”
Mehta: B2B big data's hardest problem is changing front-line behavior
“The last mile of all this cool technology and what we've struggled with just in terms of how do you take that and turn that into changes in behavior for sales people, for customer service people, that type of thing.”
Mehta: Calling B2B data 'garbage in' ignores past business logic
“Just saying garbage in, garbage out, is kind of like just throwing your hands up, right? The reality is, there's actually a good reason why this happened. At every given point, there was logic why they made the changes.”
Mehta: Most B2B companies lack sample size needed for predictive analytics
“The reality is, most companies, the sample size is far too small. They're just few, too few data points over too little time.”