Understanding customer intent remains a moving target, regardless of how deep your data sets run. But in this clip, Bill Schmarzo of Dell Technologies explains the concept of nanoeconomics and individualized predicted behavioral performance propensities for determining customer intent at every touchpoint.

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Timestamped Section [44:26-47:44]:
If you think back in time, what is an innovation or trend that has most excited you?

Nanoeconomics. Nanoeconomics. It was something that that occurred to me when I was at Yahoo, which was, I go 17 years ago or so. I was the vice president advertiser analytics. Um, our challenge at Yahoo was, um, when somebody came to our site, we had 500 million visitors a day coming to our site. And the sites we managed is, how do I know what ad to show that person?

Yeah. And how do I know the level of interest they have in that? So, cause sometimes I have to bid for that, that that visit. And so, We knew every site you'd gone to. We knew every ad you clicked on every ad. You didn't click on every keyword you searched. We knew everything about you. And from that we could create a really good idea of what your intent was.

What are you most interested in? And I could build that intent score across all 500 million. Of our visitors. I knew what you're interested. I knew what your level of interest was. So when you came to a site, I knew how valuable is it if you're interested in cars or vacations? Your value is really high.

Interested in coffee value is not so high, right? I knew what you're interested. I knew what your value was. I knew what to bid and what ad to show you. So it was that granular level, that individualized predicted behavioral performance propensity. And it wasn't only probably until. Five or six years ago that occurred to me that it, it isn't macroeconomics, it isn't microeconomics, it's actually nanoeconomics.

It's the economics of individualized predicted behavioral performance propensities. To me, that was the aha moment, and maybe I coined the term nanoeconomics. I'm gonna claim it for now. I know, but it's, it's this, I, it's this idea that I can make very granular decisions on each individual. I should be able to figure out, for example, and the book that you're reading, of course.

Preface, I talk about the motivation for my book was the frustration I had with how we were treating Covid. We were making all these covid policy decisions based on average, on average, on averages. When if you wanted to find out, we could easily have built a an A score that measured everybody in the country's likelihood to die from covid.

If you. It was, I had a class at Menlo undergrad class who took a spreadsheet, talked to some nurses, and we mocked up what that would look like. Yeah, so it's this whole, so I do think that there's this opportunity. Sorry. You're

killing me. No, you're K. You're killing me. Because that's what I wanted us to do as a country, as a data and analytics industry.

And the only country that came close because they had the data was the uk and they didn't share it. Yes. I will send you after this. They have a risk model. based on your different attributes of are you high risk or low risk? And yet I think the general public didn't understand it, and so we never acted on that.

And I will call this the tyranny of averages, and I will consider this the biggest failure I love of the data and analytics industry, the tyranny of averages.