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Speaker 1: Welcome, Welcome, Welcome to the Bob Left Sets podcast. My

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Speaker 1: guest this week is CEO of Civic Science, John Dick.

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Speaker 1: John Bob good to be here. I met John we

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Speaker 1: were on a piano for an Access TV show. Because

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Speaker 1: Mark Cuban is involved on the board of Civic Science

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Speaker 1: doing Grammy predictions and John is in town. He sends

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Speaker 1: a weekly email talking about trends in the world at large.

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Speaker 1: Because ultimately, what would you call your company? Wouldn't call

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Speaker 1: a polling company. I would call us a polling company.

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Speaker 1: I wish there was a sexier way to describe it,

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Speaker 1: but we're a new breed of polling company, okay. And

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Speaker 1: he's always revealing fasciniating insights, so I thought it'd be

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Speaker 1: great to have him on the podcast. Let's start from

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Speaker 1: the beginning. You're in Pittsburgh. Correct, Why Pittsburgh the next

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Speaker 1: big thing? Pittsburgh is going through a tremendous renaissance. But

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Speaker 1: actually we're there. The company is there because carnegiel And

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Speaker 1: University is there, and the talent and technology that powers

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Speaker 1: the foundation of our company came from there, and so

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Speaker 1: um and it's a great place to live, and it's

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Speaker 1: a really emerging city with lots of talent and just

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Speaker 1: a smart place for us to Are you from Pittsburgh

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Speaker 1: born and raised? Yeah? Did you go to Carnegie Mill?

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Speaker 1: I did not. Where do you go to to Rollins

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Speaker 1: College in Winter Park, Florida? Okay? So then how did

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Speaker 1: you hook up with all these new technology things at

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Speaker 1: Carnegie Miller. Well, I started a company earlier in my

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Speaker 1: early twenties. Um sold that company in my late twenties.

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Speaker 1: What was that company's called GSP. We helped early stage

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Speaker 1: tech and biotech companies do business with the government, very

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Speaker 1: different business. And so I exited that company and had

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Speaker 1: the business plan for what became Civic Science shortly after,

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Speaker 1: and I knew it had some technology needs and found

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Speaker 1: a couple of guys that knew how to solve those

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Speaker 1: technology needs. Okay, So why did you want to be

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Speaker 1: in the polling business? Because it's broken? And uh, well

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Speaker 1: two reasons. When it was broken and at a little

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Speaker 1: slower Why is it broken because people don't participate the

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Speaker 1: way they used to because we're too busy. Um, it's

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Speaker 1: not a pleasant experience for people to participate in polling

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Speaker 1: as it's sort of originally constructed. My phone's ringing while

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Speaker 1: I'm having dinner, or I have to sign up at

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Speaker 1: some web page and answer surveys all day for five bucks.

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Speaker 1: And it's just not a user centric experience for anyone.

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Speaker 1: And so what happened is landline phone ownerships started to plummet,

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Speaker 1: and the people who have landline phones don't look like

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Speaker 1: the ones who don't, and so the quality of the

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Speaker 1: data started to suffer. And then posters said, well, that's fine,

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Speaker 1: we'll we'll call cell phones. But people don't answer cell

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Speaker 1: phone calls from numbers they don't recognize, or at least

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Speaker 1: the people who do don't look like the ones who don't.

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Speaker 1: And then you had the advent of what are called

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Speaker 1: survey panels, which are these websites people sign up at

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Speaker 1: and answer surveys for money. And those people don't look

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Speaker 1: like either of us. They are too much time on

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Speaker 1: their hands. Okay, just one other thing, generally speaking, because

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Speaker 1: you've lived in a place in Los Angeles, you're constantly

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Speaker 1: getting email whatever, Come participate for fifty a hundred dollars.

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Speaker 1: What are those panels worth? Well, I mean, what are

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Speaker 1: they worth? They certainly are powering a lot of decision

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Speaker 1: making in commercial America. I don't think they should be

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Speaker 1: because the people who answer those surveys don't look like

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Speaker 1: the real world. They are people who are highly incentivized

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Speaker 1: by that financial incentive, and so they're heavily biased. Um,

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Speaker 1: and then that financial reward inspire some of the wrong

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Speaker 1: behaviors too, And when you're trying to research someone as

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Speaker 1: opposed to getting their honest opinion about something. So that's

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Speaker 1: all been broken, and it started breaking twenty years ago.

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Speaker 1: It started to really sort of, Um, the chickens kind

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Speaker 1: of came home to roost in the last election cycle.

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Speaker 1: We see polling just isn't working the way that it

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Speaker 1: used to. Okay, we got we got a bunchet things

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Speaker 1: on what is the other reason you wanted to go

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Speaker 1: into this business? Because I think it needs to work.

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Speaker 1: It needs we need to figure out a way to

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Speaker 1: understand what people think. There was a mindset ten or

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Speaker 1: fifteen years ago that social media was somehow going to

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Speaker 1: replace survey research is the way that we understood what

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Speaker 1: the world thinks, and it just doesn't work that way.

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Speaker 1: I'm not myself on social media. I'm a curated version

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Speaker 1: of myself. We need a discreet means of understanding people's opinion.

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Speaker 1: There's a reason we pull a curtain behind us on

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Speaker 1: election day, because if our opinions are subject to scrutiny

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Speaker 1: and peer pressure and invective, then we um, we don't

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Speaker 1: tell people what we think. Why did everybody get it so?

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Speaker 1: I mean, now we have Nate Silver, who now presently

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Speaker 1: at five thirty eight, used to be The New York

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Speaker 1: Times the previous election cycle. He balances polls. He got

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Speaker 1: it wrong, although he will say he thought the odds

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Speaker 1: of Trump being successful. Hire New York Times got it

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Speaker 1: completely wrong. What did we learn there? Well, on a

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Speaker 1: national level, from a popular vote standpoint, the posters were

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Speaker 1: pretty strong. I mean they said most of the averages

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Speaker 1: said Hillary Clinton would win by three percent, and she

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Speaker 1: wrote one roughly by three Um, we're broke down. Was

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Speaker 1: at state levels, where a couple of hundred thousand votes

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Speaker 1: across three or four states were able to swing the election.

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Speaker 1: What did we learn about it in the grand scheme

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Speaker 1: of things was that there was an entire universe of

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Speaker 1: people out there who were piste off and we didn't

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Speaker 1: know about it. Um. One of the reasons, and we

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Speaker 1: study this a lot in our own data, Conservatives tend

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Speaker 1: to be much less outspoken on social media than their

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Speaker 1: liberal counterparts are. Why because they believe that when they

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Speaker 1: speak out on conservative issues, they are opening themselves up

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Speaker 1: to attack from the left, and so they tend to

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Speaker 1: keep their mouth shut. We've studied this extensively, and so

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Speaker 1: there was an entire um sub layer of us, the

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Speaker 1: US population that was piste off. But because they weren't

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Speaker 1: trumpeting it from the rooftops on social media, we didn't

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Speaker 1: know about it, and we didn't know about it until November. Okay,

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Speaker 1: so you move back to Pittsburgh, you align with these

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Speaker 1: people at Carnegie mellon what are the next steps in

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Speaker 1: starting civic science. Well, we had a lot of technology

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Speaker 1: and to build what was that technology, database and database

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Speaker 1: architecture designed to kind of find all the stuff we

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Speaker 1: can find in our data. We had to prove that

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Speaker 1: the data the methodology we were inventing worked. Now, the

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Speaker 1: methodology you started with when was this two eight? Is

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Speaker 1: that the same methodology you're still employing. Absolutely, So explained

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Speaker 1: to my what you with that methodology. So we encounter

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Speaker 1: people and polls and quizzes across the web. Inside of

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Speaker 1: content they're reading, it might be a polar quiz you

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Speaker 1: might see on a buzz feeder or Facebook of what

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Speaker 1: kind of wine or what kind of Simpson's character are you?

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Speaker 1: But more it's an opinion type of poll of the

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Speaker 1: day that you might find in an article you're reading

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Speaker 1: on your local newspaper, on a site like Paris Hilton

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Speaker 1: or Univision. We're generally asking somebody a couple of questions

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Speaker 1: at a time, no more than three or four, because

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Speaker 1: we know that if we add too many questions, we

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Speaker 1: start to see a decline and participation and that's no good. Um.

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Speaker 1: We give people back, um instant results, which we think

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Speaker 1: is really important. So I answer a question, you know,

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Speaker 1: what is my favorite color? And after I click, I'll

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Speaker 1: see what everybody else's favorite color is. Sure. Sure, we

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Speaker 1: don't generally ask favorite color, but yeah, sure, yeah, And

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Speaker 1: and we think there's some a lot of particularly today. Um.

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Speaker 1: And what we hear back from people is they appreciate

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Speaker 1: that we're giving them the answers right back. It's unvarnished,

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Speaker 1: unfiltered truth that here's what the world thinks, and here's

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Speaker 1: what people like you think, and it's not being passed

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Speaker 1: to you through the lens of a media company that

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Speaker 1: may have some bias now with somewhat Okay, what is

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Speaker 1: incentivizing them to answer the questions to begin with? Uh,

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Speaker 1: it's that intrinsic benefit of a feeling like I'm participating

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Speaker 1: in something good, be the interestingness of the results that

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Speaker 1: I get back feeling like, Okay, I just learned something

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Speaker 1: about the rest of the world by virtue of my

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Speaker 1: participation in it. And do you compensate the sites where

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Speaker 1: you're quizzes are now? So how do they end up

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Speaker 1: being placed there? Um? They it's effectively a bartering arrangement. Um.

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Speaker 1: But what they they learn things from the information that

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Speaker 1: we gather that help them grow their businesses, that we

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Speaker 1: help them improve their editorial functions and their ad sales. Okay,

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Speaker 1: So let's say you're on website A. You would approach

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Speaker 1: them and say, I want to do this, but I'm

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Speaker 1: going to share data with you that's going to be

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Speaker 1: your compensation. Correct. So how many sites are you presently on?

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Speaker 1: Well over a thousand at last count. Um? Very uh

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Speaker 1: piece together to give us a diverse representative view of

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Speaker 1: the US population. So we want to make sure we

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Speaker 1: have ample number of media companies that reach hispanics. An

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Speaker 1: ample number of media companies that reach women and so forth. Um,

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Speaker 1: the media companies make a distinction from us that the

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Speaker 1: size of our poll sitting on their web page, that

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Speaker 1: we are delivering more value to them from that information

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Speaker 1: than they would get from a banner ad that was

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Speaker 1: sitting in that same place. And as long as we

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Speaker 1: can maintain that equilibrium, we can continue to collect that data.

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Speaker 1: How do you choose what sites to place your quizzes on? Um? Well,

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Speaker 1: it's we have a team who goes out and sells

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Speaker 1: that essentially, and we do we prioritize at any given time,

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Speaker 1: We prioritize sales to publishers or partnerships with publishers who

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Speaker 1: meet demographic imbalances in our sampling. So if we if

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Speaker 1: we notice over time, hey we're starting to get a

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Speaker 1: disproportionate number of men answering our polls, we want to

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Speaker 1: go and target sites that have a disproportionate audience of women. Okay,

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Speaker 1: I am someone who never re answers those questions, So

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Speaker 1: that begs the question are certain people ultimately not included

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Speaker 1: in the sample? Yes? What do you do about that? Well,

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Speaker 1: it's a fundamental flaw. UM, I guess what we tell

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Speaker 1: the marketplace. And what we can prove in our research

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Speaker 1: is that because we've limited those barriers as much as possible,

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Speaker 1: that our audience looks more like the real world than

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Speaker 1: any other methodology you would think of. So you're not

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Speaker 1: going to be the person who signs up and answers

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Speaker 1: surveys all day for five bucks either. Um we are.

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Speaker 1: We are all missing some portion of the US population.

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Speaker 1: We're just missing a much smaller portion of the population.

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Speaker 1: And do you sit in your office and say, how

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Speaker 1: do we reach those people? You just tell your customers

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Speaker 1: some people are unreachable. Um, we're not giving up. I

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Speaker 1: think we spend a ton of time trying to understand

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Speaker 1: what types of questions or what kind of interaction will

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Speaker 1: bring people who otherwise don't want to participate to participate in.

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Speaker 1: Our response rates as a result, have gone up a lot.

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Speaker 1: The percentage of of people who see our polls on

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Speaker 1: a website and answer them has skyrocketed in the last

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Speaker 1: two years because we've invested very heavily in solving that problem.

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Speaker 1: I would assume it's still below ten percent. Sure, sure, um,

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Speaker 1: but if you think about what's that, how's that relate

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Speaker 1: to say, the likelihood of somebody clicking on an ad,

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Speaker 1: it's a hundred times more than that, right, because there's

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Speaker 1: there's something engaging about it, as opposed to, Oh, I'm

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Speaker 1: gonna click on this ad and somebody's gonna try and

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Speaker 1: try to try to sell me. And how many people

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Speaker 1: work at SIVEN Civic Science? Um? I think? And are

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Speaker 1: they all in Pittsburgh? All in way of one person

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Speaker 1: in New York? Okay? So what kind of companies uh,

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Speaker 1: by your data? Um, pretty broad, large ones, primarily very

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Speaker 1: bigger sort of fortune five hundred types of enterprises. Consumer

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Speaker 1: facing companies were really strong and tech and telcom, media, banking, healthcare, restaurant,

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Speaker 1: and some retail. Okay, we were talking off camera, and

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Speaker 1: these are all household name the names you think they are,

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Speaker 1: they are, but let's assume point of discussion. Telcom Okay,

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Speaker 1: it is a very competitive marketplace. Everyone already has a smartphone.

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Speaker 1: There are four competitors in the United States. What you

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Speaker 1: be able to tell a telecom company, Uh, there's going

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Speaker 1: to be a mix of things about attitudes towards their

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Speaker 1: specific company and their specific products. There's going to be

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Speaker 1: a mix of things about trends within their category. So

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Speaker 1: which of their competitors and maybe are more vulnerable than

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Speaker 1: others at a given point in time. And then the

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Speaker 1: more valuable stuff we do tends to look at how

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Speaker 1: various larger consumer trends and macro forces are affecting consumers

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Speaker 1: and how that relates to what mobile carriers they're going

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Speaker 1: to use, and and and um, how much of their

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Speaker 1: plan they're going to use. One thing we learned was

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Speaker 1: really fascinating pre and post election was the composition of

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Speaker 1: the people who were likely to switch mobile carriers changed

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Speaker 1: very dramatically from the summer of two thousand sixteen through

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Speaker 1: the summer. And what did your research tell you, Um,

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Speaker 1: Democrats were more likely to switch mobile carriers after the

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Speaker 1: election than they were before because they, UM, I switched

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Speaker 1: my mobile carrier generally to get a better financial deal, right,

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Speaker 1: and so it tends to correlate with my personal financial

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Speaker 1: situation or my consumer confidence. And so once um, Donald

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Speaker 1: Trump was elected, these people, UM, a lot of Democrats

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Speaker 1: began to have a bleaker view of the economy and said,

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Speaker 1: you know what, I better go find a better deal

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Speaker 1: for myself. And we saw a higher rate of switchers

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Speaker 1: among Democrats during that period of time as opposed to

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Speaker 1: the summer before when they tended to be more likely

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Speaker 1: to be Republicans. Now, sitting here as a lay person,

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Speaker 1: one could say, also Sprint was offering comprehensive plans with

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Speaker 1: very cheaper free phones. T Mobile was very cheap, So

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Speaker 1: one might say, really it was plans were so cheap

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Speaker 1: people were incentivized for that reason as opposed to consumer confidence. Well,

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Speaker 1: but still there's I chase a cheaper opportunity when I'm

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Speaker 1: looking when I have a less positive view of my

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Speaker 1: personal finances. So I would whether there was cause and

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Speaker 1: effect there or whether those companies were smart enough to

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Speaker 1: capitalize them what they knew was coming, which was more

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Speaker 1: price sensitivity in their core markets. UM. Hard to tell. Okay, Now,

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Speaker 1: many all these companies do use consumer research, but there's

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Speaker 1: a school of thought that research will tell you where

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Speaker 1: you've been, but it won't tell you where you're going. Oh,

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Speaker 1: that's the big part of our ethos as we focus

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Speaker 1: on looking through the windshield. So how do you do that? UM?

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Speaker 1: We look a lot at patterns and trends in this

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Speaker 1: history of data now that we have UM, those trends

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Speaker 1: tend to have a trajectory that we can look two months,

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Speaker 1: six months out and see where they're headed. We ask

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Speaker 1: a lot of questions that are forward looking of people.

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Speaker 1: What are your intentions? How likely you to do something

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Speaker 1: in the future. Um, there's ample backward looking data that

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Speaker 1: credit card companies have and people on the web have

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Speaker 1: based on things I clicked on yesterday and bought yesterday.

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Speaker 1: We don't we don't need to augment that information. We

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Speaker 1: focus on where are people going to be spending their

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Speaker 1: money next week or next month. But you look famously,

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Speaker 1: Steve Jobs did no consumer research, and he would come

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Speaker 1: up with products that people literally hadn't heard of, I

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Speaker 1: didn't think they needed, and then would clamor for good,

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Speaker 1: good work if you can get it. Okay, So he's

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Speaker 1: an anomaly. He's the outline absolutely. Okay. Yeah, how about

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Speaker 1: Elon Musk probably of that ilk. But I think those

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Speaker 1: people are the exceptions, not the rule. Okay. So where's

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Speaker 1: it going? Well, Um, it's a mix of exciting and

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Speaker 1: terrifying all at once. Um, we are seeing some amazing

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Speaker 1: see changes right now, a lot of things related. UM.

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Speaker 1: I don't think we fully appreciate yet the impact that

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Speaker 1: social media will have on our culture forever. I think

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Speaker 1: we intuitively get some of it, but I don't think

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Speaker 1: we're going to be able to diagnose any of that

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Speaker 1: for decades. But one of the things that that that

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Speaker 1: we notice a lot in our data is and and

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Speaker 1: everyone kind of can relate to this, is that because

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Speaker 1: I'm this kind of curated version of myself on social media,

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Speaker 1: I use social media as a way to make myself

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Speaker 1: look funnier and smarter and a better parent and more

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Speaker 1: adventurous and a better eater. And I think about that

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Speaker 1: consciously or not when I choose what to share, where

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Speaker 1: to check in. Uh. The flip side of that is

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Speaker 1: I have a high disincentive to say anything that is unpopular.

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Speaker 1: And so we see and it goes back to the

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Speaker 1: common I made earlier about the election. People who believed

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Speaker 1: it was unpopular to support Donald Trump in two thousand

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Speaker 1: and sixteen just didn't talk about it, but they showed

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Speaker 1: up on election day and did. Now we're seeing it's

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Speaker 1: a little more um. People are a little bolder about

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Speaker 1: their support now than they were in But what's happening

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Speaker 1: is unpopular opinions tend to be suppressed on social media,

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Speaker 1: and so we have a sense of consensus that doesn't

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Speaker 1: really exist. But yes, if if, if you remember the

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Speaker 1: summer of two thousand and fifteen, UM when the Supreme

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Speaker 1: Court was ruling on gay marriage, it was a very

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Speaker 1: popular thing to have this rainbow tinted profile picture as

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Speaker 1: a as a solidarity. Right around that same summer, the

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Speaker 1: South Carolina State Legislature was being was debating taking the

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Speaker 1: Confederate flag down from over the state Capitol building. There

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Speaker 1: were no equivalent Confederate flag filters over anybody's profile pictures,

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Speaker 1: which is kind of surprising to me because I grew

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Speaker 1: up in a pretty rural part of Pennsylvania, where I

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Speaker 1: know lots of gun toting, right wing evangelical Christian types.

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Speaker 1: You'd think that I would have an equal number of

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Speaker 1: rainbow printed tinted profile pictures and Confederate flag ones. But

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Speaker 1: I didn't, um because once Facebook had become a wash

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Speaker 1: with those rainbow filters, everybody who believed otherwise had a

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Speaker 1: significant disincentive from saying anything to the contrary because they

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Speaker 1: knew they'd be open to attack. So narratives that emerge

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Speaker 1: on social media and take take hold are very, very

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Speaker 1: difficult to change, and the impact that has on the

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Speaker 1: popularity of music, the popularity of brands and fashion. It's

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Speaker 1: it's almost a measurable today. Okay, Just so I understand

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Speaker 1: it is is it a skewed view of reality or

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Speaker 1: does it change reality? Or both both? I mean, I

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Speaker 1: think I think what we're what what what I'm suggesting

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Speaker 1: is that when we look back on this period of

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Speaker 1: history that it will have changed reality. Um. Well, I'm

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Speaker 1: a big believer that the reason the younger generation is

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Speaker 1: not as racist and gay marriage is accepted because they

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Speaker 1: saw a rainbow of colors and uh, different choices on

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Speaker 1: MTV and MTV dominated. So are we saying the same

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Speaker 1: thing that social media will ultimately affect what people believe?

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Speaker 1: There's no question about it. I think that it is.

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Speaker 1: Once a wave of popular opinion washes over, it moves people. Um,

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Speaker 1: it gets them to reconsider their points of view. Some

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Speaker 1: it gets others to really stig their heels in. So

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Speaker 1: what what are we learning with the backlash against Facebook

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Speaker 1: and social media presently? Well, there's a lot of technical

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Speaker 1: reasons for some of that that I don't think Facebook

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Speaker 1: should be blamed for. Um, this has all been happening

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Speaker 1: so fast, nobody, It's not like everybody saw it coming

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Speaker 1: and told Facebook to prepare itself. This was a whole

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Speaker 1: new frontier for them. I think they can fix it.

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Speaker 1: I think what we're learning about from the election through

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Speaker 1: now is what and and social media is driving a

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Speaker 1: lot of this, which is this incessant kind of tribalism

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Speaker 1: in America that we have where um I want to

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Speaker 1: and it's and it's affecting UM this or what we

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Speaker 1: call sort of the stay at home economy as well,

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Speaker 1: people don't want to go out in public as much

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Speaker 1: as that. We have a question that we track in

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Speaker 1: our database, which is do you would you say that

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Speaker 1: you want to go out, that you're going out more

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Speaker 1: or less than you did six months ago? Thirty three

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Speaker 1: percent of people and the last time we looked at

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Speaker 1: the state of a few weeks ago said that they're

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Speaker 1: going out in public, want to go out in public

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Speaker 1: less than they did six months ago. Why every time

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Speaker 1: there's a shooting, one person says or more people say,

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Speaker 1: I'm not going to go out today, I'm just gonna

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Speaker 1: stay home. And and we've made it super easy for

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Speaker 1: people to stay home. And we're controlling our surroundings, were

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Speaker 1: curating the people that were around in in two thousand

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Speaker 1: and fifteen, we had at we have a question that says,

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Speaker 1: do you generally like to be around people a lot?

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Speaker 1: It's very simple question. Percent of people in two thousand

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Speaker 1: fifteen said yes, I like to be around people a lot.

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Speaker 1: Today that number so it's about a decline in that

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Speaker 1: and that and that, and that doesn't seem like a

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Speaker 1: huge number or might. But what it's telling us is

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Speaker 1: people are saying, I don't want to go out and

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Speaker 1: sit in a restaurant and run a risk of sitting

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Speaker 1: next to somebody who's ranting on about some political issue

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Speaker 1: I disagree with. I'm going to stay at my home

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Speaker 1: where I've where I've curated my friend group on social media,

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Speaker 1: I've curated the media that I'm going to read and watch,

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Speaker 1: and I want to create a safe nest around myself.

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Speaker 1: And I think social media has helped to propagate a

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Speaker 1: lot of that. And so projecting five years out as

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Speaker 1: opposed to three months, do you have a thought on that? Yeah,

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Speaker 1: I think it's gonna continue to play out that way.

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Speaker 1: I think we're going to have UM. I think people

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Speaker 1: are a large portion of people are inherently introverted, but

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Speaker 1: it was always hard to be an introvert um. Now

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Speaker 1: I can have everything delivered to my doorstep. I never

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Speaker 1: have to the shop. I never have to leave to

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Speaker 1: see a movie. I actually and what happened. What's happening

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Speaker 1: is the commercial marketplace is moving towards those people were making.

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Speaker 1: We're saying, if you don't want to leave your house,

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Speaker 1: you never have to. UM. Companies are vying to own

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Speaker 1: my quote unquote ecosystem. They want to have my internet,

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Speaker 1: my cell phone, my content, my They want my refrigerator

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Speaker 1: to talk to my doorbell, to my smartphone, to my

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Speaker 1: watch on my hand, and the companies are all fighting

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Speaker 1: to own that so that when I choose to stay

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Speaker 1: home and never leave, I'm only using one company. And

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Speaker 1: I think maybe five years from now that peaks and saturates,

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Speaker 1: but we're far from that. Stay right there. We'll be

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Speaker 1: back with more of my conversation with John Dick, an

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Speaker 1: expert on pulling an analytics right here on the Bob

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Speaker 1: Left Sets podcast. You're listening to the Bob Left Sets

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00:20:48,440 --> 00:20:51,720
Speaker 1: podcast recorded here in Venice, California, at the tune In Studios.

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00:20:52,080 --> 00:20:54,160
Speaker 1: Each week, I interview a new guest that dive into

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00:20:54,240 --> 00:20:57,520
Speaker 1: their backgrounds, their career, current events and everything in between.

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00:20:58,119 --> 00:21:01,159
Speaker 1: If you like the podcast, subscribe, review the show, and

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00:21:01,240 --> 00:21:03,840
Speaker 1: check out earlier episodes. You can hear them all on

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Speaker 1: tune in, Apple Podcasts, or your podcast player of choice.

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Speaker 1: And now more with John Dick. In light of gun tragedies,

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Speaker 1: Have you done any research on that that tells us

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Speaker 1: really were what America thinks? Um, And nothing that would

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Speaker 1: surprise you. UM. I think a lot of people and

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Speaker 1: this is not just our date. I think most of

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Speaker 1: the most data that you would see would say that

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Speaker 1: the majority of Americans, UM, support some stricter interpretation of,

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Speaker 1: if not if not stricter gun laws, stricter interpretation of

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Speaker 1: the gun laws that we have today. UM. But the

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Speaker 1: political environment is such that people don't want to give

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Speaker 1: any ground. One of the one of the unfortunate consequences

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Speaker 1: of tribalism isn't so much just that I want to

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Speaker 1: kind of organize only with people who agree with me

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Speaker 1: and believe me. But I start finding myself distrusting everybody else.

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Speaker 1: And that's that's the part of this kind socio cultural

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Speaker 1: shift that scares us when we look at our data. Okay,

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Speaker 1: tell my audience, certain things you've learned that are counterintuitive. Um, well,

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Speaker 1: there's certainly I felt that it was counterintuitive that we

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Speaker 1: see this trend towards introversion among people, because I thought

430
00:22:19,200 --> 00:22:21,080
Speaker 1: we tended to be more likely to be so I

431
00:22:21,280 --> 00:22:24,879
Speaker 1: thought we were more social beings than we really are. UM.

432
00:22:24,920 --> 00:22:28,120
Speaker 1: I don't think most of us ultimately want to be UM,

433
00:22:28,160 --> 00:22:31,680
Speaker 1: So that that kind of surprised me. Um. We see

434
00:22:31,720 --> 00:22:34,560
Speaker 1: relationships between things that I wouldn't have expected a few

435
00:22:34,600 --> 00:22:37,400
Speaker 1: years ago when restaurants spending started to decline in the US,

436
00:22:37,480 --> 00:22:41,080
Speaker 1: and it was kind of inexplicable at the time. Traditionally,

437
00:22:41,119 --> 00:22:45,920
Speaker 1: for for decades and decades, UM, restaurant spending is really

438
00:22:45,920 --> 00:22:50,800
Speaker 1: closely correlated to fuel prices, and because it's disposable income

439
00:22:50,920 --> 00:22:53,080
Speaker 1: and and and fuel prices affects everybody. If I have

440
00:22:53,119 --> 00:22:55,400
Speaker 1: a couple extra if I live in a rural town

441
00:22:55,400 --> 00:22:59,720
Speaker 1: in middle middle part of Pennsylvania, or I live in

442
00:22:59,720 --> 00:23:01,560
Speaker 1: a big city and drive a car, one of the

443
00:23:01,560 --> 00:23:03,800
Speaker 1: first things that I can notice in my bank account

444
00:23:03,800 --> 00:23:06,919
Speaker 1: as if I'm spending less money on gas and so Historically,

445
00:23:07,240 --> 00:23:10,359
Speaker 1: restaurant spending was very closely aligned with shifts and fuel prices,

446
00:23:10,400 --> 00:23:13,440
Speaker 1: but two years ago, restaurant spending was going down on

447
00:23:13,520 --> 00:23:17,119
Speaker 1: fuel prices were not going up. And we found in

448
00:23:17,119 --> 00:23:19,119
Speaker 1: our data, which is part of the benefit of being

449
00:23:19,160 --> 00:23:21,200
Speaker 1: able to study all of these things at once, is

450
00:23:21,240 --> 00:23:24,240
Speaker 1: that it turned out was very heavily correlated with how

451
00:23:24,320 --> 00:23:27,640
Speaker 1: much people were experiencing increases experiencing increases in their health

452
00:23:27,760 --> 00:23:31,399
Speaker 1: insurance premiums, that that was beginning to chip away more

453
00:23:31,440 --> 00:23:34,199
Speaker 1: at disposed healthcare costs were beginning to chip away at

454
00:23:34,200 --> 00:23:36,680
Speaker 1: disposable income at a faster rate than fuel prices were,

455
00:23:37,040 --> 00:23:39,840
Speaker 1: and that had never happened. How do you find that, Well,

456
00:23:39,880 --> 00:23:42,000
Speaker 1: because we're studying all of this stuff at once, we

457
00:23:42,080 --> 00:23:43,800
Speaker 1: look at all these lines on a chart to see

458
00:23:43,800 --> 00:23:46,960
Speaker 1: how they which one leads, one lags the other, How

459
00:23:47,000 --> 00:23:49,200
Speaker 1: related are they, does one seem to be a cause

460
00:23:49,240 --> 00:23:51,720
Speaker 1: of another, or do they just tend to follow or lead?

461
00:23:52,280 --> 00:23:55,840
Speaker 1: And it was pretty evident to us in looking at

462
00:23:55,880 --> 00:23:58,760
Speaker 1: two So we have two questions, say, um, it's actually

463
00:23:58,840 --> 00:24:01,560
Speaker 1: more complicated that, but imagine. And one question is would

464
00:24:01,560 --> 00:24:03,240
Speaker 1: you say you're spending more or less to eat out?

465
00:24:03,280 --> 00:24:05,080
Speaker 1: And another one is would you say you're spending more

466
00:24:05,160 --> 00:24:08,080
Speaker 1: or less on your health care? And we looked at

467
00:24:08,080 --> 00:24:10,640
Speaker 1: the people who answered yes to those two questions, they

468
00:24:10,680 --> 00:24:14,919
Speaker 1: were closely aligned to one another. Okay, but sometimes there

469
00:24:14,920 --> 00:24:17,840
Speaker 1: can be false correlations. Yeah, but that's just I mean,

470
00:24:18,160 --> 00:24:20,720
Speaker 1: really smart statisticians know how to fare at that stuff out.

471
00:24:20,800 --> 00:24:23,560
Speaker 1: How do you do it? Well? You test them, Um,

472
00:24:23,600 --> 00:24:27,200
Speaker 1: you continue to see if they if they continue to

473
00:24:27,240 --> 00:24:29,879
Speaker 1: play out over time, You ask questions different ways to

474
00:24:29,880 --> 00:24:31,960
Speaker 1: see if it's maybe just an error in the way

475
00:24:31,960 --> 00:24:38,000
Speaker 1: a person is interpreting the question. Um, we're we're certainly

476
00:24:38,040 --> 00:24:40,240
Speaker 1: mindful of that. We have a it's called a false

477
00:24:40,280 --> 00:24:44,159
Speaker 1: detection procedure that we use and it's essentially a coefficient

478
00:24:44,200 --> 00:24:47,440
Speaker 1: that we use to measure whether whether there's a false

479
00:24:47,480 --> 00:24:51,040
Speaker 1: positive in our data. Wow, no, wonder you have the

480
00:24:51,040 --> 00:24:53,480
Speaker 1: cardig email and people involved. Yeah that's so. Did you

481
00:24:53,560 --> 00:24:57,399
Speaker 1: learn anything else about health care? Um? Well no, I

482
00:24:57,400 --> 00:25:02,560
Speaker 1: mean it's a mess. Um. I think that we're we

483
00:25:02,600 --> 00:25:05,680
Speaker 1: saw a period of choice that hadn't existed before because

484
00:25:05,760 --> 00:25:08,280
Speaker 1: the Affordable Care Act and a lot of that's uncertain

485
00:25:08,400 --> 00:25:11,240
Speaker 1: right now. So it's a healthcare is a gray area

486
00:25:11,240 --> 00:25:13,000
Speaker 1: at the moment because we don't know what the future

487
00:25:13,080 --> 00:25:16,080
Speaker 1: of it's going to look like. Okay, so you're working

488
00:25:16,080 --> 00:25:21,680
Speaker 1: with Fortune five companies, how would they find you? Um?

489
00:25:21,720 --> 00:25:23,960
Speaker 1: They're either referred to us by somebody else we already

490
00:25:23,960 --> 00:25:27,280
Speaker 1: work with. We publish a lot of information. You mentioned

491
00:25:27,760 --> 00:25:30,119
Speaker 1: a little email that we do every Saturday morning that

492
00:25:30,200 --> 00:25:33,440
Speaker 1: finds its way into the hands of leaders. We don't

493
00:25:33,480 --> 00:25:35,800
Speaker 1: really have. We have a very very very small salesforce.

494
00:25:35,880 --> 00:25:38,520
Speaker 1: We're not the kind of company that's trying to um

495
00:25:38,840 --> 00:25:41,199
Speaker 1: barge our way into your office to demo something for you.

496
00:25:41,240 --> 00:25:45,359
Speaker 1: We typically, UM, somebody typically reaches out to us and says, hey,

497
00:25:45,440 --> 00:25:47,920
Speaker 1: I saw this study you published about this thing, and

498
00:25:47,960 --> 00:25:50,120
Speaker 1: I'd really like to understand how it affects my business.

499
00:25:50,200 --> 00:25:53,199
Speaker 1: And that's usually where a business. Okay, so I hire you.

500
00:25:53,280 --> 00:25:56,120
Speaker 1: Let's say I'm a software company. What will you do

501
00:25:56,200 --> 00:25:58,399
Speaker 1: for me? Well, first thing we have to do is

502
00:25:58,480 --> 00:26:01,240
Speaker 1: understand your business. How do you do that? UM? We

503
00:26:01,359 --> 00:26:03,640
Speaker 1: just spend time together. We do. We have what's called

504
00:26:03,640 --> 00:26:06,520
Speaker 1: a discovery process and a solution design process, which sounds

505
00:26:06,600 --> 00:26:10,520
Speaker 1: very bureaucratic, but it's just simply a process of us learning,

506
00:26:11,119 --> 00:26:13,719
Speaker 1: really what your blind spots are as a business, what

507
00:26:13,760 --> 00:26:15,680
Speaker 1: do you and and no one never knows what those

508
00:26:15,680 --> 00:26:17,600
Speaker 1: are in the first conversation because they don't think they

509
00:26:17,600 --> 00:26:20,800
Speaker 1: have any UM. So we we start to. We challenge

510
00:26:20,840 --> 00:26:23,080
Speaker 1: some of that, We ask what they know, and we

511
00:26:23,480 --> 00:26:25,240
Speaker 1: try to push back and we get a sense of

512
00:26:25,280 --> 00:26:27,160
Speaker 1: all right, where can we fill in some blanks about

513
00:26:27,200 --> 00:26:30,560
Speaker 1: their business? I told you earlier one of the unintuitive

514
00:26:30,560 --> 00:26:36,040
Speaker 1: things for me is that UM our our biggest clients

515
00:26:36,160 --> 00:26:39,480
Speaker 1: UM are companies that you would think don't need any

516
00:26:39,480 --> 00:26:41,960
Speaker 1: more data. I told you we have sort of one

517
00:26:42,040 --> 00:26:44,800
Speaker 1: open square on the tech Giant Bingo card of our business.

518
00:26:44,840 --> 00:26:47,919
Speaker 1: That that, but all the others we have, And you

519
00:26:47,960 --> 00:26:50,240
Speaker 1: would think I would mention the names of those companies

520
00:26:50,280 --> 00:26:52,080
Speaker 1: and you'd say, well, I thought they already knew everything

521
00:26:52,119 --> 00:26:55,280
Speaker 1: about everyone. But what fascinates me about them is that,

522
00:26:55,520 --> 00:26:58,280
Speaker 1: and I think was what's a fundamental truth is the

523
00:26:58,320 --> 00:27:00,760
Speaker 1: more data those businesses have, the more questions they have,

524
00:27:01,280 --> 00:27:03,199
Speaker 1: the more things they don't know. And I think the

525
00:27:03,240 --> 00:27:05,879
Speaker 1: reason they've become the giants they are is because they

526
00:27:05,960 --> 00:27:09,320
Speaker 1: continue to be inquisitive and they continue to look under

527
00:27:09,440 --> 00:27:12,960
Speaker 1: rocks for the next big insight, and those tend to

528
00:27:13,000 --> 00:27:14,800
Speaker 1: be our best clients. So let's say you're a big

529
00:27:14,840 --> 00:27:19,560
Speaker 1: Silicon Valley company which is collecting reams of data constantly,

530
00:27:20,720 --> 00:27:26,639
Speaker 1: what would you literally provide UM context? Sometimes UM a

531
00:27:26,720 --> 00:27:29,879
Speaker 1: lot of the data that those companies have tells you what,

532
00:27:30,119 --> 00:27:33,240
Speaker 1: but they may not necessarily answer the why, and you

533
00:27:33,240 --> 00:27:37,240
Speaker 1: can answer that, yeah, they Our our specialty is the

534
00:27:37,320 --> 00:27:39,639
Speaker 1: why and the what next? Where do we think this

535
00:27:39,720 --> 00:27:42,480
Speaker 1: is going? Let's talk specifically about the music business. Sure,

536
00:27:42,600 --> 00:27:45,639
Speaker 1: what have you learned about the music business in your research? Um,

537
00:27:45,680 --> 00:27:50,560
Speaker 1: it's in a interesting stage of of cannibalization. That doesn't

538
00:27:50,680 --> 00:27:56,640
Speaker 1: people aren't listening to music more, um, cannibalization. So there's

539
00:27:56,640 --> 00:27:59,639
Speaker 1: a it's it's something of a zero sum game for

540
00:27:59,720 --> 00:28:03,000
Speaker 1: the players in the space. There. There aren't more hours

541
00:28:03,000 --> 00:28:05,320
Speaker 1: in the day. There aren't more people listening to music

542
00:28:05,359 --> 00:28:08,760
Speaker 1: than listen to it twenty years ago. So the fact

543
00:28:08,760 --> 00:28:12,399
Speaker 1: that music is more accessible people are or not listening

544
00:28:12,400 --> 00:28:14,240
Speaker 1: to more music there does not appear to be any

545
00:28:14,280 --> 00:28:18,119
Speaker 1: evidence of that, or at least new people listening to

546
00:28:18,200 --> 00:28:21,000
Speaker 1: music who aren't listening to it a lot before. And

547
00:28:21,040 --> 00:28:23,520
Speaker 1: so with all of the providers coming into the marketplace,

548
00:28:23,800 --> 00:28:26,879
Speaker 1: they are fighting over a finite pool of and now

549
00:28:26,880 --> 00:28:29,239
Speaker 1: it's a large pool, don't get me wrong, but but

550
00:28:29,280 --> 00:28:33,159
Speaker 1: there's it's finite, and there's not room for there's certainly

551
00:28:33,160 --> 00:28:36,000
Speaker 1: a room for, you know, multiple providers, but only to

552
00:28:36,040 --> 00:28:38,480
Speaker 1: the extent that they are dividing up our attention. I

553
00:28:38,480 --> 00:28:41,719
Speaker 1: think we will see some consolidation, and I don't mean

554
00:28:41,720 --> 00:28:45,520
Speaker 1: necessarily from a business standpoint, Um, one company buying another necessarily.

555
00:28:45,520 --> 00:28:47,920
Speaker 1: But I think we will see more and more music

556
00:28:47,960 --> 00:28:51,000
Speaker 1: listeners gravitating the single sources of music than well. I

557
00:28:51,040 --> 00:28:53,400
Speaker 1: believe that for different ways we live in a we

558
00:28:53,440 --> 00:28:56,080
Speaker 1: will live in a winter and losery economy. Across the board,

559
00:28:56,080 --> 00:28:58,640
Speaker 1: we have incommittee, quality, etcetera. You want to go where

560
00:28:58,640 --> 00:29:01,719
Speaker 1: your friends are, and let's do successful companies have sixty

561
00:29:02,400 --> 00:29:05,400
Speaker 1: plus percent market You're online the roll click away, right,

562
00:29:05,800 --> 00:29:10,600
Speaker 1: and I think, well, that's a part of the virtuous circle. Um,

563
00:29:10,640 --> 00:29:14,000
Speaker 1: it's easier, but it's also desirable. Um. One of the

564
00:29:14,040 --> 00:29:16,680
Speaker 1: same thing with the kind of the at home tech ecosystem.

565
00:29:16,720 --> 00:29:19,160
Speaker 1: If I have fewer passwords I have to remember, if

566
00:29:19,160 --> 00:29:22,120
Speaker 1: your tech support people I have to call, fewer interfaces

567
00:29:22,160 --> 00:29:24,280
Speaker 1: I have to learn. I mean, that's that's a highly

568
00:29:24,360 --> 00:29:26,760
Speaker 1: that's as desirable to me as my refrigerator talking to

569
00:29:26,800 --> 00:29:29,560
Speaker 1: my doorbell would be. Just the simplicity and the convenience

570
00:29:29,560 --> 00:29:32,160
Speaker 1: of it. I think that's all driving. Um, it's driving

571
00:29:32,200 --> 00:29:36,000
Speaker 1: what will what we expect to be a rapid pace

572
00:29:36,040 --> 00:29:39,520
Speaker 1: of consolidation, particularly in all media, but music in particular. Okay,

573
00:29:39,520 --> 00:29:42,160
Speaker 1: so there, let's assume there'll be fewer places that you

574
00:29:42,160 --> 00:29:44,600
Speaker 1: would ultimately gravitate to listen to music, or they have

575
00:29:44,680 --> 00:29:51,360
Speaker 1: larger market share. What about the acts themselves, Well, we

576
00:29:51,360 --> 00:29:53,520
Speaker 1: we don't need our data to tell anyone and you're

577
00:29:53,600 --> 00:29:55,320
Speaker 1: you know this better than we do that. It's just

578
00:29:55,560 --> 00:30:01,120
Speaker 1: much much easier for people to record at right. Um,

579
00:30:01,360 --> 00:30:04,000
Speaker 1: it's happening at an almost immeasurable pace because I can

580
00:30:04,040 --> 00:30:05,920
Speaker 1: just take a quick video of myself and slap it

581
00:30:05,920 --> 00:30:11,920
Speaker 1: on YouTube. Um. I think we are because of a

582
00:30:11,960 --> 00:30:15,320
Speaker 1: lot of the trends we see in social media. Um,

583
00:30:15,440 --> 00:30:19,320
Speaker 1: we see why. I know you have some thoughts on virality. Um,

584
00:30:19,520 --> 00:30:21,520
Speaker 1: things can go from small to big very fast, but

585
00:30:21,560 --> 00:30:23,680
Speaker 1: they can also go from big small very fast with

586
00:30:23,720 --> 00:30:28,640
Speaker 1: a misstep. And I think the adoption curve of of everything,

587
00:30:28,680 --> 00:30:31,800
Speaker 1: including music is truncated. So it's happening much faster than

588
00:30:31,800 --> 00:30:34,600
Speaker 1: it did years ago because there's fewer barriers to entry.

589
00:30:35,040 --> 00:30:38,000
Speaker 1: The adoption curve. What is that, Well, it's it's the

590
00:30:38,120 --> 00:30:41,479
Speaker 1: extent to which Um, there are people out there who

591
00:30:41,480 --> 00:30:43,720
Speaker 1: will try everything. There are music fans who will listen

592
00:30:43,760 --> 00:30:46,640
Speaker 1: to every single artist they hear about, and then we

593
00:30:46,880 --> 00:30:50,600
Speaker 1: call those an early adopter. Um, there's a second phase

594
00:30:50,880 --> 00:30:53,280
Speaker 1: group of people will call a market maven in our terminology,

595
00:30:53,320 --> 00:30:55,400
Speaker 1: which is I try it and then I tell everyone

596
00:30:55,440 --> 00:30:58,080
Speaker 1: about it, and I go to social media and I say,

597
00:30:58,120 --> 00:30:59,800
Speaker 1: look at these songs I listened to check out this

598
00:30:59,800 --> 00:31:01,680
Speaker 1: band that I like. And those people have a lot

599
00:31:01,720 --> 00:31:04,080
Speaker 1: of power today. My sister is one of those people

600
00:31:04,080 --> 00:31:05,840
Speaker 1: in music. When she tells me about a band a

601
00:31:05,920 --> 00:31:08,640
Speaker 1: year later there at the Grammys, you know she's always

602
00:31:08,640 --> 00:31:12,239
Speaker 1: been that way. But UM, there's a third group of

603
00:31:12,240 --> 00:31:14,880
Speaker 1: people then, who wait for those market maven's to tell

604
00:31:14,880 --> 00:31:17,120
Speaker 1: them what to think. They don't want to make decisions

605
00:31:17,120 --> 00:31:19,320
Speaker 1: for themselves and not doing a ton of their own research,

606
00:31:19,400 --> 00:31:21,400
Speaker 1: going and reading reviews or any of that. They wait

607
00:31:21,440 --> 00:31:23,120
Speaker 1: for those friends they have like I do with my

608
00:31:23,160 --> 00:31:24,920
Speaker 1: sister who says, oh, you should go check out this

609
00:31:24,960 --> 00:31:27,520
Speaker 1: band because I don't have time to follow music. Now

610
00:31:27,560 --> 00:31:31,200
Speaker 1: with an overwhelming number of marketing messages, is the percentages

611
00:31:31,720 --> 00:31:36,960
Speaker 1: of early adopters, market maven's and passive listeners have those

612
00:31:37,040 --> 00:31:40,880
Speaker 1: numbers changed percentage wise? Not really? Um, it's a in fact,

613
00:31:41,160 --> 00:31:42,920
Speaker 1: we're working on a study on this right now. It's

614
00:31:42,960 --> 00:31:45,920
Speaker 1: it's remarkably consistent the percentage of people that fall into

615
00:31:45,960 --> 00:31:48,520
Speaker 1: those buckets. But what's happening is the progression from one

616
00:31:48,520 --> 00:31:51,160
Speaker 1: group to the next is happening much faster because the

617
00:31:51,160 --> 00:31:53,880
Speaker 1: connectivity between the early adopter to the market may even

618
00:31:53,920 --> 00:31:55,640
Speaker 1: to the lemmings who wait for those market mayven's to

619
00:31:55,640 --> 00:31:57,280
Speaker 1: tell them what to do. We're just much closer to

620
00:31:57,320 --> 00:32:00,280
Speaker 1: each other than we everywhere before. So every time I

621
00:32:00,320 --> 00:32:03,200
Speaker 1: turn on Facebook, there's the likelihood of my sister posting

622
00:32:03,280 --> 00:32:05,240
Speaker 1: some song that she just listened to that inspires me

623
00:32:05,280 --> 00:32:07,360
Speaker 1: to listen to it that I where five years ago

624
00:32:07,360 --> 00:32:08,360
Speaker 1: I had to wait for to call me on the

625
00:32:08,360 --> 00:32:11,000
Speaker 1: phone to do that. Yes, but also I would argue

626
00:32:11,280 --> 00:32:13,760
Speaker 1: that there's so many messages that it is harder to

627
00:32:13,760 --> 00:32:16,080
Speaker 1: get somebody to click or harder to pass on your

628
00:32:16,080 --> 00:32:20,320
Speaker 1: information today from a marketing standpoint, Sure, so that's why

629
00:32:20,600 --> 00:32:22,720
Speaker 1: social media is so powerful, because I don't know what

630
00:32:22,760 --> 00:32:25,280
Speaker 1: else to trust. But if my sister tells me, that's different,

631
00:32:25,320 --> 00:32:27,520
Speaker 1: and that's a hard thing for brands and marketers to

632
00:32:27,560 --> 00:32:30,640
Speaker 1: get their head around the power of the influencer, which

633
00:32:30,680 --> 00:32:33,320
Speaker 1: isn't necessary and I don't mean in a YouTube influencer

634
00:32:33,320 --> 00:32:36,360
Speaker 1: as much as I mean somebody in my social network

635
00:32:36,400 --> 00:32:38,800
Speaker 1: who I trust. One thing that we've seen in our

636
00:32:38,880 --> 00:32:42,440
Speaker 1: data indisputably over the last four or five years, we

637
00:32:42,520 --> 00:32:45,280
Speaker 1: track things like which of these kinds of advertising has

638
00:32:45,280 --> 00:32:48,920
Speaker 1: the most influence over your purchase decisions TV, radio, print, whatever.

639
00:32:49,400 --> 00:32:51,320
Speaker 1: One of the lines on that chart is comments and

640
00:32:51,320 --> 00:32:53,760
Speaker 1: recommendations from my friends on social media, and that line

641
00:32:53,760 --> 00:32:56,400
Speaker 1: has climbed and climbed. It has its surpassed television for

642
00:32:56,440 --> 00:32:58,880
Speaker 1: the first time two summers ago and has stayed there.

643
00:32:59,240 --> 00:33:02,160
Speaker 1: So I trust because I don't know what other media

644
00:33:02,200 --> 00:33:04,200
Speaker 1: to trust, and that's actually getting worse right. We can't

645
00:33:04,200 --> 00:33:06,400
Speaker 1: discern fake news from real news anymore. So the only

646
00:33:06,440 --> 00:33:08,160
Speaker 1: thing I can trust is what my sister tells me.

647
00:33:08,880 --> 00:33:13,920
Speaker 1: So what is the future of advertising? I think what

648
00:33:13,960 --> 00:33:17,240
Speaker 1: we're seeing and what we're telling our clients is that

649
00:33:17,320 --> 00:33:20,200
Speaker 1: they have to understand that adoption curve, and they have

650
00:33:20,320 --> 00:33:22,480
Speaker 1: to understand that the way you communicate to an early

651
00:33:22,520 --> 00:33:24,560
Speaker 1: adopter is different than the way you communicate to a

652
00:33:24,600 --> 00:33:26,640
Speaker 1: market maven, which is different too than a way you

653
00:33:26,680 --> 00:33:29,440
Speaker 1: communicate to that third tier. To what degree your people

654
00:33:29,480 --> 00:33:34,600
Speaker 1: just tuning out advertising completely. Oh a lot. UM. I

655
00:33:34,640 --> 00:33:38,640
Speaker 1: think you've seen the advent of UM so called native

656
00:33:38,640 --> 00:33:41,080
Speaker 1: advertising in the last five or six years. So it's

657
00:33:41,640 --> 00:33:47,080
Speaker 1: advertising masquerading is a news article. UM. Advertising has to

658
00:33:47,080 --> 00:33:51,680
Speaker 1: be a lot less obtrusive in my life or intrusive. UM.

659
00:33:52,800 --> 00:33:56,920
Speaker 1: And what you're seeing is brands focused on creating more

660
00:33:56,960 --> 00:33:59,280
Speaker 1: of an ongoing connection with somebody so they don't feel

661
00:33:59,280 --> 00:34:01,600
Speaker 1: like I'm being advert ties to all the time. It's

662
00:34:01,680 --> 00:34:03,920
Speaker 1: it's a much more subtle process than it was twenty

663
00:34:03,960 --> 00:34:08,560
Speaker 1: years ago. Okay, we'll return to this conversation with John Dick,

664
00:34:08,680 --> 00:34:12,319
Speaker 1: CEO of Civic Science in a moment. This is Bob

665
00:34:12,400 --> 00:34:13,960
Speaker 1: left Up. So I'm a writer and you could read

666
00:34:13,960 --> 00:34:16,720
Speaker 1: me at left Steps dot com if you haven't noticed already.

667
00:34:16,800 --> 00:34:19,120
Speaker 1: I love getting a person story. I like to know

668
00:34:19,160 --> 00:34:22,520
Speaker 1: what makes them tick, specifically as it relates to successful

669
00:34:22,560 --> 00:34:25,680
Speaker 1: people who are changing the game in their respective field.

670
00:34:26,000 --> 00:34:27,719
Speaker 1: I'm eager to get to the bottom of it on

671
00:34:27,760 --> 00:34:30,120
Speaker 1: this show and that same thing, and I'd like to

672
00:34:30,160 --> 00:34:32,760
Speaker 1: invite you to attend my music media summit in Santa

673
00:34:32,800 --> 00:34:34,960
Speaker 1: Barbara the last weekend in April. It's gonna be a

674
00:34:34,960 --> 00:34:36,800
Speaker 1: great way to connect with the movers and shakers in

675
00:34:36,840 --> 00:34:39,280
Speaker 1: the world of tech and music. Go to Music Media

676
00:34:39,360 --> 00:34:43,120
Speaker 1: Summit dot com for tickets and more information. And now

677
00:34:43,640 --> 00:34:48,360
Speaker 1: more with John Dick. Let's go back to uh lessons

678
00:34:48,360 --> 00:34:50,600
Speaker 1: you've learned. You were telling me earlier that you learned

679
00:34:51,000 --> 00:34:55,279
Speaker 1: that Android users correlate with more heart problems. Yeah, I

680
00:34:55,320 --> 00:34:57,160
Speaker 1: mean those are those are the kind of fun and

681
00:34:57,239 --> 00:35:00,400
Speaker 1: quirky but often useful things in our data. When you

682
00:35:00,440 --> 00:35:04,080
Speaker 1: study hundred thousand different questions that have been answered a

683
00:35:04,080 --> 00:35:07,279
Speaker 1: billion times, you find relationships between stuff that, Yes, if

684
00:35:07,360 --> 00:35:10,279
Speaker 1: you have an Android phone, you're more likely to have

685
00:35:10,320 --> 00:35:12,200
Speaker 1: a history of heart disease in your family. That is

686
00:35:12,200 --> 00:35:14,759
Speaker 1: not causality. That doesn't mean if you switch phones you

687
00:35:14,800 --> 00:35:18,960
Speaker 1: won't have heart disease. It means that people who have

688
00:35:19,120 --> 00:35:21,080
Speaker 1: the types of people who have Android phones are the

689
00:35:21,080 --> 00:35:22,719
Speaker 1: types of people who tend to have a higher rate

690
00:35:22,760 --> 00:35:24,880
Speaker 1: of heart disease in their family. And there's income and

691
00:35:25,200 --> 00:35:28,759
Speaker 1: and other kinds of proxy reasons for that, which makes

692
00:35:28,840 --> 00:35:30,600
Speaker 1: lots of sense when you explain it. But if I'm

693
00:35:30,600 --> 00:35:33,720
Speaker 1: a marketer at a pharmaceutical company, that's a pretty useful

694
00:35:33,760 --> 00:35:36,560
Speaker 1: discovery to find in a database that that relationship exists.

695
00:35:36,920 --> 00:35:39,839
Speaker 1: One of our absolute favorites is um, there is a

696
00:35:39,880 --> 00:35:42,840
Speaker 1: staggering correlation between how much people like Gary Busey and

697
00:35:42,880 --> 00:35:46,200
Speaker 1: how much they like Kia cars. Never been able to

698
00:35:46,200 --> 00:35:50,080
Speaker 1: explain it, but but I can tell you. I can

699
00:35:50,120 --> 00:35:53,560
Speaker 1: tell you five or six years ago that that little

700
00:35:53,560 --> 00:35:58,040
Speaker 1: bit of information formed the catalyst of a television campaign

701
00:35:58,040 --> 00:36:01,560
Speaker 1: for a regional network of Keya dealerships Star and Gary Boucy.

702
00:36:01,800 --> 00:36:06,120
Speaker 1: I did that works? It worked like a charm. Wow. Okay,

703
00:36:06,239 --> 00:36:08,880
Speaker 1: a little bit more. How did you find that correlation?

704
00:36:09,000 --> 00:36:12,920
Speaker 1: Same thing? So we just have these really smart people

705
00:36:13,000 --> 00:36:15,640
Speaker 1: in smart technology that crawls around and all of this

706
00:36:15,760 --> 00:36:18,000
Speaker 1: data all the time and looks for those kinds of things.

707
00:36:18,160 --> 00:36:22,120
Speaker 1: The kind of unexpected insight that one of our kind

708
00:36:22,120 --> 00:36:24,879
Speaker 1: of marketing sticks is that we answer questions you never

709
00:36:24,880 --> 00:36:28,160
Speaker 1: thought to ask. Okay, so do you go further and

710
00:36:28,200 --> 00:36:31,759
Speaker 1: try to get Okay, Gary Busey, he has kind of

711
00:36:31,800 --> 00:36:34,120
Speaker 1: an irreverence gear, gives a shit about life. And if

712
00:36:34,120 --> 00:36:36,799
Speaker 1: you're driving the Kia Squirrel car, it's kind of an

713
00:36:36,840 --> 00:36:38,919
Speaker 1: ugly car, but you're sort of saying the same thing. Yeah,

714
00:36:38,960 --> 00:36:43,720
Speaker 1: I mean, we get at we eventually you find the why.

715
00:36:43,800 --> 00:36:45,840
Speaker 1: And as I mentioned, I think That's the kind of

716
00:36:45,840 --> 00:36:48,520
Speaker 1: the gray space we're trying to fill in the world

717
00:36:48,640 --> 00:36:51,359
Speaker 1: is why is that true? So finding that it's true

718
00:36:51,440 --> 00:36:54,360
Speaker 1: is one thing. Understanding why it's true is another. Because

719
00:36:54,440 --> 00:36:57,480
Speaker 1: the advertising or marketing or product decisions that that inspire

720
00:36:57,560 --> 00:36:59,480
Speaker 1: have to be They can't just be Oh, I don't

721
00:36:59,480 --> 00:37:00,960
Speaker 1: know why, but we're gonna do it anyway. We have

722
00:37:01,000 --> 00:37:03,160
Speaker 1: to help them understand why. Okay, so you're on a roll.

723
00:37:03,239 --> 00:37:05,200
Speaker 1: Now tell us some more things you've learned in the data.

724
00:37:05,520 --> 00:37:08,960
Speaker 1: Oh geez, you know there are things that millennials are

725
00:37:08,960 --> 00:37:11,879
Speaker 1: more likely to pick rock and rock paper scissors than

726
00:37:11,920 --> 00:37:17,640
Speaker 1: other generations. Yeah, I'm trying to think. What what do

727
00:37:17,760 --> 00:37:20,160
Speaker 1: we learn from that? Well, you can learn how to

728
00:37:20,200 --> 00:37:24,960
Speaker 1: take money off of millennials at rock paper scissors. Yes, Uh,

729
00:37:25,000 --> 00:37:29,920
Speaker 1: that one's kind of trite. Um. Lots of what I mentioned,

730
00:37:29,920 --> 00:37:32,560
Speaker 1: the healthcare and restaurants spending, those are the more serious

731
00:37:32,640 --> 00:37:36,240
Speaker 1: kinds of useful, impactful, well, irrelevant of whether it's counterintuitive.

732
00:37:36,280 --> 00:37:38,759
Speaker 1: What other things might want to learn? What if someone

733
00:37:38,840 --> 00:37:42,080
Speaker 1: is an iPhone owner? What did you learn about iPhone owners? Well?

734
00:37:42,160 --> 00:37:47,720
Speaker 1: I can't touch that one. What I can say is, um,

735
00:37:48,160 --> 00:37:51,080
Speaker 1: we find one of my favorite areas of studies height.

736
00:37:51,560 --> 00:37:54,800
Speaker 1: There is a there are tons and tons of correlations

737
00:37:54,800 --> 00:37:58,319
Speaker 1: in our data tied to whether somebody is taller or

738
00:37:58,320 --> 00:38:01,200
Speaker 1: shorter than their peer group or people their age and gender.

739
00:38:01,320 --> 00:38:04,680
Speaker 1: There's there's and there's sociological reasons why that's true. But

740
00:38:05,440 --> 00:38:08,160
Speaker 1: taller people have different brand preferences, they have different media

741
00:38:08,200 --> 00:38:11,960
Speaker 1: consumption habits than shorter people do. And why is that? So?

742
00:38:12,160 --> 00:38:16,880
Speaker 1: We know over history taller people, generally when they were younger,

743
00:38:17,280 --> 00:38:21,480
Speaker 1: were better at physical sports than others. They tended to

744
00:38:22,200 --> 00:38:25,960
Speaker 1: that bread some confidence that manifests itself in the classroom

745
00:38:26,080 --> 00:38:29,479
Speaker 1: that um maybe helped the tall person stand out saying

746
00:38:29,480 --> 00:38:32,280
Speaker 1: a job interview or an admissions interview for a college.

747
00:38:32,280 --> 00:38:34,600
Speaker 1: And you do that millions and millions and billions of

748
00:38:34,600 --> 00:38:41,120
Speaker 1: times over human history. And there are certain um proclivities

749
00:38:41,160 --> 00:38:43,759
Speaker 1: that tall people have that short people don't, and vice versa.

750
00:38:43,800 --> 00:38:47,160
Speaker 1: And it's not always benefiting the tall people, by the way. UM.

751
00:38:47,160 --> 00:38:48,960
Speaker 1: But but we have a question in our database that

752
00:38:49,040 --> 00:38:51,640
Speaker 1: essentially is like are you taller shorter? And people answer

753
00:38:51,640 --> 00:38:53,440
Speaker 1: that question because they want to know how they compare

754
00:38:53,480 --> 00:38:55,279
Speaker 1: to other tall people and other short people. But when

755
00:38:55,320 --> 00:38:58,120
Speaker 1: we've asked that question, hundreds of thousands of times and

756
00:38:58,160 --> 00:39:01,359
Speaker 1: studied millions of things about people based on their height.

757
00:39:01,440 --> 00:39:04,799
Speaker 1: We find that there are indisputable relationships between your height

758
00:39:04,920 --> 00:39:08,360
Speaker 1: and your consumer behavior. For example, can you remember any

759
00:39:08,400 --> 00:39:12,040
Speaker 1: specific correlations. Um, Yeah, some of them are funny, I mean,

760
00:39:12,280 --> 00:39:14,560
Speaker 1: very obvious ones. Shorter people are less likely to go

761
00:39:14,640 --> 00:39:19,080
Speaker 1: see live music because they can't they can't see it.

762
00:39:19,280 --> 00:39:22,240
Speaker 1: I think they see your answer. That's probably your answer. Actually,

763
00:39:22,239 --> 00:39:25,640
Speaker 1: the way we the insight was that shorter people were

764
00:39:25,640 --> 00:39:30,120
Speaker 1: actually more likely to watch access concerts on television. Now,

765
00:39:30,160 --> 00:39:34,279
Speaker 1: in the music business, we've gone to so called, you know,

766
00:39:34,719 --> 00:39:38,960
Speaker 1: festival seating in that the people stand. I personally hate it.

767
00:39:39,000 --> 00:39:40,960
Speaker 1: I remember the old days when there was a seat

768
00:39:41,000 --> 00:39:45,239
Speaker 1: at the fill More East. Now the live companies would

769
00:39:45,239 --> 00:39:47,080
Speaker 1: tell you they can only put in certain number of

770
00:39:47,080 --> 00:39:50,120
Speaker 1: people because of fire marshal rules. That's a bunch of crap.

771
00:39:50,360 --> 00:39:53,919
Speaker 1: They squeeze more people in if they're standing. But would

772
00:39:53,960 --> 00:39:56,799
Speaker 1: their business be increased if they had seats and the

773
00:39:56,800 --> 00:39:59,720
Speaker 1: short people could see One would think yeah, I mean certainly.

774
00:39:59,719 --> 00:40:03,760
Speaker 1: If is an extent to which there's a person choosing

775
00:40:03,800 --> 00:40:05,759
Speaker 1: not to buy a ticket to a concert because they

776
00:40:05,760 --> 00:40:07,120
Speaker 1: don't feel like they're going to be able to see

777
00:40:07,120 --> 00:40:09,239
Speaker 1: well enough when they get there. That's what seemed to

778
00:40:09,239 --> 00:40:12,880
Speaker 1: me to be a pretty obvious measure for them to take. Wow,

779
00:40:13,080 --> 00:40:16,480
Speaker 1: that's very stimulating. Let's go to the reverse. Okay, No,

780
00:40:16,640 --> 00:40:20,040
Speaker 1: it really is, because I think this goes back to

781
00:40:20,080 --> 00:40:25,360
Speaker 1: your point that you're advising or working with household named companies,

782
00:40:25,600 --> 00:40:28,520
Speaker 1: Silicon Valley companies. We think of all the data, but

783
00:40:29,000 --> 00:40:32,520
Speaker 1: their insights. I mean, there's a lot of there's this

784
00:40:32,560 --> 00:40:34,799
Speaker 1: is something I have a lot of institutional beliefs that

785
00:40:34,880 --> 00:40:37,239
Speaker 1: I don't think are true, and the data can help

786
00:40:37,280 --> 00:40:44,120
Speaker 1: prove otherwise we are. There are certainly truisms um that

787
00:40:44,160 --> 00:40:48,520
Speaker 1: have at least evolved, if not are inherently untrue. UM

788
00:40:48,840 --> 00:40:51,239
Speaker 1: Like what really putting me on the spots some of

789
00:40:51,239 --> 00:40:56,359
Speaker 1: these No, I mean, I'll I'll think of um, well,

790
00:40:56,400 --> 00:41:00,799
Speaker 1: we don't millennials today, so hate you know, I feel

791
00:41:00,800 --> 00:41:02,080
Speaker 1: like I talked about it all them all the time,

792
00:41:02,080 --> 00:41:03,960
Speaker 1: because that's so much of what our clients want to

793
00:41:04,000 --> 00:41:07,400
Speaker 1: know about. But this sort of trend towards introversion among

794
00:41:07,440 --> 00:41:10,520
Speaker 1: consumers is really being driven by a millennial population. They're

795
00:41:10,520 --> 00:41:13,600
Speaker 1: going to bed earlier, they're waking up earlier, they're drinking

796
00:41:13,680 --> 00:41:17,800
Speaker 1: less um. There are lots of trends about young people

797
00:41:17,880 --> 00:41:22,200
Speaker 1: today that UM and younger, I mean young could be

798
00:41:22,239 --> 00:41:27,480
Speaker 1: anywhere from you know, thirty five and under that UM

799
00:41:27,640 --> 00:41:32,920
Speaker 1: are perceived to be a function of them their generation. Oh,

800
00:41:32,960 --> 00:41:38,239
Speaker 1: millennials are aren't going to drive cars because millennials, well, uh,

801
00:41:38,600 --> 00:41:40,920
Speaker 1: we want to draw distinction between what's a generation and

802
00:41:40,960 --> 00:41:44,080
Speaker 1: what's a life stage phenomenon. So, yeah, twenty four year

803
00:41:44,080 --> 00:41:45,480
Speaker 1: olds don't want to drive cars. I don't want to

804
00:41:45,520 --> 00:41:47,360
Speaker 1: drive a car when I was twenty four either, But

805
00:41:47,000 --> 00:41:48,640
Speaker 1: but all of a sudden, you grow up and you

806
00:41:48,640 --> 00:41:50,000
Speaker 1: have kids and you've got to truck them around a

807
00:41:50,040 --> 00:41:52,200
Speaker 1: soccer practice. Guess what, Millennials are starting to buy cars.

808
00:41:52,600 --> 00:41:54,839
Speaker 1: There was this, There were you know, gloom and doom

809
00:41:54,920 --> 00:41:58,200
Speaker 1: stories ten years ago that the auto manufacturers were screwed

810
00:41:58,239 --> 00:42:00,200
Speaker 1: because millennials were just going to take uber and ride

811
00:42:00,239 --> 00:42:02,680
Speaker 1: public transportation everywhere they went, and it's just not true.

812
00:42:03,239 --> 00:42:05,759
Speaker 1: We had a call a few years ago from the

813
00:42:05,800 --> 00:42:08,399
Speaker 1: director of a very large zoo who called to ask

814
00:42:08,480 --> 00:42:10,960
Speaker 1: why millennials weren't coming to the zoo. Now this might

815
00:42:10,960 --> 00:42:13,879
Speaker 1: have been longer six seven years ago. They were freaking

816
00:42:13,880 --> 00:42:16,240
Speaker 1: out because millennials weren't coming to the zoo, And I said,

817
00:42:16,760 --> 00:42:19,200
Speaker 1: year olds ever come to the zoo? Right? You know?

818
00:42:19,840 --> 00:42:23,520
Speaker 1: We we want to draw these these sort of hyperbolic

819
00:42:23,800 --> 00:42:28,160
Speaker 1: conclusions about things that quote unquote millennials are doing today.

820
00:42:28,239 --> 00:42:30,399
Speaker 1: But there were twenty two year old millennials. I mean,

821
00:42:30,480 --> 00:42:33,360
Speaker 1: so millennials technically what roughly eighteen to thirty four today?

822
00:42:33,520 --> 00:42:35,879
Speaker 1: I think of the life stages that encompasses. I could

823
00:42:35,880 --> 00:42:38,480
Speaker 1: be twenty two, smoking pots, sitting on my parents couch,

824
00:42:39,080 --> 00:42:41,560
Speaker 1: or I could be thirty three with two kids and

825
00:42:41,600 --> 00:42:44,879
Speaker 1: a mortgage and two cars in my garage. And so

826
00:42:44,960 --> 00:42:48,960
Speaker 1: to make some blanket statements about what's definitively millennial versus

827
00:42:48,960 --> 00:42:52,560
Speaker 1: definitively gen X versus baby boomers, A, it's a okay,

828
00:42:52,560 --> 00:42:55,440
Speaker 1: but let's go on a broad scale. In what ways

829
00:42:55,560 --> 00:42:58,799
Speaker 1: are millennials different from the generations they proceeded them? Well,

830
00:42:58,840 --> 00:43:04,239
Speaker 1: certainly digital connectivity and media consumption are absolutely true. Um,

831
00:43:04,280 --> 00:43:09,520
Speaker 1: these these these phenomena of the stay at home economy

832
00:43:09,640 --> 00:43:15,120
Speaker 1: and the the tribalism I mentioned before. Whether they're causing

833
00:43:15,160 --> 00:43:18,000
Speaker 1: it or whether they will be the first generation to

834
00:43:18,200 --> 00:43:20,839
Speaker 1: live fully within the confines of that, I don't know,

835
00:43:20,920 --> 00:43:23,160
Speaker 1: but it's certainly going to be the latter. They are

836
00:43:23,200 --> 00:43:27,040
Speaker 1: definitely going to be. They are driving a an era

837
00:43:27,360 --> 00:43:33,400
Speaker 1: of UM people staying in echo chambers, UM confining themselves

838
00:43:33,480 --> 00:43:37,000
Speaker 1: to a smaller group of people that they can curate

839
00:43:37,120 --> 00:43:39,600
Speaker 1: and can same thing with their media consumption. But you

840
00:43:39,640 --> 00:43:44,000
Speaker 1: can't open a major publication without people either selling or

841
00:43:44,080 --> 00:43:49,400
Speaker 1: analyzing ways to interact with millennials. Like especially millennials. You know,

842
00:43:49,480 --> 00:43:51,960
Speaker 1: you can't say anything negative to them. They're gonna cry,

843
00:43:52,000 --> 00:43:54,600
Speaker 1: They're gonna quit. Is that all horseshit or is that true?

844
00:43:54,760 --> 00:43:57,480
Speaker 1: I think that's a little overblown. I mean, most of

845
00:43:57,520 --> 00:43:59,640
Speaker 1: the people in my company fit the bill, fit the

846
00:43:59,680 --> 00:44:01,359
Speaker 1: deaf and isation of a millennial, and I don't find

847
00:44:01,360 --> 00:44:03,319
Speaker 1: that to be true at all. I think those are

848
00:44:03,480 --> 00:44:07,080
Speaker 1: those are fun stories for people to tell. I don't.

849
00:44:07,120 --> 00:44:11,400
Speaker 1: I don't have the gloomy view of, you know, millennials

850
00:44:11,560 --> 00:44:13,759
Speaker 1: killing the fabric of America or that will you read

851
00:44:13,800 --> 00:44:17,880
Speaker 1: a lot of I don't. It's I think it's overblown. UM.

852
00:44:17,920 --> 00:44:20,600
Speaker 1: I'll tell you another another thing that's that's maybe a

853
00:44:20,640 --> 00:44:24,400
Speaker 1: truism that we can dispel is that one of the

854
00:44:24,680 --> 00:44:27,120
Speaker 1: this is a huge sort of shifted topic. One of

855
00:44:27,120 --> 00:44:29,680
Speaker 1: the biggest fallacies in the world today is that Donald

856
00:44:29,680 --> 00:44:32,200
Speaker 1: Trump uses Twitter to talk to the American people. The

857
00:44:32,239 --> 00:44:36,040
Speaker 1: American people are not on Twitter. Of Americans at most

858
00:44:36,040 --> 00:44:38,759
Speaker 1: are on Twitter, even of his voters, give or take.

859
00:44:39,480 --> 00:44:43,520
Speaker 1: He is using Twitter to talk to the media who

860
00:44:43,560 --> 00:44:46,200
Speaker 1: carry his message to their little tribes of people who

861
00:44:46,200 --> 00:44:50,840
Speaker 1: read it, and so he doesn't care. And it's actually

862
00:44:50,920 --> 00:44:52,680
Speaker 1: quite brilliant. I'd have to tip my hat to the

863
00:44:52,680 --> 00:44:55,400
Speaker 1: way he's he's he's he handles it. I mean, he

864
00:44:55,960 --> 00:44:58,080
Speaker 1: tweets at three o'clock in the morning because he knows

865
00:44:58,080 --> 00:45:00,120
Speaker 1: he's setting the media landscape for the He's telling the

866
00:45:00,120 --> 00:45:01,920
Speaker 1: media what they're going to talk about that day. And

867
00:45:01,960 --> 00:45:03,920
Speaker 1: he doesn't care what CNN says about him because his

868
00:45:03,920 --> 00:45:08,040
Speaker 1: followers aren't watching CNN anyway. People don't trust information anymore.

869
00:45:08,080 --> 00:45:12,440
Speaker 1: They trust the distributors of that information. And so Donald

870
00:45:12,440 --> 00:45:16,240
Speaker 1: Trump seeds has ferry dust into Twitter, and those publications

871
00:45:16,320 --> 00:45:21,240
Speaker 1: run it to their individual tribes of readers. One um

872
00:45:21,280 --> 00:45:24,000
Speaker 1: one of the more profound trends that we see today

873
00:45:24,120 --> 00:45:27,560
Speaker 1: is even as even as consumption of every other form

874
00:45:27,600 --> 00:45:30,960
Speaker 1: of media is in decline, television ratings or down, sports

875
00:45:31,040 --> 00:45:34,799
Speaker 1: or down, newspaper readership is down radio listening is down.

876
00:45:35,239 --> 00:45:37,520
Speaker 1: The one thing that is skyrocketing is the readership of

877
00:45:37,520 --> 00:45:42,520
Speaker 1: political blogs political content online, which is a is a

878
00:45:42,840 --> 00:45:47,680
Speaker 1: harbinger of an ill future for so called objective media outlets,

879
00:45:47,719 --> 00:45:51,400
Speaker 1: because I don't want objective media. I want media that

880
00:45:51,640 --> 00:45:54,840
Speaker 1: affirms my existing beliefs and I and now there's so

881
00:45:54,880 --> 00:45:56,680
Speaker 1: much variety out there that I can go find it,

882
00:45:57,080 --> 00:45:59,200
Speaker 1: and it tends to be a blog who isn't trying

883
00:45:59,200 --> 00:46:01,799
Speaker 1: to appeal to the masses. UM that can tell me

884
00:46:01,880 --> 00:46:03,759
Speaker 1: exactly what I want to hear every time I click

885
00:46:03,840 --> 00:46:06,719
Speaker 1: on the web page. And Donald Trump has come along

886
00:46:06,760 --> 00:46:11,000
Speaker 1: in an era where he's benefiting from that immensely. Um So,

887
00:46:11,000 --> 00:46:16,200
Speaker 1: so he puts out his UM two morning tweets and

888
00:46:16,440 --> 00:46:18,640
Speaker 1: it's carried directly to the people he wanted wants to

889
00:46:18,680 --> 00:46:21,120
Speaker 1: carry it. Is he you believe he's consciously doing that?

890
00:46:21,960 --> 00:46:24,880
Speaker 1: I mean I think so. I think he knows that

891
00:46:24,920 --> 00:46:31,200
Speaker 1: he's communicating to his supporters through UM through the megaphones

892
00:46:31,280 --> 00:46:34,719
Speaker 1: of the media outlets that support him. What is your

893
00:46:34,719 --> 00:46:37,719
Speaker 1: research told you about the mid term elections? Well, uh,

894
00:46:37,920 --> 00:46:39,920
Speaker 1: funny you should ask that, because just today I was

895
00:46:39,960 --> 00:46:43,200
Speaker 1: reading some report on some of our data that is

896
00:46:43,239 --> 00:46:48,799
Speaker 1: speaking of um, we're seeing after sixteen months of very

897
00:46:48,840 --> 00:46:52,640
Speaker 1: positive growth and consumer sentiment, we've started to see over

898
00:46:52,680 --> 00:46:56,879
Speaker 1: the last month of fairly significant decline consumer confidence, economics sense,

899
00:46:56,920 --> 00:46:59,600
Speaker 1: people's attitudes or the job market and their personal finances,

900
00:46:59,600 --> 00:47:01,440
Speaker 1: and the out for the U s economy seems to

901
00:47:01,480 --> 00:47:05,600
Speaker 1: have peaked and is now beginning to slide. Um donivity

902
00:47:06,000 --> 00:47:09,279
Speaker 1: flawed as to why that is. Um, Well, I think

903
00:47:09,320 --> 00:47:11,799
Speaker 1: there was an artificial high in the first place. There

904
00:47:11,840 --> 00:47:13,239
Speaker 1: was a lot of sizzle, not a whole lot of

905
00:47:13,239 --> 00:47:16,640
Speaker 1: steak in terms of there was just optimism for optimism's sake.

906
00:47:16,760 --> 00:47:20,080
Speaker 1: There was a large sector of the US consumer population

907
00:47:20,080 --> 00:47:23,560
Speaker 1: who just believed things would get better. They tended to

908
00:47:23,560 --> 00:47:25,759
Speaker 1: be Republicans and the people who voted for Donald Trump

909
00:47:25,800 --> 00:47:28,120
Speaker 1: in the first place, even though they maybe not necessarily

910
00:47:28,120 --> 00:47:32,160
Speaker 1: had any reason to feel that way. Um. And so

911
00:47:32,880 --> 00:47:38,080
Speaker 1: we're next Tuesday in my hometown. There's a very significant

912
00:47:38,600 --> 00:47:42,160
Speaker 1: mid term special congressional election that the President's going there

913
00:47:42,160 --> 00:47:45,920
Speaker 1: on Saturday. Joe Biden was there yesterday, and I'm we're

914
00:47:45,920 --> 00:47:48,840
Speaker 1: not in the political prognostication business, but there's reason to

915
00:47:48,880 --> 00:47:52,040
Speaker 1: believe that this downturn in consumer sentiment because we do

916
00:47:52,120 --> 00:47:55,240
Speaker 1: know that that effects election cycles could have an impact

917
00:47:55,480 --> 00:47:57,719
Speaker 1: as as soon as next Tuesday on on a very

918
00:47:57,760 --> 00:48:01,680
Speaker 1: important it's a razor thin margin in that election, and

919
00:48:01,719 --> 00:48:03,759
Speaker 1: that could be the kind of downturn that might tip

920
00:48:03,760 --> 00:48:09,080
Speaker 1: it in the favor of the Democrat and Trump himself.

921
00:48:09,560 --> 00:48:12,840
Speaker 1: Now we read about his approval rating constantly. Is that

922
00:48:12,920 --> 00:48:16,640
Speaker 1: worth anything? Sure? I think so? I mean, is it

923
00:48:16,680 --> 00:48:21,440
Speaker 1: accurate the aggregate of If you take and one thing

924
00:48:21,440 --> 00:48:23,640
Speaker 1: we admire a lot about Nate Silver is he does

925
00:48:23,719 --> 00:48:27,000
Speaker 1: take into consideration a lot of different sources, and if

926
00:48:27,000 --> 00:48:29,520
Speaker 1: you normalize for the biases of one versus the other,

927
00:48:29,600 --> 00:48:34,560
Speaker 1: and those numbers, his approval numbers are fairly consistent. Um,

928
00:48:34,760 --> 00:48:37,760
Speaker 1: what matters more is whether who shows up to vote.

929
00:48:38,239 --> 00:48:41,239
Speaker 1: His approval ratings have haven't barged much at all. And

930
00:48:41,239 --> 00:48:44,000
Speaker 1: and and frankly, you know, there's what I think. The

931
00:48:44,080 --> 00:48:47,799
Speaker 1: estimate is about thirty of US adults actually voted for him.

932
00:48:47,960 --> 00:48:51,160
Speaker 1: If you can take into consideration the the people who

933
00:48:51,160 --> 00:48:52,799
Speaker 1: are registered to vote, in the portion that actually came

934
00:48:52,800 --> 00:48:54,439
Speaker 1: out and voted for him, we're talking about just over

935
00:48:54,520 --> 00:48:57,600
Speaker 1: one third of the entire US population. And that's roughly

936
00:48:57,640 --> 00:48:59,799
Speaker 1: give or take three or four points at a time,

937
00:48:59,840 --> 00:49:02,320
Speaker 1: that people who still approve of him, And so public

938
00:49:02,320 --> 00:49:05,960
Speaker 1: approval doesn't mean anything unless people vote, and we know

939
00:49:06,040 --> 00:49:08,560
Speaker 1: that they don't. And and the biggest problem that posters

940
00:49:08,600 --> 00:49:11,960
Speaker 1: have isn't can I measure public opinion? It's can I

941
00:49:12,000 --> 00:49:14,440
Speaker 1: predict who's going to turn out on election day? Okay,

942
00:49:14,480 --> 00:49:17,759
Speaker 1: we talked about millennials. What have we learned about baby boomers? Well,

943
00:49:17,800 --> 00:49:20,759
Speaker 1: I have to um putting my personal points of view

944
00:49:20,800 --> 00:49:23,800
Speaker 1: aside on that. Um, you consider yourself a gen x er.

945
00:49:23,880 --> 00:49:25,840
Speaker 1: I'm a gen xer. So should we start when we

946
00:49:25,920 --> 00:49:30,680
Speaker 1: learned about gen xers? Yeah? I mean, UM, conventional wisdom

947
00:49:30,719 --> 00:49:34,040
Speaker 1: would be there are a smaller bulge than the baby boomers.

948
00:49:34,360 --> 00:49:36,800
Speaker 1: They were in the wake of the baby boomers. They

949
00:49:36,800 --> 00:49:39,160
Speaker 1: feel on some level they missed out on woodstock in

950
00:49:39,160 --> 00:49:42,240
Speaker 1: the heyday, and they feel that the baby boomers raped

951
00:49:42,239 --> 00:49:44,879
Speaker 1: in pillage to their detriment. I think the latter is true.

952
00:49:44,920 --> 00:49:47,239
Speaker 1: I'm not necessarily sure. We feel feel like we missed

953
00:49:47,239 --> 00:49:49,880
Speaker 1: out on much. Um. I think we feel like we're

954
00:49:50,360 --> 00:49:53,320
Speaker 1: being we're responsible to clean up one group's mess to

955
00:49:53,360 --> 00:49:55,040
Speaker 1: make things better for another group, and we're not going

956
00:49:55,120 --> 00:49:57,759
Speaker 1: to get a benefit from either of them. That is

957
00:49:57,760 --> 00:49:59,640
Speaker 1: not a data that is a purely that is a

958
00:49:59,719 --> 00:50:02,080
Speaker 1: pure a subjective point of view of my own on

959
00:50:02,080 --> 00:50:05,200
Speaker 1: on the subject um. But we do see um. So

960
00:50:05,320 --> 00:50:07,600
Speaker 1: bringing it back to data, we see low we see

961
00:50:07,680 --> 00:50:12,080
Speaker 1: much lower divorce rates among gen xers. We see um

962
00:50:12,200 --> 00:50:16,400
Speaker 1: uh if you look at sort of declines in crime

963
00:50:16,480 --> 00:50:19,680
Speaker 1: rates over the last twenty years in the US. I'm

964
00:50:19,680 --> 00:50:22,120
Speaker 1: not saying that we're a more virtuous generation. We just

965
00:50:22,200 --> 00:50:25,960
Speaker 1: maybe didn't have as much time to to We're we're

966
00:50:26,000 --> 00:50:29,279
Speaker 1: all very busy. Um. What we do see one of

967
00:50:29,280 --> 00:50:31,800
Speaker 1: the biggest trends in the in the that's being driven

968
00:50:31,800 --> 00:50:34,879
Speaker 1: by gen X is the shift in um gender roles

969
00:50:34,920 --> 00:50:39,120
Speaker 1: in the family. Household responsibilities are shifting more and more

970
00:50:39,160 --> 00:50:41,799
Speaker 1: towards women, and men aren't picking up the slack um.

971
00:50:41,840 --> 00:50:45,000
Speaker 1: And that doesn't mean you know, uh, women are making

972
00:50:45,040 --> 00:50:49,200
Speaker 1: more larger household decisions about real estate, banking, things that

973
00:50:49,320 --> 00:50:53,040
Speaker 1: used to be kind of traditionally male decisions. Women are

974
00:50:53,080 --> 00:50:55,040
Speaker 1: making more of those decisions than they did in the past,

975
00:50:55,080 --> 00:50:57,319
Speaker 1: and men aren't doing much more. I mean, there are

976
00:50:57,480 --> 00:50:59,960
Speaker 1: men doing more cooking and more grocery shopping, but they're

977
00:51:00,000 --> 00:51:04,160
Speaker 1: not doing it at a rate that outweighs the increased

978
00:51:04,200 --> 00:51:07,319
Speaker 1: responsibility women are taking on in households. Um. I've we

979
00:51:07,360 --> 00:51:09,279
Speaker 1: have a piece that we wrote a couple of years

980
00:51:09,280 --> 00:51:11,239
Speaker 1: ago that still gets circulated around a lot, and it

981
00:51:11,320 --> 00:51:15,799
Speaker 1: was entitled women are getting a terrible deal because of that. Um,

982
00:51:15,840 --> 00:51:21,120
Speaker 1: you've got multi um spouse working or multi partner working households.

983
00:51:21,440 --> 00:51:24,640
Speaker 1: Women are bringing our while still making less money than

984
00:51:24,680 --> 00:51:27,080
Speaker 1: men overall, are starting to make more money and carrying

985
00:51:27,080 --> 00:51:30,000
Speaker 1: as much responsibility for households, and men are starting to

986
00:51:30,040 --> 00:51:32,400
Speaker 1: be to be free riders a little bit more than

987
00:51:32,440 --> 00:51:34,520
Speaker 1: we were before. And to the degree you've researched this,

988
00:51:34,600 --> 00:51:40,000
Speaker 1: what about the me too movement? Um, we don't. We

989
00:51:40,080 --> 00:51:44,319
Speaker 1: don't research that very much. Um, because it's unresearchable. Your

990
00:51:44,360 --> 00:51:46,600
Speaker 1: clients don't demand it now, I mean we don't, just

991
00:51:46,719 --> 00:51:49,680
Speaker 1: you know, we we don't. A lot of the not

992
00:51:49,719 --> 00:51:51,680
Speaker 1: all the research we do is because clients demanded a

993
00:51:51,680 --> 00:51:53,600
Speaker 1: lot of our clients say go research what's important and

994
00:51:53,640 --> 00:51:56,040
Speaker 1: come back and tell us the truth. You know, we are.

995
00:51:56,080 --> 00:51:59,400
Speaker 1: We are out there searching for truth and then we

996
00:51:59,440 --> 00:52:01,480
Speaker 1: share it with the people we think can benefit from

997
00:52:01,480 --> 00:52:04,719
Speaker 1: the knowledge. And I think the me too movement is

998
00:52:04,760 --> 00:52:09,959
Speaker 1: a is somewhat related to this topic of of male

999
00:52:10,120 --> 00:52:14,640
Speaker 1: entitlement that that is pervasive or has been pervasive for years.

1000
00:52:14,640 --> 00:52:16,640
Speaker 1: I think that's starting to lift. I just don't think

1001
00:52:16,640 --> 00:52:18,280
Speaker 1: there's a whole lot we can add to the discussion

1002
00:52:18,320 --> 00:52:24,799
Speaker 1: about it. Um it's a huge problem. It's wrong, it's um.

1003
00:52:24,840 --> 00:52:27,360
Speaker 1: There's just not a lot of nuance that any data

1004
00:52:27,400 --> 00:52:30,520
Speaker 1: is going to say about um, how right or wrong

1005
00:52:30,680 --> 00:52:32,800
Speaker 1: or how wrong it is. Okay, so what about baby boomers?

1006
00:52:32,840 --> 00:52:35,400
Speaker 1: You remember any insights on the baby boomers? Well, I

1007
00:52:35,400 --> 00:52:40,719
Speaker 1: mean yes, Um, so baby boomers are much closer to

1008
00:52:40,800 --> 00:52:43,880
Speaker 1: their kids than generations before them, and that has a

1009
00:52:43,880 --> 00:52:47,480
Speaker 1: lot of effect on how they're making decisions about their

1010
00:52:47,480 --> 00:52:51,920
Speaker 1: health care and and everything from their consumer decisions. We

1011
00:52:52,000 --> 00:52:55,080
Speaker 1: work with a number of healthcare companies who sell devices.

1012
00:52:55,680 --> 00:52:58,840
Speaker 1: I'll use an example of like a sleep apnea company.

1013
00:52:59,320 --> 00:53:01,399
Speaker 1: They have a sleep at the machine. So for years

1014
00:53:01,400 --> 00:53:03,520
Speaker 1: and years and years, you would advertise to the person

1015
00:53:03,560 --> 00:53:07,000
Speaker 1: who suffered from sleep apnea, Well should you really or

1016
00:53:07,000 --> 00:53:10,560
Speaker 1: should you advertise to the spouse who lives with the

1017
00:53:10,600 --> 00:53:13,720
Speaker 1: person sleep apnea? And so we've seen that shift, of course.

1018
00:53:14,239 --> 00:53:17,520
Speaker 1: But but but across other areas of healthcare, when it

1019
00:53:17,520 --> 00:53:19,959
Speaker 1: comes to medical devices, like say, say maybe not sleep

1020
00:53:19,960 --> 00:53:22,120
Speaker 1: apnea where I can't sleep because the person beside me

1021
00:53:22,160 --> 00:53:26,480
Speaker 1: is snoring. But other ailments like COPD or what have you. UM,

1022
00:53:26,680 --> 00:53:32,719
Speaker 1: the the millennial is of significant influence over the baby

1023
00:53:32,760 --> 00:53:36,000
Speaker 1: boomers decisions, and they're because they're closer than they were before.

1024
00:53:36,239 --> 00:53:39,960
Speaker 1: So we are challenging our our clients to think a

1025
00:53:40,000 --> 00:53:45,359
Speaker 1: lot about UM advertising or influencing baby boomers by influencing

1026
00:53:45,400 --> 00:53:47,920
Speaker 1: the people who influence them. And and that is a

1027
00:53:47,960 --> 00:53:51,040
Speaker 1: trend that we've seen shift quite a bit. So ironically,

1028
00:53:51,080 --> 00:53:54,720
Speaker 1: because baby boomers think they know everything, they don't. Well,

1029
00:53:54,920 --> 00:53:57,840
Speaker 1: they may still think they do, but they are influenced

1030
00:53:57,880 --> 00:53:59,719
Speaker 1: by other things. They are they are, and a lot

1031
00:53:59,800 --> 00:54:02,719
Speaker 1: of it is driven by the closeness of the relationships

1032
00:54:02,760 --> 00:54:04,759
Speaker 1: they've maintained with their kids, which is a very good thing.

1033
00:54:04,920 --> 00:54:10,279
Speaker 1: By the way, UM that there is UM they're they're

1034
00:54:10,280 --> 00:54:15,239
Speaker 1: either reliant on their younger children or they've just had

1035
00:54:15,239 --> 00:54:18,759
Speaker 1: a closer relationship through as their kids got older. And

1036
00:54:18,760 --> 00:54:21,399
Speaker 1: when you hear the stories of the millennials who are still,

1037
00:54:21,640 --> 00:54:24,360
Speaker 1: you know, living in their parents houses, there's one of

1038
00:54:24,360 --> 00:54:26,440
Speaker 1: the positive ends of that is the relationships between them

1039
00:54:26,440 --> 00:54:31,240
Speaker 1: tend to be pretty good, pretty strong. Okay, we'll return

1040
00:54:31,280 --> 00:54:34,360
Speaker 1: to this conversation with John Dick, CEO of Civic Science,

1041
00:54:34,640 --> 00:54:37,799
Speaker 1: in a moment. Hi, this is Bob left sense. My

1042
00:54:37,840 --> 00:54:40,400
Speaker 1: guests come to the tune In studios in Venice, California

1043
00:54:40,600 --> 00:54:42,799
Speaker 1: to have these conversations with me. And if you ever

1044
00:54:42,800 --> 00:54:44,520
Speaker 1: want to see what they look like where we take

1045
00:54:44,600 --> 00:54:48,000
Speaker 1: the show, check out the photos and videos search at

1046
00:54:48,040 --> 00:54:52,319
Speaker 1: tune In on Twitter, Facebook and Instagram. And now more

1047
00:54:52,400 --> 00:54:58,000
Speaker 1: with CEO of Civic Science, John Dick, And what about

1048
00:54:58,000 --> 00:55:00,799
Speaker 1: more macro trends. Who you mentioned cars? What is the

1049
00:55:00,840 --> 00:55:03,759
Speaker 1: future of audibobiles because we read all the time in

1050
00:55:03,800 --> 00:55:06,960
Speaker 1: the straight media that you know, autonomous driving is coming,

1051
00:55:08,080 --> 00:55:11,640
Speaker 1: I think, okay, so that's definitely something we studied very closely.

1052
00:55:12,320 --> 00:55:14,800
Speaker 1: The vast majority of people are still very scared about

1053
00:55:14,840 --> 00:55:17,279
Speaker 1: autonomous vehicles, particularly women, do not want to get in

1054
00:55:17,320 --> 00:55:19,520
Speaker 1: a car that's driving itself. By the same time, and

1055
00:55:19,600 --> 00:55:22,520
Speaker 1: we see things slip overnight, like we heard we heard

1056
00:55:23,120 --> 00:55:26,040
Speaker 1: digital photography was gonna place film for at least ten years,

1057
00:55:26,200 --> 00:55:28,080
Speaker 1: and then with the space of twelve months, seemed like

1058
00:55:28,120 --> 00:55:31,960
Speaker 1: film disappeared and digital came upon us. So a lot

1059
00:55:31,960 --> 00:55:34,640
Speaker 1: of people are fearful, but then when the product comes

1060
00:55:34,640 --> 00:55:36,719
Speaker 1: in their influences like a O. Well, you know that

1061
00:55:36,960 --> 00:55:38,560
Speaker 1: no one was online that all of a sudden, for

1062
00:55:38,560 --> 00:55:40,799
Speaker 1: a couple of years, everyone was buying a computer just

1063
00:55:40,880 --> 00:55:44,000
Speaker 1: to get online. So I'm just when people are fearful

1064
00:55:44,040 --> 00:55:46,520
Speaker 1: of something, could the switch be faster than it used

1065
00:55:46,560 --> 00:55:48,040
Speaker 1: to be? It could be, but it also, in the

1066
00:55:48,080 --> 00:55:50,400
Speaker 1: case of autonomous vehicles, could go the other direction to

1067
00:55:50,800 --> 00:55:54,480
Speaker 1: it only takes one hacker and some eastern block country

1068
00:55:54,560 --> 00:55:56,280
Speaker 1: to take over a bunch of cars with a computer

1069
00:55:56,360 --> 00:55:57,799
Speaker 1: and drive them into a crowd of people that no

1070
00:55:57,800 --> 00:55:59,319
Speaker 1: one's ever gonna want to get into a driver less

1071
00:55:59,400 --> 00:56:03,000
Speaker 1: vehicle for twenty years. I think that there's there's enough

1072
00:56:03,080 --> 00:56:08,000
Speaker 1: fear and and skepticism about autonomous vehicles and artificial intelligence

1073
00:56:08,000 --> 00:56:10,600
Speaker 1: in general that a couple of bad pr nightmares for

1074
00:56:10,640 --> 00:56:12,440
Speaker 1: one of those cars doing what it's not supposed to

1075
00:56:12,520 --> 00:56:15,080
Speaker 1: could kill it. Um there's no reason to believe that

1076
00:56:15,080 --> 00:56:18,160
Speaker 1: will happen, but it's you could certainly foresee a situation

1077
00:56:18,239 --> 00:56:21,680
Speaker 1: where whatever momentum driverless cars are gaining could be stopped

1078
00:56:21,680 --> 00:56:25,640
Speaker 1: in its tracks. I think it's more likely we'll see

1079
00:56:25,680 --> 00:56:29,919
Speaker 1: an evolution towards autonomous vehicles like we see evolution toward

1080
00:56:29,960 --> 00:56:34,720
Speaker 1: alternative fuels um our alternative energy UM. You've got huge

1081
00:56:35,000 --> 00:56:37,800
Speaker 1: infrastructure in the automobile industry and the fuel industry that

1082
00:56:37,840 --> 00:56:41,200
Speaker 1: are going to slow a lot of that progress down UM.

1083
00:56:41,239 --> 00:56:43,560
Speaker 1: And then you've got a lot of people who are

1084
00:56:43,640 --> 00:56:48,759
Speaker 1: just skeptical about or or simply uncomfortable with the idea

1085
00:56:48,800 --> 00:56:50,640
Speaker 1: of being in a car that they can't control. So

1086
00:56:50,680 --> 00:56:52,360
Speaker 1: I don't expect it to be an overnight thing, And

1087
00:56:52,400 --> 00:56:54,759
Speaker 1: there's nothing in our evidence. The evidence in our data

1088
00:56:54,840 --> 00:56:58,560
Speaker 1: is that the percentage of people who are comfortable with

1089
00:56:58,640 --> 00:57:01,120
Speaker 1: a driverless car has not grown much in the last

1090
00:57:01,160 --> 00:57:04,200
Speaker 1: two years. It's the same group of people. I vastly

1091
00:57:04,239 --> 00:57:07,000
Speaker 1: disagree with this, okay, just because I think that's what

1092
00:57:07,080 --> 00:57:11,000
Speaker 1: people believe. But I think when you know, we have

1093
00:57:11,120 --> 00:57:14,120
Speaker 1: the Tesla accident in Florida where the guy crashed into

1094
00:57:14,160 --> 00:57:17,480
Speaker 1: a car, these stories are really amplified. And of course

1095
00:57:17,480 --> 00:57:19,840
Speaker 1: we're talking about the pace of changes more quickly. It

1096
00:57:20,000 --> 00:57:22,439
Speaker 1: happens more quickly these days. But if you go back

1097
00:57:22,480 --> 00:57:25,680
Speaker 1: to even in the commercial jet age in the sixties,

1098
00:57:26,040 --> 00:57:29,240
Speaker 1: they did not have the flight control that they have today,

1099
00:57:29,240 --> 00:57:33,000
Speaker 1: and they had accidents. Okay, people were fearful. They're still

1100
00:57:33,040 --> 00:57:35,600
Speaker 1: fearful today, even the odds of having an accident are

1101
00:57:35,640 --> 00:57:37,840
Speaker 1: really low, but the barrier to entry to getting a

1102
00:57:37,840 --> 00:57:41,600
Speaker 1: ticket because deregulation in in airlines, there's a there's a

1103
00:57:41,600 --> 00:57:44,160
Speaker 1: lot of things that may not be directly applicable. But

1104
00:57:44,240 --> 00:57:47,440
Speaker 1: when the person next door gets a car or can

1105
00:57:47,720 --> 00:57:51,000
Speaker 1: even better, can call up a car like Uber, and

1106
00:57:51,000 --> 00:57:52,160
Speaker 1: you don't have to own a car, you don't have

1107
00:57:52,200 --> 00:57:54,320
Speaker 1: to have a garage. I think the pace of change

1108
00:57:54,640 --> 00:57:56,600
Speaker 1: happens very quickly. I mean I look at this like

1109
00:57:56,640 --> 00:58:00,080
Speaker 1: the Internet. Most of the public was not Internet savvy,

1110
00:58:00,520 --> 00:58:04,600
Speaker 1: and over the course of one to five years, everybody

1111
00:58:04,640 --> 00:58:07,680
Speaker 1: was Internet savvy. So I'm not sure I agree with that.

1112
00:58:07,680 --> 00:58:09,880
Speaker 1: What about climate change? You learn anything with climate change?

1113
00:58:09,960 --> 00:58:12,680
Speaker 1: It's not something we study a lot um. People are

1114
00:58:12,680 --> 00:58:14,880
Speaker 1: pretty setting their mindsets about that. We don't see a

1115
00:58:14,920 --> 00:58:17,680
Speaker 1: lot of change. I will go back, so I appreciate

1116
00:58:17,720 --> 00:58:20,080
Speaker 1: your your argument on the drive and let me let

1117
00:58:20,120 --> 00:58:21,680
Speaker 1: me say that I'll be the first person to call

1118
00:58:21,720 --> 00:58:24,280
Speaker 1: that driverless car to come pick me up. So I'm

1119
00:58:24,360 --> 00:58:26,640
Speaker 1: hoping it comes as fast as possible because I could

1120
00:58:26,720 --> 00:58:28,640
Speaker 1: do away with my car in a second, and I

1121
00:58:28,640 --> 00:58:30,400
Speaker 1: think there's a chance that that could happen. All I'm

1122
00:58:30,400 --> 00:58:31,840
Speaker 1: saying is there's also a chance you could go the

1123
00:58:31,920 --> 00:58:34,080
Speaker 1: other direction pretty quickly. But I guess what I'm saying

1124
00:58:34,160 --> 00:58:37,840
Speaker 1: is something it's irrelevant of driverless cars. One to what

1125
00:58:38,040 --> 00:58:43,360
Speaker 1: degree our stories being amplified that ultimately have no impact. Okay,

1126
00:58:43,360 --> 00:58:45,760
Speaker 1: there was a story back when these portals used to

1127
00:58:45,840 --> 00:58:48,800
Speaker 1: be of much greater impact. I think it was on Yahoo.

1128
00:58:48,840 --> 00:58:51,400
Speaker 1: They had a big thing saying tongue splitting was a

1129
00:58:51,440 --> 00:58:54,280
Speaker 1: big thing. Okay, well, you literally split the end of

1130
00:58:54,280 --> 00:58:57,440
Speaker 1: your tongue whatever. In reality, there were fewer than I believe,

1131
00:58:57,720 --> 00:58:58,920
Speaker 1: certainly a few of the ten. I think it was

1132
00:58:58,960 --> 00:59:02,040
Speaker 1: fewer than five people who'd ever done. There wasn't really

1133
00:59:02,080 --> 00:59:04,840
Speaker 1: a trend, but people were writing about it. It's sort

1134
00:59:04,840 --> 00:59:09,240
Speaker 1: of the flip side of the uh. People with unpopular opinions,

1135
00:59:09,280 --> 00:59:12,840
Speaker 1: we're not putting them on social media so that these

1136
00:59:12,840 --> 00:59:15,320
Speaker 1: stories that are amplified. This is something that's changed in

1137
00:59:15,320 --> 00:59:17,760
Speaker 1: the Internet. There used to be something called a turntable hit. Well,

1138
00:59:17,800 --> 00:59:20,680
Speaker 1: all the gatekeepers they were going to bang something and

1139
00:59:20,680 --> 00:59:22,760
Speaker 1: it makes it looks like it's popular, but the public

1140
00:59:22,800 --> 00:59:25,400
Speaker 1: isn't buying it. Now we can see whether the public

1141
00:59:25,440 --> 00:59:27,960
Speaker 1: buys so I see. I mean, I think the New

1142
00:59:28,040 --> 00:59:29,760
Speaker 1: York Times is the best we've got. I don't want

1143
00:59:29,760 --> 00:59:31,600
Speaker 1: to make it right about right or left. It's just

1144
00:59:31,680 --> 00:59:33,920
Speaker 1: that they have boots on the ground and in all

1145
00:59:33,960 --> 00:59:38,320
Speaker 1: these places other people don't. Okay, but now more than ever,

1146
00:59:38,840 --> 00:59:41,440
Speaker 1: the mainstream media will prints. I'm not talking about printing

1147
00:59:41,640 --> 00:59:45,200
Speaker 1: hard news. Okay, there was a war here, that's something different.

1148
00:59:45,720 --> 00:59:48,400
Speaker 1: But when they print softer news, I think it has

1149
00:59:48,480 --> 00:59:51,280
Speaker 1: less impact and less accuracy than it ever did before.

1150
00:59:51,520 --> 00:59:58,480
Speaker 1: That's indisputable, and it's and it's getting worse by the day. Uh.

1151
00:59:59,040 --> 01:00:02,200
Speaker 1: Two weeks ago, people were I had half dozen friends

1152
01:00:02,200 --> 01:00:05,080
Speaker 1: of mine on social media shared a story about the

1153
01:00:05,080 --> 01:00:07,800
Speaker 1: the idea that the students in Parkland were what were

1154
01:00:07,840 --> 01:00:11,360
Speaker 1: the crisis actors? That meant there were a hundred and

1155
01:00:11,360 --> 01:00:13,480
Speaker 1: seventeen thousand The one article that I saw had been

1156
01:00:13,480 --> 01:00:16,360
Speaker 1: shared a hundred and seventeen thousand times. The average Facebook

1157
01:00:16,440 --> 01:00:19,560
Speaker 1: user has three hundred and sixty eight friends. That means

1158
01:00:19,720 --> 01:00:22,560
Speaker 1: roughly what thirty seven some million people saw that article

1159
01:00:22,640 --> 01:00:24,960
Speaker 1: from a friend, leading them to believe it was true. Right,

1160
01:00:25,040 --> 01:00:27,600
Speaker 1: So it doesn't. So so that the scale at which

1161
01:00:27,640 --> 01:00:30,280
Speaker 1: misinformation can spread is is and we and we will

1162
01:00:30,320 --> 01:00:33,040
Speaker 1: retract from that as consumers because we're gonna only get

1163
01:00:33,040 --> 01:00:35,080
Speaker 1: burned by that so many times. So you're absolutely right.

1164
01:00:35,120 --> 01:00:38,400
Speaker 1: I think that's happening, and and I think it will

1165
01:00:38,440 --> 01:00:43,800
Speaker 1: take five misguided stories about bad driverless cars probably could

1166
01:00:43,800 --> 01:00:48,520
Speaker 1: spoil some group of people. Um but but uh, the

1167
01:00:48,520 --> 01:00:50,880
Speaker 1: pace of misinformation, I don't know where that ends. By

1168
01:00:50,920 --> 01:00:53,360
Speaker 1: the way, if I hope that people wise up and

1169
01:00:53,400 --> 01:00:55,919
Speaker 1: become more scrutinizing and what they believe, but maybe they won't.

1170
01:00:56,080 --> 01:00:58,200
Speaker 1: I don't believe people. You know, it's interesting you're selling

1171
01:00:58,240 --> 01:01:00,720
Speaker 1: that an analysis. I don't believe the average person has

1172
01:01:00,760 --> 01:01:03,320
Speaker 1: the power of analysis. Oh no, That's why I was

1173
01:01:03,320 --> 01:01:05,400
Speaker 1: saying earlier. I think one of the biggest challenges we

1174
01:01:05,480 --> 01:01:09,000
Speaker 1: face as an erosion of trust of data. We talked

1175
01:01:09,040 --> 01:01:11,080
Speaker 1: so much about big data and data science and all

1176
01:01:11,080 --> 01:01:13,920
Speaker 1: of that, but I think the average consumer distrusts numbers

1177
01:01:14,040 --> 01:01:17,840
Speaker 1: because based on who puts those numbers in front of them. Um.

1178
01:01:17,960 --> 01:01:21,040
Speaker 1: We I mentioned to you earlier when we were talking

1179
01:01:21,080 --> 01:01:23,720
Speaker 1: that we were we've been approached from by media outlets

1180
01:01:23,720 --> 01:01:26,400
Speaker 1: about sort of publishing or syndicating our numbers, and we

1181
01:01:26,440 --> 01:01:28,160
Speaker 1: just won't do it because all of a sudden, we

1182
01:01:28,240 --> 01:01:32,120
Speaker 1: lose we we we inherit the perception of bias that

1183
01:01:32,200 --> 01:01:34,800
Speaker 1: those outlets have if they put their names. But the irony,

1184
01:01:34,840 --> 01:01:38,080
Speaker 1: and I'm sure you're hearing it anyway, is people believe

1185
01:01:38,120 --> 01:01:40,960
Speaker 1: you're biased anyway. True, true, But I can sleep better

1186
01:01:40,960 --> 01:01:42,480
Speaker 1: at night if I have some control over well, I know.

1187
01:01:42,520 --> 01:01:46,160
Speaker 1: I mean, I I write about Spotify, and every day

1188
01:01:46,200 --> 01:01:47,680
Speaker 1: I get people said, well, you're on the payroll. I'm

1189
01:01:47,680 --> 01:01:49,800
Speaker 1: not on the perrol. It never took a cent. Okay,

1190
01:01:49,800 --> 01:01:51,520
Speaker 1: I'm not saying therefore the opposite is gonna hapen to

1191
01:01:51,600 --> 01:01:54,640
Speaker 1: take the money. But even if you are literally white clean,

1192
01:01:54,720 --> 01:01:57,440
Speaker 1: these days, people believe you're not. Oh, I mean, we'll

1193
01:01:57,640 --> 01:02:00,480
Speaker 1: we'll write, we'll put one blog post out about a topic,

1194
01:02:00,600 --> 01:02:02,760
Speaker 1: and on our Facebook page will be in the same

1195
01:02:02,840 --> 01:02:05,240
Speaker 1: by one person accused of being a right wing hill

1196
01:02:05,360 --> 01:02:07,720
Speaker 1: and on the next comment will be a left wing ship.

1197
01:02:07,800 --> 01:02:10,640
Speaker 1: I mean it. People will find the story they want

1198
01:02:10,640 --> 01:02:14,200
Speaker 1: to find if your data doesn't precisely affirm their existing belief.

1199
01:02:14,320 --> 01:02:16,280
Speaker 1: What's a sad state of affairs? I mean, I grew

1200
01:02:16,320 --> 01:02:18,680
Speaker 1: up an inner where there were many fewer media outlets

1201
01:02:18,840 --> 01:02:21,000
Speaker 1: and you could agree on the facts. You can't even

1202
01:02:21,000 --> 01:02:23,160
Speaker 1: agree on the facts, and even no you cannot know

1203
01:02:23,320 --> 01:02:25,320
Speaker 1: and it's hard to give it. So any other macro

1204
01:02:25,520 --> 01:02:28,320
Speaker 1: trends that you can give us. You're talking about, uh,

1205
01:02:28,400 --> 01:02:31,600
Speaker 1: the cocooning, which ironically is what Faith cop popcorns at

1206
01:02:31,600 --> 01:02:33,880
Speaker 1: a few decades back. But now we have the ability

1207
01:02:33,920 --> 01:02:36,640
Speaker 1: with all these instat cart and Netflix to do that.

1208
01:02:36,880 --> 01:02:40,720
Speaker 1: Any other macro trends in the public. Well, So I

1209
01:02:40,800 --> 01:02:47,120
Speaker 1: mentioned the the the true, the aspirational nature of social

1210
01:02:47,160 --> 01:02:51,560
Speaker 1: media and what that's doing. UM. The impact that's having

1211
01:02:51,600 --> 01:02:56,480
Speaker 1: on brands I think is pretty dramatic in terms of UM.

1212
01:02:56,520 --> 01:02:59,160
Speaker 1: I was on a I gave a talk at a

1213
01:02:59,280 --> 01:03:02,320
Speaker 1: conference of about a year ago as a beauty industry conference,

1214
01:03:02,320 --> 01:03:04,480
Speaker 1: and I sat with a woman who was an executive

1215
01:03:04,480 --> 01:03:06,000
Speaker 1: of a big beauty company, and she was telling a

1216
01:03:06,000 --> 01:03:08,040
Speaker 1: story about how the night before she had driven her son.

1217
01:03:08,280 --> 01:03:09,760
Speaker 1: She was taking her son. That we were in New York.

1218
01:03:10,080 --> 01:03:13,680
Speaker 1: She was taking her son to soccer practice, and I

1219
01:03:13,680 --> 01:03:16,720
Speaker 1: picture her in her land Rover in Westchester, right, and

1220
01:03:17,000 --> 01:03:19,240
Speaker 1: he hadn't eaten dinner yet, so she drove him through

1221
01:03:19,280 --> 01:03:21,680
Speaker 1: the drive through at a fast food restaurant to get

1222
01:03:21,720 --> 01:03:24,960
Speaker 1: him some burgers. And she gets to the soccer fields

1223
01:03:25,080 --> 01:03:28,360
Speaker 1: and all the suburban Westchester moms were standing there, and

1224
01:03:28,400 --> 01:03:30,120
Speaker 1: she has a panic attack that these moms are going

1225
01:03:30,160 --> 01:03:31,840
Speaker 1: to see that she'd taken her son of this fast

1226
01:03:33,080 --> 01:03:35,440
Speaker 1: So she's telling the story and we're all laughing, but

1227
01:03:35,520 --> 01:03:38,520
Speaker 1: she she stands up and she's shoving the stuff in

1228
01:03:38,560 --> 01:03:41,080
Speaker 1: her bag so that moms won't see it. And so

1229
01:03:41,720 --> 01:03:46,280
Speaker 1: we've been materialists are since one person had a nicer

1230
01:03:46,320 --> 01:03:48,640
Speaker 1: loincloth than the other. Right, it's going back as much

1231
01:03:48,680 --> 01:03:50,240
Speaker 1: as you know, as far back as you can go.

1232
01:03:50,320 --> 01:03:54,360
Speaker 1: Janis Joplin wrote about it in nineteen seventy and um,

1233
01:03:54,400 --> 01:03:57,919
Speaker 1: but we've never had the tools to or the purse,

1234
01:03:58,080 --> 01:04:00,400
Speaker 1: or the fear that ever that are that are purchase

1235
01:04:00,440 --> 01:04:03,280
Speaker 1: and consumption decisions were on public display like they are now.

1236
01:04:03,480 --> 01:04:05,080
Speaker 1: And this was a case of a woman driving to

1237
01:04:05,120 --> 01:04:07,640
Speaker 1: a parking lot with food in her car. But with

1238
01:04:07,680 --> 01:04:10,040
Speaker 1: the cameras and the social media and the sharing and

1239
01:04:10,080 --> 01:04:11,760
Speaker 1: the posting and all that, the decisions that I make

1240
01:04:11,800 --> 01:04:14,560
Speaker 1: about where I eat and where I buy are are

1241
01:04:14,600 --> 01:04:17,000
Speaker 1: on public display and subject to scrutiny like they've never

1242
01:04:17,040 --> 01:04:20,240
Speaker 1: been before. So we've always had guilty pleasures, and I

1243
01:04:20,360 --> 01:04:23,280
Speaker 1: might I may choose not to eat at a particular

1244
01:04:23,320 --> 01:04:26,240
Speaker 1: fast food restaurant because it's unhealthy for me. But if

1245
01:04:26,280 --> 01:04:27,919
Speaker 1: I do, I just don't post about it or brag

1246
01:04:27,960 --> 01:04:30,600
Speaker 1: about it. But what about that woman who next time

1247
01:04:30,640 --> 01:04:34,920
Speaker 1: her son hasn't eaten dinner, she actively chooses not to

1248
01:04:34,960 --> 01:04:37,240
Speaker 1: go to that fast food restaurant because of the potential

1249
01:04:37,240 --> 01:04:39,200
Speaker 1: trauma of being seen taking her son there. And now

1250
01:04:39,240 --> 01:04:41,800
Speaker 1: that that bit very large fast food restaurant has lost

1251
01:04:41,880 --> 01:04:44,880
Speaker 1: a customer or at least a visit, and think about

1252
01:04:44,920 --> 01:04:46,920
Speaker 1: how often that might be happening at the scale. And

1253
01:04:46,960 --> 01:04:49,280
Speaker 1: you look at some of these large brands, the Blue Chips,

1254
01:04:49,320 --> 01:04:52,040
Speaker 1: CpG companies and others who have There are a lot

1255
01:04:52,080 --> 01:04:54,520
Speaker 1: of reasons they can they can explain some of the

1256
01:04:54,520 --> 01:04:56,120
Speaker 1: declines that they've had, But that has to be a

1257
01:04:56,120 --> 01:04:58,720
Speaker 1: big part of it is that it's just not cool

1258
01:04:58,800 --> 01:05:00,800
Speaker 1: to go there. It's not as cool to eat there.

1259
01:05:00,840 --> 01:05:03,040
Speaker 1: It's and I think that's going to affect music as well.

1260
01:05:03,080 --> 01:05:05,440
Speaker 1: I think I think being able to brag about the

1261
01:05:05,800 --> 01:05:08,520
Speaker 1: concert I was at and that's always been there, but

1262
01:05:08,560 --> 01:05:10,520
Speaker 1: it's never been amplified the way that it is now.

1263
01:05:11,440 --> 01:05:15,360
Speaker 1: Staying with the same thing, because this crosses bombs, we

1264
01:05:15,560 --> 01:05:20,360
Speaker 1: learn that acquisition is down and experiences are up, especially

1265
01:05:20,400 --> 01:05:25,560
Speaker 1: amongst millennials. Is your data absolutely very true? Yes, UM,

1266
01:05:25,840 --> 01:05:31,400
Speaker 1: wanting to experience restaurant spending is down, but the types

1267
01:05:31,440 --> 01:05:33,560
Speaker 1: of restaurants that people are choosing to eat at are

1268
01:05:33,600 --> 01:05:36,560
Speaker 1: more experiential, So certain types of restaurants are doing well.

1269
01:05:37,040 --> 01:05:39,760
Speaker 1: UM travel is certainly up. It's making it. It's a

1270
01:05:39,840 --> 01:05:42,480
Speaker 1: it's quite a big blind spot for UM the FED

1271
01:05:42,560 --> 01:05:45,520
Speaker 1: in terms of forecasting consumer spending because a lot of

1272
01:05:45,520 --> 01:05:48,560
Speaker 1: those categories aren't measured by a lot of the federal

1273
01:05:48,640 --> 01:05:52,000
Speaker 1: resources in terms of UM. You know, it's not store

1274
01:05:52,040 --> 01:05:54,880
Speaker 1: receipts from Target and Walmart anymore. There's money being spent

1275
01:05:54,960 --> 01:05:57,600
Speaker 1: somewhere that's not as visible, and that is a that

1276
01:05:57,760 --> 01:06:00,880
Speaker 1: is a that is a fundamentally true trend. And it's

1277
01:06:00,920 --> 01:06:04,400
Speaker 1: not just millennials. It's happening in gen X as well. Okay,

1278
01:06:04,400 --> 01:06:06,480
Speaker 1: so if someone wants to check you out, they can

1279
01:06:06,480 --> 01:06:08,240
Speaker 1: listen to this podcast. Is there anywhere they can go

1280
01:06:08,240 --> 01:06:11,360
Speaker 1: online to see some of your data other than your quizzes? Uh,

1281
01:06:11,520 --> 01:06:15,520
Speaker 1: Civic science dot com. Pretty straightforward, UM, anybody who would

1282
01:06:15,520 --> 01:06:19,640
Speaker 1: like to UM. You mentioned earlier that little Saturday email

1283
01:06:19,640 --> 01:06:22,480
Speaker 1: that I send out. It is it's kind of invitation

1284
01:06:22,560 --> 01:06:26,520
Speaker 1: only but it is a it is a an awesome

1285
01:06:26,560 --> 01:06:29,080
Speaker 1: list of people who read it and if anybody would

1286
01:06:29,120 --> 01:06:31,640
Speaker 1: like to be added to that, they would could reach

1287
01:06:31,680 --> 01:06:35,520
Speaker 1: out to you maybe to do that, but otherwise, UM yes,

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01:06:35,560 --> 01:06:38,439
Speaker 1: Civic Science dot com and um, we write a lot.

1289
01:06:38,720 --> 01:06:42,280
Speaker 1: It's um our blog is is a lot of content

1290
01:06:42,360 --> 01:06:45,680
Speaker 1: every week, new things were studying, trends worth finding. Um.

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01:06:45,720 --> 01:06:48,280
Speaker 1: We do have some partnerships with some media outlets that

1292
01:06:48,320 --> 01:06:51,080
Speaker 1: take our information and give it a spotlight for us.

1293
01:06:51,120 --> 01:06:54,000
Speaker 1: But um, yeah, they can certainly reach out to us

1294
01:06:54,000 --> 01:06:55,240
Speaker 1: if they want to be added to any of the

1295
01:06:55,240 --> 01:06:57,480
Speaker 1: distribution lists of these kind of things that we're writing about.

1296
01:06:57,640 --> 01:06:59,560
Speaker 1: And once again we haven't mentioned the names of the

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01:06:59,600 --> 01:07:03,760
Speaker 1: company that employ Civic Science, but I know those names

1298
01:07:03,760 --> 01:07:06,600
Speaker 1: and believe me, they're all the alphabet soup that you

1299
01:07:06,720 --> 01:07:10,400
Speaker 1: believe they are. So once again, John Dick, CEO of

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01:07:10,400 --> 01:07:12,040
Speaker 1: Civic Science, thanks so much for being here on the

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01:07:12,040 --> 01:07:14,760
Speaker 1: Bob Left Sets Podcast. It was my pleasure, Bob, thank you.

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01:07:17,120 --> 01:07:19,320
Speaker 1: That wraps up this week's episode of the Bob Left

1303
01:07:19,320 --> 01:07:22,320
Speaker 1: Sets Podcast, recorded here at Venice, California at the tune

1304
01:07:22,320 --> 01:07:26,080
Speaker 1: In Studios. I hope you enjoyed this fascinating conversation with

1305
01:07:26,120 --> 01:07:30,000
Speaker 1: the CEO of Civic Science John Dick, about market analytics,

1306
01:07:30,040 --> 01:07:33,080
Speaker 1: the future of tech, the music industry, and consumer habits.

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01:07:33,560 --> 01:07:36,800
Speaker 1: As always, I welcome your feedback. Email me at Bob

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01:07:36,840 --> 01:07:52,560
Speaker 1: at left sets dot com.