WEBVTT - AI Spices Up Consumer Connection, Marketing

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<v Speaker 1>Welcome to Chopping It Up. I'm your host Michael Halen,

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<v Speaker 1>the senior restaurant and food service analyst at Bloomberg Intelligence.

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<v Speaker 1>Today's guest is Benny Gradwall. Benny is the go founder

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<v Speaker 1>and CEO of cognoviy Labs. It's a company that analyzes

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<v Speaker 1>social media posts to help form insights into consumer behavior.

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<v Speaker 1>Benny and the team have provided Bloomberg with some very

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<v Speaker 1>timely analyzes over the years on companies like McDonald's, Starbucks,

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<v Speaker 1>Young Brands, and most recently, Target. So I'm excited to

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<v Speaker 1>have him join me this morning. Thanks for doing this, Benny.

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<v Speaker 2>Well, good morning, Mike. Great being here, and thank you

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<v Speaker 2>very much for inviting me to your podcast.

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<v Speaker 1>Yeah, of course AI has been all over the news,

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<v Speaker 1>and you know, I thought i'd I'd hop on the

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<v Speaker 1>hype train, and when it comes to AI, you're my expert.

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<v Speaker 1>So it was really a no brainer for asking you

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<v Speaker 1>to come on with me.

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<v Speaker 2>Oh that's a big task, but yeah, happy, do we

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<v Speaker 2>hear an answer anything and you know, talk AI psychology

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<v Speaker 2>or whatever you want to go?

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<v Speaker 1>Cool? Can't wait, man, So I guess please if you

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<v Speaker 1>could please tell the audience about your career path and

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<v Speaker 1>how it led to your founding of Cognoviy Labs.

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<v Speaker 2>Yeah, it's it's an interesting story. I started in academia

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<v Speaker 2>a long time ago, and you know, on the quantity

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<v Speaker 2>decided and asked physics, but then switched into finance in

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<v Speaker 2>the early nineties. And you know, for me, everything was

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<v Speaker 2>around numbers and analytics and you know, hard stuff. And

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<v Speaker 2>you know, I was very fortunate to have a chance

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<v Speaker 2>to take a class at Harvard in behavior economics pretty

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<v Speaker 2>early on, I think it was nineteen ninety seven, and

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<v Speaker 2>that was a real eye opener. You know. I got

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<v Speaker 2>back and I was working out an investment firmat in

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<v Speaker 2>San Diego, and I came back and said, oh my gosh,

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<v Speaker 2>the world actually doesn't revolve just around numbers, revolves around

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<v Speaker 2>people making decisions. And that just became a passion. I

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<v Speaker 2>was trying for the last twenty five years or so

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<v Speaker 2>figuring out how we humans make decisions. And the more

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<v Speaker 2>I got into it, the more fascinating it was. And

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<v Speaker 2>you know, I went through a financial career and then

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<v Speaker 2>into emerging technologies and I say, look, I need to

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<v Speaker 2>really figure that out, you know, I it's it's it's

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<v Speaker 2>just fascinating how you go into through a financial career

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<v Speaker 2>and you work and you know, I was a market

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<v Speaker 2>standing then at City during the financial crisis, and with

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<v Speaker 2>negotiating on the mortgage site with homeowners without knowing anything

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<v Speaker 2>around human decision making, just felt, you know, that something

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<v Speaker 2>is missing, and so I was on a you know,

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<v Speaker 2>on a mission to figure that out. And that's how

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<v Speaker 2>we started knob Labs to really understand human decision making processes.

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<v Speaker 1>Well, that's very cool, and I'd imagine that's a very

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<v Speaker 1>emotional process for somebody right at that at that time,

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<v Speaker 1>right a home with you know, having to maybe sell

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<v Speaker 1>your home or move out of a home that you've

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<v Speaker 1>been living in, you know for some time. I think

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<v Speaker 1>there's probably a lot of emotion in that process, right.

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<v Speaker 2>Yeah, there's a lot of emotions and there's a lot

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<v Speaker 2>of pain, and you know, so we came in. I

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<v Speaker 2>came in in two thousand and eight to help stabilize

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<v Speaker 2>City in the bank and it was responsible for I've

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<v Speaker 2>pretty seen a significant mogtile portfolio. The hold loans about

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<v Speaker 2>twenty to eighty billion dollars and the question was, well,

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<v Speaker 2>you can't sell it, that's toxic assets, So what do

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<v Speaker 2>you do with it? And so you have to negotiate

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<v Speaker 2>with the homeowner. That's the beautiful mortgage portfolio. There is

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<v Speaker 2>actually an on on the other side. And so you

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<v Speaker 2>can modify and and change the asset and take the

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<v Speaker 2>homeowners who are you know, process and into waste management

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<v Speaker 2>something which is good for the bank and something is

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<v Speaker 2>good for the homeowner. And so I went to my

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<v Speaker 2>boss very early on and say, okay, that's great, but

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<v Speaker 2>now I have to negotiate, to find a way to negotiate,

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<v Speaker 2>you know in quotation marks negotiate, but through through analytics

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<v Speaker 2>and outreaches with millions of homeowners. How do I do that?

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<v Speaker 2>Where do I find the chief psychology officer within city

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<v Speaker 2>who can guide me how to do that? Then guess what,

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<v Speaker 2>there wasn't any There isn't the chief psychology officers of

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<v Speaker 2>the city. So you have investment banks and you have

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<v Speaker 2>consumer banks, which by definition work with individual consumers, and

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<v Speaker 2>there's no psychological understanding how that engagement should work. And

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<v Speaker 2>it's just mind boding, correct. And so it was very

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<v Speaker 2>clear that there is a world out there which goes

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<v Speaker 2>beyond the hard data, which is on touch to some extent,

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<v Speaker 2>but probably more important than the hard data. And so

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<v Speaker 2>how did we figure it out? And by the way,

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<v Speaker 2>I think the team at City at that time Citimot

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<v Speaker 2>did a great job. We kept about a million and

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<v Speaker 2>a half American homes of families in the home. So

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<v Speaker 2>whatever we did, we did. You know, it worked and

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<v Speaker 2>it was a great outcome. But still something was missing,

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<v Speaker 2>and and that's where I am now. Well, look, think

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<v Speaker 2>about the US economy. Two third of the US economy

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<v Speaker 2>is a consumer dreaming about the consumer. Yeah, how consumers

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<v Speaker 2>make decisions? You had bloomberg, open up a blooming terminal. Yeah,

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<v Speaker 2>every piece of information out there, take data and you know,

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<v Speaker 2>revenues and learning scrolls and and anything. Where's the cognitive

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<v Speaker 2>the behavioral signals on your screen? If two third of

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<v Speaker 2>the economy is dreaming of the consumer, where is that

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<v Speaker 2>piece on your terminal?

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<v Speaker 1>Will sitting on our restaurant dashboard with our Cognovy Labs data.

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<v Speaker 2>So I got it at least the starting.

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<v Speaker 1>Point, Yeah for sure. All right, So who are Cognoby's

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<v Speaker 1>customers and what problems are you helping them solve?

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<v Speaker 2>Yeah? So look out of technology is applicable anyway humans

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<v Speaker 2>make decisions. So when we started, we started in politics

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<v Speaker 2>and went into finance and and and and the corporate world.

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<v Speaker 2>And so it's it's really understanding. You know, if you

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<v Speaker 2>if you're in the finance and understanding you are the

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<v Speaker 2>customers of your investment. To think about a retail company

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<v Speaker 2>and or a restaurant. You know, who's going into the restaurant,

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<v Speaker 2>who's who's going to McDonald's with a Starbucks, and what's

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<v Speaker 2>driving them really or on the consumer side, you know,

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<v Speaker 2>why would somebody you know buy a Peloton bike or

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<v Speaker 2>not buy a Peloton bike? And and really understanding the

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<v Speaker 2>decision making process and so the customers. So the financial

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<v Speaker 2>companies obviously are a great customers, but also the corporations

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<v Speaker 2>are because they have to reach out to their clients

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<v Speaker 2>and emotionally engage with them in a way that they

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<v Speaker 2>can drive performance. And so let me step back, if

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<v Speaker 2>you can give me a couple of minutes, just explain

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<v Speaker 2>why that's actually relevant. So you know, if you go

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<v Speaker 2>back even to Sigmund Freud who said, look, we have

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<v Speaker 2>a rational mind, that we have a subconscious mind, and

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<v Speaker 2>the subconscious mind makes a lot of decisions. It was

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<v Speaker 2>never really identified how much at that time. But what

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<v Speaker 2>became interesting is a few things which came out of it.

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<v Speaker 2>One is, yes, there is a subconscious mind. Second, the

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<v Speaker 2>subconscious mind makes actually the majority of decisions. We assumans

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<v Speaker 2>are not as rational as we think we are. You know,

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<v Speaker 2>we're not making rational decisions. And you see that in

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<v Speaker 2>finance correctly. We started off with you know, efficient marketing particies.

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<v Speaker 2>Everyone is rational, but now we understand the cognitive biases

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<v Speaker 2>and all that stuff. All of a sudden, Oh my gosh,

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<v Speaker 2>there are actually inefficiencies in the market that are driven

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<v Speaker 2>by cognitive bvices. Oh we're not as rational. We're not

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<v Speaker 2>rational machines. So we saw that there, and but we

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<v Speaker 2>have validation, you know, down the Economan and Donostoreski in

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<v Speaker 2>the seventies did their whole analysis down. Economan got then

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<v Speaker 2>a Nobel Price in two thousand and two around you know,

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<v Speaker 2>prospect theory, how we are not as rational, and then

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<v Speaker 2>you have the So we know that the rational mind, well,

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<v Speaker 2>while it's important, it doesn't make that many decisions. Really

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<v Speaker 2>the subconscious mind, which makes the majority the rational mind,

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<v Speaker 2>is more controlled process correct and so sometimes it breaks down.

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<v Speaker 2>If you in wage for example, it may wage break down,

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<v Speaker 2>But otherwise it really is a controlled process. But the

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<v Speaker 2>subconscious mind makes most of the decisions. So that's great, Well,

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<v Speaker 2>how does it make those decisions? Are they similar to

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<v Speaker 2>the decision a rational mind would make, Right, the answer

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<v Speaker 2>is yes, Then all I would say there is around

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<v Speaker 2>the rational mind and the factual decision making process still hold.

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<v Speaker 2>If the rational mind, however, makes the subconscious might make

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<v Speaker 2>the decision in a different way than, we're in trouble. Well,

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<v Speaker 2>it turns out we are in trouble. The selfconscious mind

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<v Speaker 2>doesn't make decisions the same way the rational mind makes it.

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<v Speaker 2>And here's why. Well, Donny Conneman found out and I'm

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<v Speaker 2>as swear to everyone else that they are cognitive biases. Correct,

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<v Speaker 2>So those cognitive vices obviously are different. So we know

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<v Speaker 2>that they make the dish decisions they're differently. Well, that's great.

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<v Speaker 2>So maybe we just asked the rational mind what decisions

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<v Speaker 2>were made, so we can still use the rational mind

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<v Speaker 2>to figure out how people make decisions, even if the

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<v Speaker 2>decisions were done differently than the rational mind would do. Well,

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<v Speaker 2>that doesn't work either. Again, we're intro why there's a

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<v Speaker 2>guy that in the name of Benjamin Libbert and scientists

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<v Speaker 2>or in the eighties that the subconscious mind makes a

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<v Speaker 2>decision a fraction of a second before the rational mind

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<v Speaker 2>actually recognizes the decision was made. Wow, So here we

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<v Speaker 2>have a problem. We know that most of the decisions

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<v Speaker 2>are made by the selbconscious mind. We know that decisions

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<v Speaker 2>the subconscious mind makes are different than the rational mind.

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<v Speaker 2>And now we find out that the rational mind doesn't

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<v Speaker 2>even know when the subconscious mind makes a decision. Therefore

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<v Speaker 2>it can't explain it. So now we're will in trouble. Correct,

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<v Speaker 2>how did you mentioned? How do we figure out therefore,

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<v Speaker 2>how people make decisions. I can't ask you, Mike, why

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<v Speaker 2>did you do something? Because you probably acted on it

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<v Speaker 2>before you even figured out that you're acting on it.

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<v Speaker 2>And so surveys are great, I can ask you, but

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<v Speaker 2>they are probably wrong some of the time, maybe not

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<v Speaker 2>all of the time, but many many times. Then we

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<v Speaker 2>can go into that and why that's relevant to AI

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<v Speaker 2>and why it has to do with a lot of

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<v Speaker 2>things which happening today with AI and how oscination and

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<v Speaker 2>so on. But this is an important understanding as a

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<v Speaker 2>starting point. We're not rational. The majority of decisions are

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<v Speaker 2>everyone non rational, and rational mind doesn't know what the

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<v Speaker 2>heck is going on. In.

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<v Speaker 1>Yeah, that's really really interesting stuff, stuff that I've been

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<v Speaker 1>interested in for some years. I think I was introduced

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<v Speaker 1>to Konomen into Versky. I don't know if I'm saying

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<v Speaker 1>his last name right, but through Against the Gods, which

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<v Speaker 1>is a great book about human and their humans and

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<v Speaker 1>their interaction with risk and the audience. Can't see my

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<v Speaker 1>bookshelf behind me, but you know, Thinking Fast and Slow

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<v Speaker 1>by Connoment is back there too. So it's definitely a

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<v Speaker 1>very cool and interesting subject, you know, And it's something

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<v Speaker 1>that comes up, you know, I see an advertising I've

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<v Speaker 1>read about advertising that sometimes you need you need to

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<v Speaker 1>get in front of the customer I think eleven times

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<v Speaker 1>or something on average before like you enter the consideration set.

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<v Speaker 1>And so different companies that we work with talk about

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<v Speaker 1>the different ways that they're trying to reach out out

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<v Speaker 1>to customers. It's just just really interesting to me.

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<v Speaker 2>Yeah, absolutely right. The petition is important. My guess is

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<v Speaker 2>if they're emotionally engaged, they will probably bring that eleven

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<v Speaker 2>down quite a bit. Yeah, because emotional connection, emotional intelligence

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<v Speaker 2>is the superpower way that one has and it's completely

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<v Speaker 2>only utilized.

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<v Speaker 1>Yeah, and so you've done a great job of you know,

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<v Speaker 1>figuring out which of our companies are creating emotional connections online,

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<v Speaker 1>whether it be through different marketing programs, whether it be

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<v Speaker 1>through their sustainability efforts, and recently it did something about

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<v Speaker 1>their charitable giving with us, which is cool. You also

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<v Speaker 1>just completed an analysis of Target for my colleague Jennifer

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<v Speaker 1>Bartashis you know what did you and the team find

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<v Speaker 1>out about Target?

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<v Speaker 2>Yeah, it's actually very interesting, so and you know, maybe

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<v Speaker 2>for the listeners, what we do is we analyze any

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<v Speaker 2>free flowing conversation, could be social media discussion, forums, blogs,

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<v Speaker 2>it could be you know, your own surveys of transcript

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<v Speaker 2>and we extract ten emotions in context and actually natively

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<v Speaker 2>into any languages and no translation. Then we map that

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<v Speaker 2>through our proprietor psychological framework into an intense score and

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<v Speaker 2>an action personas what we found with Target, and the

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<v Speaker 2>question was Target has been on a downward trend and

0:13:39.600 --> 0:13:43.800
<v Speaker 2>actually was hit obviously with its Pride merchandise. When they

0:13:43.800 --> 0:13:47.520
<v Speaker 2>came out of that, there was a whole controversy around that,

0:13:47.800 --> 0:13:51.280
<v Speaker 2>and the stock really took a dive and as been

0:13:51.400 --> 0:13:54.480
<v Speaker 2>settling somewhere and the question is, you know, what's actually

0:13:54.559 --> 0:14:01.439
<v Speaker 2>happening in terms of the consumer's emotional actitude towards target.

0:14:02.760 --> 0:14:06.520
<v Speaker 2>And so what we did is our team they analyzed

0:14:07.120 --> 0:14:11.280
<v Speaker 2>social media and discussion forms around target, conversations free frond conversation,

0:14:11.400 --> 0:14:14.680
<v Speaker 2>extracted those emotions and as I mentioned, the intent score,

0:14:14.960 --> 0:14:18.320
<v Speaker 2>and what we saw is that a couple of things.

0:14:21.040 --> 0:14:24.840
<v Speaker 2>It's not the first time target is getting into a order.

0:14:25.240 --> 0:14:28.680
<v Speaker 2>I mean, that's part of who they are, and that's

0:14:28.680 --> 0:14:31.440
<v Speaker 2>perfectly fine. If you look at the intent score, you

0:14:31.480 --> 0:14:33.880
<v Speaker 2>see spikes up and down, and the intent is really

0:14:34.160 --> 0:14:38.440
<v Speaker 2>how emotionally engaging the consumer is visa v target and

0:14:38.440 --> 0:14:41.560
<v Speaker 2>how emotionally engaging they are in to go and shop,

0:14:41.760 --> 0:14:44.480
<v Speaker 2>and the motivation score we see ups and downs, and

0:14:44.560 --> 0:14:46.600
<v Speaker 2>you know, we see we saw quite a bit of

0:14:46.720 --> 0:14:52.520
<v Speaker 2>a some kind of an action around obviously the controversy.

0:14:53.360 --> 0:14:56.520
<v Speaker 2>When you look at and I think that's an interesting component.

0:14:56.560 --> 0:15:01.440
<v Speaker 2>We always look at what is their base line emotions,

0:15:01.760 --> 0:15:08.360
<v Speaker 2>what are the typical emotions they evoke in the consumer,

0:15:08.800 --> 0:15:15.119
<v Speaker 2>and how has that been disrupted by the private merchandise.

0:15:17.000 --> 0:15:22.280
<v Speaker 2>And while that's then disrupted, is it coming back to

0:15:22.360 --> 0:15:25.960
<v Speaker 2>a symbol or emotional profile and an emotional attitude of

0:15:25.960 --> 0:15:31.080
<v Speaker 2>the consumer, or is there a structural shift which would

0:15:31.120 --> 0:15:36.280
<v Speaker 2>impact consumer behavior. That was really the question. And I'll

0:15:36.280 --> 0:15:38.160
<v Speaker 2>tell you how that works for Target, and then I'll

0:15:38.360 --> 0:15:41.520
<v Speaker 2>explain that how that actually works with Pepsi I'm sorry,

0:15:41.560 --> 0:15:45.160
<v Speaker 2>with bud Light, not Pepsi, bud Light, which went through

0:15:45.200 --> 0:15:51.000
<v Speaker 2>a similar event in April. So when you look at Target,

0:15:52.680 --> 0:15:56.520
<v Speaker 2>you clearly see that there was a there was a

0:15:56.560 --> 0:16:07.200
<v Speaker 2>certain profile, emotional profile which was somewhat reflective of the consumers,

0:16:07.240 --> 0:16:12.280
<v Speaker 2>you know, emotional attitude and responses towards Target, which was

0:16:12.360 --> 0:16:16.280
<v Speaker 2>disrupted when they came out with the merchandise. What we

0:16:16.480 --> 0:16:19.640
<v Speaker 2>have seen just recently over the last couple of weeks

0:16:20.200 --> 0:16:23.760
<v Speaker 2>is that there has been a reversal back to that

0:16:23.920 --> 0:16:30.160
<v Speaker 2>emotional baseline. Not yet there, but we see a relaxation

0:16:30.560 --> 0:16:34.200
<v Speaker 2>and a return to that which from a I'm not

0:16:34.240 --> 0:16:36.000
<v Speaker 2>going to say anything about the start, but what it

0:16:36.080 --> 0:16:40.680
<v Speaker 2>means is that emotionally, it seems at least that we're

0:16:40.760 --> 0:16:44.360
<v Speaker 2>settling back to the status quo it was before the event.

0:16:45.360 --> 0:16:47.800
<v Speaker 2>And leave that up to you to figure out what

0:16:47.800 --> 0:16:50.560
<v Speaker 2>that actually means in terms of human behavior. But that's

0:16:50.600 --> 0:16:54.160
<v Speaker 2>an important component. Why is an important component again, because

0:16:54.200 --> 0:16:58.840
<v Speaker 2>emotions strive decisions, and if the emotional profile. A certain profile,

0:16:58.960 --> 0:17:04.399
<v Speaker 2>complex profile leads to certain action tendencies, and they're coming

0:17:04.440 --> 0:17:07.560
<v Speaker 2>back now to a similar baseline they were before. They're

0:17:07.560 --> 0:17:10.359
<v Speaker 2>not there yet, but they seem to go back. It

0:17:10.400 --> 0:17:13.280
<v Speaker 2>will tell you something here about consumer behavior going forward.

0:17:14.440 --> 0:17:18.800
<v Speaker 2>This is not what's happening with but Light yet. It's

0:17:18.840 --> 0:17:23.159
<v Speaker 2>interesting but Light that has a completely different profile and

0:17:23.359 --> 0:17:27.119
<v Speaker 2>had a different profile before, you know, the April first

0:17:28.359 --> 0:17:31.960
<v Speaker 2>event where the transgender influence of Dylan more Money came

0:17:32.000 --> 0:17:37.080
<v Speaker 2>out and had that video, And what we saw is

0:17:37.119 --> 0:17:41.160
<v Speaker 2>a complete disruption of that as well of that profile

0:17:42.200 --> 0:17:46.439
<v Speaker 2>and no reversal yet. And what's interesting in addition, what

0:17:46.480 --> 0:17:51.720
<v Speaker 2>we also measure is not just the consumers response emotional

0:17:51.760 --> 0:17:54.880
<v Speaker 2>response to an event or a company or a brand

0:17:55.000 --> 0:18:00.000
<v Speaker 2>or marketing. We also measure the response from the company itself,

0:18:00.080 --> 0:18:03.680
<v Speaker 2>from the CEO, what they put out. And what we

0:18:03.800 --> 0:18:07.560
<v Speaker 2>found in the case of bod Light is a complete

0:18:07.640 --> 0:18:13.679
<v Speaker 2>disconnect between the feeling of the customer after the event

0:18:13.760 --> 0:18:20.200
<v Speaker 2>after April first and the CEO's positioning. So think about it.

0:18:20.560 --> 0:18:23.679
<v Speaker 2>I have a problem, I call you. You are my

0:18:23.880 --> 0:18:25.960
<v Speaker 2>call center, correct, I have a problem with you with

0:18:26.040 --> 0:18:29.040
<v Speaker 2>a product, and I'm really angry and you just tell

0:18:29.119 --> 0:18:31.639
<v Speaker 2>me everything is homepy door, and all is fine. I

0:18:31.680 --> 0:18:34.320
<v Speaker 2>will feel as a customer, you're not you're not listening.

0:18:34.560 --> 0:18:37.280
<v Speaker 2>You don't hear me, you don't hear you hear my word,

0:18:37.600 --> 0:18:40.280
<v Speaker 2>my words, but you don't hear me emotionally. You do

0:18:40.480 --> 0:18:44.639
<v Speaker 2>not connect with me emotionally, and therefore you don't solve

0:18:44.680 --> 0:18:47.320
<v Speaker 2>the problem I'm in. And that's exactly what's happening with

0:18:47.400 --> 0:18:50.840
<v Speaker 2>but Light. They actually put something out which reflected their

0:18:50.840 --> 0:18:54.800
<v Speaker 2>emotional profile pre event, pre April first, without taking into

0:18:54.840 --> 0:18:58.680
<v Speaker 2>account and appreciating that that's not where their customer is

0:18:58.760 --> 0:19:03.000
<v Speaker 2>right now, and so we haven't seen that reversal yet

0:19:03.040 --> 0:19:06.359
<v Speaker 2>there whereas in the case of Target we start seeing

0:19:07.560 --> 0:19:08.200
<v Speaker 2>a reversal.

0:19:09.640 --> 0:19:12.720
<v Speaker 1>Yeah, it's really it's really interesting. Yeah, and it's interesting

0:19:12.800 --> 0:19:16.440
<v Speaker 1>too that you said, you know, you'll you'll analyze statements

0:19:16.480 --> 0:19:18.359
<v Speaker 1>from the companies and I think you've mentioned the past

0:19:18.359 --> 0:19:21.240
<v Speaker 1>two that you've done some stuff on earnings transcripts and

0:19:21.280 --> 0:19:21.680
<v Speaker 1>things like.

0:19:21.640 --> 0:19:24.560
<v Speaker 2>That, right, Yeah, we have. We have done some of

0:19:24.600 --> 0:19:27.879
<v Speaker 2>it with Noodled a little bit on it. And you know,

0:19:27.960 --> 0:19:31.919
<v Speaker 2>there's a lot of you know, the companies who analyze

0:19:31.960 --> 0:19:35.560
<v Speaker 2>the free flne conversation in terms of the sentiments which

0:19:35.600 --> 0:19:41.080
<v Speaker 2>is positive, negative, and neutral. And I just want to

0:19:41.080 --> 0:19:44.000
<v Speaker 2>mention that why we have probably the first or one

0:19:44.040 --> 0:19:47.640
<v Speaker 2>of the first patterns on sentiment analysis. We have moved

0:19:47.680 --> 0:19:51.040
<v Speaker 2>away from sentiment analysis because we don't believe it does

0:19:51.240 --> 0:19:54.600
<v Speaker 2>give you what you expect. It's really it shows you

0:19:54.680 --> 0:19:58.120
<v Speaker 2>the tone of the conversations, how people talk, not what

0:19:58.160 --> 0:19:59.960
<v Speaker 2>they feel and how they're going to act. And you know,

0:20:00.359 --> 0:20:02.199
<v Speaker 2>the example I always give is, you know, you go

0:20:02.280 --> 0:20:05.359
<v Speaker 2>to a restaurant and you sit down, you get dinner.

0:20:05.720 --> 0:20:08.320
<v Speaker 2>Five minutes later, the waiter comes and ask is everything okay?

0:20:08.400 --> 0:20:12.280
<v Speaker 2>And what do you say? Yeah, yeah, it's all fine,

0:20:12.600 --> 0:20:14.880
<v Speaker 2>and you give a positive sentiment and even the food

0:20:14.920 --> 0:20:17.400
<v Speaker 2>came late and doesn't look appealing, you'll never go back

0:20:17.400 --> 0:20:19.560
<v Speaker 2>to the restaurant. Just gave a positive center. And that's

0:20:19.560 --> 0:20:21.880
<v Speaker 2>why sentiment does really work in clients contours and say, hey,

0:20:22.160 --> 0:20:24.960
<v Speaker 2>centim is always positive, except in the reare cases when

0:20:24.960 --> 0:20:26.840
<v Speaker 2>it's not. It's usually positive, and we don't know what

0:20:26.840 --> 0:20:29.480
<v Speaker 2>the drivers are. So what we do is it really

0:20:29.520 --> 0:20:32.000
<v Speaker 2>have to go to the emotional level. And more importantly,

0:20:32.320 --> 0:20:38.080
<v Speaker 2>you have to understand how emotions combine into complex emotions

0:20:38.080 --> 0:20:40.879
<v Speaker 2>which lead to action. So people always think, oh, they

0:20:40.880 --> 0:20:44.359
<v Speaker 2>have good emotions bad emotions. There is joy and hope

0:20:44.440 --> 0:20:47.240
<v Speaker 2>and trust which is good, and anger, fear and and

0:20:47.320 --> 0:20:52.240
<v Speaker 2>you know, and contempt of sadness is bad. But that's

0:20:52.280 --> 0:20:56.000
<v Speaker 2>not the way the human psyche works. We have always

0:20:56.080 --> 0:20:59.160
<v Speaker 2>all the emotions right, there's no bad and good, it's

0:20:59.200 --> 0:21:01.960
<v Speaker 2>it's a combination. So, for example, fear can be very

0:21:01.960 --> 0:21:04.359
<v Speaker 2>good for marketing. You know, too much fear is bad,

0:21:04.400 --> 0:21:07.040
<v Speaker 2>it freezes you. But a little bit of fear creates

0:21:07.520 --> 0:21:10.280
<v Speaker 2>fear of missing out leads to call to action. So

0:21:10.400 --> 0:21:12.720
<v Speaker 2>the combination is important, and so we always look at

0:21:12.760 --> 0:21:15.840
<v Speaker 2>the combination. That's where our unique psychological framework comes in.

0:21:17.720 --> 0:21:20.840
<v Speaker 2>And we're able to do that because we're really complaining

0:21:21.840 --> 0:21:26.200
<v Speaker 2>deep machine learning with behavior psychology. So we have software

0:21:26.200 --> 0:21:28.640
<v Speaker 2>engineers and data scientists. We also have a chief psychology

0:21:28.640 --> 0:21:30.520
<v Speaker 2>officer as part of the core team. So we built

0:21:30.520 --> 0:21:32.720
<v Speaker 2>a psychology into the technology.

0:21:33.359 --> 0:21:35.920
<v Speaker 1>Yeah, and it's cool. One of the things that I've

0:21:35.960 --> 0:21:38.120
<v Speaker 1>learned working with you is that you know a lot

0:21:38.119 --> 0:21:41.000
<v Speaker 1>of these marketing programs need a broad range of emotions.

0:21:41.040 --> 0:21:42.639
<v Speaker 1>You know, there's I don't know how many it is,

0:21:42.680 --> 0:21:45.320
<v Speaker 1>it's but maybe in the low teens of different emotions

0:21:45.320 --> 0:21:47.800
<v Speaker 1>that you track, and if a company doesn't hit on

0:21:48.040 --> 0:21:51.440
<v Speaker 1>a wide array of those emotions, some of these marketing

0:21:51.520 --> 0:21:54.320
<v Speaker 1>plans fall a little bit flat, right.

0:21:55.080 --> 0:21:57.760
<v Speaker 2>Oh absolutely, And you see that again and again. People

0:21:57.880 --> 0:22:02.240
<v Speaker 2>are putting out on the marketing campaigns, you know, whether

0:22:02.240 --> 0:22:05.399
<v Speaker 2>it's a website, whether it's a it's a PR or

0:22:05.520 --> 0:22:08.840
<v Speaker 2>marketing campaign on social media, whatever it is that they

0:22:08.960 --> 0:22:12.840
<v Speaker 2>just put out open joy. And to some extent, nothing

0:22:12.880 --> 0:22:14.720
<v Speaker 2>is wrong with open joy. Because if I asked you

0:22:14.760 --> 0:22:16.520
<v Speaker 2>where you want to be, you know what's a good model.

0:22:16.520 --> 0:22:19.200
<v Speaker 2>I want to be happy. Of course we want to

0:22:19.240 --> 0:22:24.280
<v Speaker 2>be happy. But if you customer is already happy, they

0:22:24.320 --> 0:22:26.919
<v Speaker 2>will not do anything. Correct. If you're in front of

0:22:27.000 --> 0:22:29.400
<v Speaker 2>with your friends, in front of a TV watching your

0:22:29.600 --> 0:22:35.439
<v Speaker 2>favorite whatever, your sports team win the finals, you're so

0:22:35.640 --> 0:22:38.000
<v Speaker 2>happy you're going to sit there and not get up

0:22:38.040 --> 0:22:41.240
<v Speaker 2>and do anything. Why would you? You're already happy, You're

0:22:41.240 --> 0:22:44.320
<v Speaker 2>already in a balanced stage. You achieved it. It's we

0:22:44.400 --> 0:22:48.480
<v Speaker 2>call it an aspirational goal. You're already there for me

0:22:48.640 --> 0:22:51.280
<v Speaker 2>to have you move, I need to get you out

0:22:51.320 --> 0:22:55.640
<v Speaker 2>of that happy stage and create a reason to move

0:22:55.680 --> 0:22:58.800
<v Speaker 2>away from that unhappy stage to a happy stage. So

0:22:58.960 --> 0:23:02.040
<v Speaker 2>to get to that how do you do that? Well,

0:23:02.720 --> 0:23:05.320
<v Speaker 2>we take them through an emotional journey. You create some

0:23:05.359 --> 0:23:08.720
<v Speaker 2>anger at the competitors, or at the lack of a product,

0:23:09.080 --> 0:23:12.200
<v Speaker 2>or at the lack of quality of a product, whatever

0:23:12.240 --> 0:23:15.320
<v Speaker 2>it is. Create some fear, some contempt. Then you come

0:23:15.359 --> 0:23:20.280
<v Speaker 2>in with with a surprise and amusement, some trust that

0:23:20.359 --> 0:23:24.000
<v Speaker 2>you have the solution. Think about every marketing campaign as

0:23:24.040 --> 0:23:26.960
<v Speaker 2>a movie. That's what I tell my clients. Think about

0:23:27.040 --> 0:23:29.440
<v Speaker 2>a movie. So let's say you like I guess if

0:23:29.440 --> 0:23:33.200
<v Speaker 2>you like going to the movies. Let's say you sit

0:23:33.320 --> 0:23:37.240
<v Speaker 2>down and everything is happy, joyful and hopeful. What happens

0:23:39.240 --> 0:23:44.119
<v Speaker 2>it's boring, You fall asleep. You need a villain to

0:23:44.200 --> 0:23:48.960
<v Speaker 2>create some action, some tension, some fear. Then the hero

0:23:49.080 --> 0:23:52.320
<v Speaker 2>comes in, there's surprise, is amusement, there's some trust, the

0:23:52.320 --> 0:23:55.879
<v Speaker 2>the the you know, there's a again, I mean, the

0:23:56.000 --> 0:23:58.720
<v Speaker 2>villain comes to and there's a battle going on. It's

0:23:58.760 --> 0:24:03.640
<v Speaker 2>an emotional journey. That's what captures us. Every marketing campaign

0:24:03.680 --> 0:24:06.680
<v Speaker 2>needs that. If you're stale and you just put out

0:24:06.880 --> 0:24:10.359
<v Speaker 2>hope and joy, you're going to fail because it's not

0:24:10.560 --> 0:24:12.960
<v Speaker 2>creates doesn't create a call to action. And we measure

0:24:13.040 --> 0:24:15.719
<v Speaker 2>that and we tell you what you need to create

0:24:15.760 --> 0:24:18.040
<v Speaker 2>to create a call to action, and that's what turns

0:24:18.040 --> 0:24:19.720
<v Speaker 2>out to be predictive. And that's what we have been

0:24:19.720 --> 0:24:23.160
<v Speaker 2>providing you for the last five years or so. Yeah.

0:24:23.280 --> 0:24:26.520
<v Speaker 1>Yeah, it's it's really interesting stuff. I love it. Yeah,

0:24:26.560 --> 0:24:29.040
<v Speaker 1>since early twenty eighteen. I think that was our first

0:24:29.440 --> 0:24:32.280
<v Speaker 1>our first one. It was a report on McDonald's and

0:24:32.320 --> 0:24:34.600
<v Speaker 1>how they could improve their marketing. We made. There were

0:24:34.640 --> 0:24:36.879
<v Speaker 1>some good calls in there, I think by both of us.

0:24:37.200 --> 0:24:40.680
<v Speaker 2>Oh absolutely, I thank you you did. Yeah, I really

0:24:40.720 --> 0:24:44.680
<v Speaker 2>love you, White Ups. And I think the McDonald's was

0:24:45.080 --> 0:24:50.040
<v Speaker 2>really the first home run. I mean I remember twenty

0:24:50.200 --> 0:24:53.480
<v Speaker 2>eight eighteen, you came to us and asked, Okay, so

0:24:53.720 --> 0:24:56.800
<v Speaker 2>McDonald's just came out with a new you know, Big Mac,

0:24:57.960 --> 0:24:59.959
<v Speaker 2>and at the same time they had the twenty four

0:25:00.040 --> 0:25:02.840
<v Speaker 2>well Breakfast and they also just launched a one to

0:25:03.000 --> 0:25:06.840
<v Speaker 2>three dollar menu, and and I think you told us

0:25:06.880 --> 0:25:09.960
<v Speaker 2>that the Big Mac is going to be the big driver,

0:25:10.080 --> 0:25:13.280
<v Speaker 2>at least in the mind of McDonald's. And so what

0:25:13.320 --> 0:25:15.680
<v Speaker 2>we did is we looked at the last several months

0:25:15.720 --> 0:25:18.560
<v Speaker 2>of social media conversation so acted emotions and created that

0:25:18.600 --> 0:25:22.440
<v Speaker 2>we intend score, Motivation Score, And what we found was fascinating.

0:25:23.400 --> 0:25:28.480
<v Speaker 2>We found that breakfast has a high motivation. Consumers highly motivated,

0:25:29.080 --> 0:25:32.239
<v Speaker 2>two awards breakfast at McDonald's and very stable, so they

0:25:32.280 --> 0:25:37.080
<v Speaker 2>can really consistently going there and buying, you know, getting

0:25:37.080 --> 0:25:38.280
<v Speaker 2>breakfast at McDonald's.

0:25:38.480 --> 0:25:41.880
<v Speaker 1>The one to my favorite day part at McDonald's breakfast,

0:25:41.960 --> 0:25:42.440
<v Speaker 1>hands down.

0:25:42.840 --> 0:25:47.440
<v Speaker 2>Yeah, and clearly for the consumer is one to three

0:25:48.600 --> 0:25:52.480
<v Speaker 2>menu had even a higher motivation and that was that

0:25:52.560 --> 0:25:55.080
<v Speaker 2>was one of the first insight higher motivation, but they

0:25:55.160 --> 0:25:58.680
<v Speaker 2>didn't get enough marketing dollars, so the awareness wasn't there.

0:25:59.480 --> 0:26:04.159
<v Speaker 2>So we are suggestion was put more money into they

0:26:04.200 --> 0:26:06.639
<v Speaker 2>won to three dollar menu, because that's what's going to

0:26:06.760 --> 0:26:09.200
<v Speaker 2>drive your performance. And when we looked at the Big Mac,

0:26:09.320 --> 0:26:14.160
<v Speaker 2>it was a disaster, a complete disaster. Motivation was volatile

0:26:14.320 --> 0:26:17.920
<v Speaker 2>up and down, it was mainly negative. People were clearly

0:26:18.359 --> 0:26:21.320
<v Speaker 2>moving away. And when we looked at it, it was

0:26:21.400 --> 0:26:23.600
<v Speaker 2>you know, it wasn't the marketing, it was the product.

0:26:24.000 --> 0:26:26.560
<v Speaker 2>And the suggestion we had and you wrote that down

0:26:26.640 --> 0:26:29.720
<v Speaker 2>in twenty eighteen perfectly in the first quarter is that

0:26:30.359 --> 0:26:34.960
<v Speaker 2>if that's the biggest driver, they're gonna miss or run

0:26:35.000 --> 0:26:39.800
<v Speaker 2>the risk of missing revenues. And so fast forward quarter

0:26:40.880 --> 0:26:45.800
<v Speaker 2>and you know, mcnolla comes out announcwer innings, the miss revenues.

0:26:45.840 --> 0:26:48.000
<v Speaker 2>They say, the miss revenues because of the Big Mac.

0:26:48.720 --> 0:26:52.040
<v Speaker 2>And by the end of the year they we did

0:26:52.320 --> 0:26:57.320
<v Speaker 2>the product and then six came back. So that was

0:26:57.359 --> 0:26:59.040
<v Speaker 2>a fascinating study.

0:26:59.760 --> 0:27:03.480
<v Speaker 1>Yeah. Yeah, they changed course and went back onto that

0:27:03.600 --> 0:27:06.760
<v Speaker 1>you know, strong trajectory they had. But that was really interesting.

0:27:06.840 --> 0:27:09.000
<v Speaker 1>I'm gonna pat myself on the back. My call was

0:27:09.280 --> 0:27:11.359
<v Speaker 1>a lot less uh you know, it was more of

0:27:11.400 --> 0:27:13.840
<v Speaker 1>a common sense call. It was about the big Max sauce.

0:27:13.840 --> 0:27:16.080
<v Speaker 1>I thought, I said, they thought they should start putting

0:27:16.080 --> 0:27:20.160
<v Speaker 1>it on their chicken, so, you know, a much more

0:27:20.720 --> 0:27:25.080
<v Speaker 1>elementary type of call. But they finally hitting on that

0:27:25.240 --> 0:27:28.359
<v Speaker 1>in twenty twenty three. So everybody loves Big Mac sauce.

0:27:28.720 --> 0:27:30.120
<v Speaker 2>No, that was a great report.

0:27:31.359 --> 0:27:35.040
<v Speaker 1>Yeah, thanks, gondn't do it out, Yeah, what else do

0:27:35.160 --> 0:27:38.600
<v Speaker 1>we do? We did plant based meat right, and plant

0:27:38.600 --> 0:27:41.760
<v Speaker 1>based meat right now is kind of struggling, and it's

0:27:41.760 --> 0:27:44.840
<v Speaker 1>something that your research warned us that that it might,

0:27:44.960 --> 0:27:48.000
<v Speaker 1>you know, especially at quick service, because that isn't the

0:27:48.040 --> 0:27:51.879
<v Speaker 1>core type of customer for plant based meat, right, And

0:27:51.920 --> 0:27:53.639
<v Speaker 1>so a lot of people tried it at a fast

0:27:53.640 --> 0:27:56.560
<v Speaker 1>food restaurant and then they were you know, once and

0:27:56.600 --> 0:27:59.040
<v Speaker 1>then they either bought it at retail or they didn't.

0:28:00.080 --> 0:28:01.880
<v Speaker 1>But can you maybe give us an update on what's

0:28:01.920 --> 0:28:05.159
<v Speaker 1>going on with plant based meat and the consumer and

0:28:05.520 --> 0:28:08.640
<v Speaker 1>what could it mean for the recent introduction of cell

0:28:08.680 --> 0:28:09.320
<v Speaker 1>cultured meat.

0:28:10.880 --> 0:28:15.400
<v Speaker 2>Yeah, I mean the plant based meat that was fascinating

0:28:16.119 --> 0:28:18.800
<v Speaker 2>analysis as well, and that goes back to was it

0:28:18.880 --> 0:28:24.240
<v Speaker 2>late twenty nineteen, So plant based meat came out, you know,

0:28:24.480 --> 0:28:28.200
<v Speaker 2>Passoerburger and beyond Burger, and there was an enormous amount

0:28:28.320 --> 0:28:35.000
<v Speaker 2>of novelty and conversations and excitement. And when we looked

0:28:35.080 --> 0:28:39.800
<v Speaker 2>at a few months into the launch and from the

0:28:39.840 --> 0:28:42.400
<v Speaker 2>beginning into the first few months Sowar's the end of

0:28:42.440 --> 0:28:46.280
<v Speaker 2>the year, we saw that motivation actually while still high,

0:28:46.400 --> 0:28:51.560
<v Speaker 2>was started to fade. And I think that's something which

0:28:51.600 --> 0:28:56.360
<v Speaker 2>is a learning for every company out there, including self,

0:28:56.880 --> 0:29:02.040
<v Speaker 2>you know, cell based, cell grown meat and any product,

0:29:02.200 --> 0:29:06.000
<v Speaker 2>is that you have to understand what is emotionally engaging,

0:29:06.120 --> 0:29:09.040
<v Speaker 2>what's not. What are the key emotional drivers of your

0:29:09.080 --> 0:29:13.720
<v Speaker 2>customers towards your product. And what we found is that

0:29:14.520 --> 0:29:18.720
<v Speaker 2>we know today we knew then that plant bait meat,

0:29:19.840 --> 0:29:22.600
<v Speaker 2>you know, was built to have the flavor and the

0:29:22.680 --> 0:29:28.280
<v Speaker 2>texture of meat, but it's actually quite unhealthy. Maybe good

0:29:28.280 --> 0:29:31.320
<v Speaker 2>for the environment, but it's unhealthy. But when you come

0:29:31.360 --> 0:29:34.200
<v Speaker 2>out with a plant based meat and that concept, the

0:29:34.280 --> 0:29:39.520
<v Speaker 2>first thing is, oh, it's vegan. Vegan is healthy. So

0:29:39.720 --> 0:29:43.000
<v Speaker 2>merely in the conversation, we saw that it is a dissonance,

0:29:43.120 --> 0:29:47.680
<v Speaker 2>emotional dissonance between what the product stands for and what

0:29:47.760 --> 0:29:52.680
<v Speaker 2>it actually delivers. And we pointed that out and said, look,

0:29:53.000 --> 0:29:56.720
<v Speaker 2>I understand it is an internal conflict because the product

0:29:56.800 --> 0:29:59.920
<v Speaker 2>was built differently than what it's what it looks like.

0:30:00.160 --> 0:30:03.200
<v Speaker 2>So maybe not calling it plant based meat and doing

0:30:03.440 --> 0:30:06.120
<v Speaker 2>calling it slightly different so it doesn't have the connotation

0:30:06.240 --> 0:30:10.760
<v Speaker 2>with the vegan of vegetarian community might actually help here.

0:30:12.160 --> 0:30:14.680
<v Speaker 2>And that started to hurt. And I think this is

0:30:14.720 --> 0:30:19.880
<v Speaker 2>a great example of why you need to understand your consumer.

0:30:19.920 --> 0:30:24.720
<v Speaker 2>And you know, I mentioned to you that maybe outside

0:30:24.720 --> 0:30:30.040
<v Speaker 2>of the of your your vertical and your interests your industry.

0:30:30.360 --> 0:30:34.640
<v Speaker 2>You know, another example of a company which completely missed

0:30:36.000 --> 0:30:41.760
<v Speaker 2>understanding its clients was Peloton. You know, we talked quite

0:30:41.800 --> 0:30:45.760
<v Speaker 2>a bit about that. So we had a private equity

0:30:45.800 --> 0:30:51.080
<v Speaker 2>company come to us in early twenty twenty one asking

0:30:51.200 --> 0:30:55.400
<v Speaker 2>us about the analysis of Peloton, and Peloton did phenomenally well.

0:30:55.720 --> 0:30:57.800
<v Speaker 2>They went to become publican now a year and a

0:30:57.840 --> 0:31:00.600
<v Speaker 2>half before in the late two thousand nineteen, and then

0:31:00.680 --> 0:31:04.520
<v Speaker 2>twenty twenty, obviously with COVID and the pandemic, everyone stayed home.

0:31:05.040 --> 0:31:10.320
<v Speaker 2>Revenues went just you know, went, you know, accelerated, and

0:31:10.680 --> 0:31:13.800
<v Speaker 2>the stock just was on the tear. In early twenty

0:31:13.840 --> 0:31:16.440
<v Speaker 2>twenty one, they announced it going to open up a

0:31:16.480 --> 0:31:20.840
<v Speaker 2>second manufacturing plan because the demand was too big. So

0:31:20.880 --> 0:31:24.000
<v Speaker 2>the question was what happens in twenty twenty one when

0:31:24.040 --> 0:31:27.520
<v Speaker 2>the vaccine comes out. COVID vaccine was just coming out

0:31:28.000 --> 0:31:33.239
<v Speaker 2>late twenty twenty, and yeah, the expectation was that the

0:31:33.280 --> 0:31:38.480
<v Speaker 2>world will open up again, and so what happens to

0:31:39.320 --> 0:31:44.520
<v Speaker 2>you know, home equipment, home sports equipment. So we didn't

0:31:44.520 --> 0:31:47.360
<v Speaker 2>talk to Peloton. We only talked to our client at

0:31:47.400 --> 0:31:52.360
<v Speaker 2>Private Equity, and we went to the website Peloton's website

0:31:52.400 --> 0:31:55.680
<v Speaker 2>better understand what is the strategy. And as soon as

0:31:55.720 --> 0:31:58.400
<v Speaker 2>you looked at the website, it became very obvious what

0:31:58.440 --> 0:32:04.000
<v Speaker 2>their strategy is. And the strategy is that to compete

0:32:04.000 --> 0:32:07.080
<v Speaker 2>against the gyms. They viewed that the gyms would be

0:32:07.120 --> 0:32:10.840
<v Speaker 2>their biggest competitor. People will go out, the world opens up,

0:32:10.840 --> 0:32:12.760
<v Speaker 2>people go back to the gym, and so it's all

0:32:12.800 --> 0:32:16.640
<v Speaker 2>about competing against gym's membership. So everything was around price.

0:32:17.520 --> 0:32:20.560
<v Speaker 2>Only forty nine dollars a months were less expensive than membership. Overusing.

0:32:20.560 --> 0:32:23.560
<v Speaker 2>It was around price. It was very honest and by

0:32:23.560 --> 0:32:27.560
<v Speaker 2>the way, this makes perfect sense. If you ask people,

0:32:27.680 --> 0:32:30.680
<v Speaker 2>and you ask the rational mind, would you think about

0:32:30.720 --> 0:32:35.320
<v Speaker 2>price and Peloton, everyone will tell you it's expensive. It's

0:32:35.360 --> 0:32:39.440
<v Speaker 2>expensive like Lululemon. For years people said Lulu Lemon is expensive.

0:32:40.200 --> 0:32:44.320
<v Speaker 2>But here's the issue. When we looked at the emotional

0:32:44.360 --> 0:32:51.320
<v Speaker 2>profile of conversations around Peloton and specifically around price and

0:32:51.360 --> 0:32:54.440
<v Speaker 2>how expensive it is in early twenty twenty one, we

0:32:54.520 --> 0:33:00.000
<v Speaker 2>saw that people clearly talked about it, but nobody emotionally cared.

0:33:01.400 --> 0:33:04.280
<v Speaker 2>And that's the key point. It took us exactly three

0:33:04.360 --> 0:33:07.760
<v Speaker 2>minutes to figure it out. People emotionally do not care

0:33:08.240 --> 0:33:12.200
<v Speaker 2>about price when they Buipeloton or don't like Peloton. What

0:33:12.280 --> 0:33:16.840
<v Speaker 2>they do care about is online classes, is instructors, it's

0:33:16.920 --> 0:33:23.880
<v Speaker 2>the social status. And if you focus on the reasons

0:33:23.920 --> 0:33:27.720
<v Speaker 2>which are unimportant to your clients, you will miss out

0:33:28.840 --> 0:33:31.480
<v Speaker 2>on actually what's important to them, and therefore you're going

0:33:31.520 --> 0:33:33.960
<v Speaker 2>to lose revenue. So twenty twenty one, we going back

0:33:33.960 --> 0:33:37.920
<v Speaker 2>to our clients said hey, I think their risks missing

0:33:38.000 --> 0:33:41.560
<v Speaker 2>revenues big time. And six months later they announce they're

0:33:41.600 --> 0:33:43.760
<v Speaker 2>going to miss a billion dollars in revenues. And then

0:33:43.800 --> 0:33:46.120
<v Speaker 2>another six months the CEO was gone and now they

0:33:46.160 --> 0:33:50.040
<v Speaker 2>look at their company. It's all about online classes and instructors.

0:33:50.240 --> 0:33:53.720
<v Speaker 2>And that's the same with plant based meats. You have

0:33:53.800 --> 0:33:58.280
<v Speaker 2>to understand what are the key emotional drivers of your customers,

0:33:58.880 --> 0:33:59.840
<v Speaker 2>otherwise you miss.

0:34:00.840 --> 0:34:07.120
<v Speaker 1>Yeah, it's very cool. You've also developed a job confidence index.

0:34:07.280 --> 0:34:10.360
<v Speaker 1>Can you talk about that and what your proprietary index

0:34:10.800 --> 0:34:12.600
<v Speaker 1>is telling you and the team right now about the

0:34:12.680 --> 0:34:13.480
<v Speaker 1>US job market.

0:34:14.040 --> 0:34:17.280
<v Speaker 2>Yeah. We have a history of trying to be also

0:34:18.880 --> 0:34:26.000
<v Speaker 2>public facing and provide services. So when COVID started, we

0:34:26.080 --> 0:34:29.560
<v Speaker 2>came out immediately with an anxiety COVID Anxiety a panic

0:34:29.600 --> 0:34:34.920
<v Speaker 2>index to better measure people's emotional attitude, so you know,

0:34:35.360 --> 0:34:39.359
<v Speaker 2>public figures can better understand, both globally and in the

0:34:39.480 --> 0:34:43.640
<v Speaker 2>US how people really feel and create policies. And then

0:34:43.680 --> 0:34:47.960
<v Speaker 2>when the vaccine came out, we had the COVID Vaccine

0:34:48.120 --> 0:34:52.480
<v Speaker 2>Confident Index again public FASIC and then about a year

0:34:52.560 --> 0:34:56.800
<v Speaker 2>later we came out with the Job Index. There was

0:34:56.840 --> 0:35:01.040
<v Speaker 2>a lot about the jobs and the silent designation and

0:35:01.080 --> 0:35:04.160
<v Speaker 2>people understanding what's happening, and it was important for us

0:35:04.200 --> 0:35:07.840
<v Speaker 2>to measure the public emotional attitude so we can actually

0:35:07.880 --> 0:35:12.840
<v Speaker 2>do something about it. I think understanding not just companies,

0:35:12.960 --> 0:35:17.600
<v Speaker 2>but the society at large, the public at large, better

0:35:17.680 --> 0:35:24.000
<v Speaker 2>understanding what their emotional policies around key policy issues is

0:35:24.040 --> 0:35:26.600
<v Speaker 2>incredibly important because we have a tool which can provide

0:35:26.600 --> 0:35:32.200
<v Speaker 2>that as a social good, so we can actually again politicians,

0:35:32.239 --> 0:35:35.839
<v Speaker 2>public figures, the media, if they use the tool, they

0:35:35.880 --> 0:35:40.560
<v Speaker 2>can address the issues which are important to us as

0:35:40.560 --> 0:35:46.800
<v Speaker 2>citizens and emotionally engaged. So we actually and emotionally engage

0:35:46.840 --> 0:35:51.760
<v Speaker 2>in an emotionally intelligent way without technology, so it provides

0:35:51.800 --> 0:35:52.520
<v Speaker 2>a better outcome.

0:35:52.920 --> 0:35:55.800
<v Speaker 1>Very cool, all right, So I'd like to talk about

0:35:55.800 --> 0:36:00.279
<v Speaker 1>AI a little bit more generally now. You know, if

0:36:00.320 --> 0:36:02.799
<v Speaker 1>you've been in this business, obviously a long time. We've

0:36:02.800 --> 0:36:08.600
<v Speaker 1>been partnered, as we said, since twenty eighteen, you know,

0:36:08.640 --> 0:36:12.360
<v Speaker 1>so AI has been around longer than most people realize.

0:36:12.400 --> 0:36:15.920
<v Speaker 1>But with chat GBT, a lot of people are being

0:36:16.120 --> 0:36:18.360
<v Speaker 1>introduced to AI for the first time. And when I

0:36:18.400 --> 0:36:20.839
<v Speaker 1>tell them, I was partnered with an AI company for

0:36:20.880 --> 0:36:26.280
<v Speaker 1>more than five years. They're like shocked, right, it's really interesting.

0:36:26.320 --> 0:36:28.520
<v Speaker 1>It's the hot topic of the day. I think it's

0:36:28.719 --> 0:36:32.840
<v Speaker 1>it's driving tech company valuations here in the first half,

0:36:33.640 --> 0:36:36.719
<v Speaker 1>so I figured we'd hit on it. In regards to

0:36:36.800 --> 0:36:40.319
<v Speaker 1>chat GBT, what are some of the opportunities for large

0:36:40.400 --> 0:36:41.240
<v Speaker 1>language models.

0:36:41.640 --> 0:36:45.160
<v Speaker 2>That's a great question. By the way, I'm really fascinated

0:36:45.280 --> 0:36:49.880
<v Speaker 2>by the awareness CHATIPT and OpenAI plot to the market.

0:36:49.920 --> 0:36:52.200
<v Speaker 2>I mean, AI has been around for a long time

0:36:52.239 --> 0:36:56.360
<v Speaker 2>and and you know, fifty years in terms of the making,

0:36:56.440 --> 0:36:58.840
<v Speaker 2>but we over the last I would say decade or

0:36:58.840 --> 0:37:01.279
<v Speaker 2>so more we have had the chance to really use

0:37:01.320 --> 0:37:05.080
<v Speaker 2>AI because of the digitization of the world and more data,

0:37:05.120 --> 0:37:08.600
<v Speaker 2>more computing power and so on. But really, what CHGPT

0:37:08.800 --> 0:37:11.800
<v Speaker 2>did is it brought the whole concept of oh AI

0:37:12.000 --> 0:37:14.279
<v Speaker 2>is really part of our can be part of our

0:37:14.320 --> 0:37:19.200
<v Speaker 2>world and professionally, personally really to a different level. And

0:37:19.880 --> 0:37:23.120
<v Speaker 2>this awareness is incredible and it opens up a lot

0:37:23.160 --> 0:37:26.080
<v Speaker 2>of opportunities, both of the good side. And obviously there

0:37:26.080 --> 0:37:31.759
<v Speaker 2>are some risks, but there's no revolution without risks, and

0:37:32.600 --> 0:37:35.440
<v Speaker 2>nothing gets solved before it's actually here. So now we're

0:37:35.440 --> 0:37:37.640
<v Speaker 2>here and now we have to face it in terms

0:37:37.640 --> 0:37:42.919
<v Speaker 2>of opportunities. I mean the opportunities I think short term

0:37:43.080 --> 0:37:47.399
<v Speaker 2>are somewhat known. The longer term I think are significant,

0:37:47.600 --> 0:37:51.840
<v Speaker 2>very significant, because all those large language models are improving

0:37:52.640 --> 0:37:56.240
<v Speaker 2>agguessively improving not just within the large organization but also

0:37:56.280 --> 0:38:02.279
<v Speaker 2>within you know, open source developer communities, and so there's

0:38:02.440 --> 0:38:06.879
<v Speaker 2>there's a lot happening there are There are really two

0:38:06.920 --> 0:38:11.520
<v Speaker 2>ways to think about this. Let's call it generitive AI.

0:38:11.800 --> 0:38:16.520
<v Speaker 2>One is depending on the large language model is exactly

0:38:16.600 --> 0:38:23.480
<v Speaker 2>what it sounds. It's trying to use training from textual conversations,

0:38:24.080 --> 0:38:26.239
<v Speaker 2>you know, WI just be pre trained on a very

0:38:26.320 --> 0:38:32.640
<v Speaker 2>very large data set to become you know, conversational and

0:38:32.760 --> 0:38:35.960
<v Speaker 2>provide answers like a human being would. Now it's all

0:38:36.040 --> 0:38:40.319
<v Speaker 2>still heavily statistical. It's like us putting something into the

0:38:40.360 --> 0:38:42.680
<v Speaker 2>Google search bar, which we have been used for years,

0:38:43.040 --> 0:38:45.520
<v Speaker 2>where when you start typing, it shows you what else

0:38:45.560 --> 0:38:48.719
<v Speaker 2>you could mean. It's predicting what it thinks you have

0:38:48.840 --> 0:38:52.680
<v Speaker 2>in mind. Large language models or genitive AI is based

0:38:52.719 --> 0:38:54.960
<v Speaker 2>on large language model. Is really that it's trying to

0:38:55.000 --> 0:38:58.000
<v Speaker 2>predict not the next word or phrase, but in the

0:38:58.280 --> 0:39:03.080
<v Speaker 2>sentences and narratives and paragraphs, but it's still a prediction model.

0:39:03.640 --> 0:39:05.839
<v Speaker 2>It's based on what it thinks, and that's why it's

0:39:05.840 --> 0:39:10.520
<v Speaker 2>also lascinating. It's making up stuff, which right away is

0:39:10.600 --> 0:39:13.000
<v Speaker 2>very similar to the brain. We make up stuff because

0:39:13.520 --> 0:39:16.399
<v Speaker 2>again the subconscious mind makes decisions, the rational mind tries

0:39:16.480 --> 0:39:19.319
<v Speaker 2>to make find a reason and narrative, so it makes

0:39:19.320 --> 0:39:21.279
<v Speaker 2>it up all the time, and we can go into that.

0:39:21.360 --> 0:39:25.920
<v Speaker 2>But this is fascinating. So that's using generaltve AI to

0:39:26.000 --> 0:39:29.799
<v Speaker 2>have an interaction with a machine to get answers in

0:39:29.840 --> 0:39:32.399
<v Speaker 2>a much easier way than it was we were able

0:39:32.480 --> 0:39:34.840
<v Speaker 2>to be able to. This is a second part of

0:39:34.880 --> 0:39:38.880
<v Speaker 2>general DEVEAI, which is or AI usage not on a

0:39:38.960 --> 0:39:41.919
<v Speaker 2>large language model, but actually on structured data. And that's

0:39:41.920 --> 0:39:44.480
<v Speaker 2>where also a lot of the usage comes where instead

0:39:44.520 --> 0:39:48.720
<v Speaker 2>of having a terminal where you look at the data,

0:39:48.920 --> 0:39:51.120
<v Speaker 2>maybe if a search find you should ask the question, hey,

0:39:52.160 --> 0:39:57.680
<v Speaker 2>who is my best salesperson? Or tell me about you know, McDonald's,

0:39:57.719 --> 0:40:04.960
<v Speaker 2>or targets behavior customer behavior around around the event which

0:40:05.080 --> 0:40:07.440
<v Speaker 2>was you know a few months ago, and it just

0:40:07.560 --> 0:40:10.120
<v Speaker 2>spits us the answers and gives you the results in

0:40:10.120 --> 0:40:13.760
<v Speaker 2>a nice chart format without you actually doing the research.

0:40:13.800 --> 0:40:17.400
<v Speaker 2>So there's a lot happening depending how you train the

0:40:17.480 --> 0:40:20.760
<v Speaker 2>models and how you use it, and that will clearly

0:40:20.840 --> 0:40:27.360
<v Speaker 2>create a lot of efficiencies, effectiveness what we call also

0:40:27.440 --> 0:40:31.920
<v Speaker 2>cognitive load reduction. It uses our need to use the

0:40:32.280 --> 0:40:35.799
<v Speaker 2>rational mind to find the results because it's there. But

0:40:35.880 --> 0:40:40.640
<v Speaker 2>it will require us to change our definitions of what

0:40:40.719 --> 0:40:44.759
<v Speaker 2>it means to be a stock picker or you know,

0:40:44.880 --> 0:40:48.640
<v Speaker 2>a writer or an artist or whatever it is, and

0:40:48.719 --> 0:40:51.080
<v Speaker 2>so does so it comes with a change, but it

0:40:51.120 --> 0:40:53.480
<v Speaker 2>has all everything has come with a change. Correct when

0:40:53.520 --> 0:40:56.759
<v Speaker 2>we started with having computers people who are open arms,

0:40:56.760 --> 0:40:59.279
<v Speaker 2>what happens to maths? Now people don't have to learn math, well,

0:40:59.560 --> 0:41:01.520
<v Speaker 2>they don't have to learn that math, but they have

0:41:01.560 --> 0:41:04.120
<v Speaker 2>to learn a different kind of logic, and so it

0:41:04.239 --> 0:41:07.720
<v Speaker 2>takes you to a different level and it enhances clearly

0:41:08.600 --> 0:41:13.799
<v Speaker 2>the human The capability is productivity and I believe longer

0:41:13.880 --> 0:41:18.480
<v Speaker 2>term also you know, satisfaction. But it comes with risks,

0:41:18.719 --> 0:41:20.960
<v Speaker 2>and I think the biggest risk here is not just

0:41:21.320 --> 0:41:24.680
<v Speaker 2>how it impacts us as humans in our worlds. They

0:41:24.960 --> 0:41:28.440
<v Speaker 2>you know, personal as well as the professional in the

0:41:28.480 --> 0:41:32.480
<v Speaker 2>professional settings, but also the risk in terms of privacy,

0:41:32.719 --> 0:41:36.280
<v Speaker 2>in terms of security, the risk in terms of ethical

0:41:36.320 --> 0:41:40.640
<v Speaker 2>behavior and the responsible behavior and all those things have

0:41:40.880 --> 0:41:45.480
<v Speaker 2>not been addressed yet to full extent as it should,

0:41:45.920 --> 0:41:48.560
<v Speaker 2>and it's not expected to because as long as the

0:41:48.560 --> 0:41:52.359
<v Speaker 2>technology isn't out there, nobody feels the need to come

0:41:52.440 --> 0:41:55.480
<v Speaker 2>up with policies and guidelines. But now it is, so

0:41:55.520 --> 0:41:58.080
<v Speaker 2>we really have to put our act together and come

0:41:58.160 --> 0:42:00.959
<v Speaker 2>up with it otherwise we're the need to a free

0:42:01.040 --> 0:42:04.120
<v Speaker 2>for all. And we see a lot of the bad

0:42:04.880 --> 0:42:09.320
<v Speaker 2>usage of the capabilities like we see today already in

0:42:09.400 --> 0:42:13.200
<v Speaker 2>terms of cyber security issues and incidents and deep fake

0:42:13.280 --> 0:42:17.239
<v Speaker 2>issues correct where somebody can pretend I could pretend on

0:42:17.360 --> 0:42:21.359
<v Speaker 2>Mike Hayleen and talk about a company to buy and

0:42:21.760 --> 0:42:24.560
<v Speaker 2>short and it's not you, and I'm just making it up,

0:42:24.600 --> 0:42:29.680
<v Speaker 2>and that has obviously illegal implications, and that's a risk,

0:42:29.960 --> 0:42:34.280
<v Speaker 2>and that's a risk which todays technology can still somewhat

0:42:34.280 --> 0:42:37.000
<v Speaker 2>figure out. So we have AI fighting AI to figure

0:42:37.000 --> 0:42:40.200
<v Speaker 2>it out. But I think that capability is slowly going

0:42:40.239 --> 0:42:43.600
<v Speaker 2>away as AI is improving, So we need to find

0:42:43.920 --> 0:42:45.840
<v Speaker 2>better ways to protect ourselves.

0:42:46.520 --> 0:42:48.960
<v Speaker 1>Yeah, that stuff's really interesting, right We're in there's a

0:42:49.000 --> 0:42:52.680
<v Speaker 1>massive distrust of government and media, and I know smart

0:42:52.680 --> 0:42:56.880
<v Speaker 1>people that tell me that they're having real difficulty discerning

0:42:56.960 --> 0:42:59.160
<v Speaker 1>fact from fiction, right, and so all that stuff is

0:42:59.719 --> 0:43:03.399
<v Speaker 1>is area and you know, I think there there needs

0:43:03.400 --> 0:43:08.879
<v Speaker 1>to be some sort of regulation on it. But yeah,

0:43:08.920 --> 0:43:11.160
<v Speaker 1>it's interesting and it's really interesting to see what happens

0:43:11.200 --> 0:43:13.520
<v Speaker 1>to the job market job market right with all of

0:43:13.560 --> 0:43:17.600
<v Speaker 1>the improvements in AI and automation, and you know, what

0:43:17.680 --> 0:43:19.880
<v Speaker 1>does that mean for workers moving forward?

0:43:20.480 --> 0:43:23.680
<v Speaker 2>Well, there's definitely going to be a rotation happening. There

0:43:23.719 --> 0:43:26.279
<v Speaker 2>has to be a redefinition of what it needs to be,

0:43:27.040 --> 0:43:30.680
<v Speaker 2>you know, a bank or an investment professional or anything.

0:43:31.400 --> 0:43:35.160
<v Speaker 2>But again, we are used to that. Every time there

0:43:35.200 --> 0:43:38.439
<v Speaker 2>is a revolution, there is a change. When the first

0:43:38.560 --> 0:43:44.600
<v Speaker 2>cast came, it changed the whole industry on tourses and

0:43:44.680 --> 0:43:48.319
<v Speaker 2>so we're used to the fact that the world is

0:43:48.520 --> 0:43:54.680
<v Speaker 2>moving forward. And you know, I think it's interesting to

0:43:54.719 --> 0:43:57.520
<v Speaker 2>see some of the conversations and you think about the educators.

0:43:57.520 --> 0:44:02.520
<v Speaker 2>The first we actual manchat ChiPT came out from educators

0:44:02.560 --> 0:44:04.759
<v Speaker 2>from teachers was oh my gosh, we're going to have

0:44:04.880 --> 0:44:09.640
<v Speaker 2>to you know, prohibit the use otherwise we can figure

0:44:09.640 --> 0:44:13.279
<v Speaker 2>out if the student actually learned it or vote it,

0:44:13.400 --> 0:44:16.160
<v Speaker 2>or whether it was done by one of the large

0:44:16.200 --> 0:44:19.399
<v Speaker 2>language models. I think that's the wrong attitude. I think

0:44:19.440 --> 0:44:22.799
<v Speaker 2>the attitude is this is here. I mean, it's going

0:44:22.880 --> 0:44:26.440
<v Speaker 2>to happen. Let's figure out how we can leverage that

0:44:27.160 --> 0:44:32.440
<v Speaker 2>and make sure that our students are even more creative

0:44:33.160 --> 0:44:35.360
<v Speaker 2>than they used to be. And so we have to

0:44:35.520 --> 0:44:38.600
<v Speaker 2>just yes, it's effort. We don't like effort. We don't

0:44:38.680 --> 0:44:42.799
<v Speaker 2>like to change our status quo, which is by the way,

0:44:42.840 --> 0:44:48.399
<v Speaker 2>emotional as well. But no, we don't want to change

0:44:48.400 --> 0:44:52.600
<v Speaker 2>the status quo. And so we're looking to, you know,

0:44:52.880 --> 0:44:58.399
<v Speaker 2>suppress new opportunities, not understanding that the opportunities are here

0:44:58.680 --> 0:45:01.960
<v Speaker 2>and they actually have a positive can have a positive

0:45:02.000 --> 0:45:04.839
<v Speaker 2>impact if we actually think it so. And so it's

0:45:04.880 --> 0:45:10.359
<v Speaker 2>a challenge for all of us, everyone, every profession, every

0:45:10.480 --> 0:45:13.200
<v Speaker 2>vertical to understand what does it mean the fact that

0:45:13.280 --> 0:45:18.720
<v Speaker 2>we now have a capability which is out there, people

0:45:18.719 --> 0:45:22.439
<v Speaker 2>are aware, which are being used day in, day out,

0:45:23.600 --> 0:45:26.920
<v Speaker 2>and how does that What does that mean to society?

0:45:27.360 --> 0:45:29.360
<v Speaker 2>What does it mean for us? That doesn't mean for me?

0:45:30.400 --> 0:45:34.759
<v Speaker 1>Yeah, a lot of answered questions. Definitely interesting times to

0:45:34.800 --> 0:45:36.920
<v Speaker 1>be alive and something I think about, Right, I have

0:45:36.960 --> 0:45:39.960
<v Speaker 1>a fifteen year old, right, and so making sure he

0:45:40.320 --> 0:45:42.080
<v Speaker 1>chooses a career that's going to be around by the

0:45:42.080 --> 0:45:43.160
<v Speaker 1>time he gets out of college.

0:45:43.239 --> 0:45:50.759
<v Speaker 2>Right, Well, for then AI behind it. But yes, it's fascinating.

0:45:50.800 --> 0:45:54.239
<v Speaker 2>So we at Kovnoby Labs are very conscientious about that

0:45:54.600 --> 0:45:59.960
<v Speaker 2>and put a lot of effort around understanding these compliance issues,

0:46:00.200 --> 0:46:04.480
<v Speaker 2>around responsibly ethical, privacy, insecurity issues to make sure that

0:46:05.080 --> 0:46:07.920
<v Speaker 2>we use it in a responsive way. And you know,

0:46:08.080 --> 0:46:11.240
<v Speaker 2>the large language model for us, it's just an additional

0:46:11.440 --> 0:46:13.319
<v Speaker 2>icing on the cake. We already had the cake. It's

0:46:13.360 --> 0:46:18.160
<v Speaker 2>our technology. It's something Chatgypt doesn't do, nobody does accept us.

0:46:18.520 --> 0:46:21.879
<v Speaker 2>So we're just you know, we're aggressively building out our

0:46:21.920 --> 0:46:26.080
<v Speaker 2>capabilities with every new capability, new functionality which is being

0:46:26.320 --> 0:46:29.239
<v Speaker 2>which is out there to provide more value to our clients.

0:46:30.160 --> 0:46:32.399
<v Speaker 1>That's awesome. I think that's a perfect spot to wrap

0:46:32.400 --> 0:46:36.040
<v Speaker 1>it up. Where can you know if any of our listeners,

0:46:36.160 --> 0:46:39.719
<v Speaker 1>you know, any chief marketing officers out there, you know,

0:46:39.800 --> 0:46:42.600
<v Speaker 1>restaurant executives you know, want to reach out to you

0:46:42.760 --> 0:46:46.000
<v Speaker 1>and inquire about your services work. How's the best way

0:46:46.000 --> 0:46:46.480
<v Speaker 1>to reach.

0:46:46.280 --> 0:46:50.440
<v Speaker 2>You, Yeah, go to our website Krognovi Labs dot com

0:46:50.840 --> 0:46:54.720
<v Speaker 2>and send us a text or send us an email

0:46:55.320 --> 0:46:59.040
<v Speaker 2>at info at Kognovi Labs dot com and love to

0:46:59.080 --> 0:47:03.040
<v Speaker 2>talk to you. We can help you emotional, engage with custom.

0:47:03.080 --> 0:47:05.360
<v Speaker 1>That's great, good stuff. Thanks for doing this and thanks

0:47:05.400 --> 0:47:08.719
<v Speaker 1>to the listeners for checking us out. Have a great

0:47:08.760 --> 0:47:13.120
<v Speaker 1>day everybody.

0:47:12.360 --> 0:47:12.400
<v Speaker 2>H