Latest / Choppin’ It Up by Bloomberg Intelligence / AI Spices Up Consumer Connection, Marketing

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AI Spices Up Consumer Connection, Marketing · Choppin’ It Up by Bloomberg Intelligence

0:16
Speaker 1: Welcome to Chopping It Up. I'm your host Michael Halen,
0:19
Speaker 1: the senior restaurant and food service analyst at Bloomberg Intelligence.
0:23
Speaker 1: Today's guest is Benny Gradwall. Benny is the go founder
0:26
Speaker 1: and CEO of cognoviy Labs. It's a company that analyzes
0:30
Speaker 1: social media posts to help form insights into consumer behavior.
0:34
Speaker 1: Benny and the team have provided Bloomberg with some very
0:37
Speaker 1: timely analyzes over the years on companies like McDonald's, Starbucks,
0:41
Speaker 1: Young Brands, and most recently, Target. So I'm excited to
0:45
Speaker 1: have him join me this morning. Thanks for doing this, Benny.
0:49
Speaker 2: Well, good morning, Mike. Great being here, and thank you
0:51
Speaker 2: very much for inviting me to your podcast.
0:55
Speaker 1: Yeah, of course AI has been all over the news,
0:57
Speaker 1: and you know, I thought i'd I'd hop on the
1:00
Speaker 1: hype train, and when it comes to AI, you're my expert.
1:04
Speaker 1: So it was really a no brainer for asking you
1:08
Speaker 1: to come on with me.
1:09
Speaker 2: Oh that's a big task, but yeah, happy, do we
1:12
Speaker 2: hear an answer anything and you know, talk AI psychology
1:15
Speaker 2: or whatever you want to go?
1:17
Speaker 1: Cool? Can't wait, man, So I guess please if you
1:20
Speaker 1: could please tell the audience about your career path and
1:23
Speaker 1: how it led to your founding of Cognoviy Labs.
1:26
Speaker 2: Yeah, it's it's an interesting story. I started in academia
1:29
Speaker 2: a long time ago, and you know, on the quantity
1:32
Speaker 2: decided and asked physics, but then switched into finance in
1:35
Speaker 2: the early nineties. And you know, for me, everything was
1:39
Speaker 2: around numbers and analytics and you know, hard stuff. And
1:45
Speaker 2: you know, I was very fortunate to have a chance
1:48
Speaker 2: to take a class at Harvard in behavior economics pretty
1:52
Speaker 2: early on, I think it was nineteen ninety seven, and
1:56
Speaker 2: that was a real eye opener. You know. I got
2:00
Speaker 2: back and I was working out an investment firmat in
2:03
Speaker 2: San Diego, and I came back and said, oh my gosh,
2:05
Speaker 2: the world actually doesn't revolve just around numbers, revolves around
2:09
Speaker 2: people making decisions. And that just became a passion. I
2:13
Speaker 2: was trying for the last twenty five years or so
2:16
Speaker 2: figuring out how we humans make decisions. And the more
2:21
Speaker 2: I got into it, the more fascinating it was. And
2:24
Speaker 2: you know, I went through a financial career and then
2:26
Speaker 2: into emerging technologies and I say, look, I need to
2:29
Speaker 2: really figure that out, you know, I it's it's it's
2:33
Speaker 2: just fascinating how you go into through a financial career
2:36
Speaker 2: and you work and you know, I was a market
2:38
Speaker 2: standing then at City during the financial crisis, and with
2:42
Speaker 2: negotiating on the mortgage site with homeowners without knowing anything
2:47
Speaker 2: around human decision making, just felt, you know, that something
2:51
Speaker 2: is missing, and so I was on a you know,
2:54
Speaker 2: on a mission to figure that out. And that's how
2:56
Speaker 2: we started knob Labs to really understand human decision making processes.
3:04
Speaker 1: Well, that's very cool, and I'd imagine that's a very
3:06
Speaker 1: emotional process for somebody right at that at that time,
3:10
Speaker 1: right a home with you know, having to maybe sell
3:14
Speaker 1: your home or move out of a home that you've
3:16
Speaker 1: been living in, you know for some time. I think
3:20
Speaker 1: there's probably a lot of emotion in that process, right.
3:24
Speaker 2: Yeah, there's a lot of emotions and there's a lot
3:27
Speaker 2: of pain, and you know, so we came in. I
3:30
Speaker 2: came in in two thousand and eight to help stabilize
3:34
Speaker 2: City in the bank and it was responsible for I've
3:37
Speaker 2: pretty seen a significant mogtile portfolio. The hold loans about
3:42
Speaker 2: twenty to eighty billion dollars and the question was, well,
3:45
Speaker 2: you can't sell it, that's toxic assets, So what do
3:48
Speaker 2: you do with it? And so you have to negotiate
3:52
Speaker 2: with the homeowner. That's the beautiful mortgage portfolio. There is
3:54
Speaker 2: actually an on on the other side. And so you
3:57
Speaker 2: can modify and and change the asset and take the
4:02
Speaker 2: homeowners who are you know, process and into waste management
4:08
Speaker 2: something which is good for the bank and something is
4:10
Speaker 2: good for the homeowner. And so I went to my
4:12
Speaker 2: boss very early on and say, okay, that's great, but
4:14
Speaker 2: now I have to negotiate, to find a way to negotiate,
4:18
Speaker 2: you know in quotation marks negotiate, but through through analytics
4:22
Speaker 2: and outreaches with millions of homeowners. How do I do that?
4:26
Speaker 2: Where do I find the chief psychology officer within city
4:28
Speaker 2: who can guide me how to do that? Then guess what,
4:32
Speaker 2: there wasn't any There isn't the chief psychology officers of
4:35
Speaker 2: the city. So you have investment banks and you have
4:38
Speaker 2: consumer banks, which by definition work with individual consumers, and
4:43
Speaker 2: there's no psychological understanding how that engagement should work. And
4:48
Speaker 2: it's just mind boding, correct. And so it was very
4:51
Speaker 2: clear that there is a world out there which goes
4:55
Speaker 2: beyond the hard data, which is on touch to some extent,
5:01
Speaker 2: but probably more important than the hard data. And so
5:04
Speaker 2: how did we figure it out? And by the way,
5:06
Speaker 2: I think the team at City at that time Citimot
5:11
Speaker 2: did a great job. We kept about a million and
5:13
Speaker 2: a half American homes of families in the home. So
5:15
Speaker 2: whatever we did, we did. You know, it worked and
5:18
Speaker 2: it was a great outcome. But still something was missing,
5:21
Speaker 2: and and that's where I am now. Well, look, think
5:25
Speaker 2: about the US economy. Two third of the US economy
5:27
Speaker 2: is a consumer dreaming about the consumer. Yeah, how consumers
5:30
Speaker 2: make decisions? You had bloomberg, open up a blooming terminal. Yeah,
5:34
Speaker 2: every piece of information out there, take data and you know,
5:38
Speaker 2: revenues and learning scrolls and and anything. Where's the cognitive
5:43
Speaker 2: the behavioral signals on your screen? If two third of
5:47
Speaker 2: the economy is dreaming of the consumer, where is that
5:50
Speaker 2: piece on your terminal?
5:52
Speaker 1: Will sitting on our restaurant dashboard with our Cognovy Labs data.
5:56
Speaker 2: So I got it at least the starting.
5:59
Speaker 1: Point, Yeah for sure. All right, So who are Cognoby's
6:04
Speaker 1: customers and what problems are you helping them solve?
6:08
Speaker 2: Yeah? So look out of technology is applicable anyway humans
6:12
Speaker 2: make decisions. So when we started, we started in politics
6:16
Speaker 2: and went into finance and and and and the corporate world.
6:22
Speaker 2: And so it's it's really understanding. You know, if you
6:28
Speaker 2: if you're in the finance and understanding you are the
6:30
Speaker 2: customers of your investment. To think about a retail company
6:33
Speaker 2: and or a restaurant. You know, who's going into the restaurant,
6:37
Speaker 2: who's who's going to McDonald's with a Starbucks, and what's
6:40
Speaker 2: driving them really or on the consumer side, you know,
6:42
Speaker 2: why would somebody you know buy a Peloton bike or
6:46
Speaker 2: not buy a Peloton bike? And and really understanding the
6:50
Speaker 2: decision making process and so the customers. So the financial
6:57
Speaker 2: companies obviously are a great customers, but also the corporations
7:01
Speaker 2: are because they have to reach out to their clients
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Speaker 2: and emotionally engage with them in a way that they
7:06
Speaker 2: can drive performance. And so let me step back, if
7:11
Speaker 2: you can give me a couple of minutes, just explain
7:13
Speaker 2: why that's actually relevant. So you know, if you go
7:17
Speaker 2: back even to Sigmund Freud who said, look, we have
7:22
Speaker 2: a rational mind, that we have a subconscious mind, and
7:26
Speaker 2: the subconscious mind makes a lot of decisions. It was
7:30
Speaker 2: never really identified how much at that time. But what
7:35
Speaker 2: became interesting is a few things which came out of it.
7:38
Speaker 2: One is, yes, there is a subconscious mind. Second, the
7:43
Speaker 2: subconscious mind makes actually the majority of decisions. We assumans
7:47
Speaker 2: are not as rational as we think we are. You know,
7:50
Speaker 2: we're not making rational decisions. And you see that in
7:52
Speaker 2: finance correctly. We started off with you know, efficient marketing particies.
7:56
Speaker 2: Everyone is rational, but now we understand the cognitive biases
7:59
Speaker 2: and all that stuff. All of a sudden, Oh my gosh,
8:01
Speaker 2: there are actually inefficiencies in the market that are driven
8:03
Speaker 2: by cognitive bvices. Oh we're not as rational. We're not
8:07
Speaker 2: rational machines. So we saw that there, and but we
8:11
Speaker 2: have validation, you know, down the Economan and Donostoreski in
8:14
Speaker 2: the seventies did their whole analysis down. Economan got then
8:17
Speaker 2: a Nobel Price in two thousand and two around you know,
8:20
Speaker 2: prospect theory, how we are not as rational, and then
8:24
Speaker 2: you have the So we know that the rational mind, well,
8:31
Speaker 2: while it's important, it doesn't make that many decisions. Really
8:34
Speaker 2: the subconscious mind, which makes the majority the rational mind,
8:37
Speaker 2: is more controlled process correct and so sometimes it breaks down.
8:41
Speaker 2: If you in wage for example, it may wage break down,
8:44
Speaker 2: But otherwise it really is a controlled process. But the
8:49
Speaker 2: subconscious mind makes most of the decisions. So that's great, Well,
8:52
Speaker 2: how does it make those decisions? Are they similar to
8:57
Speaker 2: the decision a rational mind would make, Right, the answer
9:00
Speaker 2: is yes, Then all I would say there is around
9:03
Speaker 2: the rational mind and the factual decision making process still hold.
9:07
Speaker 2: If the rational mind, however, makes the subconscious might make
9:10
Speaker 2: the decision in a different way than, we're in trouble. Well,
9:13
Speaker 2: it turns out we are in trouble. The selfconscious mind
9:19
Speaker 2: doesn't make decisions the same way the rational mind makes it.
9:23
Speaker 2: And here's why. Well, Donny Conneman found out and I'm
9:27
Speaker 2: as swear to everyone else that they are cognitive biases. Correct,
9:32
Speaker 2: So those cognitive vices obviously are different. So we know
9:35
Speaker 2: that they make the dish decisions they're differently. Well, that's great.
9:38
Speaker 2: So maybe we just asked the rational mind what decisions
9:42
Speaker 2: were made, so we can still use the rational mind
9:44
Speaker 2: to figure out how people make decisions, even if the
9:46
Speaker 2: decisions were done differently than the rational mind would do. Well,
9:50
Speaker 2: that doesn't work either. Again, we're intro why there's a
9:54
Speaker 2: guy that in the name of Benjamin Libbert and scientists
9:59
Speaker 2: or in the eighties that the subconscious mind makes a
10:04
Speaker 2: decision a fraction of a second before the rational mind
10:10
Speaker 2: actually recognizes the decision was made. Wow, So here we
10:15
Speaker 2: have a problem. We know that most of the decisions
10:18
Speaker 2: are made by the selbconscious mind. We know that decisions
10:21
Speaker 2: the subconscious mind makes are different than the rational mind.
10:24
Speaker 2: And now we find out that the rational mind doesn't
10:26
Speaker 2: even know when the subconscious mind makes a decision. Therefore
10:30
Speaker 2: it can't explain it. So now we're will in trouble. Correct,
10:34
Speaker 2: how did you mentioned? How do we figure out therefore,
10:38
Speaker 2: how people make decisions. I can't ask you, Mike, why
10:41
Speaker 2: did you do something? Because you probably acted on it
10:46
Speaker 2: before you even figured out that you're acting on it.
10:49
Speaker 2: And so surveys are great, I can ask you, but
10:53
Speaker 2: they are probably wrong some of the time, maybe not
10:56
Speaker 2: all of the time, but many many times. Then we
10:59
Speaker 2: can go into that and why that's relevant to AI
11:02
Speaker 2: and why it has to do with a lot of
11:04
Speaker 2: things which happening today with AI and how oscination and
11:07
Speaker 2: so on. But this is an important understanding as a
11:11
Speaker 2: starting point. We're not rational. The majority of decisions are
11:15
Speaker 2: everyone non rational, and rational mind doesn't know what the
11:18
Speaker 2: heck is going on. In.
11:20
Speaker 1: Yeah, that's really really interesting stuff, stuff that I've been
11:24
Speaker 1: interested in for some years. I think I was introduced
11:27
Speaker 1: to Konomen into Versky. I don't know if I'm saying
11:29
Speaker 1: his last name right, but through Against the Gods, which
11:32
Speaker 1: is a great book about human and their humans and
11:34
Speaker 1: their interaction with risk and the audience. Can't see my
11:37
Speaker 1: bookshelf behind me, but you know, Thinking Fast and Slow
11:41
Speaker 1: by Connoment is back there too. So it's definitely a
11:44
Speaker 1: very cool and interesting subject, you know, And it's something
11:48
Speaker 1: that comes up, you know, I see an advertising I've
11:52
Speaker 1: read about advertising that sometimes you need you need to
11:55
Speaker 1: get in front of the customer I think eleven times
11:58
Speaker 1: or something on average before like you enter the consideration set.
12:02
Speaker 1: And so different companies that we work with talk about
12:06
Speaker 1: the different ways that they're trying to reach out out
12:08
Speaker 1: to customers. It's just just really interesting to me.
12:11
Speaker 2: Yeah, absolutely right. The petition is important. My guess is
12:15
Speaker 2: if they're emotionally engaged, they will probably bring that eleven
12:19
Speaker 2: down quite a bit. Yeah, because emotional connection, emotional intelligence
12:24
Speaker 2: is the superpower way that one has and it's completely
12:29
Speaker 2: only utilized.
12:32
Speaker 1: Yeah, and so you've done a great job of you know,
12:35
Speaker 1: figuring out which of our companies are creating emotional connections online,
12:40
Speaker 1: whether it be through different marketing programs, whether it be
12:44
Speaker 1: through their sustainability efforts, and recently it did something about
12:49
Speaker 1: their charitable giving with us, which is cool. You also
12:54
Speaker 1: just completed an analysis of Target for my colleague Jennifer
12:59
Speaker 1: Bartashis you know what did you and the team find
13:02
Speaker 1: out about Target?
13:03
Speaker 2: Yeah, it's actually very interesting, so and you know, maybe
13:08
Speaker 2: for the listeners, what we do is we analyze any
13:12
Speaker 2: free flowing conversation, could be social media discussion, forums, blogs,
13:15
Speaker 2: it could be you know, your own surveys of transcript
13:19
Speaker 2: and we extract ten emotions in context and actually natively
13:24
Speaker 2: into any languages and no translation. Then we map that
13:26
Speaker 2: through our proprietor psychological framework into an intense score and
13:31
Speaker 2: an action personas what we found with Target, and the
13:35
Speaker 2: question was Target has been on a downward trend and
13:39
Speaker 2: actually was hit obviously with its Pride merchandise. When they
13:43
Speaker 2: came out of that, there was a whole controversy around that,
13:47
Speaker 2: and the stock really took a dive and as been
13:51
Speaker 2: settling somewhere and the question is, you know, what's actually
13:54
Speaker 2: happening in terms of the consumer's emotional actitude towards target.
14:02
Speaker 2: And so what we did is our team they analyzed
14:07
Speaker 2: social media and discussion forms around target, conversations free frond conversation,
14:11
Speaker 2: extracted those emotions and as I mentioned, the intent score,
14:14
Speaker 2: and what we saw is that a couple of things.
14:21
Speaker 2: It's not the first time target is getting into a order.
14:25
Speaker 2: I mean, that's part of who they are, and that's
14:28
Speaker 2: perfectly fine. If you look at the intent score, you
14:31
Speaker 2: see spikes up and down, and the intent is really
14:34
Speaker 2: how emotionally engaging the consumer is visa v target and
14:38
Speaker 2: how emotionally engaging they are in to go and shop,
14:41
Speaker 2: and the motivation score we see ups and downs, and
14:44
Speaker 2: you know, we see we saw quite a bit of
14:46
Speaker 2: a some kind of an action around obviously the controversy.
14:53
Speaker 2: When you look at and I think that's an interesting component.
14:56
Speaker 2: We always look at what is their base line emotions,
15:01
Speaker 2: what are the typical emotions they evoke in the consumer,
15:08
Speaker 2: and how has that been disrupted by the private merchandise.
15:17
Speaker 2: And while that's then disrupted, is it coming back to
15:22
Speaker 2: a symbol or emotional profile and an emotional attitude of
15:25
Speaker 2: the consumer, or is there a structural shift which would
15:31
Speaker 2: impact consumer behavior. That was really the question. And I'll
15:36
Speaker 2: tell you how that works for Target, and then I'll
15:38
Speaker 2: explain that how that actually works with Pepsi I'm sorry,
15:41
Speaker 2: with bud Light, not Pepsi, bud Light, which went through
15:45
Speaker 2: a similar event in April. So when you look at Target,
15:52
Speaker 2: you clearly see that there was a there was a
15:56
Speaker 2: certain profile, emotional profile which was somewhat reflective of the consumers,
16:07
Speaker 2: you know, emotional attitude and responses towards Target, which was
16:12
Speaker 2: disrupted when they came out with the merchandise. What we
16:16
Speaker 2: have seen just recently over the last couple of weeks
16:20
Speaker 2: is that there has been a reversal back to that
16:23
Speaker 2: emotional baseline. Not yet there, but we see a relaxation
16:30
Speaker 2: and a return to that which from a I'm not
16:34
Speaker 2: going to say anything about the start, but what it
16:36
Speaker 2: means is that emotionally, it seems at least that we're
16:40
Speaker 2: settling back to the status quo it was before the event.
16:45
Speaker 2: And leave that up to you to figure out what
16:47
Speaker 2: that actually means in terms of human behavior. But that's
16:50
Speaker 2: an important component. Why is an important component again, because
16:54
Speaker 2: emotions strive decisions, and if the emotional profile. A certain profile,
16:58
Speaker 2: complex profile leads to certain action tendencies, and they're coming
17:04
Speaker 2: back now to a similar baseline they were before. They're
17:07
Speaker 2: not there yet, but they seem to go back. It
17:10
Speaker 2: will tell you something here about consumer behavior going forward.
17:14
Speaker 2: This is not what's happening with but Light yet. It's
17:18
Speaker 2: interesting but Light that has a completely different profile and
17:23
Speaker 2: had a different profile before, you know, the April first
17:28
Speaker 2: event where the transgender influence of Dylan more Money came
17:32
Speaker 2: out and had that video, And what we saw is
17:37
Speaker 2: a complete disruption of that as well of that profile
17:42
Speaker 2: and no reversal yet. And what's interesting in addition, what
17:46
Speaker 2: we also measure is not just the consumers response emotional
17:51
Speaker 2: response to an event or a company or a brand
17:55
Speaker 2: or marketing. We also measure the response from the company itself,
18:00
Speaker 2: from the CEO, what they put out. And what we
18:03
Speaker 2: found in the case of bod Light is a complete
18:07
Speaker 2: disconnect between the feeling of the customer after the event
18:13
Speaker 2: after April first and the CEO's positioning. So think about it.
18:20
Speaker 2: I have a problem, I call you. You are my
18:23
Speaker 2: call center, correct, I have a problem with you with
18:26
Speaker 2: a product, and I'm really angry and you just tell
18:29
Speaker 2: me everything is homepy door, and all is fine. I
18:31
Speaker 2: will feel as a customer, you're not you're not listening.
18:34
Speaker 2: You don't hear me, you don't hear you hear my word,
18:37
Speaker 2: my words, but you don't hear me emotionally. You do
18:40
Speaker 2: not connect with me emotionally, and therefore you don't solve
18:44
Speaker 2: the problem I'm in. And that's exactly what's happening with
18:47
Speaker 2: but Light. They actually put something out which reflected their
18:50
Speaker 2: emotional profile pre event, pre April first, without taking into
18:54
Speaker 2: account and appreciating that that's not where their customer is
18:58
Speaker 2: right now, and so we haven't seen that reversal yet
19:03
Speaker 2: there whereas in the case of Target we start seeing
19:07
Speaker 2: a reversal.
19:09
Speaker 1: Yeah, it's really it's really interesting. Yeah, and it's interesting
19:12
Speaker 1: too that you said, you know, you'll you'll analyze statements
19:16
Speaker 1: from the companies and I think you've mentioned the past
19:18
Speaker 1: two that you've done some stuff on earnings transcripts and
19:21
Speaker 1: things like.
19:21
Speaker 2: That, right, Yeah, we have. We have done some of
19:24
Speaker 2: it with Noodled a little bit on it. And you know,
19:27
Speaker 2: there's a lot of you know, the companies who analyze
19:31
Speaker 2: the free flne conversation in terms of the sentiments which
19:35
Speaker 2: is positive, negative, and neutral. And I just want to
19:41
Speaker 2: mention that why we have probably the first or one
19:44
Speaker 2: of the first patterns on sentiment analysis. We have moved
19:47
Speaker 2: away from sentiment analysis because we don't believe it does
19:51
Speaker 2: give you what you expect. It's really it shows you
19:54
Speaker 2: the tone of the conversations, how people talk, not what
19:58
Speaker 2: they feel and how they're going to act. And you know,
20:00
Speaker 2: the example I always give is, you know, you go
20:02
Speaker 2: to a restaurant and you sit down, you get dinner.
20:05
Speaker 2: Five minutes later, the waiter comes and ask is everything okay?
20:08
Speaker 2: And what do you say? Yeah, yeah, it's all fine,
20:12
Speaker 2: and you give a positive sentiment and even the food
20:14
Speaker 2: came late and doesn't look appealing, you'll never go back
20:17
Speaker 2: to the restaurant. Just gave a positive center. And that's
20:19
Speaker 2: why sentiment does really work in clients contours and say, hey,
20:22
Speaker 2: centim is always positive, except in the reare cases when
20:24
Speaker 2: it's not. It's usually positive, and we don't know what
20:26
Speaker 2: the drivers are. So what we do is it really
20:29
Speaker 2: have to go to the emotional level. And more importantly,
20:32
Speaker 2: you have to understand how emotions combine into complex emotions
20:38
Speaker 2: which lead to action. So people always think, oh, they
20:40
Speaker 2: have good emotions bad emotions. There is joy and hope
20:44
Speaker 2: and trust which is good, and anger, fear and and
20:47
Speaker 2: you know, and contempt of sadness is bad. But that's
20:52
Speaker 2: not the way the human psyche works. We have always
20:56
Speaker 2: all the emotions right, there's no bad and good, it's
20:59
Speaker 2: it's a combination. So, for example, fear can be very
21:01
Speaker 2: good for marketing. You know, too much fear is bad,
21:04
Speaker 2: it freezes you. But a little bit of fear creates
21:07
Speaker 2: fear of missing out leads to call to action. So
21:10
Speaker 2: the combination is important, and so we always look at
21:12
Speaker 2: the combination. That's where our unique psychological framework comes in.
21:17
Speaker 2: And we're able to do that because we're really complaining
21:21
Speaker 2: deep machine learning with behavior psychology. So we have software
21:26
Speaker 2: engineers and data scientists. We also have a chief psychology
21:28
Speaker 2: officer as part of the core team. So we built
21:30
Speaker 2: a psychology into the technology.
21:33
Speaker 1: Yeah, and it's cool. One of the things that I've
21:35
Speaker 1: learned working with you is that you know a lot
21:38
Speaker 1: of these marketing programs need a broad range of emotions.
21:41
Speaker 1: You know, there's I don't know how many it is,
21:42
Speaker 1: it's but maybe in the low teens of different emotions
21:45
Speaker 1: that you track, and if a company doesn't hit on
21:48
Speaker 1: a wide array of those emotions, some of these marketing
21:51
Speaker 1: plans fall a little bit flat, right.
21:55
Speaker 2: Oh absolutely, And you see that again and again. People
21:57
Speaker 2: are putting out on the marketing campaigns, you know, whether
22:02
Speaker 2: it's a website, whether it's a it's a PR or
22:05
Speaker 2: marketing campaign on social media, whatever it is that they
22:08
Speaker 2: just put out open joy. And to some extent, nothing
22:12
Speaker 2: is wrong with open joy. Because if I asked you
22:14
Speaker 2: where you want to be, you know what's a good model.
22:16
Speaker 2: I want to be happy. Of course we want to
22:19
Speaker 2: be happy. But if you customer is already happy, they
22:24
Speaker 2: will not do anything. Correct. If you're in front of
22:27
Speaker 2: with your friends, in front of a TV watching your
22:29
Speaker 2: favorite whatever, your sports team win the finals, you're so
22:35
Speaker 2: happy you're going to sit there and not get up
22:38
Speaker 2: and do anything. Why would you? You're already happy, You're
22:41
Speaker 2: already in a balanced stage. You achieved it. It's we
22:44
Speaker 2: call it an aspirational goal. You're already there for me
22:48
Speaker 2: to have you move, I need to get you out
22:51
Speaker 2: of that happy stage and create a reason to move
22:55
Speaker 2: away from that unhappy stage to a happy stage. So
22:58
Speaker 2: to get to that how do you do that? Well,
23:02
Speaker 2: we take them through an emotional journey. You create some
23:05
Speaker 2: anger at the competitors, or at the lack of a product,
23:09
Speaker 2: or at the lack of quality of a product, whatever
23:12
Speaker 2: it is. Create some fear, some contempt. Then you come
23:15
Speaker 2: in with with a surprise and amusement, some trust that
23:20
Speaker 2: you have the solution. Think about every marketing campaign as
23:24
Speaker 2: a movie. That's what I tell my clients. Think about
23:27
Speaker 2: a movie. So let's say you like I guess if
23:29
Speaker 2: you like going to the movies. Let's say you sit
23:33
Speaker 2: down and everything is happy, joyful and hopeful. What happens
23:39
Speaker 2: it's boring, You fall asleep. You need a villain to
23:44
Speaker 2: create some action, some tension, some fear. Then the hero
23:49
Speaker 2: comes in, there's surprise, is amusement, there's some trust, the
23:52
Speaker 2: the the you know, there's a again, I mean, the
23:56
Speaker 2: villain comes to and there's a battle going on. It's
23:58
Speaker 2: an emotional journey. That's what captures us. Every marketing campaign
24:03
Speaker 2: needs that. If you're stale and you just put out
24:06
Speaker 2: hope and joy, you're going to fail because it's not
24:10
Speaker 2: creates doesn't create a call to action. And we measure
24:13
Speaker 2: that and we tell you what you need to create
24:15
Speaker 2: to create a call to action, and that's what turns
24:18
Speaker 2: out to be predictive. And that's what we have been
24:19
Speaker 2: providing you for the last five years or so. Yeah.
24:23
Speaker 1: Yeah, it's it's really interesting stuff. I love it. Yeah,
24:26
Speaker 1: since early twenty eighteen. I think that was our first
24:29
Speaker 1: our first one. It was a report on McDonald's and
24:32
Speaker 1: how they could improve their marketing. We made. There were
24:34
Speaker 1: some good calls in there, I think by both of us.
24:37
Speaker 2: Oh absolutely, I thank you you did. Yeah, I really
24:40
Speaker 2: love you, White Ups. And I think the McDonald's was
24:45
Speaker 2: really the first home run. I mean I remember twenty
24:50
Speaker 2: eight eighteen, you came to us and asked, Okay, so
24:53
Speaker 2: McDonald's just came out with a new you know, Big Mac,
24:57
Speaker 2: and at the same time they had the twenty four
25:00
Speaker 2: well Breakfast and they also just launched a one to
25:03
Speaker 2: three dollar menu, and and I think you told us
25:06
Speaker 2: that the Big Mac is going to be the big driver,
25:10
Speaker 2: at least in the mind of McDonald's. And so what
25:13
Speaker 2: we did is we looked at the last several months
25:15
Speaker 2: of social media conversation so acted emotions and created that
25:18
Speaker 2: we intend score, Motivation Score, And what we found was fascinating.
25:23
Speaker 2: We found that breakfast has a high motivation. Consumers highly motivated,
25:29
Speaker 2: two awards breakfast at McDonald's and very stable, so they
25:32
Speaker 2: can really consistently going there and buying, you know, getting
25:37
Speaker 2: breakfast at McDonald's.
25:38
Speaker 1: The one to my favorite day part at McDonald's breakfast,
25:41
Speaker 1: hands down.
25:42
Speaker 2: Yeah, and clearly for the consumer is one to three
25:48
Speaker 2: menu had even a higher motivation and that was that
25:52
Speaker 2: was one of the first insight higher motivation, but they
25:55
Speaker 2: didn't get enough marketing dollars, so the awareness wasn't there.
25:59
Speaker 2: So we are suggestion was put more money into they
26:04
Speaker 2: won to three dollar menu, because that's what's going to
26:06
Speaker 2: drive your performance. And when we looked at the Big Mac,
26:09
Speaker 2: it was a disaster, a complete disaster. Motivation was volatile
26:14
Speaker 2: up and down, it was mainly negative. People were clearly
26:18
Speaker 2: moving away. And when we looked at it, it was
26:21
Speaker 2: you know, it wasn't the marketing, it was the product.
26:24
Speaker 2: And the suggestion we had and you wrote that down
26:26
Speaker 2: in twenty eighteen perfectly in the first quarter is that
26:30
Speaker 2: if that's the biggest driver, they're gonna miss or run
26:35
Speaker 2: the risk of missing revenues. And so fast forward quarter
26:40
Speaker 2: and you know, mcnolla comes out announcwer innings, the miss revenues.
26:45
Speaker 2: They say, the miss revenues because of the Big Mac.
26:48
Speaker 2: And by the end of the year they we did
26:52
Speaker 2: the product and then six came back. So that was
26:57
Speaker 2: a fascinating study.
26:59
Speaker 1: Yeah. Yeah, they changed course and went back onto that
27:03
Speaker 1: you know, strong trajectory they had. But that was really interesting.
27:06
Speaker 1: I'm gonna pat myself on the back. My call was
27:09
Speaker 1: a lot less uh you know, it was more of
27:11
Speaker 1: a common sense call. It was about the big Max sauce.
27:13
Speaker 1: I thought, I said, they thought they should start putting
27:16
Speaker 1: it on their chicken, so, you know, a much more
27:20
Speaker 1: elementary type of call. But they finally hitting on that
27:25
Speaker 1: in twenty twenty three. So everybody loves Big Mac sauce.
27:28
Speaker 2: No, that was a great report.
27:31
Speaker 1: Yeah, thanks, gondn't do it out, Yeah, what else do
27:35
Speaker 1: we do? We did plant based meat right, and plant
27:38
Speaker 1: based meat right now is kind of struggling, and it's
27:41
Speaker 1: something that your research warned us that that it might,
27:44
Speaker 1: you know, especially at quick service, because that isn't the
27:48
Speaker 1: core type of customer for plant based meat, right, And
27:51
Speaker 1: so a lot of people tried it at a fast
27:53
Speaker 1: food restaurant and then they were you know, once and
27:56
Speaker 1: then they either bought it at retail or they didn't.
28:00
Speaker 1: But can you maybe give us an update on what's
28:01
Speaker 1: going on with plant based meat and the consumer and
28:05
Speaker 1: what could it mean for the recent introduction of cell
28:08
Speaker 1: cultured meat.
28:10
Speaker 2: Yeah, I mean the plant based meat that was fascinating
28:16
Speaker 2: analysis as well, and that goes back to was it
28:18
Speaker 2: late twenty nineteen, So plant based meat came out, you know,
28:24
Speaker 2: Passoerburger and beyond Burger, and there was an enormous amount
28:28
Speaker 2: of novelty and conversations and excitement. And when we looked
28:35
Speaker 2: at a few months into the launch and from the
28:39
Speaker 2: beginning into the first few months Sowar's the end of
28:42
Speaker 2: the year, we saw that motivation actually while still high,
28:46
Speaker 2: was started to fade. And I think that's something which
28:51
Speaker 2: is a learning for every company out there, including self,
28:56
Speaker 2: you know, cell based, cell grown meat and any product,
29:02
Speaker 2: is that you have to understand what is emotionally engaging,
29:06
Speaker 2: what's not. What are the key emotional drivers of your
29:09
Speaker 2: customers towards your product. And what we found is that
29:14
Speaker 2: we know today we knew then that plant bait meat,
29:19
Speaker 2: you know, was built to have the flavor and the
29:22
Speaker 2: texture of meat, but it's actually quite unhealthy. Maybe good
29:28
Speaker 2: for the environment, but it's unhealthy. But when you come
29:31
Speaker 2: out with a plant based meat and that concept, the
29:34
Speaker 2: first thing is, oh, it's vegan. Vegan is healthy. So
29:39
Speaker 2: merely in the conversation, we saw that it is a dissonance,
29:43
Speaker 2: emotional dissonance between what the product stands for and what
29:47
Speaker 2: it actually delivers. And we pointed that out and said, look,
29:53
Speaker 2: I understand it is an internal conflict because the product
29:56
Speaker 2: was built differently than what it's what it looks like.
30:00
Speaker 2: So maybe not calling it plant based meat and doing
30:03
Speaker 2: calling it slightly different so it doesn't have the connotation
30:06
Speaker 2: with the vegan of vegetarian community might actually help here.
30:12
Speaker 2: And that started to hurt. And I think this is
30:14
Speaker 2: a great example of why you need to understand your consumer.
30:19
Speaker 2: And you know, I mentioned to you that maybe outside
30:24
Speaker 2: of the of your your vertical and your interests your industry.
30:30
Speaker 2: You know, another example of a company which completely missed
30:36
Speaker 2: understanding its clients was Peloton. You know, we talked quite
30:41
Speaker 2: a bit about that. So we had a private equity
30:45
Speaker 2: company come to us in early twenty twenty one asking
30:51
Speaker 2: us about the analysis of Peloton, and Peloton did phenomenally well.
30:55
Speaker 2: They went to become publican now a year and a
30:57
Speaker 2: half before in the late two thousand nineteen, and then
31:00
Speaker 2: twenty twenty, obviously with COVID and the pandemic, everyone stayed home.
31:05
Speaker 2: Revenues went just you know, went, you know, accelerated, and
31:10
Speaker 2: the stock just was on the tear. In early twenty
31:13
Speaker 2: twenty one, they announced it going to open up a
31:16
Speaker 2: second manufacturing plan because the demand was too big. So
31:20
Speaker 2: the question was what happens in twenty twenty one when
31:24
Speaker 2: the vaccine comes out. COVID vaccine was just coming out
31:28
Speaker 2: late twenty twenty, and yeah, the expectation was that the
31:33
Speaker 2: world will open up again, and so what happens to
31:39
Speaker 2: you know, home equipment, home sports equipment. So we didn't
31:44
Speaker 2: talk to Peloton. We only talked to our client at
31:47
Speaker 2: Private Equity, and we went to the website Peloton's website
31:52
Speaker 2: better understand what is the strategy. And as soon as
31:55
Speaker 2: you looked at the website, it became very obvious what
31:58
Speaker 2: their strategy is. And the strategy is that to compete
32:04
Speaker 2: against the gyms. They viewed that the gyms would be
32:07
Speaker 2: their biggest competitor. People will go out, the world opens up,
32:10
Speaker 2: people go back to the gym, and so it's all
32:12
Speaker 2: about competing against gym's membership. So everything was around price.
32:17
Speaker 2: Only forty nine dollars a months were less expensive than membership. Overusing.
32:20
Speaker 2: It was around price. It was very honest and by
32:23
Speaker 2: the way, this makes perfect sense. If you ask people,
32:27
Speaker 2: and you ask the rational mind, would you think about
32:30
Speaker 2: price and Peloton, everyone will tell you it's expensive. It's
32:35
Speaker 2: expensive like Lululemon. For years people said Lulu Lemon is expensive.
32:40
Speaker 2: But here's the issue. When we looked at the emotional
32:44
Speaker 2: profile of conversations around Peloton and specifically around price and
32:51
Speaker 2: how expensive it is in early twenty twenty one, we
32:54
Speaker 2: saw that people clearly talked about it, but nobody emotionally cared.
33:01
Speaker 2: And that's the key point. It took us exactly three
33:04
Speaker 2: minutes to figure it out. People emotionally do not care
33:08
Speaker 2: about price when they Buipeloton or don't like Peloton. What
33:12
Speaker 2: they do care about is online classes, is instructors, it's
33:16
Speaker 2: the social status. And if you focus on the reasons
33:23
Speaker 2: which are unimportant to your clients, you will miss out
33:28
Speaker 2: on actually what's important to them, and therefore you're going
33:31
Speaker 2: to lose revenue. So twenty twenty one, we going back
33:33
Speaker 2: to our clients said hey, I think their risks missing
33:38
Speaker 2: revenues big time. And six months later they announce they're
33:41
Speaker 2: going to miss a billion dollars in revenues. And then
33:43
Speaker 2: another six months the CEO was gone and now they
33:46
Speaker 2: look at their company. It's all about online classes and instructors.
33:50
Speaker 2: And that's the same with plant based meats. You have
33:53
Speaker 2: to understand what are the key emotional drivers of your customers,
33:58
Speaker 2: otherwise you miss.
34:00
Speaker 1: Yeah, it's very cool. You've also developed a job confidence index.
34:07
Speaker 1: Can you talk about that and what your proprietary index
34:10
Speaker 1: is telling you and the team right now about the
34:12
Speaker 1: US job market.
34:14
Speaker 2: Yeah. We have a history of trying to be also
34:18
Speaker 2: public facing and provide services. So when COVID started, we
34:26
Speaker 2: came out immediately with an anxiety COVID Anxiety a panic
34:29
Speaker 2: index to better measure people's emotional attitude, so you know,
34:35
Speaker 2: public figures can better understand, both globally and in the
34:39
Speaker 2: US how people really feel and create policies. And then
34:43
Speaker 2: when the vaccine came out, we had the COVID Vaccine
34:48
Speaker 2: Confident Index again public FASIC and then about a year
34:52
Speaker 2: later we came out with the Job Index. There was
34:56
Speaker 2: a lot about the jobs and the silent designation and
35:01
Speaker 2: people understanding what's happening, and it was important for us
35:04
Speaker 2: to measure the public emotional attitude so we can actually
35:07
Speaker 2: do something about it. I think understanding not just companies,
35:12
Speaker 2: but the society at large, the public at large, better
35:17
Speaker 2: understanding what their emotional policies around key policy issues is
35:24
Speaker 2: incredibly important because we have a tool which can provide
35:26
Speaker 2: that as a social good, so we can actually again politicians,
35:32
Speaker 2: public figures, the media, if they use the tool, they
35:35
Speaker 2: can address the issues which are important to us as
35:40
Speaker 2: citizens and emotionally engaged. So we actually and emotionally engage
35:46
Speaker 2: in an emotionally intelligent way without technology, so it provides
35:51
Speaker 2: a better outcome.
35:52
Speaker 1: Very cool, all right, So I'd like to talk about
35:55
Speaker 1: AI a little bit more generally now. You know, if
36:00
Speaker 1: you've been in this business, obviously a long time. We've
36:02
Speaker 1: been partnered, as we said, since twenty eighteen, you know,
36:08
Speaker 1: so AI has been around longer than most people realize.
36:12
Speaker 1: But with chat GBT, a lot of people are being
36:16
Speaker 1: introduced to AI for the first time. And when I
36:18
Speaker 1: tell them, I was partnered with an AI company for
36:20
Speaker 1: more than five years. They're like shocked, right, it's really interesting.
36:26
Speaker 1: It's the hot topic of the day. I think it's
36:28
Speaker 1: it's driving tech company valuations here in the first half,
36:33
Speaker 1: so I figured we'd hit on it. In regards to
36:36
Speaker 1: chat GBT, what are some of the opportunities for large
36:40
Speaker 1: language models.
36:41
Speaker 2: That's a great question. By the way, I'm really fascinated
36:45
Speaker 2: by the awareness CHATIPT and OpenAI plot to the market.
36:49
Speaker 2: I mean, AI has been around for a long time
36:52
Speaker 2: and and you know, fifty years in terms of the making,
36:56
Speaker 2: but we over the last I would say decade or
36:58
Speaker 2: so more we have had the chance to really use
37:01
Speaker 2: AI because of the digitization of the world and more data,
37:05
Speaker 2: more computing power and so on. But really, what CHGPT
37:08
Speaker 2: did is it brought the whole concept of oh AI
37:12
Speaker 2: is really part of our can be part of our
37:14
Speaker 2: world and professionally, personally really to a different level. And
37:19
Speaker 2: this awareness is incredible and it opens up a lot
37:23
Speaker 2: of opportunities, both of the good side. And obviously there
37:26
Speaker 2: are some risks, but there's no revolution without risks, and
37:32
Speaker 2: nothing gets solved before it's actually here. So now we're
37:35
Speaker 2: here and now we have to face it in terms
37:37
Speaker 2: of opportunities. I mean the opportunities I think short term
37:43
Speaker 2: are somewhat known. The longer term I think are significant,
37:47
Speaker 2: very significant, because all those large language models are improving
37:52
Speaker 2: agguessively improving not just within the large organization but also
37:56
Speaker 2: within you know, open source developer communities, and so there's
38:02
Speaker 2: there's a lot happening there are There are really two
38:06
Speaker 2: ways to think about this. Let's call it generitive AI.
38:11
Speaker 2: One is depending on the large language model is exactly
38:16
Speaker 2: what it sounds. It's trying to use training from textual conversations,
38:24
Speaker 2: you know, WI just be pre trained on a very
38:26
Speaker 2: very large data set to become you know, conversational and
38:32
Speaker 2: provide answers like a human being would. Now it's all
38:36
Speaker 2: still heavily statistical. It's like us putting something into the
38:40
Speaker 2: Google search bar, which we have been used for years,
38:43
Speaker 2: where when you start typing, it shows you what else
38:45
Speaker 2: you could mean. It's predicting what it thinks you have
38:48
Speaker 2: in mind. Large language models or genitive AI is based
38:52
Speaker 2: on large language model. Is really that it's trying to
38:55
Speaker 2: predict not the next word or phrase, but in the
38:58
Speaker 2: sentences and narratives and paragraphs, but it's still a prediction model.
39:03
Speaker 2: It's based on what it thinks, and that's why it's
39:05
Speaker 2: also lascinating. It's making up stuff, which right away is
39:10
Speaker 2: very similar to the brain. We make up stuff because
39:13
Speaker 2: again the subconscious mind makes decisions, the rational mind tries
39:16
Speaker 2: to make find a reason and narrative, so it makes
39:19
Speaker 2: it up all the time, and we can go into that.
39:21
Speaker 2: But this is fascinating. So that's using generaltve AI to
39:26
Speaker 2: have an interaction with a machine to get answers in
39:29
Speaker 2: a much easier way than it was we were able
39:32
Speaker 2: to be able to. This is a second part of
39:34
Speaker 2: general DEVEAI, which is or AI usage not on a
39:38
Speaker 2: large language model, but actually on structured data. And that's
39:41
Speaker 2: where also a lot of the usage comes where instead
39:44
Speaker 2: of having a terminal where you look at the data,
39:48
Speaker 2: maybe if a search find you should ask the question, hey,
39:52
Speaker 2: who is my best salesperson? Or tell me about you know, McDonald's,
39:57
Speaker 2: or targets behavior customer behavior around around the event which
40:05
Speaker 2: was you know a few months ago, and it just
40:07
Speaker 2: spits us the answers and gives you the results in
40:10
Speaker 2: a nice chart format without you actually doing the research.
40:13
Speaker 2: So there's a lot happening depending how you train the
40:17
Speaker 2: models and how you use it, and that will clearly
40:20
Speaker 2: create a lot of efficiencies, effectiveness what we call also
40:27
Speaker 2: cognitive load reduction. It uses our need to use the
40:32
Speaker 2: rational mind to find the results because it's there. But
40:35
Speaker 2: it will require us to change our definitions of what
40:40
Speaker 2: it means to be a stock picker or you know,
40:44
Speaker 2: a writer or an artist or whatever it is, and
40:48
Speaker 2: so does so it comes with a change, but it
40:51
Speaker 2: has all everything has come with a change. Correct when
40:53
Speaker 2: we started with having computers people who are open arms,
40:56
Speaker 2: what happens to maths? Now people don't have to learn math, well,
40:59
Speaker 2: they don't have to learn that math, but they have
41:01
Speaker 2: to learn a different kind of logic, and so it
41:04
Speaker 2: takes you to a different level and it enhances clearly
41:08
Speaker 2: the human The capability is productivity and I believe longer
41:13
Speaker 2: term also you know, satisfaction. But it comes with risks,
41:18
Speaker 2: and I think the biggest risk here is not just
41:21
Speaker 2: how it impacts us as humans in our worlds. They
41:24
Speaker 2: you know, personal as well as the professional in the
41:28
Speaker 2: professional settings, but also the risk in terms of privacy,
41:32
Speaker 2: in terms of security, the risk in terms of ethical
41:36
Speaker 2: behavior and the responsible behavior and all those things have
41:40
Speaker 2: not been addressed yet to full extent as it should,
41:45
Speaker 2: and it's not expected to because as long as the
41:48
Speaker 2: technology isn't out there, nobody feels the need to come
41:52
Speaker 2: up with policies and guidelines. But now it is, so
41:55
Speaker 2: we really have to put our act together and come
41:58
Speaker 2: up with it otherwise we're the need to a free
42:01
Speaker 2: for all. And we see a lot of the bad
42:04
Speaker 2: usage of the capabilities like we see today already in
42:09
Speaker 2: terms of cyber security issues and incidents and deep fake
42:13
Speaker 2: issues correct where somebody can pretend I could pretend on
42:17
Speaker 2: Mike Hayleen and talk about a company to buy and
42:21
Speaker 2: short and it's not you, and I'm just making it up,
42:24
Speaker 2: and that has obviously illegal implications, and that's a risk,
42:29
Speaker 2: and that's a risk which todays technology can still somewhat
42:34
Speaker 2: figure out. So we have AI fighting AI to figure
42:37
Speaker 2: it out. But I think that capability is slowly going
42:40
Speaker 2: away as AI is improving, So we need to find
42:43
Speaker 2: better ways to protect ourselves.
42:46
Speaker 1: Yeah, that stuff's really interesting, right We're in there's a
42:49
Speaker 1: massive distrust of government and media, and I know smart
42:52
Speaker 1: people that tell me that they're having real difficulty discerning
42:56
Speaker 1: fact from fiction, right, and so all that stuff is
42:59
Speaker 1: is area and you know, I think there there needs
43:03
Speaker 1: to be some sort of regulation on it. But yeah,
43:08
Speaker 1: it's interesting and it's really interesting to see what happens
43:11
Speaker 1: to the job market job market right with all of
43:13
Speaker 1: the improvements in AI and automation, and you know, what
43:17
Speaker 1: does that mean for workers moving forward?
43:20
Speaker 2: Well, there's definitely going to be a rotation happening. There
43:23
Speaker 2: has to be a redefinition of what it needs to be,
43:27
Speaker 2: you know, a bank or an investment professional or anything.
43:31
Speaker 2: But again, we are used to that. Every time there
43:35
Speaker 2: is a revolution, there is a change. When the first
43:38
Speaker 2: cast came, it changed the whole industry on tourses and
43:44
Speaker 2: so we're used to the fact that the world is
43:48
Speaker 2: moving forward. And you know, I think it's interesting to
43:54
Speaker 2: see some of the conversations and you think about the educators.
43:57
Speaker 2: The first we actual manchat ChiPT came out from educators
44:02
Speaker 2: from teachers was oh my gosh, we're going to have
44:04
Speaker 2: to you know, prohibit the use otherwise we can figure
44:09
Speaker 2: out if the student actually learned it or vote it,
44:13
Speaker 2: or whether it was done by one of the large
44:16
Speaker 2: language models. I think that's the wrong attitude. I think
44:19
Speaker 2: the attitude is this is here. I mean, it's going
44:22
Speaker 2: to happen. Let's figure out how we can leverage that
44:27
Speaker 2: and make sure that our students are even more creative
44:33
Speaker 2: than they used to be. And so we have to
44:35
Speaker 2: just yes, it's effort. We don't like effort. We don't
44:38
Speaker 2: like to change our status quo, which is by the way,
44:42
Speaker 2: emotional as well. But no, we don't want to change
44:48
Speaker 2: the status quo. And so we're looking to, you know,
44:52
Speaker 2: suppress new opportunities, not understanding that the opportunities are here
44:58
Speaker 2: and they actually have a positive can have a positive
45:02
Speaker 2: impact if we actually think it so. And so it's
45:04
Speaker 2: a challenge for all of us, everyone, every profession, every
45:10
Speaker 2: vertical to understand what does it mean the fact that
45:13
Speaker 2: we now have a capability which is out there, people
45:18
Speaker 2: are aware, which are being used day in, day out,
45:23
Speaker 2: and how does that What does that mean to society?
45:27
Speaker 2: What does it mean for us? That doesn't mean for me?
45:30
Speaker 1: Yeah, a lot of answered questions. Definitely interesting times to
45:34
Speaker 1: be alive and something I think about, Right, I have
45:36
Speaker 1: a fifteen year old, right, and so making sure he
45:40
Speaker 1: chooses a career that's going to be around by the
45:42
Speaker 1: time he gets out of college.
45:43
Speaker 2: Right, Well, for then AI behind it. But yes, it's fascinating.
45:50
Speaker 2: So we at Kovnoby Labs are very conscientious about that
45:54
Speaker 2: and put a lot of effort around understanding these compliance issues,
46:00
Speaker 2: around responsibly ethical, privacy, insecurity issues to make sure that
46:05
Speaker 2: we use it in a responsive way. And you know,
46:08
Speaker 2: the large language model for us, it's just an additional
46:11
Speaker 2: icing on the cake. We already had the cake. It's
46:13
Speaker 2: our technology. It's something Chatgypt doesn't do, nobody does accept us.
46:18
Speaker 2: So we're just you know, we're aggressively building out our
46:21
Speaker 2: capabilities with every new capability, new functionality which is being
46:26
Speaker 2: which is out there to provide more value to our clients.
46:30
Speaker 1: That's awesome. I think that's a perfect spot to wrap
46:32
Speaker 1: it up. Where can you know if any of our listeners,
46:36
Speaker 1: you know, any chief marketing officers out there, you know,
46:39
Speaker 1: restaurant executives you know, want to reach out to you
46:42
Speaker 1: and inquire about your services work. How's the best way
46:46
Speaker 1: to reach.
46:46
Speaker 2: You, Yeah, go to our website Krognovi Labs dot com
46:50
Speaker 2: and send us a text or send us an email
46:55
Speaker 2: at info at Kognovi Labs dot com and love to
46:59
Speaker 2: talk to you. We can help you emotional, engage with custom.
47:03
Speaker 1: That's great, good stuff. Thanks for doing this and thanks
47:05
Speaker 1: to the listeners for checking us out. Have a great
47:08
Speaker 1: day everybody.
47:12
Speaker 2: H