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Hello and welcome. You're listening to Marketing Matters on the Wharton Podcast
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Network, which is our weekly podcast where we analyze the latest in advertising, marketing, customer
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behavior, new product launches, retailing, branding, anything marketing. I'm Barbara Khan, the Patty and JH Baker
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Professor of Marketing, and I'm joined by my co-host, America Reed, the Whitney
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M. Young Jr. Professor of Marketing and the Brand identity theorist. Hello Americus.
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>> Hello Barbara. So last week we sat in on a presentation in our department uh
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professor by the name of Kevin Lee, University of Michigan marketing prof talking about AI and how AI can be used
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agentically to help design your emails and outreach and protocols and different collaterals
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for your marketing efforts. and they actually introduced this idea of having AI sort of help you figure out in AB
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testing what's working, what's you know what's not working and so on. So really
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>> really cool result. They actually reported a 33% lift. So now I'm wondering I I want to go deeper on this
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idea of of agentic AI and like the the marriage Barbara between these new tools and us as marketers trying to measure
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stuff trying to elevate and amplify. Is there anything you've got for me that
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can help shed some light on this today? >> Why don't we get someone who really
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knows what he's talking about? Uh, and I'm really happy to have with us in the
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studio today Chris O'Neal who's the CEO of Growth Loop. And Growth Loop is
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trying to redefine that future of marketing using, as you're suggesting, AI and data. Chris, welcome to our show.
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>> Hi, Chris. >> It's great to be here with you. >> So, Chris, before you tell us about the
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world of AI and marketing and how to improve everything, can you tell us your background, how you got to where you
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are? Yeah, absolutely. I grew up in a very small town of 7,000 people in the middle
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of nowhere up in Canada and I found my way to the valley for two months uh before graduate school and that's turned
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into 25 years. I've been very fortunate to be part of some incredible companies
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like Google, some amazing teams and brands. Um and really uh the first wave of that was really trying to figure out
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how you actually use this thing called digital marketing to help um build personal connections or just connections
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with with consumers and businesses. So that was the first wave and now we're uh
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here an exciting new wave with applying AI in uh in an even more transformative way. So I'm really lucky. I've been part
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of some incredible teams and boards and um and that's that's really uh been a
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fun journey and I'm really excited to share some of it with you. >> So I mean just because Americus went to
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that lecture but I didn't. So like there's like a bunch of stuff that I don't even understand the words of. So
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can so I understand growth loop which is the company you're CEO of uh has something to do with compound marketing
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like what what does that term mean and and what does aentic AI mean? Can you just start at the really basic?
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>> Yes. Yes. >> Cuz we know compound we know compound interest is a good thing, Chris.
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>> 100%. And that's where it started. You know, in that small town, we used to get
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a lot of snow in the winter. And I remember my we'd have parents, friends of our of ours come in and sometimes
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they get stuck. Like literally, it' be that much snow. And I was given a book
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during one of these four-day stays when some some family members read some books
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and they left me this book. And it was called The Wealthy Barber. And it was a story about a proverbial wealthy barber
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who did some really small investing and compounded it over time and he became wealthy. And that started a lifelong
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fascination with the notion of compounding. And when you stop and think about it, compounding is everywhere.
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Albert Einstein famously said it's the eighth wonder of the world. Compound interest, right? It's in nature, right?
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If you think about how forests happen, it's actually compounding. There's like
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the worldwide wood uh worldwide wood they call it. It's a mitochondria like underneath the soil. It's in nature.
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It's in technology. You know, when you think about network effects, that's
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compounding. Um James Clear, who I'm a huge fan of, if you get 1% better every
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day, by the end of the year, you'll be 37 times better. So, it's the notion of
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little things that are done consistently over time that start to lead to exponential results. And we got to
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thinking, okay, gosh, we're trying to combine data and AI with people in the
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loop to unleash the brand's potential, right? whether it's a personal connection with a brand or whether it's
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the different variety of of tools and channels you can bring together to offer truly personalized and really great
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experiences. So we borrowed the term compounding shamelessly and say hey what if we could compound marketing and so
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that's really where it starts. Um the second part with with aantic really like
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AI is interesting in so many ways and we'll unpack some of it today but many
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of the changes in marketing were were kind of the demand side meaning we changed the consumers changed how they
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interacted right we went from desktops to mobile right analog to digital and certainly there's elements of demand
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side change here but the bigger change is supply side what I mean by that is the way in which we do marketing the way
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in which we go about workflows uh with AI in the middle uh is really exciting and we're just scratching the surface
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and again I'm keen to get into it with you today because I think it's a supply
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side meaning it's changing the way in which we interact with people in a very
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personal way in a way which in many ways has been the holy grail forever and we've been living with this unfulfilled
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promise of marketing as a cost center or marketing as some interruption and there's so much more we can do together
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and I know you feel the same uh and I'm keen to as I said get into it a little
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bit with you today. >> So, okay. So, you had this big idea of compound marketing and you understand
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more of how all this works. So, with that in mind, did you start growth loop or like how did the whole company get
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started or what is what is it exactly? >> So, I've been fortunate remember I
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talked about great brands and teams and Google was one of them. I was at uh Google for 10 years during a very great
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in you know really really wonderful time during a very obviously generational company and during that time I met some
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incredible people including the two founders of growth loop uh they worked for an old CMO of mine when I was up
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running Google in Canada uh and they are like just incredible entrepreneurs and they they looked at how Google did
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marketing and they said gosh for a company that's allegedly so innovative they sure don't don't like really always
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think about marketing an interesting way So Chris and David, the founders, said,
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"Hey, why is it that I, as a product marketing manager with goals to drive value for a particular product, have to
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go and line up outside a data team just to basically get a hypothesis tested with SQL and all this code and that
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bounces back and forth and takes days and weeks and up to a month just to do one campaign. They thought that was in
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that was silly, right? So basically they had the innovation to to build on top of
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a data cloud, right? So increasingly we're putting data in fewer places these
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things called data clouds that's Google bigquery this is Google cloud company
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like snowflake data bricks Azure um Amazon there are many data clouds but the notion was how about you start there
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by putting an intelligence layer on top of the data as opposed to pushing the data everywhere really and this is
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incredibly important from a cost perspective from a compute perspective security perspective and now of course
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with AI you want to have your data and your understanding and your intelligence on your customers in one place so you
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can apply the full effect of this magical technology called AI. So that's the insight where it started. And then
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the other part with compounding is the notion that it's not a funnel so much as
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it's a loop, right? You basically have ideas who you should be speaking to, where you should be speaking to them and
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then do closed loop experiments where you get a little bit better every time and then it really compounds as you do.
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So >> wait, I got you got a because I'm like big on this funnel and this loop. So I I
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really need you to unpack that a little more. So you have a different structural
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model of dealing with the customer rather than thinking about it like >> a funnel which is traditional. So you
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know are you in the product category then you're considering the brand then you know my brand compared to others
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you're suggesting some kind of loop. So what exactly does that mean? I I I don't
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really understand. >> Yeah. No great question. Like so in a funnel, we basically treated the world
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as like this big uh this big pool and then you basically pull them through a funnel. Um and there's a consideration
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all that stuff we've been we taught when I was in business school. We were taught
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about consideration, awareness, all that good stuff. And look, I'm here to say
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look, you need to continue to invest in your purpose and your brand and why you why you exist. That is that is as
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important if not more important than ever. However, you can do better, right? Rather than treating everyone as like
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one big segment, we're actually getting close to the holy grail where you can
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actually reasonably into it something interesting about a person at the onetoone level. This is the importance
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of what sometimes called first party data information you have on that person. Okay. Um so that you can inter
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you can intercept them at the at the right moment >> to ensure loyalty surprise and delight
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them. the customer journey kind of model then >> it's an iterative customer journey that
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says you're going to treat people or sub subsegments or even onetoone for the
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first time in an iterative loop as opposed to just doing what I sometimes call waterfall marketing.
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>> It's more agile just like we develop software. >> Yeah. Just let me let me just see if I
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got if I have this correct, Chris, because I I don't think what you're saying is somehow
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>> uh falsifying the funnel as much as it's saying, hey, you know what? We can make
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this funnel super efficient by almost pointing and using tools that allow us >> to kind of get to that that behavioral
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part faster. In other words, we can have some understanding and then we can use artificial intelligence to sort of test
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kind of immediate sort of intuitions that we have and quote get to the right answer as fast as possible. So there's
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less guesswork I guess in this >> also predicated on this notion of touch points like I mean how do you know when
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to interact? It has to be when the customer engages I guess. >> Exactly. Exactly. So, quick story. I
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serve on the board with a very senior um member at Starbucks, executive at Starbucks, rather, and he he's he he and
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I were talking about this this very topic, and he says, "Look, we know that
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uh Sally is a vegetarian. So, why is it when Sally visits her local Starbucks, we're offering her meat products? We're
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meat." Like, that just seems odd, right? when you can reasonably know with permission and Sally wants to know like
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what she she's gonna get when she greet is greeted by the barista they probably
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know that but systematically how about you offer something that would be relevant to Sally as opposed to relevant
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so it does start absolutely touch points a great a great point >> so when Sally comes to the cash register
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>> yeah you learn about >> a script about Sally that's what you're
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saying right >> exactly that's customer engages with us. Is that what you're saying?
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>> Yeah. >> Exactly. Exactly. And that's just one touch point. I love that term. We
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actually, you know, we talk about touch points and moments that matter. Uh because again, sometimes it's it's
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leaving Sally alone, right? It's not just constantly interrupting. It's >> implies you know Sally and Sally's going
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to get to know you, right? I mean, so are you only talking about a situation where I've already had interaction with
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the customer and then the way I interact with her at the next moment should reflect that history. But that implies I
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have that relationship already. >> Correct. That's correct. Right. So what
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we but I am talking about both, right? So you can reasonably intuit it. And this is where back to machine learning,
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right? So we we sometimes intermix the term machine learning with AI. Machine learning says, okay, how can we go about
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into the rest of the the potential prospects or potential touch points to basically inform the set of next actions
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which would reasonably do do the right thing for the brand and for the long-term customer relationship. And
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this is a really exciting part where you can actually get to causality, right? So
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most of marketing is correlative. It's like we think these things worked but we
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don't know, right? And that's the thing that's holding back a lot of marketing
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is like, you know, BF know the CFO and the CMO need to be BFFs, right? It needs to be you talk the language of the CFO
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and that's really one of the things that I think separates the great CMOs from
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the sort of mediocre ones. >> Yeah, that's I love the CFO and the CMO need to be BFFs. That's fantastic. I
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need that on a t-shirt, Chris. All right. Now, listen. So I I want to make sure I'm I'm unpacking this correctly
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because I think what you're saying is like hey the fact that we have tools that allow us to unpack insights and
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data very quickly now allows us to be much more >> uh much more inductive right so we can
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kind of go to Barbara's point we can kind of immediately we have a touch point with Sally and we have we have
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some inferences about what she's done in the marketplace and not what she reported on a survey but what she's
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actually done and we start building that intelligence and testing and iter Iterating and testing and iterating and
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testing to get that clear picture of Sally and then see to what extent can we uh predict other Sally's out there as a
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function of is that close at all in terms of >> you know so fast personalized data
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driven and continuously optimized. This is the notion of a funnel and this is exactly what we're talking about. So you
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absolutely nailed it. But so his point though because I I understand once you know the person using that data that
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makes sense to me but it I what America said which is taking it another level. And is that what you're saying? The way
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you reach people you don't know >> is by optimizing the data you do know.
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So that's where you start and then you look for other people like that's what
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America said. Is that what you're saying too? >> Yeah. Correct. So you're absolutely
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right. There's two modalities. Do you know the person? Do you know something
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about them or do you not? And that's like the different techniques are used to to tease those apart. But where it
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starts to get magical is when you start to for inform uh one from the other. Um a lot of the a lot of the science is b
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sort of the techniques are born from a lot of probabilistic statistics, right? So you'll you'll hear things like
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multi-armmed bandits and all that's a fancy way of saying is you're assigning
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people to a group and then you have a test and control, right? A lot of the power and things we're super excited
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about is that you actually have a test and control group. So you can prove causality. You can basically and have a
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hold out group that lasts for a time. Now here's an interesting magic we're
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we're really excited about is over time you can use math to basically approximate the control group. So you
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don't actually have to hold them out and still determine causality and still
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determine is it having the lift and the desired personalized impact and moving the needle on a metric like your
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customer lifetime value. That's like whoa. That's like mind-blowing for many
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people and we're really just scratching the surface on what's possible there.
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>> Interesting. Let me ask this question. I love this point because in some senses
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I've heard this quote, Barbara, that marketing starts with math and ends with
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art. Uh kind of I don't we'll unpack that later, Barbara. But Chris, help me
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because I want to get to to Barbara's level of of understanding here. very specific example to say all right I come
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to you Chris in growth loop and I've got a data set >> is that how this happens and then and
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then you you look at my data >> and then you build out a kind of AI infrastructure andor agentic
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architecture to lay on top of my data in order to help me pull as much insights about what I can understand from the
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people in that data set as fast as possible is that some is that correct at all correct let's make it let's make Get
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specific. Okay. So, a brand comes to usific. >> Yeah. Yeah. So, so they have a brief,
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right? We're familiar with marketing briefs. Just say this is what we were trying to accomplish is the business
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objectives is who we're trying to talk to, right? Literally drag and drop the
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brief into our agent, our campaign agent, right? We basically start the following. Say discerning what the what
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the market is trying to accomplish. And you talk about art. I think art and and and math intertwine at different stages
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because it's like the the real role for humans is ingenuity and creativity to
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say hey what's happening in this in the zeitgeist that really is relevant to our
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customers. So it's a really important role marry that up with what has worked
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in the past. So a brief comes in it basically gets processed by an agent and it says what do we know about this um
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this objective and these set of campaigns we've run in the past and the people that might be relevant. So it
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then it then offers up to the marketer a set of different audiences to basically
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go and and and you know treat with something and then it would suggest a journey right to say okay maybe you send
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them uh something through your app or your email or you know may maybe you have to give them some time and maybe
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then offer them an ad or some form of incentive. So that's that's what happens, right? A brief gets turned into
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a specific set of audiences and a specific journey which then gets pushed into the world and then executed and
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then run back into what worked and read it back into your data cloud. Lather, rinse and repeat. This is exactly the
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work that we're doing with some >> Can you give me like a real concrete
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example with specific like just specifics exactly how this worked like a big success story?
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>> Sure. I I'll give you a recent one. We we are so fortunate to work with Costco,
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right? Everyone knows and loves Costco. >> Chris, you're speaking her language.
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COSTCO ALREADY. >> I love Costco. So, um I'm going to give a little bit of a little spin on this,
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but Costco is super super into their members, right? And they they put their members above all else. Their business
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model is well predicated on selling memberships and they're very tight. Like
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if you ever um you know what the acquired podcast has done a deep deep dive >> into I've listened to that several
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times. It's great >> and I love it. So okay so people are familiar with Costco and and Costco is
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very in the in the business of working with their brands to basically ensure delightful experiences and connecting
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them to you know say it's DeWalt tools right so basically how can you infer the
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people in the Costco membership that might have a propensity to buy DeWalt tools we can do that with math right and
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then what we're doing is connecting the two we basically say DeWalt Costco here
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is here's a subset of customers that used to take you days and weeks just to
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do to to to really do the math and really surface the people. We now do that in hours, right? In terms of really
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understanding and automating the connection between in this case a brand and a set of high propensity customers.
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>> What do you do with that information? Can you show me like >> Yeah. Then then you do you send them an
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email? Do you send them an offer? Do you push something into the the Costco app?
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It really depends. and you're constantly testing to understand both what offer
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resonates with the subset, which channels offer the off um seem to work the best. Maybe it's an instore um
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experience. So, it's it's a really multi- channelannel or omni channel. Sometimes people use that term
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experience that says, okay, how do we broker this in a way that is additive for the merchant in the home improvement
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category and of course the members? It has to be a balance between the two. >> So, so let me just get the it's just
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hard for me. So your client is DeWalt Tools and they want to know >> or or your client is Costco and so
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Costco pays you to figure out how to optimize the purchases of specific brands within their environment. Is that
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how it works or >> that that is one use case exactly? Right. So it is and really just to
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abstract it away and talk just in terms like it allows their marketing velocity to be faster. Right. Like where that
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used to take weeks, it now takes hours. So therefore, right, DeWalt has money they want to spend and there's many
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DeWalts, right? There's n number of brands >> and if it's very manual and takes a long
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time. >> Yeah. >> Well, so this is like a retail media network. Is that who your clients are?
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>> In this case, that's what it is. Correct. Correct. But we do we do life
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cycle marketing and retail media networks and uh really we do the similar stuff with Albertson's and you know
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price line and many others. >> Okay. Okay. So your client is the retailer and the retailer's customers
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are these brands and you're trying to figure out a way to optimize what the brand can get from the retail media's
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data and the way they optimize all that is through you. Is that right? >> Correct. Correct. We we play we play a
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part in a broader ecosystem. I want to be clear. But yeah, it's it's it's the
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how do you accelerate that get the velocity? We talk about speed. Yeah. And then secondly, did it work? Right. It's
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not just did you did you throw out some impressions. No, no, no. Like the the brands with all respect, they care about
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moving their product and having dectual experiences. So, we also help them measure incrementality,
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>> right? Right. >> In the store, right? And that's that's sounds easier than it is in terms of the
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actual math and the calculation. But >> no, incrementality defined as if you
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didn't do anything, how much more with what you did matters in some sense. Bingo, right?
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>> Yeah. It's a resource allocation problem in some senses. Let me ask you this,
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Chris, because I I want to make sure I understand this part because the math gets the retailer to some hypothetical
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uh set of audiences to think about. >> Does the retailer then step in because
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now it's sort of like, okay, what do I prioritize? Is it an ad? Is it a this?
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Is it is it a that? Is that where the marketers then step in and say, "Well, I
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know something about some of these groups and now I'm going to bring sort of a creative piece to sort of
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prioritize some of the marketing uh protocols that might be used to again is that what happens in this case?"
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>> Yeah. Exact. We believe in human in the loop, right? So these agents are working
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on behalf of the brands and the customers and they can suggest things the audience the journey the creative
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creative like Nano Banana and all these images are getting like so good, right? So I wouldn't have expected that uh the
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pace we can we can talk about that separately but yeah it's about suggesting things and yeah it's using
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intuition right it's using the understanding of what has worked in the past so so marketers arguably they're
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getting out of the mundane stuff right and a lot of that you know the work of the work is so not why marketers signed
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up for the job so we're freeing them up to do the job that maybe they wanted to
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do in the first place >> along the lines of what America is asking are a lot of the suggestions more
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price related or deal related or I mean typically Great question. Yeah. Um, so you know this is where machine learning
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and AI actually interact. So machine learning would be a propensity model. So we have high propensity like take
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Albertson's. Albertson's knows who's a vegan, right? So they have like
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propensity models to say vegans uh have high propensity to provide these products, right? These products and not
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these ones. Uh or to your point Barbara, price elasticity, right? Um I serve on the board of GAP and tariffs have had
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enormous impact on anyone who's importing things and GAP certainly falls into that camp.
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>> So getting an understanding of where and how you can change prices that's a price
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elasticity game right. So how do you take >> that would be wonderful. >> I mean it's it's f it's a hard problem
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but it's like not just not just in a category all the way down to a skew level and then ideally at an individual
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level. So this is where the the math those are those are math problems right machine learning like propensity or
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price elasticity. >> Yeah. So, let me let me just go back to the old old world of grocery. Like
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before we had all this data and all this fancy stuff and you can do it on each customer, whatever. I remember Catalina
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would would put out coupons in the grocery based on what you purchased just at the cash register that time because
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they didn't have all the sophistication. And they do one thing or another, which
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is kind of what I hear you saying. They'd either give me a price deal of some kind, some kind of coupon for
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future purchase, or if I was Pepsi and paying for it and the customer bought Coke, they might give me a coupon for
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Pepsi to try to convert me. So, like what you're saying now is in multi-dimensions, much more in a
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sophisticated way than Catalina used to do it at the one time at the cash register. I can either give you
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different kinds of price deals or like you said if I'm vegan or I can give you
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a constellation of brands where I'd be more that's what you meant by propensity
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I think more likely to buy that product because I see you bought this in the past therefore you're more likely to buy
00:24:55
carrots than you are to buy steak >> with the veganism is that what is that
00:25:01
kind of very I'm saying in super simple way and you're doing it in a multi-dimensional way but is and 10
00:25:07
times as fast probably. Well 10 that is well said Barbara exactly I remember Catalina well you studied him
00:25:15
and that's exactly now it's it's a it's a version of that but it's like it's
00:25:18
it's so it's faster to your point and is much more much more it take is taking in
00:25:23
so much more data right that's a very simple if this >> is a very simple example but I can
00:25:28
understand it but now if you multiply it by all these different directions and do
00:25:33
it like instantly the power is incredible >> it is it is and you know so let's weave
00:25:38
in you asked early off the top, aantic, what does that mean? Like the reason, you know, I spent three hours yesterday
00:25:44
doing a cloud code um course for PMs in this case, but like I I so recommend people jumping in and trying it and and
00:25:51
why do I get excited? So the the the innovation that cloud code and and variants of uh of it represent or what
00:25:57
they're calling long horizon uh agents, right? So you know when when uh the chat
00:26:03
pimo would happen that was all about pre-training a model and then we had reasoning and inference time compute
00:26:08
like that was where like it's thinking to basically really really run lots of
00:26:12
different permutations and this long horizon agents are a gamecher because it actually can look long horizon and hold
00:26:19
context and look at the whole system. So to your point, Barbara, this is not only
00:26:24
just taking in more information. It's actually thinking about the long horizon
00:26:27
of the relationship, right? So we're just starting to see this where it's
00:26:30
like, well, maybe maybe it is like these se these 10 things. I'm making that
00:26:35
number up, but it's thinking about the entire system and that's where the agent
00:26:40
it is. >> So you're maximizing lifetime value. You're not only at this one occasion
00:26:45
doing it in a million different directions. So, it's not only multi-dimensional in that way with the
00:26:50
different tools you can use and all the different questions you can ask, but it's dynamic. It's long term. So, you're
00:26:55
looking about how to maximize the the That's incredible. >> Ladies and gentlemen, Chris O'Neal has
00:27:03
just split the atom slide on our podcast. >> I mean, amazing. I I can't I mean, I see
00:27:10
why it's so hard to explain because it's just so much. Then you have metrics that
00:27:16
prove you're able to do this somehow or another. >> I mean, you're not just taking it on
00:27:20
faith cuz it's pretty hard to get your brain around all of these things. >> It it is, but you know, you know, the
00:27:25
the math the math we've applied it in ad tech for a long time. Those previous,
00:27:31
you know, run it Google and now this is like how do you bring it into marketing personalization. So, it's borrowing
00:27:36
similar concepts and then and then turbocharging with AI. It's just like it's it is my my bending. You know the
00:27:42
hardest thing is actually changing the people's minds and the workflows in their life. People get used to different
00:27:48
ways of thinking and the longest pull the hardest change is actually changing the ways in which people go about their
00:27:54
business and that's the hardest thing and that's you know I find it deeply
00:27:57
rewarding once you do that. So you're not just applying your existing workflows and your mindsets to a whole
00:28:02
new set of tools or vice versa right new tools on top. When you say people, are you talking about the marketers, the end
00:28:07
users, the retailers? Who are the people you're talking about? >> All the all the above, but particularly
00:28:12
the marketers, right? And so like what's happened? There's always been a
00:28:14
tugof-war between technical people and like the data teams and lots of different parts of large organizations.
00:28:21
>> You know, if you'd asked me 10 years ago, I would have thought that the the
00:28:24
marketing leaders of today or now would be a blend of art and science, right? These people who are have data driven
00:28:31
thing and and deep appreciation for personalization and brand. I would have thought they sort of merged those
00:28:35
together and that's still largely true. But what's happening is the data teams
00:28:40
and the technologists are really leaning in and they're saying, "Hey, you know,
00:28:43
for a long time this promise has not been kept, right? The the BFF thing I mentioned." The reason that's not true
00:28:50
as a rule is that basically marketers haven't leaned in consistently to show
00:28:55
that this isn't just a cost center. This is a growth engine. Right? So the technologist with all these tools we're
00:29:00
talking about, I mean, they're so powerful, it's unbelievable. I wouldn't
00:29:03
have the slope of improvement is incredible. So you're seeing the technologists really play a role and
00:29:10
they're trying to learn marketing faster than marketing is trying to learn tech
00:29:13
and it's a fascinating time to be in business and but it does require a mindset and a partnership of
00:29:18
collaboration that takes time and you know really really um the culture. >> So let's you know just we we are almost
00:29:26
at the end of time if we're not at the end of time. So let's just big picture
00:29:29
this like just to get like >> with all of this power and all this ability that you have to make all these
00:29:35
predictions you know are you would you maintain that you are not only increasing
00:29:43
bottom line for the retailer because the retailer sending this information so the
00:29:47
more useful the information is the more money they'll get for that information
00:29:50
the more value they'll have the marketer who's selling more product but are you
00:29:55
also maximizing the experience for the end user like is that one of the goals too? I mean,
00:30:01
>> I think it's the main goal. Absolutely. Right. For too long, I mean, the reason
00:30:05
I'm in this line of work is like, look, we all experience this in our day-to-day
00:30:09
life, some sub-optimal experiences, whether it's the actual product experience or the marketing experience,
00:30:13
the customer service experience. And like I just think that, you know, this is one of going to be one of the use
00:30:17
cases just like it is for DevOps developing code or customer service interaction where you're applying
00:30:23
agents. Same as in marketing, too. For goodness sake, we know we know information and people are willing to
00:30:29
offer it. in a fair exchange for more delightful experiences and that's what
00:30:34
right great brands I know I think I'm preaching to the to the choir here that
00:30:37
like that's what it's all about that's the higher order bit you know the tech
00:30:40
can be a service to wonderful experiences >> and that's what um that's what gets us
00:30:45
out of bed every morning and we're really really thrilled to put gonna put up for me next like that's I'm going to
00:30:54
be even happier shopping at Costco now >> that's funny I love it though Barbara to
00:30:58
your point though because as Chris saying it's the trifecta, right? I mean, consumer welfare goes up, marketing
00:31:04
efficiency, marketers do their jobs better. I mean, every it's a just win-winwin. This is fantastic.
00:31:10
>> Well, Chris, it's been kind of an amazing little session. If I understand
00:31:14
an eighth of what you're talking about, I'm thrilled. But, uh, it's been
00:31:19
wonderful talking to you. Can you tell us where consumers can learn more about this or our listeners can learn more
00:31:24
about what you're talking about and get into more of the details? you have is
00:31:28
that available for people to dig in? >> Yeah. Yeah, people can track me down on
00:31:32
LinkedIn. That's usually where I spend spend my uh my my social time. Um so people want to track me down on LinkedIn
00:31:38
or check out Growth Loop. Um we'd be happy to to chat or or just really like
00:31:43
help us move this this whole uh industry one one step forward. So thank you so much for for the time and discussion.
00:31:49
I've enjoyed it very much. So thank you very much for having me here. >> Thank you. Chris O'Neal, CEO of Growth
00:31:55
Loop. That's all we have time for today. We'd like to thank our producers, Dion
00:31:59
Simpkins and Marissa Rena. Thank you all for listening. We'll be back next week.
00:32:04
Till then, this has been Marketing Matters on the Wharton Podcast Network. I'm Barbara Khan here with Americas
00:32:11
Reed.