Marketing, AI, and You: A Fireside Chat with Christopher Penn and Cathy McPhillips

Hi folks, and this is a fireside chat. I'm Christopher Penn. With me today is Kathy McPhillips from the Marketing AI Institute. And we're talking about uh the uses of artificial intelligence and machine learning within the space of marketing, uh the domain of marketing. If you have comments or questions about anything as you're watching this, uh go ahead and just use the comments box on whatever platform you're on.

If you're on Facebook, you're gonna have to go to the little thing here, streamyard.com slash Facebook, if you want your name to be seen. If you just want to be in an anonymous person, that's fine. You can just leave comments, however. So, Kathy, welcome. Uh and uh tell us very briefly about who you are, what you do, and why you're here.

Well, I'm here because you asked me to be on, which makes me very excited. Um I am Kathy McPhillips, I'm the uh Chief Growth Officer at the Marketing AI Institute. I've been with the company about 10 weeks, and I think that you know I'm excited to be here because I come with a marketer's perspective with this AI stuff. And I work with Paul Reitzer and Mike Kaput, and they're so in the weeds on AI that I'm excited to kind of ask tell you some of the questions that I've been asking them for the past 10 weeks, weeks incessantly. So yeah.

In your first 10 weeks, what have you found from the perspective of a marketer to be some of the obstacles? Because one of the things that uh we've seen, I'm gonna pull up a slide here. This is from the CMO survey uh from February 2021. Um, they ask about 500 CMOs, uh, how, if at all, has your investment in AI and machine learning influenced how much time your marketers are able to spend? So the impact of AI.

And what you see is of those companies that are doing something with AI, most of them have not seen very much impact at all. There's they're trying it out and it's just not happening for them. So when you think about, again, from the marketers' perspective, this chart, why is this? Well, I think a couple things. One, I think this artificial intelligence, the term scares people.

I mean, guilty. I, you know, you know, a year ago, two years ago, if I heard that, I'd be like, you know, that's just we don't need that. We like the personal touch, we want to be more involved. We don't want machines to do a lot of the things, you know. I don't you know me well enough to know that like my biggest thing is customer experience, and I want community and I want to have that relationship and will AI jeopardize that?

Well, what I've learned is that no, it actually would give you will give you more time to do those sorts of things because it'll take away a lot of the data-driven repetitive things and give you more time to focus on what you really want to focus on. Um, I think between that and people, um, there is bias. We'll get into that a little bit with AI, and people worry about what will this machine tell me? Um, and I think there's a whole thing of like, will I become obsolete? Well, machine take my job away.

So I think you know, if people are onboarding AI, I would ask them, are you really? Um, is what you're using really an artificial intelligence solution? And are you doing it right? Do you have the right people doing it? Are you investing and just because you have a technology doesn't mean you're doing it right?

It's or even doing it because if you're not having someone behind it to use it, then you're not really using it. And in terms of the different technologies that are out there, there's natural language processing, regression analysis, all sorts of really uh fun and technical terms. What are the things that you're seeing most uh people trying to discuss, trying to say, like, yeah, this is uh something that I'm I'm excited about, uh, or this is something that I don't understand. So what what are the the hot areas that from a marketer's perspective you think people should be paying more attention to? Well, I'm gonna kind of answer this in a roundabout way, but I've been doing some Google ads around MACON, our event that's coming up in a few weeks, and you know, I did AI and mark and um and analysis, AI and social media, AI and content creation, AI and and agencies.

And AI and social media has been the one that's surfaced the most and has gotten the most impression. So I think there's a big opportunity there for social media, you know, how can we it's just such a it's a time suck. It's it but you to do it right, you need to invest a lot of time in social media. So what what can you shortcut? What can you streamline?

What can sort AI help you at least get from point A to point B, not from point A to finish, but how can it help you take a few hours off your plate? Um, and then content creation, Mike Kaput, our chief content officer, posted something on LinkedIn today. He wrote a blog post a few weeks ago. He actually didn't write it, AI wrote it, and we read it and we're like, this is really good. So just being able to start drafting some content for you, whether it's social, whether it's a blog post, um, and then funny enough, last week someone, I'm not sure if you were in this conversation, but someone posted on LinkedIn about how AI really doesn't work for marketers.

So I chimed in and just said, you know, here are some use cases on how AI might be beneficial for you. And he wrote back and he said, I tried to write a blog post and AI is never gonna give me a final version of my blog post that I want. And I was like, Well, it's not really supposed to give you the final version. And if you don't try to get that first draft, you're never gonna get a final version anyway. So you have to at least jump in and get started.

Like machine learning doesn't say we're going to give you this beautiful thing at the end. It's going to say, We're let's we'll take you, we'll keep learning from you and making it better and better each time. So yeah. Then I just stopped to stop the conversation because you know, you know how social media goes. Well, yeah, so inevitably somebody ends up you know insulting somebody else.

But I I don't know if I agree with that in terms of what you know natural language processing in particular is able to do. Um, this is a a funny example. So this is a press release that um I found on the newswire from champion plumbing, right? In Oklahoma City, I have no connection to these people, I have no idea who they are. Uh they just were on the unlucky random draw that morning on the newswire.

And you could see this whole press release about you know uh plumbing in trees, and you know, like basically they're saying like don't plant trees uh near your house because it'll it'll mess up your plumbing. And what I did was I said, okay, I'm gonna slice off the the press release in half, and I'm gonna get rid of the second half, but I'm gonna feed it to one of these models and say, you try to auto-complete the rest of the press release. So here's the uh you know, I should probably share my screen because you can't see that. Um so here's the press release, right? And it's it's not it's not terrible, right?

It's you know, we encourage our neighbors to plant and maintain trees, but it's critically smart where they're located, and then you know, another wall of text from from the person who's speaking. So I fed it to um the model. This model is uh poorly branded named GPT J6B. Uh AI needs to work on uh naming things. And I said, I'm gonna cut off the second half.

What you auto-complete the rest of the release. So they're in the top of this, uh, let's make this bigger. Um, is the the original release, and then the bottom here, uh, the non-bolded text is what it came up with, right? Uh and it's a nice bullet list plant fruit and nut tree at least 12 feet away from the house. Maintain your tree so you know, keep your plant mulch so the tree roots don't have to go dig down as far.

And I was like, this is not a first draft. Like this is almost ready to go. Yeah. And first of all, it's a lot prettier with those bulleted list. Exactly.

So, you know, to the person who's saying, you know, this is you know, it's never gonna create a finished product. I don't know that that's going to be true for you know for the long term. It it is true now, um, but all these models are getting so much better so much more quickly, uh, that I don't know that holds true. I mean, so for somebody to to have those beliefs, how do you you know do you think something like this is convincing enough to get them to go, maybe I should be hopping on the bandwagon? Absolutely.

I mean, and to the other point is like, okay, so you go in here and you add a bullet or you tweak a bullet or something, and then the machine sees what you did, and the next time it's gonna know that. So you have to give the computer data, the machine data for it to know and learn. So next time it's gonna be better, you know, and it's you can't expect this perfect thing without you putting in some some information. And those are the kind of things that I've you know learned and said, okay, we can give this a shot. I I'm curious to you to dig in a bit more.

You said with regard to social media, um, what is it that you're hearing people asking about the use of AI within social media? I think from like um not from an engagement standpoint, but more of the distribution standpoint. You know, you write a blog post, you want to do a LinkedIn post, a Twitter post, a Facebook post, and different versions. So maybe one blog post might be 20 different versions of a social media post without duplicating something. And you want to pull out different snippets or use different hashtags and all these different things, like how can AI help me do that?

Because the blog post took me 10 hours to write, and social media is taking me another hour to two hours to draft, to publish, to schedule. So how can AI help me in that? Like, can it go through and just do a quick sweep of the post and to strap something up so I can just go in and edit that, save an hour of my life, and then um, and then go and then you know, go in and say, Can you even schedule it for me? Because we know here's the optimal times that our community is engaging with our posts. How can AI help us there?

And just constantly learn. You know, you six months ago, 10 a.m. might have been a great time for you to be on Twitter, but machines learning that your engagement's happening more at two in the morning because no one else is on. And that's those are the kind of things that we you know, me as a marketer, I've gone in and I've looked at that data and said, Let's shift up our times. But how can the machine actually tell me, well, you you're looking at it this way, but actually try this instead.

How much value do you think there is though in marketers trying to not necessarily um just use AI for creation and working at their own stuff, but trying to understand the other people's AI. So, you know, the uh the the classic thing, for example, in SEO is people have been you know spending years of their lives trying to understand how Google does things. Um people spent years of their lives trying to understand how Facebook and LinkedIn and all these different social networks, what their underlying algorithms and models do. And for those who can read the technical stuff, you can extract a lot of value and come up with tactics that map to the way the underlying machines work. Do you feel like the marketers are in a position, like even in in your own efforts as you've as in the last 10 weeks, do you feel like you're in a position now where if somebody handed you, you know, um some documentation about how LinkedIn is using its natural language processing, you could say, ah, now I know what to change in my LinkedIn strategy.

I mean, is it trying to like work with the LinkedIn algorithm? Because I mean, or is it really trying is that beneficial for you and your business and your customers? Sure. I mean, I go and read it, I probably read it anyways because I'm a big nerd and I read stuff, but I don't know if I'd invest, you're not trying to learn that the technology, you're not trying to learn AI. And you and I talked about this a few weeks ago when we were just on the phone, but I'm not trying, I don't need to learn about AI and be a data scientist and understand all that.

I just need to learn how AI is helping me. Um, I don't know if you said this analogy or I did, but you know, just be to drive a car, you don't need to be an auto mechanic. You don't need to know how to how a car works to drive it. You just need to drive the car. So if I know how AI can help me, I don't really that's pretty much the end of it.

I don't need to know all the ins and outs unless I really want to. Gotcha. Um because I think it's interesting. What you hear a lot about with particularly in in more technical um pieces of documentation is you learn more about the inputs. Like the underlying algorithm.

There's only really a handful of algorithms that these big tech companies could use at scale because they've got to do it for you know millions or billions of people. So they can't come up, they can't use the fanciest stuff that there is out there because they uh they simply don't have enough computational cost. But there's plenty of it's sort of like a you know the analogy I use is a blender. Like if you know what to put in the blender, you know what a blender does, um, then what comes out of the blender shouldn't really be a surprise. Like if you if you put in sand, uh a fruit smoothie is not gonna come out of the other end, right?

Um and so there's this interesting post that has since been deleted, uh which I find very interesting, um, by the technical head of Instagram saying, here's what the signals we look at in rough order of importance for what how we show things to to people. So post itself, uh signals like how many, how quickly people are liking, commenting, sharing, and saving a post. You know, and they matter in explore more than they do in feed of stories. Your history of interaction with someone, your activity, uh, what you do um and how people interact with you, and uh interactions with people who who are uh about their posts. And I find that again, you may not necessarily need a data science or an AI engineering background to read this and go, oh, so these are the things in order that you take into account.

So as a marketer, when you read this now, does that change how you think about how you should be using Instagram to to promote stuff? For sure. I mean Why aren't more marketers doing this then? I don't know. And why'd they take this down?

Because people are trying to gain the system. Uh I don't know. I speculate, and this is pure speculation. This is the I have no basis in fact for this. Um, but I speculate that he gave away a little too much information.

Sure. Yeah, or didn't have the appropriate approvals to to to give it away. But there's a lot of information here. Like, oh, well, if you know this about what Instagram's model is taking into into account, then you know that what you need to do. So if there's a post, for example, that you need to have really do well, the fact that they say use how many and how quickly people like, comment, and share, you may want to send out a text alert to your fans.

Like, I need you all to like this post right now. Right? You run some ads, real spin on some ads really quick on it, or you send out an email or a notification to your Slack community, but whatever it is. The fact that how many and how quickly is the most important signal is a velocity algorithm, means that you can now go and take advantage of it. And so, again, from the perspective of a marketer, why don't more marketers pay attention to the technical stuff?

There's no code here, there's no math. We're not asking anybody to do math. Yeah. But this could change your social media strategy. All those marketers who are looking for AI on social media, like they're giving it to you when they don't delete it on you.

Right, right. Unless your Chris Pennant screenshot it and save it. You know, yeah. Totally. I mean, this is such useful information because you know, we're working on that right now.

We're trying to build our Instagram strategy for the institute, and it's just like, I mean, I could take this back to our team and say, okay, here's what we're gonna do, you know. Exactly. And something you know, uh our CEO uh Katie Robert is always saying is like, so what? Like here's you know, you have all this AI as a so what? Well, when you're decomposing other people's AI and trying to figure out how it works, the so what is you know how their stuff works better so that you can take advantage of it.

There's another paper, I don't have it handy. Um LinkedIn published. LinkedIn actually is, I think one of the most interesting social networks because they publish and share a lot of their underlying technology. Like they tell you exactly how they do things, how they train their models and things. But again, marketers don't read them.

Now, in in that case, I I think it's fair to give marketers a break because their papers are really technical, like really like this there's calculus and linear algebra and all through them. But if you can decode it, you you can recognize, oh, so for example, it's not just what you post on LinkedIn that determines you know what gets seen. It's the language you use on your profile. It's the language of the people in their profiles and your first degree connections around you. So like if all your friends uh on LinkedIn, all your first degree connections are you know talking about e-commerce and you're over here talking about AI, there's kind of this mismatch, and LinkedIn will say, well, you know what, they're not really interested in AI, so we're not going to show your post to them.

But if they show some interactivity, and then one of the things I thought was kind of a cool dead giveaway was it looks at the language you use and other people use in comments, like when you're commenting on other posts. Um so if you are engaging with you know political content on there, and all you talk about is so-and-so did this thing, it thinks that that's what you're about then. And so then when you publish your thing about the marketing AI conference, it's like, well, you're you're posting something that has nothing to do with what you talk about most of the time. Right. So again, I would I would wish more marketers would read these things.

So I think it's a new side business of Trust Insights is to have you analyze, you know, decompose all of those into marketer speaks. We can all understand it and pay you to do that for us. Okay, Katie on that. Um we'll we'll we'll do what we can. Um there are folks who actually make a living, and you know, that's sort of the especially uh one gentleman comes to mind is Bill Slosky over in SEO community.

Um he runs a blog, SEO by the C. And his whole thing is he reads and analyzes in detail every patent that Google puts out and every you know academic paper. And he's like, okay, this is what Google is doing based on what they have patented. Um so if you you know if you read his stuff, it's like okay, he invests like a ton of time on it. But again, I think there's a niche here for folks who are in marketing, interested in AI.

Again, you don't have to be a coder, you do have to be able to read academic papers. Um okay, let's move on to something else. You had said that earlier on um a bit of a focus on like bias and ethics and what's going on with AI. What have you found out in your in your first 10 weeks about that topic? Um that a lot of marketers take what the A what AI what the machine is giving them, and they take it at face value, and that really is not a really good decision.

You know, and um Paul Rates, our our CEO and I have talked a lot about you know, he has this whole model of marketer plus machine, where a marketer needs a machine, but a machine needs a marketer. So there has to be someone, a real life person on the end of that after the machine tells you something to say, is this true? Um, is this beneficial? And are we comfortable using the data in this way? Um, so you know, whether it's implicit bias or whether, you know, just there's a lot more to it than just taking what the machine is telling you at face value.

You know, and there's Karen Howe from the MIT Um Tech Review who's speaking at MAKON who's got getting into this topic. And she, I mean, she's been phenomenal. I've read a lot of her stuff, and just she just constantly pushes back saying, Are you sure? Are you sure? Are we doing this right?

And especially now where marketers are where if you're if you're talking about AI, even if you're not using AI, you're ahead of most people. Um, and as this continues to grow, we're we have this opportunity and a big responsibility as marketers leading this AI charge that we need to set this, set some ground rules and set this up now to do it the right way. Um, so I'm excited to hear her speak at the event about that. What are some of the ground rules you think need to be set up? I don't know.

I think that's one of the reasons I'm I want to hear from her is just um putting some, you know, some checks in place. And I don't know who the right people are to do that, whether it's making sure we have a data scientist somewhere in our team and our agency and our you know, some type of partner that can help us do that. Um or you know, having having someone look at the and just an analyst within our company look at the data and say, you know, is this right? How do you reconcile that, particularly when you have things where there aren't necessarily always good right answers? So a real simple example, um, social networks, Facebook in particular, but social networks in general have received a lot of criticism, much of it very valid, um, that they are essentially uh breeding grounds for misinformation um and and for just outright garbage, you know, particularly around uh the pandemic, but politics in general.

How do you balance that as uh of saying like yes, you have the right to free expression, even if what you're expressing is completely incorrect. Um with things like, hey, by you know, 40% of the population failing to take action on this thing, you will eventually create a mutant strain of of you know SARS COV2 that will probably uh reinfect us all. So how do you balance the public good with the individual when it comes to the way these models are trained? Because right now no one's doing much of anything on this front, and you know, the outcomes we're getting are not great. I mean, this might be like a really silly answer, but I feel like if if you have that gut feeling that I don't know if this is right, or are we sure?

Like if you I just feel like we as marketers need to be good humans and just make sure we're doing good by our company and by our customers. You know, if if it gives you pause, it probably you probably need to dig a little further and you need to do a little bit more. Um I think you need to do that anyways, even if you know you are confident with the data, but and what I just feel like we have to just be good people, you know. But I don't I don't but I don't I don't know, I don't know. And that's some of the things, you know, that's like I said, I'm coming into this as and it's it's funny because I've been marketing for 30 years, but this is all new to me.

So I'm coming in with like an expertish level of marketing with no experience in AI. So trying to learn that and being a custom customer of our company, um, just trying to understand it, it's like there's a lot of questions that I need answered. And that's, you know, that's one of them. You know, you know, you say, What do you do? I'm like, I'm figuring it out as we're going along, which is how I've kind of lived the last 30 years of my marketing world is just you just figure it out.

Right. Do you think that some of these things, at least for the for big things that have societal impact might need to go through a review process, you know, um something, for example, with clinical trials, you can't just release a drug on the market uh without uh in at least in the United States, the FDA saying, hey, you should probably provide some evidence that this thing works the way it says it works and that it's not actually just going to go and kill a whole bunch of people. There is no oversight like that in artificial intelligence. Should there be? Well we have talked about you know within our team we have kind of like an ad hoc advisory board of sorts, you know, where he'll reach out to someone like you or Tim Hayden or, you know, can can you look at this or what do you think about this or some CMOs that that we know that we're making sure we're getting gut checks from them just saying you know are we on the right path or what do you think of this?

But yeah, I think there should be some some checks in place along the way. How much of the problems do you think are because of uh upstream issues with the people creating the AI Well I do know that we have talked to some folks about you know that wanting to partner with us on something and the team has pushed back and said you know either one there just doesn't seem like it's a good fit for a number of reasons to what you're doing really isn't AI. Um, and so just trying to make sure that we're we're with working with the right people and what they're doing is something that we believe in. Dig more into that. So what about the people who are telling me about those those companies that say they're doing AI but they're not?

What's what's the you don't have to name names, obviously, but you know, what's the storyline that well, I think that some, you know, and as marketers, you know, back like I'm kind of jumping off topic a little bit, but like way back before when I was working for CMI, prior to that, you know, I was a CMI customer. I was reading the blog, and I'm like, we're doing content marketing. And once I started at CMI, I'm like, we are not doing content marketing at all. We're publishing promotional blog posts. So you know, you just learn.

Um, I think some companies and some marketers think, oh, because we're using a marketing automation tool, we're using AI. Or we're doing, you know, we're using this tool, we're using AI. But that's not you're using a technology, you're not using an AI-powered technology. So I think the marketer it sh should. I mean, it should, if you know that it can the machine's going to help you in a more beneficial way by learning what what you're doing and learning what your customer needs, then yes, in the long run, that's going to save you a boatload of time and give you more, you know, better um better outcomes.

Okay. Because in 2018, the Financial Times did an assessment of a hundred companies that that said they did AI, that their products were AI enabled, and found that 35% of them were just outright lying. Like there is literally not a scrap of technology. They had an outsourced, you know, cube farm somewhere in like Kyrgyzstan that was doing all the uh the stuff behind the scenes. But it's good for SEO to have machine learning in your in your website, right?

And and there and ultimately the question is, if the customer gets the the benefit at the end of the day, does it actually matter whether a machine did it or not? I mean, I see your point. You know, is the cost of outsourcing all this stuff is comparable in price to using an AI technology? I mean, I guess what are you doing, right? But I mean, I guess I would I would say if you want to want to know really what your customers want and what you're gonna save time on, and you as a as a business leader want to know more, I feel like you gotta get on this AI train eventually and start using some of these technologies.

Because what you're you're giving you're giving this other group, this partner of yours, all this data, and they're just sending it back. Are you sure it's are you sure it's what it's right? Are they doing what you want what you want them to do? Right. But if you're if you're a marketer who's not a technologist, do you necessarily know what you want them to do?

Or do you just want them to say, like, hey, I just need my social post, I just need more people like my tweets. So they just see that the more they're getting their engagements up so everything's good, right? Exactly. Um because one of the things that I wonder about a lot when it comes to the use of AI and you know, particularly with bias and ethics, is machines write their own software, but they do it from the data we provide them. And they do it using algorithms in many cases that we specify, which means that if we're doing it wrong, we're gonna teach that to the machines.

The most um powerful and unpleasant example I can come up with is when you look at this is Facebook's website. Uh, these are their core values. Be bold, focus on impact, move fast, be open, build social value. At no point in here does it say make the world a better place, make people happier, uh improve the productivity of society, right? Um, even when you go you zoom in on a few of these these values and things, you know, we're trying to bring the world closer together.

Yes, you've done that. Congratulations. You've created entire tribes of people uh who are like wacky conspiracy theorists. So these are the values of the company, it shows up in their AI. It shows up in how the Facebook news feed works.

You don't have to, you know, you can look at it and say, okay, they have done exactly what they said they were going to do, and in the process made the world a much worse place in the world. At the detriment of society. Exactly, because that's not in the values. And so when you hear people, you know, companies coming to you, that um how much is anybody thinking about okay, have we hired people who are biased in in a way that's would be generally accepted as not acceptable? Um, you know, has anybody looked at the data going in?

Has anybody looked at the data coming out? Do you do you see anybody doing that kind of deep investigation? There have been a few people we've worked with, um, and obviously, you know, Paul and Mike on on a much longer on longer scale, that that's what kind of what they're doing is they're being, are you being strategic with your data? Are you using it or are you like you said, are you inputting it the right way? Are you teaching the machine the right way?

And like you said, I mean, that's bias right there. You think you're doing it right, even with the best of intentions, but you might not be. And having someone to help you do that is, you know, is a is an important thing to have. But yeah, I mean, there are definitely go ahead. No, please, go ahead.

I was gonna say there are definitely some companies that you know we've kind of shied away from because it just kind of gives you that feeling of like, I don't know if this is if this is right. I don't know if this is an association we really want to be part of. Let's just watch and see maybe in the next six, twelve months how how things change with their business, and maybe it's something that we might want to consider. But you know, that's something that Paul, you know, for as long as I've known him way before you know I was involved, um, when it was when he with his agency, that he's you just kind of find the people that you want to be associated with that are good people and that are working toward a good a good cause. Helping for the work of the makeup of the companies themselves in terms of the people they've hired.

With when we partner with people, uh with when you're looking at a company and trying to evaluate, for example, whether they're telling the truth about their AI and or whether their AI is is it has potential biases when you look at the company itself uh and say, like, gosh, it yeah, I'll give you an example. I used to work at a company uh that was based in Atlanta, and they claimed they were an equal opportunity employer, all this stuff and things like that. And you walk in the office the first day on the job, and out of the hundred employees, there's not a single black person there. I'm like, this is Atlanta, 54% of the population is black, and you're telling me you can't find a single person who's black to work here. Like, oh yeah, but we're an equal opportunity employer.

Like, no, you're not. Otherwise, you would represent the population you're in. And so one of the things that I wonder when I look at, you know, because we will look at companies and technologies and look at the the team, like go to the their their team page and look at it and say, huh, you're all exactly the same people. It's like, you know, uh something right out of Stepford-wise, just you know, you you're you're the same person cloned over and over again. Right.

And that makes me worry about their AI because if you have a homogenous culture of people you're going to create a homo uh a uh an algorithm or a model with problems so how much do you look at at the people behind these companies it's a good question I honestly don't know the answer to that question but I do know that um we talked off camera about our AI in action series that we're starting next week and some of the the um sponsors of MakeCon who will be on these AI in action sessions next week you know Paul got on the phone with them and talked for 30 60 minutes and just said let's talk about your AI let's talk about this talk about this doing like we don't want to partner with someone who's saying they're using AI and they're not for example um as far as getting into team and culture and all those sorts of things I don't know but it's something I'll definitely um you know you know you know me I we like when we were at CMI that was something that Mo and I really were it was important to us um you know with the with a larger informal community so I think I feel like that's something that I could bring back to the team for sure. It's a great question. Right. I know we I know we talk about it like in the lessons and in the certifications that those are slides saying you know check out these things but have we put it into our own practice? I would like to say yes but I'll make sure what about with the conference itself like when you're choosing speakers and things oh definitely you know um and it's it's hard right now because it's been a lot of you know we know that you're an expert we know like I said Tim Hayden and Matthew Sweezy um three men, you know, it's like, but we know that you'll give the content to our attendees that they need to know.

Um, so it's been an it's been an effort to you know scour the internet, talk to people, talk to people that are our customers, but he's also reaching out to people that we've never talked to. You know, a couple of our speakers are brand new to to the whole team because Paul took the initiative to go out there and find and find folks and ask around and make sure that um, you know, so is it 5050? Is it um, you know, is there a diverse audience? Um could could we do better? Sure, we could do better, but it's better than 2019, you know.

So we just have to continue to improve. And I think um, I think that's very definitely important. But we just, you know, it takes 12 months to plan an event and it we just have and that gives us now September 15th, we'll start planning 2022. And that's gives us a new new chance to get out there and and try to find out who those experts are, or train some, you know, get somebody that the means to be that next expert. Is the issue because there aren't enough people who are skilled in both marketing and artificial intelligence?

Probably. Okay, or we just don't know we or we just don't know them. Right. Because I know there's, for example, you know, there's the whole black and AI community um and the Latinx in AI community and the women in AI, women and analytics organization, which I think is actually either in Cleveland or Cincinnati. So there's groups out there, but I know a lot of those folks are like in bioinformatics and stuff.

And where these are folks who a hundred percent know the cutting edge, but they're not doing they don't work in marketing at all. They have no connection to marketing. Yeah, I mean, definitely. And I've been a few women in um AI groups, and I'm on this inside AI Slack channel and I'm and I'm looking around, like you said, it's like trying to find the marketing people within those groups, um, and then approaching them in a way that's you know, I would love to connect with you, not like I need something from you. So it's building relationships too.

It's not just, hey, you're you know, you're you're a black woman who works in AI and marketing. Can you come speak at our event? Like, I wouldn't do that. So we have to, you know, it's gonna take a long time to build that relationship up and up and have them want to do that for us. Yep.

When you look at um the audience, uh who is a member of the institute who's attends the conference, who is it and how has it changed in the last three years? Well, I'm still diving into all that data, but what I have seen is um a lot more people that are you know downloading our state of the industry report or downloading our beginner guides. And I look at their title and I'm like, you're so not a beginner, you are um a CMO of a brand, but they're trying to like just you know dip their toes in the water and say, is this something I should be be investing in? We've got a nice international presence, um, which I think that's been consistent, consistently growing. And um I mean, I'm even trying to grow the market in Cleveland, you know, just how many people in Cleveland know that you know, Paul has been around for a long time, PR2020's been around for a long time.

The institute could kind of heard about it, but they don't know about they haven't heard about the event. So I'm like, wow, in our own backyard, we need to expand our audience a little bit. What are those? What are folks who are what are their their top questions that they have? Obviously, you know, the the big ones are easy, like how does this stuff work?

Do I need this and stuff? But what are some of the more nuanced questions that you're getting that you see as a theme? Am I ready for this? What do I need to do with with my team with my data before I can start? Um, what's it what does it even really mean?

You know, what is what is AI? I mean, just what is AI? Um so I think it's a lot of just that fear, and also you know, the fear of can I do this and should we be doing this? But do I have time? You know, where where do I fit?

You know, onboarding technology alone is is a time commitment. But now you're like changing your whole entire marketing strategy. And can you onboard a new technology that might help you with one small task before you change overhaul your strategy? Like what I feel like you need to do both, but when can you just start with something small and then work on the bigger picture? How do you answer to people who ask, you know, how do I know if I'm ready for it?

I tell them to call Paul. But I mean, in terms of what you've learned in the last 10 weeks. Yeah, I mean, I think if it's something that you know, if it's something you do every single week, if it's something repetitive, if it's something that you have data to support, um, and if it's just consistent, I feel like that's a good use case. You know, it could be A B testing subject lines, it could be drafting social media posts, it could be looking at some analytics, um, just some of those things and versioning creative. You know, I one of our AI in action sessions next week is with a company called Celtra, and they talk about how they you can put in a piece of creative and it'll version it for all the web, you know, all the banner ad sizes.

And I but there are companies that do that, but how they're different is that they you might you you can go down, you can manipulate where the logo is and to do certain things on the versions they pump out, but then it learns and says, okay, next time we'll know that, you know, or the other um companies who aren't AI powered don't do that. And I was just like, oh my gosh, I just I did that two weeks ago for all our A in action and our make on sessions I'm I spent a whole entire day doing creative and I was like that would have saved me maybe half the time you know I could have done it in half the time versus um eight hours it could have been four hours and that would have been glorious because I have so many other things I need to do so just finding that that thing um and what's or what don't you like doing you know I love doing you know creative ideation and putting something together but do I like sitting there and making all the versions not really um so usually I do it at night watching a movie which is not a good way to spend my time either because I should be enjoying my evenings with my family and I'm versioning and creative. So just you know what don't you like doing what you know what isn't fulfilling to you I know we all have things that aren't fulfilling that we just have to do because it's part of our jobs what's repetitive and what do you have data to support the that a machine can help you. How do you answer people when they ask the the perennial buy or build question like should we should we go out and just you know get a vendor or should we try and build this ourselves what do you think is the are the criteria that makes sense to you I think from an AI standpoint if I mean if people haven't figured this out yet and there are companies that are working on it I feel like why would you start your own I mean someone like you could but do you have the team do you have the team that could do that? Are you investing in the right people go see what other technology companies are doing first is where what's what I would say.

Okay. Yeah. I the decision we usually factor on is uh A, we're cheap because we're a small business. So we're like, okay, if we can build ourselves, we will. Uh and and more important.

If it's something that is is ancillary, like I just want like your example of ad creative. That's not that's never gonna be a core competency of ours. Um so there's no reason to to build that technology. It doesn't, you know, it's somebody else has solved that problem. But if it's gonna be something that is core to what we do part of the secret sauce, I personally feel like that's risky to have in the hands of a vendor.

You no matter how good the vendor is. Um I feel like that's something that you don't want to be held hostage to with by a vendor. Right. So to wrap up here, um where can we find out more about uh uh the the conference itself? The conference itself can be found at Macon M-A-I-C-O-N dot AI.

And that will take you to the homepage and there's you know our agenda, our sessions, speakers, everything's up there. Um yeah, so it's in September thirteenth and fourteenth. And yesterday we did a lot of the filming and everything, and it's it's I'm really excited about it. Excellent. What are you looking forward to most?

Um, one, I'm excited because we're using swap card, which is an AI powered um solution for our platform for the event. And the nice thing about that is that say I connect with you and I connect with with Katie. Um if she's coming to the event, then I then the algorithm will say, Okay, they're looking at folks in this industry, or they're looking at, you know, this job level or function or whatever, and they'll say, Why don't you go talk to these per you know, call talk to John Wall, I'll talk to this person, and it'll connect you. So that that we, you know, we thought if we're using a platform, we should use an AI powered platform. So we're um we're testing out some things.

So I'm so excited about that just to one, just to see people and start to build this community a little bit more. And then two, just some of the sessions. I think um, I think one of the things that I keep asking a lot, which I'm probably driving everyone crazy, is just the okay. We talk a lot about the what is marketing AI and why you should be doing it, but I'm just like, how do you do it? Show me how to do it.

And so I think you know, one between our between our AI and action sessions that are happening next week, along with some of our breakout sessions, it really is diving into the how are you doing it, how can we be doing it? And that will answer my question of should we be doing it right now? Do or what do we need? What do we how what position do we need to be in to be able to start doing this on our own or with a partner to help us? So I think that's gonna help answer a lot of the questions for some folks.

And then some of the other sessions, you know, we talked about Karen's um Cade Metz is our closing keynote, and he wrote the book Genius Makers, which is a was a really really fascinating read on the world of AI from Google and Facebook and all you know, Deep Mind through now, and how that whole industry has evolved. Um, and to your point, there was a lot of white men 30 years ago, 40 years ago, you know, doing 50 years ago, you know, doing all this, and look where look where we are today. So the industry's evolved. There's a lot of the whys and how we got from this point to this point. Um, and he's a the technology um correspondent for the New York Times, and he's written for Wired Magazine.

So a lot of his stuff has just been really great to read. But he was actually one of the people that Paul started following first that got him into the into this whole marketing AI space. Um so he's doing the closing keynote, and then um just some Matt Matthew Sweezy will be there. He was supposed to keynote last year, and we know how 2020 went, so that didn't happen. Um, but he's gonna come back for this one, and he's always brilliant to listen to and so great to listen to.

He's really good at taking something strategic and bringing it to a tactical level so you can really understand it and figure out like, okay, this is something that I really understand and as a marketing leader can take to my team on things we should be doing or things we should, you know, how we should be approaching our marketing strategy. And then there's lots of in between. Yeah. Awesome. I have a uh question here from uh Dr.

Ashley Lidyard asking uh what aspects of marketing have higher activity of AI. I think right now content creation has been the biggest one that I've seen, but I but um and there is a state of um the industry report on our website. I think it's just state of I should know this off the top of my head. Um state of marketingai.com. There was a report that's it that would answer some of those questions.

Okay. I know from our own experience, we have seen the attribution modeling by far probably uses the most the most models and data, uh at least in for what companies are selling uh or or building, like the new Google Analytics 4 has some brand new attribution modeling that has some questions to the Google team uh about what they're using on the on the back end. Uh and I know SEO has a ton of machine learning uh really being used right now, where it's like you said, content creation. Uh it is also very much uh analysis, like people trying to reverse engineer search results and figure out well, what are the factors that that correlate most to the known outcomes? Uh when you look at the you know the people doing data analysis on on search results and ranking positions and things, there's a tremendous amount of data, and because it's all in reasonably good condition, um, you can build models on it.

I think those are the things that I've seen where people using more complex stuff, but there's not there's not as much of it as as I would have thought there would have been because again, there's not that many people who can sit down and say, okay, take you know your Google Search Console data and let's you know run it through a gradient boosting algorithm uh you know, and and see what are the the variable importance uh factors that go with these things. Um there's not too as many folks that can do that, and there's not a lot of off the shelf stuff that I've seen that does that well because everybody's data is different and really messy. Well, that's what I was gonna ask you. So how many and I'm I'm interviewing the interviewer right now. So how many how many CMOs and marketing leaders do you think are reluctant to dive into that because they don't want people to see that things are messy?

More than we think, and certainly more than anyone's willing to admit, um we had a client a couple of years ago. Their marketing team was forbidden to see any data from sales. We're like, how do you make decisions? Sales just tells us yes, that's you know, things are good or no, things are bad. I'm like, why?

And you know, a couple of years later it turns out that um sales was so bad at their job, like they closed like one percent of the leads they got, and the the chief sales officer didn't want anybody to see just how bad things were. Yeah. I mean, you think a good mark um mark or good leader would say, you know, this is not my area of expertise, I need help. But you know, are we if someone's been in a role for you know for a long time or they're like, I don't want anyone to see what that we have been kind of muddling our way through this for so long. You know, you and you and I talked about that a couple of years ago, and I'm like, I need help on some stuff.

Um it's hard for people to ask for help. Uh it's hard for people to admit they don't know something, especially if they're in a leadership position where they're ex their the whoever their stakeholders are expect them to to know things to you know put your hand up and say, I have no idea what that thing is. Um, but don't go go find out about it, is is very difficult. Right. Um follow-up question here.

When you talk, say content creation, are you talking more granular customer segmentation, like specific content for specific people, personalization. Again, I'm still learning, but I mean that's a great way to look at it. You know, we're talking a little bit about um we're actually I just did it. We're doing some ad copy for for different j for different functions within within marketing. Um, it's a great way to to use a tool if you if you have the data.

Um I was talking more about just like legit content creation, but you know, blog posts, articles, social media posts, things like that. And I think I think I keep going back to that because I think it's a it's a very tangible thing to see the results of. So that might just be a good place for a lot of people just to to to look at it. We used one tool called hyper hyperwrite.ai. I'm not endorsing them.

We're not we have no affiliation with them. We used them, but it was like, okay, here's what you plugged in and here's what came out. And it was just a very easy thing to see. Wow, look at that. It was actually turned out it turned out pretty cool.

Um, so I think just seeing what AI can do with a limit with a small amount of data versus a large amount of data, um, has been pretty fascinating to see like what AI can do. Yeah I I would agree a personalization um is is e relatively easy in terms of like recommendations content and recommendations and creating content that's targeted towards certain audiences uh the folks over demand base who we've worked with relatively recently have a lot of systems like that that we recommend content that um has a higher propensity for somebody to buy uh a third question here uh is how do you sell AI and and market and machine learning to leadership that either are unfamiliar with it or opposed to it like send them to make on September 13th and 14th I'm just joking um I'm not really joking I'm not really joking at all um so again this is a little bit of a sales pitch but we have this these sessions starting next week called AI in action um and what they are are there are six webinars Tuesday Wednesday Thursday over the next two weeks and we're actually talking about you know showing AI in action so the creative example that I was talking about it's there's this company Celtra so they took one of their customers and they've got some pretty big name B2C customers and they took some of their creative and they ran it through their system and here was here was the output and you could see it and it was like this is what AI did the first time we did it. Then the next campaign we did this and here was the outcome um so it's not a demo but it really goes into the tactical show me your how your AI is working and what's what's the AI doing that another technology can't do. So I think a lot of those just visualizing some of those things I don't know about you, but I'm a very visual learner. So me seeing it, I'm like, aha.

Or, you know, getting an actual use case is just really beneficial. Um, I think some of the things like the state of the industry report, whether it's ours or whether it's somebody else's, just having them see what other companies are doing, having them see what your competitors are doing, um, having them like if there's something that your company is spending a lot of time doing one thing, you know, could we just pilot AI on this one project and just so you can see how it's working? I think some things like that, you know, just without taking your whole entire budget and trying to put it towards something and just saying, can I just have a little bit of a portion just so I can show you this one thing? I think that's a very um easy low cost, low um, you know, you're not locked into something longer term way to show people something. Yeah, I mean, generally speaking, leadership cares about three things, right?

It's gonna save me time, is it gonna save me money? Is it gonna make me money? I remember, you know, uh when I was starting a data science initiative at my last agency, the the agency owners said, How the are you gonna make me money? Like that's the uh that was the the blunt question in the interview is like, well, we can resell these services. And where I think a lot of folks, myself included, my hand is is totally up, is we don't spend enough time figuring out, okay, well, here's like what you're gonna get.

Like you're gonna improve your ROI on your ad uh on your ad spent by 14%. Right. So if you want 14% more results for for your ad dollar, um, use the system rather than this system. Or um, like in your case, um you're your your team's gonna spend you know half the time creating uh you know creative variations. So it'll it'll really, you know, help your marketing move faster.

And I think that's something they can wrap their brains around is say, okay, uh you're you're gonna make me more money. Cool. I'm I can I can deal with that. Then because they don't obviously, a lot of the stakeholders I've talked to, they don't really care. They could be a box full of gerbils um you know with a some wires coming in and out and they wouldn't care what was in the box it was fairies gerbils AI you know uh aliens from outer space as long as when they put a dollar in the machine more than a dollar comes out that's just really all they care about.

Yeah for sure. So well thanks for the great questions. Thanks for the great conversation today. If you've got comments or questions things you want to follow up with afterwards pop on over to our free Slack group or trust insights dot AI slash analytics for marketers where you can ask Kathy who's in the the that Slack along with nineteen other nineteen hundred other folks about your questions around our artificial intelligence machine learning and also just like how do we do this thing. Um and if you'd like to to read more about this stuff on a regular basis pop on over to our our free newsletter to go to trust insights dot AI slash newsletter and hopefully we'll see you at uh the Marketing AI conference September 13th and 14th go to Macon.ai and if you'd like to learn more about uh Kathy and the institute you can go to marketingai institute dot com.

Uh Kathy thanks for coming and hanging out for almost an hour today and uh thank you so much at MACOM yes for sure. Thanks Chris