Mastering the Art and Science of AI Marketing
Episode Description:
Today we sit down with Adriana Gil Miner, CMO at Iterable, for a discussion on the power of AI in marketing. Adriana shares her unique perspective on leveraging data and storytelling to drive customer engagement, and provides invaluable insights into the ethical considerations and future trends of AI. Join us as we explore the art and science of modern marketing, and discover how to harness the potential of AI to deliver personalized, impactful customer experiences at scale.
Resources Mentioned:
Full Transcript:
Andrew Miller:
We have a lot to cover today, so we're going to go ahead and jump right into it.
We're joined by a very special guest, someone I've been personally wanting to chat with for a while.
Let me go ahead and introduce you to Adriana Gil-Miner.
She is the CMO at Iterable, which is an AI-powered customer communication platform.
Adriana is passionate about customer engagement, and her leadership has contributed to Iterable's success in assisting brands such as Redfin, Priceline, and Volvo in delivering individualized, harmonized, and dynamic communications at scale.
Now, before her role at Iterable, Adriana held influential senior leadership positions at Tableau, where she significantly contributed to the company's remarkable growth from $250 million to over a billion, with notable impact which helped lead to the successful Salesforce acquisition.
Her leadership also left a very positive mark on Kumolo, a data storage startup, and Artifact with its spinoff 10,000 Foot, which eventually got acquired by Smartsheet in 2019.
Her 20 plus years of marketing experience span prominent names like American Express, Digitas, and Weber Shandwick.
It's so great to have you here today. Thank you for being on the show.
Adriana Gil Miner:
Thank you, Andrew. Great to be here.
Andrew Miller:
Now, I know I gave a bit of an introduction, but is there anything else that maybe I have missed out or you just want to, you know, double click on?
Adriana Gil Miner:
That's so comprehensive. And it does make me feel a little bit old, but I think maybe the thing that, you know, it's not on the on the resume is a little bit of like, what what is all this journey?
And, you know, I've been a marketer now for 25 years, so I'm in the space of reflecting. And to me, it really comes down to just the art and science.
You know, funny enough, my background is in art. I went to a primary and secondary school in Venezuela. And thank you for saying my name very well.
So in Venezuela. And so I have an arts background. I'm married to an artist.
I was a dancer as well. So, you know, performing arts and visual arts is my first love, I would say. And of that came a love for writing and literature.
So originally, I wanted to be a writer, but that didn't quite work out.
And funny enough, I come from a very techie family. Like my mother was a CIO.
She was a computer science career, lots of engineers. My uncle is a nuclear physicist.
And the reason I say that is because I was the rebel in my family.
You know, I'm like, I'm a humanist.
And yet the first job that I had in corporate America was a data analyst.
It was called New Media Analyst because at the time, the internet was new media.
And it's funny because in that first job, my breakthrough really came through.
And understanding that all that data that I had on, you know, clicks and web visits and all that stuff really was a proxy for human behavior.
And that there was a story behind all those numbers.
And like my job was like literally cut and paste from two different Excel spreadsheets, PDF, and then create a weekly report. It was very boring.
But that grunt work changed for me the day that I realized there's a story here and there's some opportunities for optimization. optimization.
And I look back at those, you know, I was only 22 at the time.
So I look back at that. And today as a CMO, I'm still doing exactly the same thing.
You know, I'm still digging into the data.
I still love, I love data in my years at Tableau.
Definitely, you know, amp that, you know, and so I still find really the insights in the, in what to do.
Well, how does the strategy, It's born out of the data, but it's not enough to just be in there. You have to bring in the human element.
You have to bring the data story in the story, telling perspective, the art side of things.
And that's where innovation comes through. So that's really what my journey is about.
And so, you know, when I pitch myself, I say I'm all about data and stories.
Andrew Miller:
Yeah, no, no, no. I think that that's beautiful, but also very applicable to today, right? You have AI coming out, and that's very data-heavy, but it's all about bringing that human aspect into it. I mean, they even have...
Courses and degrees now specifically tailored to merge both of those together, right?
So like behavioral economics, that takes the human psychology and the understanding of what nudges us, what makes us tick, what is that creative and emotional way that we can relate on in a one-to-one aspect, but also the numbers.
You have to have the numbers because we're such a data-driven society now and merging those together to push people forward.
So I think the background that that you have is probably the most in demand and most relevant background that there is for making a holistic marketer in today.
So I love hearing that the creative aspect and the, the, the numbers it's that that's what makes true marketing. I think beautiful today.
Adriana Gil Miner:
That's true. I like that. I like to be in the man.
Andrew Miller:
Yeah, absolutely. Absolutely. So, so let me, let me ask you what attracted you to AI and marketing Kind of like the intersection of both, you know, with Iterable.
Adriana Gil Miner:
Yeah, I think, you know, first of all, I've made a career of being at the forefront of every, I would say, pivotal computing or technology change we've had.
And I think in particular, marketing is one of those functions in an organization that has the opportunity to be at the forefront of that and really be in the change.
So, you know, like I said, when I was starting my career, that was, oof, 99.
And so when, you know, e-commerce and sort of like affiliate marketing was an email marketing was just coming out.
And so just being in sort of like that first generation of marketers that were trying to figure out, okay, how do, you know, how do we go from direct mail to marketing?
And what some of the most practices that we were building from direct mail that, you know, definitely led to basically 10 years, like the first half of my career was really dedicated to digital marketing, building, you know, email programs, engagement programs, websites, and going from the idea of brochureware to actually, you know, transactional stuff like at American Express, like how do you, you know, you went from being online and being able to do, for example, disputing charges online.
And so that was a very big transformation at the organization.
And many marketers, digital marketers like me, we're at the forefront of that organization transformation.
Adriana Gil Miner:
And if I, you know, continue in my career, the next big pivot was when social media came out.
And that really changed how we engage with customers and all that stuff.
So this is when I switch over to PR and brand.
And then, of course, you know, cloud technology, which it gave birth to the whole SaaS business.
And again, very, very transformational.
So here I am. And so to me, of course, you know, I don't, I can't even like conceive yet.
I don't think any of us can, the sort of transformational power of AI, but it really excites me because I know that at every, you know, in my history and I want to be in there, but seeing not only because of the change that it can does to market, but to me, the really interesting thing is how can can it change organizations and how can it change like the individual worker day to day?
Um, so, so that's like my, my sort of motivation.
Um, and so it's, it's a very exciting, like, why wouldn't I be here? You know?
Andrew Miller:
Absolutely. Absolutely. That, that makes a lot of sense. Um, if we, if we dug down into that a little bit further, how would you say that maybe AI is revolutionizing the space that you're in today and maybe share a real world example, example, if you have one.
Adriana Gil Miner:
AI is broad. So let's just zero in into generative AI, because I think that's really kind of like the newer thing that's giving opening to a lot of things.
So we recently did a survey to 1,200 marketers, and we found out that 91% of them are already using AI in their work, which honestly really surprised me.
We did it last September, October of last year. So that's really early.
And there's been other studies that I've seen that show that marketing is the first function to adopt AI, particularly generative AI, you know?
And so we see things like basically on two fronts, how is it changing?
And a good example of this, one of them is how is it changing productivity?
So care.com, I don't know if you're a parent.
Andrew Miller:
I've used them to get my babysitter.
Adriana Gil Miner:
There you go. Yes, exactly. So Care.com is a customer of Iterable and they have been using one of our AI functionality channel optimization.
And by using that, you know, trying to like basically have the machines decide what's the right channel for the right message.
And just by doing that, they've seen a gain of 25% time back for the workers.
And so that's what I would call the first phase of revolution.
Right now, a lot of generative AI or AI to help you optimize automation of things are really focused on productivity gains.
And it's a race. I would say like any company, especially now with all the financial pressures that we have on profitability and stuff, any company that's not quickly adopting AI technologies to gain in productivity is going to be fall behind because the ratio of like, you know, human or productivities are really changing with AI.
Now, to me, that's just like the very beginning of this sort of revolution.
Adriana Gil Miner:
I think the next thing that we're going to see is really AI.
Unlocking sort of like the smartness of what we can do in marketing.
So think about when the machines are analyzing and recommending or even actioning on data that we see all the time.
So think about, for example, in marketing, a big thing is predictive models, right?
The next purchase, churn, like all kinds of things. That's like a core thing.
Adriana Gil Miner:
Today, still most corporations will We'll have a data science team that develops that, you know, it takes you a few months to do it or maybe a few weeks to do it, but you still need historical data.
When we're in a place where we have all that data in models that it's like automatically and all the time updating and generating predictive models and then actioning on that, what you are going to start is discovering opportunities that we don't see today, you know, that we can't humanly, you know, process at that level.
And I think like that's the next sort of phase is like, what are the new opportunities for say product, dynamic pricing, you know, like the four P's of marketing, but automating, like think about like what that's going to uncover.
And then, and we're starting to see a little bit of that already, you know, like if you go back to sort of like more of the ML technologies, like recommendations and stuff, like it's not that there's no precedent to this, but now we're seeing it a much more, much more more sort of scale level and many more applications.
And I think that the next thing, and I'm not suggesting that these will happen, you know, sort of sequentially, they might happen in parallel.
But the next big area, which is, I think, both the risk, we're starting to see already, you know, like any new technology, we're starting to see already some backlash, right?
So you have compliance challenges, you have biases, you have a lot of, we don't understand these models. So there's going to we're going to go through a trough of dissolution.
It's already we're starting to see, you know, little seeds of that.
And I think one of the things that worries me as a as a marketer is that, you know, in the world where we can generate, you know, videos automatically and like emails and all this stuff, it feels like we're we are facing a challenge of a sea of sameness.
You know, like what really distinguishes one brand versus the other or one interaction versus the other where, you know, machines can produce this, you know?
And so I think we're going to go into some sort of like crisis of creativity where the human touch, we're going to recalibrate what do we value and what do we really see as authentic?
Authentic um and so that that sort of i think marrying of almost very traditional.
Um marketing and advertising uh and we're seeing it in certain things like in design in certain retail brands you're seeing a um a movement from like typical like grid design to more like handmade things so i think that there's going to be that next phase of really what is the the rebalance or the recalibration of the human touch value, the human value and the machine value.
So I'd call that like the intersection of, of creativity and AI, because that's, you know, and we, we got to figure that out. Right.
So I think that's, that's the sort of final, or that at least I can see of like the things that can really like change how we work.
Andrew Miller:
Yeah, yeah, absolutely. There's a lot to unpack there.
You gave me so much insight about like the development.
I think if we go back to maybe like your first point, you know, productivity gains.
At this point, it's becoming almost like table stakes, right?
Like you're incorporating AI because you have to do more with less.
And so if there's these basic, you know, AI tools out there that are allowing you to do that, like handle your scheduling, handle, you know, note taking, handle, you know, you know, task assignments, you know, basic things like that, that that needs to just be adopted.
You know, if you haven't done that, you're already falling behind.
But it's that's not taking you to the next level. That's what is just now being expected. affected, like you mentioned, I mean, open AI and I'd say like the consumerization of AI has only been public facing for like two and a half years.
It's AI has been around and machine learning has been around for over a decade, if not longer, but it's just now getting to the point where people, it's more accessible to everybody.
And so with that adoption, like you said, 91% of marketers were at the forefront because we know if we're not using everything in our arsenal, we're going going to get replaced.
You know, you need to show that revenue attribution. You need to be able to focus on the meaningful, which is that higher strategic aspect that you're talking about, and not fall into that sea of sameness, which you're kind of already seeing happen right now.
I usually run an experiment every couple of weeks where I post on LinkedIn saying, one of these paragraphs was written by me and one was written by an AI.
And I always feel like like people are going to, they mark, oh, I can call that out immediately. I know that was written by an AI.
And I'd say when earlier on, when I started doing this a few months ago, there was, you know, they were mostly right.
But recently, I'd say in the past three or four times that I've shared it, everybody's wrong.
Everybody calls the one that was written by AI as more human and more on point because you can now start shaping the prompts.
You can start, you know, it's starting to learn and it's sounding more human.
But there are those little things that, You see showing up again and again, if you're not good at actually explaining what you're trying to get, you know, that the end results and working with it.
So it's an interesting kind of dynamic, but you are seeing that sea of sameness starting to appear more and more.
Adriana Gil Miner:
Yeah, that's very interesting. And you bring an important point in there about, again, like the learning, really.
And if you think about like our role as humans, right, in training, and I think, you know, I've read some things about like, the most common programming language is going to be English.
So, okay, so we need to. And one thing that really fascinated me as I've, you know, worked with ChatGPT and also, you know, been learning a lot about it is that one of the most effective ways to teach it to vary, you know, its responses or humanize more their responses is actually to humanize the prompt.
And so that changes us because we're really used to saying, hey, you know, write me, blah, blah, blah, or analyze this. And we tend to be very neutral.
But it turns out when you say to child GPT things like this is very important to me, if you can really balance your response and be caring with your tone, because it can really like make me upset.
That, uh, it, it really responds. So that's really interesting.
I mean, I would have never thought to use those type of emotional prompts in the emotional language to a, to a machine, you know, like, um, so I think this is really, again, changing to your point, like there's that even how we interact with computers, right? Like very fundamentally.
Andrew Miller:
Absolutely. Absolutely. Um, I guess if we wanted to get into some application here, and I know you talked about the predictive models, and I spoke to, what was it, Pocus, the CTO at Pocus, Isaac, last week.
And with that, they use product-led sales, and it's all about following that digital body language, but modeling it out on really a one-to-one basis as much as possible so that you have those revenue insights.
Could you maybe describe a fascinating AI application that you've come across maybe maybe within your marketing community, you know, recently?
Adriana Gil Miner:
I think one of the things that comes to mind is how AI can also, like many technologies, be a great democratizer or, you know, allow you to really compete, especially if you're a smaller company, you know, with the bigger ones.
And there's this company in Australia, Redbubble. It's an e-commerce platform for artists, actually, so close to my heart.
Um and uh there's there's one person there you know this guy josh geiser um and he he's a senior manager for crm for lifecycle and he uses um like everything we have for ai features um you know he he uh augments all of his segmentation as ai base he uses up you know a time of or ai base um time optimization.
So to like, when to have that message, he does, of course, personalized recommendations that are, you know, also machine driven.
And, and I think one of the key things is he uses AI to help him optimize the coding.
So one of the things that are really, I think, perhaps underutilized for marketers is AI is a great QA tool.
You know, so if you're, you know, many of us have to develop front end code or, you know, you have things like handlebars or, you know, your email, you can put that or you can put that through, you know, through tools like ChatGPT and actually check your, your, your code and it finds errors.
Um so josh is i think like a very resourceful and i think he embodies the the type of marketer that we want to see you know he um you know it's a it's a small shop very small team he has a huge reach um and he's been able to like drive some direct results increasing um his open rate from like 10 um 10 basis points his click rate to 30 so he's he's seen some in in a very like tactical way But you think about like, fundamentally, we still as marketers, at least today, we still need to be sending messages and engaging.
Now, how do we do that? And how do we optimize, you know, the results with that by using these tools?
You know, people like Josh are doing some really good things using it and every little bit of his workflow process. process.
Andrew Miller:
I love that. I love that. And I don't think there's enough people out there using it for core things like QA or like just reviewing your code.
One thing that I, I use it. I don't know.
I don't remember the last time that I wrote a formula for like an Excel or Google sheet because I just type it in to ChatGPT. I'm like, Hey, I'm trying to do this.
Can you give me that formula? And then I just throw it over there.
And that's been, that's been a lifesaver that has saved me so much time.
Adriana Gil Miner:
I do that too. And one other one that I recently used, I hope my team doesn't hate me for this, although I have told them.
So, you know, AI also like, again, Chowdhury PT, great summary tool.
So we just, we just completing our, you know, performance reviews.
And what I did was I fed, I anonymized it, but fed, you know, okay, this person, here's all the 360 feedback, help me, you know, give me a summary and like what are the strengths of.
Areas of opportunities. And of course I, you know, personalize it, humanize it, et cetera.
But honestly, like I just got through so much in very little time.
And, and I think that's, that's the thing that we are is just finding little places, which may be, um.
Meaningless individually but when you like kind of edit up all the little things that you can do like formulas and excels and stuff that's where and the more important thing is finding excuses to interact with chat to pt or if you have whatever platform you have like there's there are lots of sales you know like gong for example that have ai features in it so any little excuse that you can find to interact to try to put it to use that's where the real learning is going to to happen.
And that's where also we're going to advance what the applications are, find the challenges, find the limitations, the biases, all the sort of dark side, you know, so by finding those excuses.
So I love that you're doing that. And I think we all need to be a little bit more like that.
Andrew Miller:
Yeah, yeah. I mean, that's one of the things I mean, everybody's out there promoting and marketing how it can help you with your email copy.
Sure. You know, it helps you with your landing page copy. Sure.
But there's so much more that it does.
If you look at it just from even just a machine standpoint, you know, you can ask it to do those things that from a human brain standpoint, you know, it's going to take you a little while to figure out a formula.
It's going to take you a little while to find that one syntaxual error in your code because you forgot that semicolon.
You know, you don't want to sit there for six hours like, oh, I forget this one thing. It's not working. Just throw that in there and it fixes it. There's so many use use cases.
It's just pushing the boundaries and throwing it in there and saying, Hey, this helps me here.
This helps me there. And, and the example you gave with the guy at red bubble, I mean, that's phenomenal.
I mean, you're a small team, you're doing everything.
So you need to find out those quick wins, uh, however you can.
So I think that those are great, great use cases.
I think one of the big things that always comes up, uh, and, and I work with a lot of like enterprise customers and I see this when I'm talking to them is the ethical considerations considerations around AI.
There's even companies now that I believe put riders inside of their contracts when you're going through the contracting process, specifically for AI and the data privacy and how you're using it if you are an AI tool.
What ethical considerations would you say are essential in AI work?
Adriana Gil Miner:
Yeah, I think there's a couple, and I've been having conversations with a couple of people.
So I think that one very important attitude is as enthusiastic as we are, is to not adopt the idea of, hey, this is a magic, you know, solution to all my problems.
Like, it's very important that we continue to approach AI technology with a lot of skepticism.
Skepticism uh doesn't mean that it should stop you from using it but it should absolutely you should have a skepticism so i think like one of the biggest issues uh when you when we talk about you know their implicit biases or it and it is the lack of transparency that that ai has we don't understand and it is very complex you know we don't understand what goes on and so i think that That is very concerning, especially for, you know, organizations, if your audience are, you know, serving or supporting traditionally underrepresented minorities or, you know, frankly, like women, which is not an underrepresented minority, it's half the world.
And so, you know, but but those things are, you know, like, let's not forget, these systems are still built by by people and people have biases, we all have biases.
And so we need to police, we need to understand, we need to be very vigilant about what is in how much power we give, you know, how much oversight we give to these tools to decision things, especially when it comes to more life.
Life, like, you know, one of the areas where it's very promising is AI in medicine, but.
Now we're talking about like human lives, right? Not just an email, right? So I do think it's very important that we do that.
I think another big, big issue has to do with, honestly, security and all the data that it's there and like data ownership.
And this is something that we've been dealing for a long time.
And we're already seeing some pretty strong legislation coming, of course, starts in Europe, but that is quickly coming into our world.
And really, the spirit of it is to protect, you know, humans, individuals.
So, you know, of course, as a marketer, I'm always like, oh, like new compliance thing.
But the reality is like the spirit behind it is that it's trying to protect consumers, trying to protect human beings behind it and sort of like your data. data.
So that is another huge area that I think we're just wrestling with that.
I'll throw one more, which is so funny because I was actually talking to my mother, as I mentioned, she was the CIO.
So I don't get the thing that most people are like, oh, my mom doesn't understand what I do.
I just go to her for advice.
Andrew Miller:
That's great. That's phenomenal.
Adriana Gil Miner:
Yeah, yeah, it's very different. So, you know, as I mentioned, I was at Tableau for a long time, and there was a lot of in this sort of BI or data revolution that we went through about 10 years.
One of the main things that would show up is that when we made data more accessible and more visible to more people in the organization, the data integrity, which power these reports, would come out, right?
Like dirty data, incorrect data, all of that stuff.
Well, it's the same thing that's happening in AI.
And so we're not talking about like AI needs data.
And it's going to be only as so good. So, for example, you know, in today's world, it's not like clean data that we feed into these language.
We can feed documents. So when you have the document that's like final, final for real, final, final, final.
It's like, you know, like all that goes into like and just remember, like the stuff that we dump in there, it's unstructured data.
So, like, there's just a lot more room for distinguishing or, like, what data is the system using to predict, to, you know, create responses, making decisions and stuff.
So the importance of the data, the cleansing, again, the data integrity that feeds and trains these models.
I think is imperative and really important as part of the whole revolution.
Andrew Miller:
Yeah, absolutely. I guess the output is only as good as the input and the source material that it's getting.
And whenever you start throwing in, like you said, well, this is the final document to use that way.
This is the final, final, I promise you. No, no, this is the final, final. And I've done that so many times.
So whenever you're talking about that, I'm like, I'm getting flashbacks.
But that's very, very applicable.
And it makes sense. I mean, it's being trained off of what we're feeding it and it's learning and trying to iterate off of that.
But you can confuse the models and it starts to try and figure things out.
And when you went back to kind of like the biases earlier, I mean, it's been intentionally created with hallucinations so that these hallucinations aren't necessarily bad and they're not necessarily good, but they're similar to us as humans, right?
We're pulling different aspects of the data, trying to assimilate it into our mind and figure out this is the next best logical guess that I have on what this is. this is.
I mean, that's all LLM models are, right? It's language, large language models that are saying, okay, based off of this input and this language, this is probably the next word that should be used.
And then this is the next word and the next word and so forth.
So that's where those hallucinations impacted by the original code base and the coders, and then the source material that interjects inside there all creates this output that we have to be very wary and and review before we're just like, oh yeah, ship it, that's good, you know?
So there's all these layers inside there that we definitely, that you pinpointed that we have to take into consideration whenever we're working with these.
Okay, what's a groundbreaking yet underutilized AI application in your industry?
Adriana Gil Miner:
As we were talking before, especially generative AI, lots of different applications and to me are not like the big things, but all the little things that we can use in our workflow.
One opportunity that I think we're under utilizing is the power of QA, avoiding errors, not just like from a coding perspective, but for example, I mean, how many times I think every email marketer has had this challenge of like, oh, I sent it to the wrong people.
Or, you know, I set up a journey and then I automated and like, oh, that didn't quite work out.
So if we can use AI as a guardrail actually for campaigns, for testing, for, you know, actually, you know, kind of like a modeling, like what would happen if these actions happen?
I think it could radically reduce the amount of errors that we have to have that as an editor.
I mean, and even the simple thing is typos and things like we already have tools like Grammarly, another great customer of Iterable, that, you know, frankly, if you really invest in training that tool, it for us has made a huge difference in how we communicate, how we write.
And there's just like a lot of promise, I think, in error reducing, not just like augmenting us, but an error reducing applications through throughout the work force.
Andrew Miller:
Yeah, yeah, absolutely. And I know we've already talked about, you know, some of the shortfalls within AI, you know, hallucinations, you know, all these like ethical considerations.
But is there something specific that maybe pops up in your mind when you're thinking about AI is falling short in this particular way?
And how do we overcome those limitations?
Adriana Gil Miner:
Yeah, to me, it really comes back to the whole thing of transparency or what I would call what we call here iterable explainability.
The as a default, not as a thing that you have to prompt it, but as a default, the better we find, we build systems that explain what they're doing, why are they doing, what are their sources.
Is um and and uh because the biggest challenge we have is like i said you know fake information like the biases and decisions that can be made based on things that we don't understand um so to me that's the biggest thing that it's really frustrating because i feel like right out of the gate now there's there are other systems you know in uh in tropic that are doing i think a better job with with trying to bring to the forefront the explainability of how the language which, uh, or how the model works, but there, there is so much to go.
Um, and I, and I see, um, This is a problem that we've had for a little while with others, but I have two teenage daughters, and I see them interacting.
Honestly, it's impossible. Even for myself, I tend to back and check things quite a bit, but the amount is so much bigger than my human capacity to go and double-check everything.
So that that that would say if I like AI developers out there that I would really prioritize it.
We need more transparency. We need explainability of AI so that we are more informed and aware and we can uncover, you know, again, the biases, the challenges, the limitations that we talked about.
Andrew Miller:
Yeah, I love that phrase as a default. You know, you need to have that transparency.
I think a lot of the terms or the term that I've heard most often when talking to executives is, you know, AI is a black box.
We don't know where everything is. We don't know what models it's working on.
It's just this is proprietary.
It's like, oh, okay, you know, and so that makes it uncomfortable for a lot of people. Uh, and, and until that transparency, I think comes out, we're always going to have that hesitation because we don't know what, again, that, that final output is going to be, if we can really trust it.
Uh, there, there's definitely a lot of work that still needs to be, be done to create that trust in that space for sure.
Um, we, we've covered a lot so far, but I always like to do a bit of a future outlook and, you know, we, we talked about the different stages, uh, that we're going through, you you know, productivity gains, smartness with predictive, you know, models.
Maybe the next thing is backlash and then eventually like the sea of sameness. But if you were to, Keep looking further. Maybe we are in the scene of sameness very quickly, but what emerging AI trends are you most excited about?
Adriana Gil Miner:
There are two things. One thing that I mentioned is that next level of intelligent or smart, finding that needle in the haystack, especially as a marker.
We have this holy grail of individualization, right?
Right? Not just personalization, but when we get to that place where we can truly treat every single customer in your database as an individual to their personal preferences at the right time, that has been a long journey.
And so to me, I'm very excited because I think we can do that at scale with the technology.
That's really what excites me about the promise of this. And then to your point about like the second part of this is then what is going to be that recalibration of the humans and the machines working together?
I do think we're going to evolve symbiotically, just like we were talking about.
It's not just the machine is going to evolve. We are going to evolve.
We are already starting to change how we work, how we interact with computers, how we value computers, you know, and what role does it play?
So it is we are changing the computers and the computers are changing us. Right.
And so that symbiotic evolution and what is that pairing to me is very exciting in the in the next in sort of the next area.
And I will say for this generation of marketers that are entering the workplace or are in the workplace today, we have the chance to be that generation that defines those rules.
And so that pioneering spirit really fills me up because, you know, it's like, hey, I get to define, be part of a generation that defines what that's going to be like.
For the next few generations. So what an exciting opportunity for all of us to be part of.
Andrew Miller:
Absolutely. Absolutely. There's so much there. I think on your first point, the one-to-one relationship, I feel like that's something that's been promised and maybe marketed to us as marketers for a long time.
I still remember, what was it, Oracle when they launched Eloqua you know, back 10, 15 years ago, this is going to be the first time, you know, we're actually going to have the one-to-one relationship and we're going to be able to really map out that digital body language.
And you can talk to, you know, know exactly what they're doing.
Not quite, you know, and it's taken some time, but I think I do see the progress of that, that we're making and we're getting closer.
And then I think to your, to your point about about building that symbiotic relationship.
But if we apply the symbiotic relationship two to one on one, I think that the statement that always pops into my mind and and you've probably heard this as a marketer is don't be creepy, you know, because we know so much information about this individual.
We have to make sure that we're not. Hey, I know that you love friends and you saw that other episode just two nights ago.
And, you know, all this we have to like back it up and make make sure that we're still being relatable as humans without being too, too, like in their face that we know everything about them because they're, That's still a little scary.
Adriana Gil Miner:
Yeah, for sure. Permission marketing is going to become more and more important.
And it's funny because in parallel to all this power that we have for personalization and targeting and data-driven marketing, we also have a huge growth in what I would call really old ways of communicating, like social media.
Social media is really just the old word of mouth, you know, like, how did you find out your friend told you about so you heard about it?
Adriana Gil Miner:
You know, like, in, in, in, it's also interesting, you know, like billboards, and there's been a resurgence of sort of like that old advertising that's not targeted, not data driven, not a none of that.
So I do think, you know, in every technology you have also sort of like the seed and this is like, you know, a social, the seed of the counter in it as well, you know.
So I think as marketers, it's important to understand sociologically, like things are not linear.
It's not like, okay, now it's AI, so now here are all the rules.
No, you're going to have many, many more ways of communicating, engaging, and it's important not to just think about our typical digital or CRM or lifecycle tactics and channels.
We have to think about engagement and essentially the act of marketing to someone is multifaceted.
It's going to continue to be multifaceted, multi-channel, and in many, many ways, because this is just the art of influence, right?
Adriana Gil Miner:
So it is important that we don't just zero in into, you know, what is it going to do to our, you know, performance marketing function.
It's truly a change of everything that we do to market.
Andrew Miller:
Absolutely. And it's revolutionary right now, you know, like Sora coming out and you're going to be able to make these videos just through text and they're going to be so beautiful and amazing, all that.
And the speed at which we're able to ship things is, you know, 10, 100x what it used to be.
But people are going to start putting up those blinders just like they did, you know, with those interstitial ads that like pop up.
So it's going to be really important to weave in what you were talking about earlier, that story and that human element and bringing back some of what we call old school techniques, but it's more of the human to human techniques.
I've seen a lot of organizations are starting to send actual handwritten notes, no longer just through a tool that fakes this is your signature, but they're literally writing handwritten notes and mailing them out.
And that's not scalable.
You know, I mean, your arm's going to fall off if you have like 100,000 customers and you're trying to handwrite a note.
But it's a way that is differentiating them and creating, you know, cutting through the noise of all this, you know, these materials that are just being thrown at you because, hey, somebody took the time, a real human, wrote it down and sent you a letter, you know?
And that's just another touch point like you were talking about.
So I think, yeah, it's interesting how these old things, you know, nostalgic aspects come back.
Um, if we, if we look at like personal insights and lessons, you know, what, what lessons would you say you've learned in your career that you wish maybe you knew earlier?
Adriana Gil Miner:
I think, I think, um, one of the biggest ones has nothing to do with AI, but about the human connection.
Um, what, one of my biggest one is, um, to, um
Earlier on to advocate for, for others. So it's very easy for us to feel bad about advocating for ourselves, right.
Cause it's kind of weird and, and sort of, but, but in, especially with a, with a discipline like marketing, um, it's misunderstood.
Um, you know, I mean, frankly, many marketers, we don't know, um, what, what, um, what we're doing.
I mean, we're kind of like, Like, because part of the beauty of marketing is that there's always something new, you know, you can't just like repeat the same campaign, you know, like you have to like, there's always something new.
And so if you learned early on in your career, I wish I would have learned this to do, I did it much later, to advocate for the function, for the industry, for the work of others.
And, you know, sort of like really, really nurture that learning together.
I think I would have advanced. I think I would have made more progress in my own career.
And more importantly, I think I would have like build, you know, the following and the teams that you need as a leader to really transform things. You can't do it alone.
So yeah, I now spend a lot of time really spotlighting, really talking about others, learning about others and spending a lot of time outside of the walls of my own company because that's how I learn the most.
But also advocating for their own work.
And that's something I wish I would have done differently earlier, I would say.
Andrew Miller:
Absolutely. I know you've spent quite a while in the industry.
You have a lot of great learnings and insights.
Are there any maybe book recommendations or, you know, I don't know, TED Talks or, you know, there's so many different ways to, you know, consume material.
Is there anything that pops out of your mind that says that you'd recommend a one? These are the one to two things that you should read or you should, you know, go watch or something.
Adriana Gil Miner:
Yeah, look, I think in every, and this is just how my brain works, so I don't know if it's for everybody, but I, maybe this is my liberal arts background, but I do like to abstract and sort of understand the bigger forces behind things.
And there were two books from grad school that I think really changed my perspective of technology that I would recommend.
So one is a little book called The Victorian Internet.
It's very funny because it is actually the story of the telegraph, the advent of the telegraph.
And and it just it's just a gem of a little book um adam savage i think it's i can't or dan savage that's the author dan savage and um and it is just and it and the interesting thing is that it relates to what's happening you know to what the internet was i still think it's very applicable so it is hilarious and is so insightful to the human reaction to communication technology like the telegraph.
And the other one that really changed my life, and this is an extremely dense book, it's the wealth of networks, obviously based on the wealth of nations, you know, but this was what happens, and we're dealing with this today, what happens to sort of the human capacity, to when you have the wealth that networks create, you know, the internet and stuff like, like, for example, space to think or, you know, not we don't have to do so many operational things, and sort of, you know, explain some of the things about like the economy is free.
You know, like if you think about like, why do people create, you know, podcasts and blogs or Wikipedia, you know, all those phenomena.
And so they're in a system of capitalism.
You have to think about what why is the motivation? Why do someone do this?
And so again, you know, these are sort of like abstract big ones.
But I do think, you know, again, I'd recommend it to people that you want to maybe abstract a little bit to what's happening today and sort of understand the forces.
And then on the other side, I do think there are fantastic thought leaders. I tend to go to.
Engineers and technologists as this woman, Harper Carroll from Stanford.
She works at, I'm forgetting, but at an AI company that you can download your own GBTs and do that.
And she does a really great job of, frankly, explaining her TikTok, which I follow a lot.
Of explaining some basics of of ai um and and you know as a non-technologist that to me is important to understand the basics like i'm not going to go and program or develop my own llm but like what is the difference between machine learning and an llm you know like stuff like that so you can be conversant and you can kind of like um sort of like generally you know know know enough to be dangerous.
So that's when I just love how, how she is able to translate to very understandable terms what's happening.
So I would recommend her on that side.
And if you're like crazy, like me, the other thing is stuff I do, I'm a huge sci fi fan, both TV and books.
And I mean, I think, you know, reading that stuff really opens to your eyes to the the possibilities of where we could go.
And so, you know, like I know, yes, like people are, I'm obsessed with us being a simulation and I read everything about that.
So that's my other like super nerdy side.
Andrew Miller:
No, I love that. I love that. I mean, and the thing is like sci-fi, whenever you start reading older stuff in it, it's come to fruition because that's helping to set the stage for like, these are things that we can do.
You know, we can actually create this. I know it's crazy to think about it now, but give it a few years and, oh, wait, that's not crazy anymore.
We've actually done it. So I think from the creative standpoint, there's so much great stuff there.
Harper Carroll, I believe that's what you said, Harper Carroll, that's a skill set in and of itself to condense tough, very technical information into easily digestible and understandable items.
That's like a true mark of an expert. So I don't follow her, but I'm going to follow her and I'll have, you know, have her in the notes and I'm definitely going to look up these other books.
I mean, all of these are going to be in the show notes for people to check out.
I think I'm going to go on Amazon right after this and buy the Victorian internet because that sounds fun. I just, I just want to read that.
Adriana Gil Miner:
It's so great. Yeah. Yeah. Good. Good. You should.
And it's like, you can get it done in like maybe three hours, maybe. It's less. It's like this thin.
Andrew Miller:
Oh, it's, it's there. It's going to be in my shopping cart in a minute.
It. Um, what would you say is like your moonshot project for the future?
And this could be AI related.
This could be team related. This could be anything, but I usually like to just ask that, like, if you're, you're wanting to just throw something out there, that's going to be like disrupts, you know, your space, the world, however big you want to go. What, what does that look like for you?
Adriana Gil Miner:
Um, my little world of marketing. Um, I basically want to or feel like this is very soon like just getting rid of websites and that like this, the next just why do we need that?
Why do we just have an agent, you know, sort of like the Google, you know, just the search bar, but the interactive part. art.
Like, so I think like fundamentally that, that is in marketing, I think it's, it's the next big thing is just to reimagine what that interaction from the get-go is, um, what our storefronts are, you know, um, you know, at the bigger thing, I think like the two, uh, biggest opportunities that, that are, um, and I hope to, you know, in some ways being part of this is, uh, I do think that, you know, medicine, nutrition and what we can do in there for more preventative care are very fascinating.
And again, perhaps because I'm getting old, but, you know, I want to live till I'm 100, maybe 110, but 100. That's like my goal.
And I think that we're entering an era where like wellness and health is sort of like much more.
You know we're not just like it's not about our productivity it's about fundamentally our health and our ability to create um and um and so i think i think where ai can really change our lives is like what happens when a lot of the daily tasks if you think about yourself like home like 90 of our time is just like coordinating or doing stuff um what happens when we can reduce that that amount to a minimum and we have the space to have you know free time free uh you know free thinking time um that that is that i think is is sort of again the the ability to human and then i mean as like i said as a sci-fi nerd yeah i mean i the reason why i want to live to 100 is because i want to go do space travel so um so we can uh if i can get in that road uh that that That would be, that would be amazing.
Andrew Miller:
Yeah, yeah, absolutely. No, I love that. Maybe, maybe we should all just move to like the blue zones so that we can be centenarians and live a hundred plus, you know?
Adriana Gil Miner:
This is, this is, uh, my thing is like sweet potatoes, people.
That's, that's, that's the thing. There you go.
Andrew Miller:
There you go. You heard it here first. Sweet potatoes, you know, lives a hundred.
Um, how can our listeners follow, you know, what you and Iterable are doing?
Adriana Gil Miner:
Oh, that's great. Well, we are in all the social platforms and we like to joke a lot.
So follow us so you can get maybe a chuckle or two and get a little bit of education.
So Iterable is our handle.
And then I'm most active in LinkedIn. I did quit TwitterX about a year and a half ago.
Just, you know, just couldn't do it anymore. more.
And as well as like all Instagram and Facebook, I'm a lurker.
It's interesting. My kids ask me not to post anymore, which is a whole thing about them because it's their own personality and their own image.
So I look at stuff, but I know currently.
So the only place or the best place right now is LinkedIn. You get like the professional side.
So I know that would be a a good place to, to, uh, connect with me.
Andrew Miller:
Perfect. Perfect. And again, those are going to be in the show notes, uh, underneath this video and this audio podcast.
Um, the very last question that I have before, you know, we, we close out is, are there any final words of wisdom that you would like to leave with the, with the audience?
Adriana Gil Miner:
Um, you know, I am going to quote, So we were talking about Josh at Redbubble.
And he said this, he actually presented in one of our events about all his case study, just really insightful person
And he said something that really stuck with me.
And he said, the most exciting benefit of, well, he said iterable AI, but let's call it AI, is giving us more time to daydream.
And I just think that's a fabulous. So thank you, Josh, for that amazing quote. It stayed with me.
Andrew Miller:
That's amazing. That's amazing. Well, Adriana, thank you so much for being on the podcast today.
There is a lot of notes that I've written out here and I've learned a lot.
So I'm sure the audience is going to enjoy this. Appreciate your time and hope you have a great rest of your day.
Adriana Gil Miner:
Thank you so much, Andrew. Thank you for having me and all these insightful questions. It made me really think. So thank you for the opportunity.
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