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Digital Velocity Podcast Hosted by Tim Curtis and Erik Martinez

42 The Advantages of AI-Generated Analytics - Mia Umanos

This week on the Digital Velocity Podcast, Mia Umanos joins Erik and Tim to discuss the advantages of AI-generated analytics in delivering actionable insights and increasing revenue.

Oftentimes, marketers are overly focused on the data without understanding what the numbers are revealing. Mia says, “That is actually what we're doing today as an industry with the data. We are so brand-focused. You know, PPC, ROI, CPC-focused that we are not listening. We forgot the analysis part, that the whole point of having data is the empathy that we can create in our marketers as a result of the knowledge of what people are doing and clicking on.”

Marketers need to change the way data is viewed and understood to increase customer connection. Mia explains, “… I don't know if the left and the right brain are both math and listening and connecting with people, but what we do know is from storytelling that you get connected to the person that you are speaking to because you understand them because you're echoing back their fears, their anxieties, their hopes, and their dreams. And all of that is in the data on what they're clicking on. And we, as marketers, we need to retrain ourselves to create data-driven empathy. I mean, performance metrics are important, but when we're being creative, that data-driven empathy is a thing that's going to really blow everything out of the water.”

AI-generated analytics can help marketers narrow in on what is important to concentrate on. Mia says, “So, when we are automating statistical research all the time, not only are you delivering these things that matter, those three things that matter right now for conversion rates, you are also eliminating all the noise that you don't have to look at.”

Listen to this week’s episode to learn more about AI-powered analytics, analysis, and insights.

About the Guest:

Mia’s superpower is her instinct for user behavior. 

Her optimization strategies have lifted revenue $4M in 90 days, created a sustained 40% increase in advertising revenue, taken a political candidate from 4th to 2nd place in 3 months, and won a Google News Initiative Grant. She is a 14-year veteran of analytics who grew up inside digital marketing agencies like Huge, Critical Mass, and Mirum. 

During her sabbatical from digital marketing, Mia worked for 3 years for independent media to re-imagine what data could do on media sites to promote civil civic engagement.


Tim Curtis: [00:00:00] Hello, and welcome to this edition of the Digital Velocity Podcast. I'm your host, Tim Curtis from CohereOne.

Erik Martinez: And I'm Erik Martinez from Blue Tangerine.

Tim Curtis: And today is a very special day because we have a friend of mine, Mia Umanos, on the show. Mia is a 15-year veteran of marketing analytics, who grew her career from junior to director inside global digital marketing agencies.

Her talent for balancing math and intuition turns her projects into gold. She lifted revenue 20 million for one client during the pandemic through conversion rate optimization, created a sustained 40% increase in ad revenue [00:01:00] for a major publisher, and won a Google News Initiative grant to rebuild a 30 million visit per month news media platform around civic engagement versus advertising.

As CEO, Mia is pouring all that expertise into Clickvoyant's AI data analyst as a service to expand the data-driven capabilities of all humans. Mia, glad to finally have you on the show.

Mia Umanos: Thank you. It's wonderful to be here. I'm so looking forward to having this conversation today.

Tim Curtis: Good. Good. Well, I know a little bit about your story, but why don't you give the listeners a brief synopsis of your story up through founding Clickvoyant?

Mia Umanos: Oh, man. Okay. Well, this is gonna be hard to be brief, but I'm gonna do my best. Um, you know, you mentioned that I was director of analytics inside big agencies and you know, I was working on like Intel, Nissan, Mattel, like Salesforce, you name it. FullFunnel also. And in that time, like every time my side hustle at an agency was also doing the pitches.

Every month or quarter there'd be a big pitch that ruined my life [00:02:00] for a bit because I had to do my billable hours in addition. But 80/20 rule, 80% of the time analytics would get cut, and it got cut because it was just too expensive. As a result, those big agencies like it's $300 an hour with all the pad. So, brands were like, neh, you know what? We're gonna try to figure this out on our own. Just give us the website. Just give us the media campaign, and what happens later, post-launch is like, what happened? Here's 10 hours. Find out. At the end of the day, that's just not enough to even get a snowflake on the iceberg.

Data analysis takes really meaningful thought and time and cleaning. If everybody cannot afford this kind of service, we're no better than the days of when, you know, Ford was like, I think it was Ford, you know, I know that marketing is half wasted. I just don't know which half.

Tim Curtis: Right. Yeah, exactly.

Mia Umanos: Yeah. So, you know, fast forward, my co-founder and I were both directors of analytics. She at Mirum and I at Critical Mass, and Huge, [00:03:00] and we built the platform to serve as like that data analyst relationship that a marketer has. Coming to ask questions about what's going on with their data. Then Clickvoyant pulls that in and answers it in a way that is palatable and meaningful for the marketer versus alternatives which is just like, here's a dashboard piece.

Tim Curtis: Right?

Mia Umanos: So, that's where we're at today. We're now in our third year. We've just closed a fundraising round and graduated Techstars. So, we're really ramping up lots of new customers, especially in this environment where GA4 is looming and everybody's trying to figure out how to analyze it.

Tim Curtis: Yeah. Well, speaking of Google Analytics, so it makes sense that with your background in analytics and what you were doing on the agency side, all three of us here are very familiar with Google Analytics. We've worked in it for years. We're toying around with Google Analytics 4 now, trying to get ourselves acclimated to the new interface, et cetera. But what for you was the catalyst of bringing AI in with Google Analytics? [00:04:00] What was that and how did that start?

Mia Umanos: Sure. Well, computation of data and the interpretation thereof are two jobs, right? The computation piece is very easily handled by a computer. In the world of Google Analytics, I'm sure most of your listeners are barely scraping the surface of what it can do.

Tim Curtis: Yep, easily.

Mia Umanos: It's like, oh, well, I've defined my KPIs because Avinash told us a few years ago, just, these are the most important things, focus on there. But, I'm sorry to say Avinash is wrong.

Erik Martinez: What? Did you just say Avinash is wrong? That is sacrilege and we will burn you at the stake, Mia.

Tim Curtis: Sacrilege. Yeah.

Mia Umanos: I believe that the dollars are in the detail. Here's what's happening. You know, we talk about the tip of the iceberg with data. In the world of like where Don't Make Me Think was written, things like Analytics: An Hour a Day, in that world it made sense. But let's just focus on [00:05:00] a few things to help drive our business and that'll help guide us, and that's true, but we're in a world of TikTok, of Facebook, of Pinterest, of all these new things coming out. There is no human possible way to get a handle on all of the content distribution that a brand has to deal with.

If we focus on like a few metrics that we care about, we're missing the deeper meaning of what people are doing. Not users, but people. When I say to my clients, the dollars are in the details, what I actually mean are the promise of big data was always that we could get to a one-to-one communication. Right? But that promise and our practices of get a dashboard, you checked the box, they don't match up in the middle.

The AI that Clickvoyant has built is allowing a marketer to be in all those [00:06:00] places at once, right? To say, I'm gonna be in device types, I'm gonna be in DMAs, I'm gonna be in these pages and those clicks so that I can then bubble it up to the top and say, here's the X on the ground. Clickvoyant says, this is the X on the ground where the money is buried, and now marketer, you could spend your time in the value-added activities.

Tim Curtis: Can you imagine the spend if you had an analyst manually try to do all that?

Mia Umanos: Oh, I've written those proposals.

Tim Curtis: Oh, I can't imagine.

Erik Martinez: I remember one time somebody was telling me there was this company that they worked with and they did a data analytics project. What they do, they come in, they'd spend a day and a half with a team, and that first day it was just data gathering. And then they'd take all this stuff, all the recordings from the meeting and everything, and they'd dump it to a team in India that night. They would put a massive team on that, and then the next [00:07:00] day they'd have a thorough analysis of all that data. It's kind of crazy, but it was a million-dollar project. Like you just spent a million dollars in one day.

Tim Curtis: Yeah, and you've solved for that.

Mia Umanos: Sure. I mean, and think about like how many brands, and often like the million dollars is the start, Erik.

Erik Martinez: Right.

Mia Umanos: It goes way up from there. Data architecture, building data lake, like we're gonna get all this in one place and then we're gonna, you know, send it offshore to, you know, maybe an analyst who may or may not understand the culture of why the behavior is behaving that way, why the data looks that way. Also, there's no industry standard definition of what an insight even is when you say that. Have you guys checked out the Google Analytics Instant Insights?

Tim Curtis: I've played around with it.

Mia Umanos: Well, first of all, it'll always say, raise your budget.

Tim Curtis: Yes. Yeah, exactly.

Erik Martinez: Yeah, that's always the first recommendation.

Tim Curtis: That's their [00:08:00] answer and that's the problem. Which is why we're talking because that's their solution right, to every problem.

Mia Umanos: Their solution to every problem, increase your budget 20%. It will never tell you, move budget from this DMA to that better performing DMA, and you will get extra, you know, 20% on the dollar of your investment. It would never say that.

Erik Martinez: As you're talking about that, what is the biggest issue or what is the biggest topic in the world of data analytics? I mean, you've just talked about, we used to have just a couple of platforms. Now we've got 20 platforms that were distributing content on and lots of different ways and different devices and all those things. But underneath that is a lot of structure and infrastructure, and I don't think people quite understand even to do what I think is a relatively simple lifetime value report requires a ton of work to get the data in [00:09:00] shape to even get that simple calculation.

Mia Umanos: Oh yeah. And well actually, Erik, you're not far off. It is simple, but because we have technology, we've made it incredibly difficult. So, the world that we're talking about is like, okay, all this bifurcate data is all over the place. We have 10,000 marketing technologies right now, all creating customer data on clicks, emails, whatever. The solution that we've been taught is big data. The solution that we've been taught is that million-dollar project where we collect it all in one place. That solution is three years in the making.

Actually, I'd said this at a conference last year. I said, who's out there building a data lake? Right? And I said, who's been waiting two years for the data lake to be ready? And so many chuckles from the audience because from an enterprise standpoint, not only is that data lake or data warehouse project years in the making, oftentimes is because the data talent pool is so competitive that the person running it leaves before its done [00:10:00] and some other cat has to come in, take it over.

Tim Curtis: All the time. All the time. All the time.

Mia Umanos: They take it over and then they're, oh, but actually let's take it because this is a better idea cause I'm the new cat and I need to make my mark and then they leave. What Clickvoyants's thesis is, is that we don't need big data. We actually need boutique data. We need boutique data in the sense that to get the lifetime value, to get to what correlates to down funnel milestones, you actually need only the data that you need from the different places to pull that data together to answer that question.

And then when we wanna answer another question, then we get the boutique pieces of data that we need to answer that question. And that is the underlying industry wish of every director of analytics out there. I don't care. Put me in front of a line of directors of analytics and tell me that they will not say to my face, I wish we would analyze the data [00:11:00] that we have already.

Erik Martinez: It's funny that you say that because what just went through my mind was the data lake will allow us to do all this incredible analysis and they end up, because it's so much data, they just build reports everybody gets really excited about for two or three days, or two or three looks, and then they set it aside and they put it away and they never look at it again until next year's meeting. They're not really looking at this stuff. So, you're pivoting the strategy.

Mia Umanos: Sure.

Erik Martinez: Really say, Hey, I'm answering one question at a time, and this is the data that I need to answer that question and get the answer. That's brilliant.

Mia Umanos: Analysis and analytics and insights are not that different from marketing needs, right message, right person, right time, right place. So, you know, let's talk about that. Let's pick it apart for a second. Right message. If I am a marketer that's like a hundred percent focused on, maybe I'm taking a brand to market and they just really need to focus on [00:12:00] awareness campaigns. If you come to that marketer with an insight about what is going on in the CRM, they don't care. Right? That's not my strategy right now. It's not my focus right now.

Let's talk about right person. So, we've done a lot of jobs to be done interviews with our customers, and that's mostly marketers. What they say most often about a dashboard, whether it's like Looker, you know, BI reports. It's like, you know what? These are all really interesting, but it's not relevant to me. It doesn't tell me what I need to do about my job today. So, users increased. Okay, great. Why? Who cares? Right? The number of people who visited that blog post or users from Canada tripled. That's what insights look like, and that's what dashboards give you.

Okay, we talked about right time, right person. Oh, right place. I've made a career of building these dashboards that come from data lakes. You know, getting into like supermetrics, putting it all together. As an experiment, you can put a pixel on those [00:13:00] dashboards. And I can tell you that my experience has been zero looks, zero views until the day of the meeting. When Mia is coming into the boardroom and sharing the insights is the moment that they look at that dashboard. It's not a daily activity. I think the issue that all of the analytics tools are trying to solve for hasn't addressed the one major thing, and that is the relationship that marketers have with data.

Erik Martinez: Right. We have that conversation all the time in inside our organization because like many agencies, we have a monthly reporting cycle and a quarterly reporting cycle, and my team, I love them to death, but they get all like frantic about the reporting cycle. It's become a thing. They made it a bigger thing than it needs to be, and we're like, no. We're slowly pulling 'em back and saying, look, it isn't about the 20-page report. What are the two or three [00:14:00] things that are most meaningful out of those 20 pages? People can read the 20 pages later, but what our clients want is us to tell them what they need to do.

Mia Umanos: Yes.

Erik Martinez: What action needs to be taken? So, I think you make a fantastic point that all the analytics tools and the big data, we've created a thing, we've created a monster. We've been guilty of it. We're trying to uh, wrestle the monster and bring him back under control.

Mia Umanos: It's an industry practice. So, we learned how to create fire 20 years ago, and Harvard Business Review said this was gonna be the sexiest job of the 21st century. You know, 20 years later that's like the tech job that's actually quitting with the most frequency. We've invented fire, but we haven't harnessed it yet. You know, you've mentioned like, oh, everybody gets up in arms in reporting, and the level of effort that it takes to do that, you only can do it on a quarterly basis.

But when you say, oh, well, we're actually coming away with three things that are the most important [00:15:00] thing, we should be doing that once a month. We should be doing that once a month. These are the three things that we're gonna work on. That's why we, our thesis and the way we're approaching software creation is not just about a report. I mean, certainly, we started out that way. We started out that way. Cause at the agency side, the billable hour was dedicated to making reports and it was like 40 hours for this dashboard, and then we would have maybe a 10 or 20-hour deep dive as a result of that.

And here's what would happen all the time. Okay, we took 40 hours to build this dashboard. Client has questions. Okay, you can take 10 hours to do that, between a junior and a director of analytics, 10 hours to answer that question. So, we go down that path. We answer that question, and you know what? It's not statistically significant research. So, we as an agency have that embarrassing moment where we gotta go, I'm gonna go present this knowing that the end answer is, I don't know.

Erik Martinez: Right?

Mia Umanos: Right. You're gonna say, here's what the data says, but in the end, it doesn't mean anything. [00:16:00] So, when we are automating statistical research all the time, not only are you like delivering these things that matter, those three things that matter right now for conversion rates, you are also eliminating all the noise that you don't have to look at.

Erik Martinez: Yeah.

Mia Umanos: Oh, Clickvoyant's been down that path. There's no there, there. Let's just move on these nuggets.

Tim Curtis: When you're talking about marketing and marketing analytics and you're talking about all that data, one of the things that I've always tried to stress, not only to our internal team at CohereOne but also just over the course of my career, is that it doesn't end with data, right? One of the things that I've always appreciated about you is you understand that the left and the right brain both play a role in the analytics, and you've got to contextualize that.

Nancy Duarte, who's one of my favorite authors and speakers, she talks oftentimes about forming a data point of view. It is taking the data, but it's [00:17:00] also understanding the other elements of what the data may be telling us. You've always really key in on that. So, for you, why was that so important?

Mia Umanos: You know, I started my career, Tim, as a journalist, actually. Pretty quickly honed my skills for listening to people. There was one moment where as a junior journalist, I was interviewing this person who wrote a book about biological invasion of invasive species. And I'm interviewing this person, I play back the tape. I've got this data, I'm playing back the tape, and I listen to myself talking all over his answers. I am just interrupting, finishing his sentences. That was an incredible learning moment to me.

That is actually what we're doing today as an industry with the data. We are so brand-focused. You know, PPC, ROI, CPC-focused that we are not listening. We forgot the analysis part, that the whole point of having data is the [00:18:00] empathy that we can create in our marketers as a result of the knowledge of what people are doing and clicking on.

The creative piece, your creative process probably similar. You go in, you listen to a couple of customers, you listen to the brand, you try to figure out what they're trying to do, and you get really deep into the why and the motivations for why people buy a yoga skirt. I'll never understand it, but people do. Why people wanna change to a new product or get another Peloton?

Tim Curtis: Right.

Mia Umanos: Why do they do these things? And it's not related to the product itself. It's related to their body image or their competitor with their neighbor. They're keeping up with the Joneses. We're not doing in data. We're not doing that anymore cuz we're sowing infrastructure, performance metrics. So, the empathy piece is what will create that killer tear-jerker straight to the heart ad creative.[00:19:00]

The left brain, right brain, the math and listening, I don't know if the left and the right brain are both math and listening and connecting with people, but what we do know is from storytelling that you get connected to the person that you are speaking to because you understand them, because you're echoing back their fears, their anxieties, their hopes, and their dreams. And all of that is in the data on what they're clicking on. And we as marketers we need to retrain ourselves to create data-driven empathy. I mean, performance metrics are important, but when we're being creative, that data-driven empathy is a thing that's gonna really like blow everything out of the water.

Tim Curtis: It's so much data, right? No one is lacking for data, at least they shouldn't be lacking for data. We have data just coming out of everywhere. Part of what I see is the inability to articulate a point of view, or to understand from the empathy, what the narrative is that the [00:20:00] data's telling us.

When we're examining the data, we're knee-deep in the data, what happens is you start to recognize that some form of paralysis begins to set in. Oftentimes what I see, it's not just the analyst, but I see executives in the C-suite who also have reached a point of paralysis and where they're really not able to always articulate what's happening in the data. And that was one of the big learnings was when we're working with a client, you need to be able to help them see what is happening with the business. Take some of the elements or talking track that you mentioned and sort of build on that with the data. Any tricks for avoiding that paralysis? I mean, that's a real deal.

Mia Umanos: Absolutely. I mean get this. This is so interesting. So, the first iteration of the Clickvoyant software, what it would do is, you know, it connects to Google Analytics, it connects to HubSpot. It then analyzes all the data for what is 95% statistically significant, proven to move the needle on revenue. What we found in our user metrics is that if there were more than 10 slides, people [00:21:00] shut down. I don't know what to do.

Tim Curtis: Absolutely.

Mia Umanos: I don't know what to do. I'm like, no, there are 10 things that you could do right now to increase your conversion rate and revenue. Then I had to, you know, get really deep underneath it. Again, people's relationship with the data. So, what we did in the software was actually just narrow it. These are the top five. These are the top five things that you can do. Never mind that there are 17 other things here.

What we've done inside the software, and you haven't seen this yet, I don't think. It was just released in November, but, so all of the insights that we come up with, it's ranked by revenue opportunity in one tab. In another tab, it's filtered by the digital marketing component that should work on it, UX, conversionary optimization, technology, media. These are the activities that they can do. And then the third tab is showing insights by part of the customer journey. It's almost like a customer journey combine board where you can then see the insights by what part of the customer journey do I care about.

Again, that decision paralysis, it's a real thing. These executives, they have to make [00:22:00] decisions all day long. When you're presenting data to them, you have to present data to them in a way that it feels like they are going to hang up on you. You might have a 30-minute meeting, but you actually have five minutes to capture their attention. You need to come out of the gate with those three things, five things, or it'll shut down.

And also, at the end of the day, the relationship with the data is, I don't necessarily wanna be told what to do. I would like this to be a relationship, a conversation. I wanna be able to influence. So, that's the psychology behind the data-driven action. So, like if you can grab the attention in the first five minutes on those top three things, the next thing is leading somebody to come to a decision on what the marketing strategy is gonna be.

This is also the failure of ML right now. You know, we've met at the Marketing AI Institute. There's lots of really cool stuff. But the reason why there isn't widespread adoption is that there is a trust issue and [00:23:00] accountability. When you say, I'm gonna leave my whole email marketing campaign up to a computer, I as a marketer don't have any accountability for when something goes south or worse. We've invested in it and it's flat.

Tim Curtis: Right, exactly. Performance doesn't follow up. Yeah.

Erik Martinez: Right.

Mia Umanos: You can't explain why. And you know what? Neither can the ML. It's a black box. We know the mechanisms for why. We might be able to explain the science behind it, but you will never be able to get to why did users do that. The empathy isn't there. That's the piece that we're missing.

Preparing your client's relationship with the data, it relies on those two things. One, you're meaningful. You don't come at it with a 40-page report in that meeting, and we're gonna talk about these three things. And then the next piece is helping them come to the decision-making, repairing their relationship with their own data that helps really get the data-driven actions in play.

Tim Curtis: Yeah. So, you know, there's two things that strike me right there, from the conversation. So, three actually. MAICON, great conference. If you haven't [00:24:00] been, put it on your radar. But AI, let's just take it AI for just a second. I go back to some of the history of AI, specifically in paid advertising, and how Level Agency, from 2012 to 2020, was testing the human bidding versus the AI bidding.

In 2021, the AI finally won, but unfortunately, by that point in time, so many CMOs and executives had walked away from AI saying it doesn't work. It just, in reality, it does. It just takes time. In some cases, a lot of time for that machine learning to really learn enough to be able to understand the context of what's exactly happening.

And the second part of that is the trust factor, which at MAICON, we talked and heard a lot about really the ethics. And I think at the end of the day, one of the big questions everyone has when you're talking about whether it's machine learning, traditional AI, what people are really worried about is the ethical [00:25:00] impact. If we can do a thing with AI, should we do that thing? Do you guys struggle with that, or do you run that through your gauntlet as well when you're working with a client?

Mia Umanos: Absolutely. I mean, that is on our manifesto. I mean, I don't know if you know this, but I ran an analytics campaign for a Nobel Peace Prize winner. I met her when I was running analytics for a presidential campaign. And then I met her when we, you know, together kind of uncovered the Cambridge Analytica scandals and this election meddling and social media.

Since then, I guess to some degree I've always known that the knowledge of analytics and AI is a bit of, you know, like knowledge of the dark arts. And so that is definitely a place that Kate and I, whenever we're going to build something, we think about what we're gonna do. So, we're analyzing customer behavior, your user behavior. We are telling brands what customers are doing to help them buy more product, you know, help them develop more loyalty. Things [00:26:00] like that could be used to, you know, drive an 18-year-old boy to buy a gun. Those are things that we talk about all the time.

We draw a clear line in the sand. Clickvoyant, you know, won't accept like any political customers. We won't accept anything related to guns or weapons. That is a company culture choice. I grew up with guns. My dad hunted. I hunted. My brother still has a small arsenal in Florida. But, you know, in terms of like a greater good, the greatest good that we could do is to, you know, inhibit those types of transactions, enabling that on our platform. So, you know, I think every company is going to air on wherever their culture leans.

I don't know if you saw in Time Magazine, they did an article on ChatGPT. The people who worked on the mitigation of any kind of risk in ChatGPT were making less than $2 a day in Kenya.

Tim Curtis: No, I did not read that. That's fascinating.

Erik Martinez: No, I did not read that. That's interesting.

Mia Umanos: [00:27:00] Go take a look at that read. I mean, to be able to train an AI to filter out horrific things on the internet, a human has to go look at those things and say, this is not appropriate.

Tim Curtis: Right.

Mia Umanos: And so they had like a team of people in Kenya making less or equal to what a receptionist would make in Kenya to expose themselves to horrible, horrific internet stuff. I mean, you know, I ran analytics in a political campaign. I know how horrible that is. And so they're getting paid the same as a receptionist to do that kind of work and now, chatGPT just sold to Microsoft.

Tim Curtis: Yeah.

Mia Umanos: So, you know, when we think about like, not only like what will the AI do, but the measures that we take to make AI safe, also should come into question. I definitely think that there is a learning curve for all of us. Technology is moving faster than humans can [00:28:00] evolve. In some ways that's part of why we build the things that we build. It's like the humanities feather in the cap that we can make these inventions, and also our downfall that sometimes we do it without thinking.

Tim Curtis: We're getting to that point where for all those companies or all those organizations that are utilizing AI and governments, by the way, utilizing AI, where the conversation's going and we're gonna have a lot more conversations related to ethics and morals and boundaries, et cetera. But it just takes a bad actor. It takes one bad actor to really utilize the power of AI to do something nefarious. That's what we're all, I think, kind of up against. So, we'll see. There's gonna be a whole lot more guardrails defined in the years to come related to AI. Yeah.

Mia Umanos: Sure. And you know, I think that one bad actor is the person that teaches us. If you believe in harmony of the universe, like, you know, there can't be a Jedi without the Sith, there can't be the dark without the light, then that one bad actor will be the person who actually gives the rest of the world the playbook.

Erik Martinez: Right. [00:29:00] Yeah, absolutely. Let's pivot that a little bit back into the human realm and the impact of AI on human beings. I was scanning headlines on my phone this morning, and we've all heard about the Microsoft layoff of 10,000 people, and the headline read Microsoft Laying Off 10,000 People because they're gonna get these huge efficiencies from the use of artificial intelligence, right?

Mia Umanos: Mm-hmm.

Erik Martinez: And I remember talking to a friend, a friend of mine, who is actually writing software for a genetic sequencing company. They do all the genome research and the data processing and they help companies kind of figure that out. We were talking about this just a couple years ago actually. He postulated that you know, at some point in time, humans should have more time to enjoy life because automation technologies, so I'm gonna use the term automation here, will allow you to free yourselves up.

[00:30:00] But in the short term where we're making this transition, there's real human impacts in terms of, Hey, you know what? Ten thousand people just lost their job, at least on the surface of that, Microsoft saying, because they're gonna improve efficiencies through the use of AI. There may be a lot of other reasons, but that's at least on the surface, and I think we're gonna hear that narrative more and more and more. Just walk into a McDonald's and the use of kiosks.

Tim Curtis: Or through the drive-thru.

Erik Martinez: Right. More and more with fewer and fewer people. We order on the app, we're doing more and more of the heavy lifting for these businesses to provide their services. So, the question is for you, how is this gonna play out? What are the potential outcomes of this over the next decade, decade, and a half? What are we gonna see in our society and how's that gonna impact poverty and wealth and all those different [00:31:00] aspects? Is it gonna increase the wealth gap or will we get to a point as a society where we're gonna say, Hey, that's not right?

Mia Umanos: Mm-hmm. I like this because it's like, you know, we get to dream about what we wanna see in the world. First of all, I would be lying if I didn't say that we built Clickvoyant because I wanted to surf more. I do not wanna answer the same questions over and over again.

Tim Curtis: Fair enough. Fair enough.

Erik Martinez: That is very, very fair.

Mia Umanos: But the reality is that when my robot does my work for me, if I find something that I'm doing repetitive, I love to get that programmed in the Clickvoyant because I can spend my brain on things that are bigger, stickier problems. I could spend my psychic energy solving, you know, what if that cashier who doesn't have her job anymore is the person who has time now to study in this fictional world where we have free education and it becomes a person to [00:32:00] solve world hunger. You know what fascinated me about the movie Hidden Figures? Have you seen it?

Tim Curtis: I love that movie.

Erik Martinez: I'm not sure I've seen that one.

Mia Umanos: It's about the NASA, the women who helped calculate the returning of Apollo.

Erik Martinez: Oh, yeah, yeah, yeah. I did see that.

Mia Umanos: You know, what their job was called? A computer and it just struck me. Think about that. Your job was to be a computer and NASA didn't slow down. Now, they're doing amazing things and I think that that is actually my vision of what the future can bring, is that when we get ourselves out of these more mundane type of jobs, that we create jobs that actually realize the human potential of everybody. You should read the antithesis of that, which is Sapiens. Have you read that book? The Brief History of Humankind by Yuval Harari.

Tim Curtis: Yep.

Mia Umanos: I love it because it's post-apocalyptic, like the feeling about like, AI's gonna do this and we're just gonna become a bunch of listless jerks. I mean, maybe.

Erik Martinez: Well, that [00:33:00] is a possibility, I guess.

Tim Curtis: That's one outcome.

Erik Martinez: I think that vision you have is fantastic. I'm worried about the short term, right? Because our economic models, the way we live today, are not set up for that society yet.

Mia Umanos: I understand that. And I can only speak to this on something specific to Clickvoyant. Clickvoyant has gone on this war up against dashboards, against the way we do analytics, but it's because we haven't, as humans, created that empathy. We're focused in the wrong place. If we need humans, then we go to this offshore resource, right?

I'm saying that there is a wall of misunderstanding from marketers on this side because we're doing data in the wrong way. And there's a wall of users who are performing these human tasks in offshore resources who don't understand American cultures delivering insights.

Tim Curtis: Yep. Exactly.

Mia Umanos: If Clickvoyant can be the product that creates this empathy [00:34:00] in a universe where, by the way, there isn't enough people to do that job, then we're using the AI to level up the human critical thinking about what analytics actually is.

Tim Curtis: Yeah.

Erik Martinez: Yeah.

Tim Curtis: Well, Mia, it's been great to have you on the show. What's a good way for people to reach out to you if they need to do

Mia Umanos: Yeah, I mean, you know, I'm the only Mia Umanos in, um, the Google sphere. So, Google Mia Umanos, and you can actually get to my email, like you can actually schedule a meeting with me at

Tim Curtis: Well, and everybody should go out and take a look at You'll do yourself quite well to do that and become familiar with what the future's gonna start looking like. Again, Mia, thank you so much for coming on the show. This is your host, Tim Curtis from CohereOne.

Erik Martinez: And I'm Erik Martinez from Blue Tangerine.


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