Today, at least, if not five years ago, if you're not doing performance marketing, you are making a big strategic mistake. My, my first advice to companies who haven't really embraced performance marketing or digital marketing is there is no valid reason today not to be in that space. But if you don't, like you rightly said, it is a disadvantage. You will be left behind. Make no mistake. Today's world, it's not a choice.
Hi, this is the marketing meeting, and I'm your host Itir Eraslan. Every two weeks I meet with experts and we talk about topics related to brands, marketing, and businesses. We sometimes add random lifestyle topics too. I hope you enjoy the show. Welcome to the Marketing Meeting Podcast. And I'm happy to host Anindya Ghose today. He's a professor at NYU Stern, specializing in quantitative marketing and tech economics.
His first book, TAP, Unlocking the Mobile Economy, is one of my favorite books, but it's also a bestseller and an award winning book. Uh, now I'm really excited that, uh, his second book is coming up, uh, which is tribe, maximizing well being in the age of AI. And we'll talk about this, this episode. Welcome, Anindya.
Thank you. Thank you for having me. Looking forward to the conversation.
Um, I learned that you are an avid high altitude mountaineer. Uh, what are some of the places that you've climbed?
Yeah, that's true. I'm very, very, you know, I guess, passionate about mountaineering. Um, I have climbed in all five continents. Uh, you know, the Himalayas, Andes, Alps, uh, lots in the Rockies and Cascades. And of course there's Kilimanjaro. Um, yeah, I mean, I guess, you know, I started climbing when I was 22, uh, my undergrad education was in the Northern part of India, which is very close to the Himalayas.
And so basically every other weekend I disappear in the mountains and I realized that there is a certain draw, a certain appeal to the mountain that really, You know, his song. And I think it was just, uh, I guess natural that the love, the love was mutual.
And you said you climbed with your, uh, daughter lately.
Yeah. So my daughter, uh, she's 15 when she was four years old, we took out of the Alps in Europe and. You know, she did a a moderate climb pretty effortlessly and I realized both my wife and I realized like maybe she's going to be A natural too. So ever since she's been four, she's been doing a lot of mostly like long day hikes and multi day tracks But I think Last year, she climbed Mount Fuji in Japan with me. And then, uh, she climbed, uh, 14 hour year in Colorado with me.
Uh, and then this year, we climbed Tibet. And she, you know, reached her personal best, almost, uh, 14, 500 feet.
Wow. Uh, it's, it's exciting to hear that and then maybe later on after the episode, I'm going to ask you how can we go into climbing in New York City? I, I'm just looking all these, uh, glass cleaners nowadays, you know, it's summertime and everyone is cleaning their doors. And, uh, when sometimes when I see, see them sitting on just like one plate. It's just like scaring me a lot. It's
a tough job. They have a tough, risky job. You know, I, I appreciate the work they do and they have to be harnessed up all the time. And it's a really risky and tough job. So a lot of kudos to those people.
Yeah. Yeah, I agree. So from there, I'm jumping on to the topic, which is AI. Uh, and before I, uh, you know, ask questions about the book, upcoming book, can you tell me how does. AI differ from previous technologies, from its technological advancements? And how is it similar?
Sure. So, um, I think of AI as another GPT, a general purpose technology. Uh, you know, every new GPT comes with its own set of challenges and opportunities. You know, by construction, they often like redefine industries. They make new farms. They make certain jobs obsolete, but they also increase productivity. What is different about AI is that it's significantly more layered and more complex and intangible than prior GPTs, you know, like think about electricity or cars, right?
Uh, when people talk about electricity, you could see the light switching on and off, but you can't see AI. You know, when people talk about transportation and computing, you can see a computer working, you can see the cars moving, you don't see AI. So AI is more intangible. And so this is the reason why we see these extreme narratives that swing from dystopia like AI is going to take over all our jobs to utopia, you know, depending on who's narrating.
And so, um, you know, that's sort of the reason why we wrote this book to kind of lift the hood of AI for regular people. I'm using the context of how it's actually embedded in, you know, everyday life. Um, in things to care about like health and education and work and relationships. I can talk more about
that. What was the driving force to write this book thrive maximizing well being in the age of AI? Because I was curious about the. Especially the second part, maximizing well being,
uh,
what made you put that headline to the book's name?
Right. So, so the, the book is a joint work with me and professor Ravi Bhatna. And both Ravi and I have been immersed in the AI space for the last 20 years, you know, through consulting and research and other kinds of projects. So we've had a very hands on experience and actually coding, building the models, the classifiers. So we've seen the good, the bad and the ugly. And we realize that all around the world, when people talk about AI, the only narrative we see is this.
Negative fear mongering is kind of a cottage industry that's trying to create a lot of fear And so we're not dismissing that they can be negative, but there's a lot of positives and so what both of you and I realized there is a market there should be a demand for a book that also Leaving the media's hunt for eyeballs and clicks, you know, we know that negativity sells, right?
So that's why it's easy to write negative stories But we also feel that look ai is here to stay and we believe that an informed and educated Citizenry is the missing ingredient for us to get ai to work for the benefit of society, you know For instance people don't realize but we actually give a lot of details in our book that ai is actually keeping us more safe Then what many people would imagine. So that's sort of the motivation for writing this book.
In the book, uh, you talk a lot about the, um, House of AI, uh, framework. Right. Uh, and I read an article this morning about that, uh, online. It was like an introduction to House of AI. And I would like to touch base on House of AI. What that is. Uh, that mean and how it works.
Sure. Sure. So, so that is one of my favorite parts of the book. Uh, and partly because it comes Mai's joint hands on work with companies. So. You know, one prevalent misconception about AI is that people equate AI with something like ChatGPT. So, ChatGPT is an indeed an impressive large language model. It's developed by OpenAI and Microsoft and so on. But that's just one small facet of artificial intelligence.
I've worked with more than 50 companies at this point and in different parts of the world. In our experience, AI encompasses a much wider range of, you know, uh, technologies and applications, including like Additional machine learning and like involving prediction and optimization, causal analytics, prescriptive optimization, uh, deep learning. So what we did is we said, Hey, look, uh, I also run the, uh, AI program at NYU, which is part of my day job.
Uh, being a professor, I am the director of the master's AI program. And so in that capacity, also, I have to be heavily hands on involved in the real world with companies. Because our students do these capstone projects that are year long projects. Uh, and these are not just like some, uh, artificial data and analytics project. These are real companies where they solve real problems.
And so in doing all of that, what we realize is that we need a proper framework to help organizations adopt and adapt to AI. And so what the house of AI does is that it, it provides a strong foundation, uh, at the bottom, the foundational layer is data engineering.
Um, and you know, between him and I, like we have like two, Four decades of experience and we believe both of us believe that between 60 to 70 Of the project time in analytics should be in data engineering Okay, so if you're wondering what is data engineering data engineering means cleaning the data aggregating the data You know curating it in a way where you account for missing variables missing observations, you know, because If you don't do the
cleaning of the data, then there's garbage in garbage out problem in AI, right?
To be able to make meaningful inferences from your AI models, you need to be able to clean the data And so we strongly believe that two thirds of your time should be spent in that And then the rest of the house of AI is basically placing the spotlight on translation very cogent translation of AI applications into business and societal value in a fair and an equitable way Uh, and so there are these four pillars of AI, which I can talk about it in
depth. Just one thing, Ben, you say two thirds of this should be spent in data engineering, which is like cleaning the data, harnessing the data. Does that mean like when a company adopts AI, uh, that house of AI work should be mainly about two thirds of it, mainly about data cleaning? Or Does it involve, let's say if marketing is using AI, then it means like two thirds of marketing needs to be data engineering. Right. Can you elaborate on that? Yeah, it's
a very, very good question. Actually, the answer is both. So it depends on, you know, the structure of the organization and how integrated the different disciplines are versus many organizations have marketing and accounting and operations and advertising and finance as silos.
And so they have they do not have a centralized data infrastructure They have siloed infrastructure on other and many organizations have an integrated centralized data base or data infrastructure where All disciplines pull from that infrastructure So if you are the farmer, that means you will have to do the data cleaning job for each individual functional discipline Okay, so marketing will have to do it on its own In operation, you'll have to do it on its own.
Finance, you'll have to do it on its own. But if you are centralized, right, then you have to do it in one place and then every discipline can pull the clean data and then they can do the model building, you know, the prescriptive analytics, the causal analytics. the descriptive, all of that can be done after that.
So in other words, what you're saying is irrespective of whether you are centralized or siloed, okay, you have to spend two thirds of your time, in our opinion, two thirds of your time doing the data engineering, cleaning the data, curating it, you know, creating it, et cetera, making it. Processable so that these AI based models can then plug and play.
In terms of, when you say about data engineering, does it, uh, does it vary? Because I'm asking, uh, sometimes 101 AI questions. So bear with me on that.
But, uh, Does it differ from one company to the other on, for example, let's say two companies, they are doing both centralized data engineering, but, uh, the way that how you data engineer your data, does it depend on your, you know, strategy on your goal on what you want to get out of this data or that data engineering and cleansing the data is. Is that very, uh, like plug and play work?
No, it has to be customized. So it has to be customized. Let's just take marketing as an example. You know, B2C marketing, as you very well know, is very different from B2B marketing. And part of the reason is because if you're doing data driven marketing, you have to start with a unit of analysis. In b2b the unit of analysis is typically another decision maker, which is a company not a consumer in b2c The unit of analysis is typically a customer level a consumer level data.
So just based on that itself There's going to be meaningful differences in how you do data engineering because you know When you have disaggregate data and b2c level you can do a lot more with disaggregate data as opposed to aggregated data in b2b In other words You cannot disaggregate aggregate data, but you can aggregate disaggregate data, and I don't know if this is clear, but you can basically go from a smaller unit to a more broader unit, but you cannot go
from a broader unit to a smaller unit. You know, people often ask me like, why is B2B marketing behind B2C? That is the reason. The reason is because of the fundamental infrastructure of the unit of data. Okay? Mm-Hmm. . So B2C has a lot more, you know, uh, juice or fuel because they are individual customer level data. You can do a lot more with that.
Okay. Yeah. That, that makes sense right now, um, my links question is one, after the data engineering, I know that there's like the descriptive, predictive, casual and prescriptive stage, right? Do, do you call them stage or is it like. Segments under the house.
Good question. I don't think of them as stages. Mostly, I think of them as individual pillars. That being said, there is some sequence. So now in my recommendation in the in the book, also, we say the first pillar should be descriptive. At this point, you're not building any fancy models. You're essentially doing what we call ice fishing, you know, fishing in a frozen lake looking for one fish.
Meaning that you're exploring the data, look for some patterns, thinking about what questions can you answer that? That's a descriptive pillar. Then the next one is either predictive or causal. Predictive means, uh, what will happen next? Okay, so descriptive is answering one simple question, what has happened so far? And predictive will answer the question, what will happen next? And causal will answer the question. Why did something happen?
And then finally is prescriptive, which is what should we do next? But that's the sequence we go. What has happened so far? What can happen next? Why did it happen? And then what should we do? Okay, so those are four And
I mean, thinking about the explanations that you gave me, I feel that especially on the prescriptive side, then human beings will play an important role because then how should we respond is something that's probably AI would be able to suggest, but then it's up to the human being to decide based on. Strategic level, right?
Yeah, so that's an interesting question. You know, uh, one of my co authors, uh, professor Avi Goldbart he wrote a very interesting book with a lot of people where they said basically that as the prediction power of this economy improves The value of intuition actually goes up not goes down And so they complementarity between prediction and human intuition and I also think the same they're not substitutes You Uh, I do think that as AI becomes more sophisticated, there are going to be
many elements and many contexts in which human intervention becomes more important, not less important, which is the same reason why we believe that we had to write this book to dispel the fear that AI will just replace jobs. They're not going to replace jobs. They will obviously, some jobs become obsolete, but they also create new jobs. In addition, the existing jobs. They will simply change the role of the individual human being. You know, a good example I give is ATM machines.
You know, 20 years ago, when ATM machines came everybody around us told, Oh, you know, the tellers, the cash tellers are gone. Their jobs are gone. They're not gone. Their jobs are not different. So that's how I think about AI and humans.
Um, that was an, that was an interesting example from yesterday. I was. You know, questioning the meaning of life, uh, while I was waiting for a live chat to happen. As a marketer, I'm familiar with AI and so on, but whenever I open a chatbot, which is a part of an AI outcome, it's like a product at the end of the day. But when I open a chatbot, uh, I was trying to change a flight, uh, in United.
Uh, and then I, I directly always write, connects me to live chats and I write it five times if I, it doesn't connect to me. I just don't want to deal with chatbots. And I said, I mean, in that chatbot world, there should be some more evolution. Yes.
And maybe United is not the best airline. You know that anyway, right? So I think part of the problem is that we are dealing with an organization that's not necessarily the most sophisticated when it comes to adopting technologies and AI, but your, your, your Roger point is very well taken, which is that I think we still have a lot of room to improve.
Our interaction with these bots and um, I have seen some some organizations go a long way especially in the far east in like So like India and Singapore, uh, they are much more sophisticated than actually back in the West.
And I think in that case, like the adoption of AI and the trust around AI is an important one, because even if the chatbot was offering me the best, and I never tried, I mean, like, I never tried using chatbot for United to see if it can help me with the refund and the change of the flight, uh, because I don't trust it yet, uh, it's not about that. I know United States. chatbot is working or not. It's not about that. It's my general perception of chatbots.
Uh, and I think in that sense, Even if the technology advances and if it's the best chatbot, then people's perception in using the technology will have to change, which will probably take a little bit more time and for the technology to be adapted as well. Um, you mentioned on top of the house, these, there's AI powered society and underneath is the AI. ethical, equitable, explainable, and fair AI. Right.
So this is the part that a lot of people are discussing right now because, you know, we can already imagine the bad use cases of AI. And then people are worried about that a lot. But for you, an equitable, fair AI is how do you think about that?
Yeah. So technically speaking, the solution is not that difficult solution is to minimize or remove algorithmic biases, right? So what does that mean? What that means? You see, an algorithm is basically as good or as bad as the data set on which it is trained. Okay, so a good data scientist before they train the algorithm, they will actually explore a lot of time in the data looking for outliers, outliers, both on the left tail and the right tail of the distribution.
Okay. Because these biases come from the outliers the biases don't come from the mean or the averages, right? They come from several standard deviations on the right or the left And so a good data scientist will basically explore and remove these outliers or at least try to understand the reason for the outliers So the technical solution to algorithmic bias is really straightforward.
It's not as complicated as you know People think it is and so that's what I would like when I consult for companies uh in and in this issue I start with the ground level and say Hey Uh, let's sit down with your data science team and spend a lot of time exploring these outliers in the data because I want to make sure that we eliminate or mitigate the biases right off the top of the pack Then we are on the right path towards a fair and equitable and more ethical AI, right?
Because these outliers have been removed. So that's the very first step Beyond that, then, of course, as the new data comes in, it's not a one shot process. You have to continually explore the new data and make sure that you continue to mitigate these algorithmic biases in the new data that's generated as well. So it's an ongoing process.
And I think in that sense, too. There is a lot of work to do for the data engineers and also from the management up to the data engineers to see, okay, what's for our company, what's equitable, what's fair, and what are the filters that the data team should be following as a company, not to fall into that traps. So, I mean, as a marketer, I have a question. So we have the house of AI framework and I'm a marketer. How can I apply this house of AI framework to My function yeah,
so, uh, you know, i'm also a professor of marketing and I do a lot of marketing consulting So we have applied the house of ai in like dozens of projects at this point.
I'll start with my favorite one My favorite one is marketing mixed modeling followed by multi touch attribution modeling Those are my two favorite ones and they're favorite because they're heavily quantitative You know, they have elements of both causal inference predictive modeling As well as prescriptive modeling and with with one caveat which i'll come to you So it covers descriptive because you first have to explore what are the
inputs to your marketing campaigns, you know, then you look at What might happen next you look at substitution? So like when I work with companies I am taking their google campaign data facebook campaign data twitter tiktok linkedin pinterest all of the data And i'm looking for substitution possibilities to see how do I increase the roi? You Of this particular company's campaign. Then I design incrementality tests, you know, for causal inference.
I do these randomized control experiments, AB testing. These are called instrumentality tests, which also helps me with this question of how do I maximize the ROI for my client? By substituting in different ad campaigns between facebook and google or google and tiktok or tiktok and twitter, you know And so the one caveat I said where you can also do prescriptive analytics for marketing is In budget allocation. So one of the most complex problems for marketer is how do I allocate?
The budget right across these different channels. I have TV and billboard and internet and mobile and, you know, direct newspapers, right? So how do I look at the budget? So I've done that kind of work maybe 15, 20 times now for different companies. And so the prescriptive analytics provides the solution to budget allocation because prescriptive analytics, the foundation of toolkit is optimization.
So the only way you can do a good job with budget allocation is if you can optimize the budget condition on these constraints So that's just like one or two good examples. We've done this with attribution Customers both from meta to youtube to instagram linkedin right every session. They open a new touch point, right?
Yeah, so
the consumer path to purchase journey is no longer linear or sequential. It's very very iterative. It is non linear It is non sequential people go back and forth, you know You That marketing funnel, we used to talk about awareness, interest, desire, action. It's not like that anymore.
It's like people skip stages, you know, people, uh, go from awareness to action and then you go back to interest and they go from interest to desire, then they go back to awareness so that old marketing funnel, right, does not exist the way it is, it's very nonlinear, it's non sequential. People skip states to be able to isolate how should this consumer be targeted at each stage. We are now using these AI based models, uh, using MTA and MMM
and so on. But it means like, uh, the data that you feed in, since you're going to do quite quantitative at the beginning, the data should be, should be measurable, which means like, I'm just thinking if for a company that has been doing performance marketing, advertising, you know, giving ads to social media and a programmatic, they would have a lot of data.
So it gives them an unfair advantage for the companies that haven't done so much of performance marketing and that they are doing like, you know, influencer marketing, uh, Well, some of it can be measured though But like events and so on I would assume that it's especially Applicable for the performance marketing and online advertising, right?
Yeah, I mean, you know, I actually think that Today at least uh, if not five years ago if you're not doing performance marketing You are making a big strategic mistake So that's sort of the lens where I work on that my first would have advised to companies and I haven't met that many But there's always a few maybe who haven't really embraced performance marketing or digital marketing And so I have this deep kind of you know question for them, which is like why not? Why haven't you done so?
And so my recommendation to them is like no matter what the reason is There is no valid reason today not to be in that space So you have to do that and then once you Get on the train, of course, then you're gonna get all of this data that you're talking about Uh, but if you don't like you rightly said it is a disadvantage you will be left behind and make no mistake Today's world it's not a choice.
You have to embrace some element of performance marketing And because of that you then have to embrace data driven marketing analytics like mmm or mta You have to be able to measure it like Five years ago, I did a survey and we did a survey and we saw that something like 72 percent of marketing organizations or rather. A 70 percent of organizations that have a prominent marketing function are already adopting MMM and MTA. This is five years ago.
Today, that number is going to be in the eighties, maybe in the nineties. So you don't want to be in that 10 percent that don't do that, right? Because that's just going to pull you back. You know, it's not regressive.
Yeah. I mean, like there's a lot of debate around performance marketing already, like the ad world and everything is just like, I mean, as a marketer, I, Sometimes question it. Although I understand that because it's measurable, you can see it and you can also segment it and those things. However, there's also like a big question mark around if online performance marketing is working for every brand at every stage.
So that's why I think maybe, you know, when adopting AI, it It's can be, uh, good to see how much of the marketing business are we evaluating right now, depending on how much of advertising we are giving online. Uh, so if it's only like 20 percent of our budget is dedicated to performance, which is measurable, then the outcome that you expect from AI at this stage, uh, is. Representing the 20%, almost 20 percent of what you have been doing as a brand.
So there will probably be something that won't be measurable, uh, in the very near future at least Uh for marketing and I think that will be like the debate that we have we we will keep on doing as a marketer Uh with the management teams. What did you do? And what's the ROI on what did you do? Uh, so there will be I think still a gray line, uh in the upcoming years That's my Personal prediction.
Yeah, I see where you're coming from. I mean, I guess what I'm saying is if a company or a marketer is willing, right, to adopt these toolkits and methodologies, the good news is they exist. They exist and they are progressively getting remarkably precise.
Yeah.
Now, the bad news is, as an outsider, I may not be able to convince the marketer that you should adopt, right? That becomes a very, sometimes a subjective decision, a cultural decision, you know, issue of inertia. So, um, in the book, uh, Ravi and I talk about the three I's, which are a big problem. Okay. So the first I is inertia. Okay. And the second I is like, like lack of imagination. The third one is like lack of innovation, but that comes afterward.
Okay. But in our, uh, you know, in this conversation we're having now, I think this inertia, And potentially lack of imagination is very relevant to this context where I can bring the horse to the lake to drink the water, but if the horse doesn't want to drink it, You know, I can't solve the problem. So, and I definitely see that.
I, I, I'm not saying that, you know, today there's going to be still enough people, uh, who often question, you know, the value of adopting these, uh, toolkits and methodologies. And I think, you know, I always look at this as a conversation, right? My job is to kind of inform you and empower you. At the end of the day, it's the marketer is not willing to take that next step.
And use the information and the empowerment then that's the most you can do like you can't really solve that problem So, uh, I guess the nutshell is the good news is we have the technologies and the models and the algorithm to solve the problem But the marketer has to first change their mindset. If at all, they're resisting, they're pushing their
mindset. Since we talked about performance marketing and how it's crucial for the brands to adapt soon because of all these aspects. And because of your, like you are the expert in this for so many years. In the era of AI, what traditional marketing principles do you believe still relevant for a marketing department?
Oh, sure. It's a really good question. Um, I would say I have maybe four or five I can think of. Uh, first of all, customer centric approach. I don't think that's changing, you know, understanding and addressing customer needs and pain points remains crucial. AI can enhance this by providing more deeper insights from your data analysis. But the core principle. Or prioritizing the customer's experience and satisfaction is timeless, right?
Another one I can think of is brand trust and authenticity, okay? So building and maintaining trust through consistent authentic brand messaging is still important It's not going to go away In fact might become even more important because of the possibility of fake news that ai can also permeate, right? So some people will say well, uh, anindo doesn't ai personalize interactions, so it'll You know, solve this problem.
Even if AI personalized interaction, I think the authenticity of the brand's voice is what resonates with consumers at a deep level. So I still think that's a fundamental principle that will not change. I think, you know, segmentation and targeting, right? Yes. AI can allow and enable more precise segmentation, but the, you know, the idea of segmenting the market into smaller units, telling messages, I think that's a cornerstone of marketing. That's not going to change.
Um, and I think maybe lastly, I'll say relationship building, you know, I still believe that a lot of good work gets done through individual relationship between people, right? So I think, especially if you want to, like, nurture loyalty and retention, you know, uh, these toolkits are going to enable it, but at the end of the day, it's a human interaction, the one to one interaction, uh, that needs to be done. And AI can enhance that relationship building.
But the underlying principle where you have to make the effort to engage with another human being that will not change
Uh, there was one thing that I want to ask one last thing from the book that I want to ask There is a word that I found which is quantified self I find it quite interesting because it's quantified and self. Can you explain that a bit?
Yeah, so this hits home because of my mountaineering hobby as well So okay quantified self refers to the practice of using the technology Like a wearable device, okay To collect data on various aspects of our everyday life like physical activity how many steps you walk, you know How many miles you run your sleeping patterns, you know, deep sleep rem sleep your nutrition and diet your biometrics because basically To be able to excel, uh, forget
mountaineering just to be able to live a good fulfilling Uh life, you know, you need to be self aware of your individual, uh, physical parameters, right? And so what this wearable device, uh movement has done is it does help us quantify our wellness, you know, so nutrition sleep and exercise the three things now you can quantify it, right?
Um, we have actually done a lot of research with you know, medical practitioners On in this space we have shown that roughly about a 15 month period after adopting a wearable device You The patient, for example, of chronic diseases, we see that, uh, patients with diabetes and blood sugar, they show a drop in their blood glucose and H1AC levels, uh, by following the three practices of, you know, uh, better nutrition, longer sleeping, and more exercise, okay?
And we do these randomized experiments to monitor patients. Uh, behavior. So that's what the quantifier cell comes in. Um, and then, you know, in the book, we talk a lot about how AI is actually improved healthcare. So we talk about personalized medicine, you know, like, um, AI can analyze genetic information of patients and offer personalized treatment plans. We talked a lot about like predictive modeling of disease outbreaks.
And the likelihood of a patient being readmitted to a hospital, uh, and one of my favorite example is actually from Hungary, uh, in, in Budapest. So, there's a, a series of breast cancer clinics in, in Budapest called Mama Clinica. Um, here, the doctors have been using AI to review and detect breast cancer diagnosis by the two radiologists. And, you know, they are human beings, but that's why they can make mistakes. They're experts. But like every expert, you can make a mistake.
I don't think that sometimes the AI agrees with the radiologist, sometimes it disagrees. But, uh, in fact, there was a New York Times case on this as well, that in these marmosite clinics in Hungary, there have been 22 cases documented in the last three years. AI actually identified cancer that was missed by both the radiologists. Oh, okay.
And these are 20, yeah, 22 people now owe their lives so to ai, and there's another 40 people that is under review, so I don't have the latest results yet, but some significant fraction of that may also turn out to be a false negative. That means you're talking about 30 or 40, 50, 60 lives being saved. And that's just like in one clinic, in one city, in one country. Now extrapolate in multiple thousands of cities and, you know, 196 countries.
So I will ask a question about, um, about mobile economy, because your first book tab, uh, this, this is the last question about, uh, on this episode. And your first book tab was about mobile advertising and with now, with the rise of AI and all the other technologies, how do you see mobile and mobile advertising evolving in the next years?
Yeah. So I think it's again, a great question, which encompasses, you know, the previous question you had, which is are certain marketing principles still relevant and what will be different, right?
So I think many of the principles we talked about will still be the same, but the something that will be different, especially in the mobile economy is again, content creation and curation, because now AI not only produces more interesting multimedia content, diverse content, but it does this at a. faster speed and at a bigger scale. I think with AI and mobile marketing, we can now map this customer journey a lot better.
You know, remember I was saying that the traditional IDA path was linear awareness, interest, design, action. Yeah. That's not how it exists anymore. And today in the mobile economy, you know, when more and more people are using mobile devices to finish their entire shopping journey, We have to do a much better job in mapping the changes in the previous journey to today's journey, which have become nonlinear and nonsequential.
So I think, you know, mapping the customer journey, better content curation, speed of execution, and more scale. Uh, and lastly, I would say that I personally still believe that marketing is going to stay. a blend of creativeness and data driven analytics. I don't believe I will replace creativity and I don't think it should replace creativity. I think these two things are like two sides of the same coin. There should be a strong intertwined relationship.
Uh, I think the job of the marketer is to think about the balance, right? What is the right balance between creativity and AI? So that data will actually inform creativity rather than stifling creativity.
Yeah. Yeah. On that side, when I think about the marketer's role and marketer jobs, uh, that's going to be available in the markets.
Uh, you know, I see a lot of data engineers getting involved, uh, but The other part of it is that the current marketer or the upcoming marketer is getting, um, skilled, uh, in using and dealing with these technologies, not necessarily need to be being a data engineer on its own, uh, but knowing how to, uh, work with a data engineer or like a company with a data engineer.
Because I mean, like I, as much as I read the upcoming jobs, for example, I was doing a search, uh, and it's all about data engineers, you know, cybersecurity and so on. And as a woman, for example, I'm just like, question. Okay. Uh, because I know that the data engineer roles are usually male driven. And I'm just like, how does that sit as a, you know, gender equity site, which is another episode, another conversation around that. Uh, but yeah, I feel that.
A new marketer should be able to, they don't necessarily need to deal with data on their own, but they need to understand and capable of understanding how data works and how it can help them on marketing. Exactly.
Totally agree.
Two questions about lifestyle. Those are my closing questions. One of them is, uh, is there a book that you would recommend highly, uh, other than the book that's coming up, which I'm quite excited to read soon. Um, first of all first question is when is the book coming out?
Oh, so, uh, the book's coming out on october 1st It's available for pre order on amazon and some exciting news Uh, it actually hit three bestseller lists on amazon last week and the week before
Well, i've seen that i'm quite excited about that. Yeah.
Yeah
So first of october it's coming up and what's your favorite book in the meanwhile what we can read
yes, so This may be a surprise, but my favorite book has nothing to do with AI or technology or analytics or data. It's a book on mountaineering. Um, it's a book written by Ed Wister my favorite, uh, role models in mountaineering and the books called no shortcuts to the top. And the reason it's my favorite is, uh, it very much. Embodies the way I live my life or pursue my career, which is diligence, diligence, diligence. I have never believed that intelligence is as important as diligence.
In other words, In my opinion, uh, whatever success metric, um, you know, one can evaluate anybody with including myself I would say 99 percent of my success has been diligence and non intelligence Meaning that you have to work hard. You have to burn the midnight oil. You have to be patient You have to study, you know, stay focused stay gritty, you know, uh, keep the mission in mind So the adversarious climbing style is very different from Someone like Steve House.
They're both brilliant, like, you know, a world class mountaineer. Steve House does what we call fast ascents. Very light expedition backpack, 20 pounds, fast ascents. And at Vistars, does not believe in that. He does long, multi week ascents, heavy backpacks, heavy loads, slow, steady at a time. You know, nothing wrong with either one of them. It's a personal style.
And the reason the book resonates with me is I believe that to be able to achieve whatever metric you have kept for yourself, you have to work hard, you have to stay patient, and you have to be diligent, tenacious, you have to persevere. So that's why the book resonates.
Perfect. I'll leave it in the links also, uh, as episode links. And what's your favorite place for coffee, uh, in New York City?
Oh, oh, that's a hard one. So on the weekends I go to Blue Bottle Coffee because I love their pour over.
Oh yeah. We can be good friends on that.
Okay. Okay. Great. Uh, yeah. There's Blue Bottle multiple places. You know, on a regular day, I think I'm always like stopped for time running from here to there. So I just grab maybe, you know, sometimes Starbucks, sometimes, well, there's a Felix Coffee in my neighborhood. I live in Met Town, so they're pretty nice. Uh, but sometimes it's Mato, sometimes it's Starbucks, so. But on the weekends, I make the 10 block walk to Blue Bottle Coffee and, uh, You know, it also helps me quantify myself.
I put some steps,
which, which branch is that?
It says, uh, 19th and Park Avenue. There's a blue bottle coffee.
19th. Okay. Okay. Um, my favorite is one of the ones in Tribeca and currently they have the Brundi, uh, single origin, which I, which I love a lot.
Yes. Both are awesome.
And I hope we can drink that together one day.
We should. We should. Definitely.
Thank you so much for joining me Anindo.
Thank you. Thanks for having me.