AIPM Event Feb10

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Held on February 10, 2021 @ 4:30 PM

Ask me Anything:
AI-Powered Marketing Programme
By IIM Calcutta and TalentSprint

Aritro Bhattacharyya
Sr. Director

AI-powered Marketing, the newest wave of data-driven marketing is here and already taking the digital world by storm. Watch this recorded webinar by that witness the attendance of marketers and leaders where experts, Saravana Jaikumar, Professor, IIM Calcutta, and Niladri Syam, Director of the Centre of Sales and Customer Development, IIM Calcutta prompted why AI adoption is imperative for making ROI worthy marketing decisions.

Watch Webinar Recording

About Advanced Programme in AI-Powered Marketing (APAIPM)

The Advanced Programme in AI-Powered Marketing is a first of its kind by an IIM. Launched by IIM Calcutta in association with TalentSprint, the programme helps professionals ride the newest wave of data-driven marketing practices and create highly personalized consumer experiences.

Ranked #3 in Asia, IIM Calcutta is well placed to launch this Programme for CXOs, Revenue Owners, Marketing Professionals, Tech Professionals in Marketing, and companies who are looking forward to investing in some form of AI in their marketing practices.

Event Transcript

Revolutionising Marketing Through AI-Powered Tools With IIM Calcutta And TalentSprint

Good afternoon ladies and gentlemen and a very warm welcome to today's webinar. Artificial Intelligence or AI and machine learning or ml are no longer buzzwords. With the acceleration of digitalization of almost every aspect of business in light of the pandemic, the adoption of AI and ml has also been rampant. Together with big data and advanced analytics, ai driven marketing has made it possible for marketers to have a clearer picture of their target audience. In this light, it becomes critical for decision makers to understand and use the power of AI effectively while making decisions on marketing campaigns, budgets, channels, identifying opportunities and creating a marketing USP. Today's webinar brought to you by at brand talentsprint and I am Calcutta brings an advanced program that helps current and aspiring cxos businesses and marketing professionals leverage the power of AI in making data driven marketing decisions. The webinar will also unveil the possibilities and approaches for using AI and ml technologies for enhancing the impact of marketing activities of their organization. We are joined by our panelists today, Professor serban, Ajay Kumar and Professor lakshminarayanan ji requested professors to say a bit about themselves before they start the presentations. And before I hand over the stage to our panelists, I would like to remind the audience that we will be taking questions at the end of the presentations. So do write in your queries over to your professor seminar.

Yeah, thanks, we'll be and you know, welcome to everyone from behalf of talentsprint at brand equity, and I am Calcutta. So we are glad to have you here that you know, during a weekday we have taken one hour off so you understand the importance of this particular topic and the implications that you can have. So thank you for being here. myself. I'm seven Ajay Kumar, I am currently with I am Calcutta, I'm a faculty in the marketing group. Prior to Calcutta, I've been here for about five to six years now prior to Calcutta, I was with I'm with a pool. Prior to that I was with I'm on the board for a while. Most of my work experience has been in marketing strategy and data driven marketing related roles in the publishing industry also for few years. Right now, most of my research work and teaching interests revolve around, you know, artificial intelligence and machine learning, and also digital marketing to some extent. Yeah. So that's basically about me. My research work is mainly on, you know, public policy related issues, business to business marketing. And in those areas. listener, can you please say a few words about yourself, and then I can start with the actual presentation. Thank you.

Sure. Thanks, Professor Sarna. Good evening, everyone. I am lakshminarayanan. And I have nearly 14 years experience in the industry. I work as an associate director with one of the big four consulting firms in analytics and cognitive practice. And last 10 years I've been with consulting post my MBA from I am Lucknow. So I've been working with different fortune 500 companies, consulting them on marketing, consulting them on how to drive decisions with the help of data. So that's where my association with this particular course comes in. That is how you can bring in analytics data, artificial intelligence, kind of concepts to help marketers take better decisions of the market. So apart from my years of experience in consulting, I also enjoy teaching. So I teach with processor and I in some of the marketing courses, as well, as I'm a visiting faculty with I am Lucknow, and I'm touching. So that's about me looking forward to have a wonderful interaction with all of you today. Thank you back to your process. Thanks.

Yeah, thanks for a selection. So I'll just start with the presentation. Let me

Yeah, thank you. So as you all know, today, we are going to talk about, you know, revolutionising marketing through AI. So we're gonna, it's not purely a technical conversation, per se, but we're going to talk about how a ml can have a huge impact on marketing and how the future of marketing actually is going to be, you know, completely dependent on machine learning and data driven marketing decisions. So that's basically the objective for today. We'll also be talking a little bit about, you know, the course that we are offering and the, you know, the, the different models that we'll be covering, and what we can, you know, hope to take away from that particular course. Right. So that's the agenda for today. If you have any questions, you may please type it in chat, or we'll have a separate q&a session after the presentation is done, will not take long through the presentation will be done when about 30 to 35 minutes. Maximum auto limit is 40 minutes, so you'll have sufficient time for q&a. Yeah, let me just jump in and jump right in with an example. This is a news article from times from last year, January around one year, one month ago, where this was in the first page actually a witch doctors with breast cancer did cancer detection, right. So this is actually based on a you know, a paper that was a academic research paper that was published in the journal Nature, which is like the top most, you know, journal in the in the world, if you might say. So they're kind of rigorous, and they kind of, you know, look at the procedures and mechanisms, the data, everything is actually scrutinized carefully, and goes through peer review process, right. So it's not just a news article that's coming out of times, it's based on an art journal paper that was published in Nature, you can see that, you know, a actually as beaten the doctors in terms of detecting breast cancer, just from the X ray x rays, better pictures of X rays, and we're talking to doctors who have been dealing with these patients for a long period of time. And they could outperform by picking out the patterns and interpreting the images and trying to find out what kind of a pattern would actually result in breast cancer. So they could actually overperform the doctor. So you can imagine the power of AI in terms of you know, something that's not really visible to the human eye. So patents and you know, includes data, there's a lot of patterns, there's a lot of common occurrences that might be happening, but we may not be able to notice them, but they can pick it up really well. And you can actually click, you can train the model to detect certain anomalies and certain issues very, very efficiently there just to give you the context that, you know, it's not just in management, or it's not just in, you know, certain things like driverless cars, but we're talking about real impact that they can have. And how would take actually a bit outperform human intervention at certain points. So we'll talk about the balance between human intervention and as well at some point, but yeah, so just wanted to start with, you know, a particular example where a can actually do some really good, good contribution to the society as a whole. Right? So when we talk about AI, I think the needs that we typically have is either, you know, Jarvis from Jarvis, or, you know, Karen, from Spider Man, or that's basically what we think of is here. But that's not where we are. Right? So that's just in Marvel Cinematic Universe. So we are somewhere much, much further before in terms of, you know, in terms of where AI has taken us. So we're not talking about that kind of AI, where it's a self inquiry, and it can actually process a context and do a lot of things. But if you think of AI, right, so the first thing that people will come up with now, because it's a buzzword, driverless cars, right, so that's the first thing that, you know, people think of when they, you know, the moment AI or ml comes into mind. So driverless cars, if you look at it, you know, if we think in a narrow way, right, what are the industries that's going to impact? And the obvious answers would be like, you know, obviously, the Ola and Uber is going to be affected to a great extent, it's a two sided market, it will become on one sided market with driverless cars. Number two, the driving practices and number of accidents are going to come down. And that's a very, very interesting concept here, the current number of accidents are actually quite high with human, you know, driving the taking control of the cars. But if a is brought in, the accidents will be very, very minimal. But in case one accident, God forbid, one issue happens, people would react in a very, very drastic manner, saying that it's unacceptable. So if you weigh the benefits and costs, you know, to rational person, it will look like, you know, minimizing the number of accidents is actually a good thing. But people when they want when they give the control back to the machines, a and ml they want it to be perfect, right. So that's just one caveat that we have to keep in mind that it's not that easy to say, I'm going to involve AI and ml and all the technologies that they're going to use because the reaction so consumer psychology is going to change drastically when they know that ml is going to take over. That's one aspect that we need to keep in mind when we think about a. So if you further take it further, one step further, right? So driverless cars, what are the other industries is going to have an impact insurance is definitely going to be impacted, the way automobile insurance is functioning, that's definitely going to be impacted. If you think about the actual underwriting process and the claims process, all of those would actually drastically change security. That is, you know, the hacking of cars, right. So the internet secure security systems and security solutions, would you know that the massive demand for those changes change the demand for those to that industry is going to get affected? If you even think a little bit outside the box breathalyzer market, the companies that are manufacturing breathalyzers, they may go out of business, because you know, you don't really have to distribute breathalyzers to public departments and traffic, police departments are listed. The role of traffic police is also going to drastically change their recruitment process on what kind of skills they want the traffic police to typically have, that will drastically change. So you're talking about, you know, not just not just a small impact in one industry, you're talking about ripple effect across several industries. And if you go even further, if you look at the commute, right, people are going to be much more productive and commute. Because if you talk about the cxos CXO levels, people are going to be involved in business to a great extent, that could change the entire demand for the cloud platforms, right. So because you want the entire office to be interconnected on the office solutions to be available in the automobile for the top management access. So cloud solutions and cloud computing platforms are going to go through a huge change in demand. Your driverless cars become a no further as a marketer, you also need to know you know, certain things, how certain things that you may not even expect to get impacted, for instance, real estate prices, right. Those who are in the outskirts right commute is no longer a problem because you're actually being productive. The high net worth individuals would actually don't mind being in the being in the outskirts because the commute is not really a problem for them anymore because they're actually being productive. So A lot of these industries are going to change. So it's not that AML is just a new technology of that particular in that particular segment alone, that ripple effects are going to be felt everywhere. So as a market in respect of which domain URL, you need to be very, very wary of, you know, what are the changes that are going to be brought brought about, because of these technological advances? And are you well equipped to actually address those right in time, that's basically the objective that you need to have, because that's going to revolutionize marketing completely. Right. The other place that, you know, you will see a huge change, right, that can happen from a marketers perspective is sales processes, right? So you would have seen the, I think many of you would have seen the Google conference and where they introduced, the A could actually take a call, and could take an order from, you know, people with different accents and could actually respond like a human right. So in fact, the tone and infer the, you know, kind of mood that the person is in and then respond accordingly. And create develop relationships, right. So automate everything happening in an automated manner at scale, right. So that's going to change the sales process to a great extent. And finally, if you look at, you know, Amazon, right, it's now we are in the shopping and then shipping model, because we shop and then the products are shipped. But a can actually invert that particular know the entire dynamic, and it can go into shipping, then shopping model. And there is companies that are already doing that, to some extent, with some, obviously some minor changes there. So if Amazon could predict right, or any, a retailer could predict what exactly we're going to purchase, and move the products to the particular battles, right on an individual basis, they could move to the arrows, then delivery becomes a lot more simpler. And that's just moving to the battles. But taking it one step further, what if they ship it directly to you, you don't even have to shop, they know exactly what is required for you, and they want to ship it directly to you. And at the doorstep, you decide whether you're going to keep it or not, right, that's the shipping then shopping model. And that's going to change customer behavior, the people will not, you know, prepare to do list anymore, people are not prepared list of items to buy, people do not plan the purchases, because they know that it's going to come on time, that's going to change the customer behavior to great extent. And Amazon did try that the economy's on wardrobe. But another company that actually did that was Stitch Fix does in the US. In the course, we also talk about Stitch Fix to a great extent, where a Stitch Fix kind of gets a user's consent, people have to sign into that service. And they get access to the data like, you know, the social media platforms, the Pinterest boards that the customers typically use using that data, they kind of figure out what style is going to actually match this person's behavior, this person's personality, and they send the dresses out every three months, once right, they ship it directly, and then the person at the door will have to figure out whether they want to keep it or not. So let's shop shipping, then shopping. And that's completely done through a done through an AML system. So we will understand that, you know, when we discuss about these examples, vented organization is proven to adopt technology to have data driven approach to actually fundamentally do business in a different way. So that's where the focus has to be when you're thinking about, you know how marketing is going to change because of a sales process. If you look at it, you know, typically when we talk about b2b marketing, it's more about following up with the leads and trying to get the conversion done. And a is already playing a great role over there in terms of automating the entire process, right. So typically, when we talk about a lead, we reach out to the lead a couple of times, if the lead is not really responding in any way, we kind of, you know, humans don't know really, when to reach out to them, or we actually disturbing them. So we're talking about b2b transactions, right? So the one example is I saw a software solution called convert seeker, which is completely built on AI. And that kind of reaches out to the customer leads at a optimal points in time, kind of converts, the lead engagement is increased to a great extent. And it could actually convert the convert more number of leads into conversions. So that's the effective few examples of effectiveness of, you know, a ml in the marketing space.

This I just wanted to start with this particular, you know, to get you to think that you know, driverless cars is a great example to think about, it's not just one particular industry that you're actually that's going to get affected. It's the entire ecosystem that's going to change when technology actually, drastically there's a nonlinear jump in the technology and there is some sort of innovation the dumb breakthrough innovation that happens right now I'd like to pass the pass it on to the purpose election arena to cover a little bit about the market, liberal job prospects of this domain, and a few applications in this domain as well overdue lectionary

Thank you processor enough thanks for setting the context. So I hope you have understood the importance of this concept of artificial intelligence. Now, let's understand where the market is heading. So if you move on to the next slide. So we will take up some basic factors which will help you to understand what the market is setting so that you you are well prepared because I personally believe in one thing after doing my postgraduate diploma in management in from luck now. I had to do one more degree in Postgraduate Diploma in a machine learning so that I can keep myself equipped for this new age stuff that's coming in, which is going to sweep us all because here is considered to be the new electricity with the changing landscape that is happening because of this powerful tool powerful concept. AI. There are going to be lot of automated processes. The automation is going to be changing exponentially coming up new things. And data driven decision making will become an hygiene factor. Because I mean, I've seen 10 years back when I joined analytics, it was more about business intelligence. It's more about organizing data within the organization. Today, people are looking at data as a kind of an asset. From data said infrastructure mindset to data as an asset mindset. People have moved towards this particular mindset to drive a lot of decisions in the organization, when I was going to impact the marketing space, if you look at this question, automatically, two keywords would come up. One is automation. The other one is the personalization, automation so that anything and everything that you do in the space of marketing, are you able to derive value out of it? Are you able to see data out of it information out of it, thereby you are able to gain wisdom, which will help you in a lot of distance? That's one way of looking at it. That is automation. The second aspect is personalization. with loads of data coming from online tools, the websites, the analytics platforms, which are available everywhere. Do you are you able to segment your customers better understand them better? Are you able to personalize offerings for them? So that's another question that comes up. And already things have started coming around to the market, where companies are trying to be more proactive and preempt what the customer they think, and thereby provide personalized products for them. And of course, thanks to the big data in the last 10 to 15 years, we have more data to analyze, we have more data to understand and come up with better insights out of it, which is helping us out. So AI ml enabled marketing decisions is something which is going to be the future for sure. And you should be prepared for it with the kind of advanced Advent and technologies that we're talking about going to the next stage. So let's look at some industry reports coming from top consulting companies like PwC says that AI is a business advantage. In fact, I don't know whether you have heard of this term recently. I mean, very recently, there was a term coming from the EA conclave which was organized by the government. We always used to hear this concept called as the term called as AI is the new oil. Remember, people used to say that two three years back today the new termas yeah is the water yeah is the new water. Without this water, not any organization can survive. So you need to ensure that the data is available within the organization and from outside the organization and are well are able to make use of that data for your analysis and decision making. MIT says that, nine out of 10 companies already use artificial intelligence to improve their customer journeys, are you able to map the kind of data that you are able to capture at every stage of the customer journey, and I was able to analyze and understand what would be the right information or the messaging or the data that you would like to share across with the customer at each stage of the customer journey, so that they are able to come back to you, they are able to have us a Top of Mind recall for them. So there is a difference that's being made. With the help of artificial intelligence, there are nearly 92% of the Fortune 3000 executives surveyed indicated that their companies are accelerating the pace of their big data and AI investments. So it's no longer a kind of an advantage. It is seen as a ranking factor. Now, it is seen as creating the foundation for the future. That's what has happened in the recent past.

So it is critical for decision makers to understand the power of EAA and how they are going to make use of it in their marketing campaigns to allocate the budget utilize the budgets effectively identify the channel mix, because today market has served very well realized what should be the mix, they cannot rely only on the conventional channels, they should rely on the New Age digital channels also. So what should be the right mix? You just cannot go with the experience rate because not everyone is experienced in the space also, because all new age technologies. So can you rely on the data? Can you look at the data that you are collecting from different channels, and take a call which channel would be a better channel for you, and what should be the right mix of channels for you to campaign and reach the customers and how well you can identify opportunities and what kind of USP you can create for yourself, your marketing campaigns as well as for your products. So all this would require data and would require a refinery like machine learning, which can actually derive better insights from the data for your day to day decision making. So this is where the market is heading. So certainly, if you can move to the next one. So now, we hope that you have understood where the market is heading. In fact, just to add a bit, we are able to see a lot of practical applications of marketing plus analytics, marketing plus data marketing plus engineering, which is coming into the picture. So I'm sure in your domain, you would have seen the prospects of this and that's the reason it Most of you are attending this particular session to know more about what is happening in this market. Let's look at some of the practical examples which can help you to understand how machine learning is being used by different companies. The most popular one, the company, which made billions during the COVID scenario, the lockdown, we all would have bought Netflix, if I'm not wrong, people who did not have Netflix before COVID would have got it now. So Netflix is one of the best companies which makes the best use of data for producing good shows, editing them, ensuring that the right streaming quality is available for the customer, depending on the net connection and other stuff. And auto generation personalization of the thumbnails and artwork. That's something all these things happen in Netflix, I don't know whether you have seen it. For example, take your mobile phone and take your mobile friend's mobile phone, open Netflix in both mobile phones. Maybe most probably you may not see the same thumbnail available for a sitcom which you'd like to watch. So it is completely customized, personalized based on what you have watched earlier in your mobile. So they use data they use machine learning to come up with such kind of recommendations such kind of personalization. So that's one good example. Let's move on to the next one. Yeah, and Amazon. So when you talk of EA, the democratization of the artificial intelligence, which is happening in the market today, very few companies have contributed to it. Amazon is one of them. And the best example for Amazon, on how it is using AI for marketing is the recommendation engine, I'm sure you would have watched it like when you order something in Amazon, immediately there is a small bit of mercury frame that is available to you with shows that look, people who bought this also bought this if you're buying a laptop, there is a laptop bag charger, which is available, you can package it together, you can order it together. This is a recommendation engine, which is nothing but an implementation of a machine learning algorithm in the space of marketing to understand what would be the right product which can go ahead with this work. So recommendation is genius. A popular one. Alex, I need not tell you people use Alexa left, right if they are all of us own and Alexa, if I'm not wrong, isn't it? So Alexa is another good example. I mean, it's one of those tools. I don't know how many of you have visited us and I've been to our ghost tours. That's one good example, if you walk through it, you will realize how much machine learning and the kind of automation that they are brought in to make the store successful. So, definitely you will find it soon in India too. And when it is there, without machine learning this store cannot be successful in any country. dynamic pricing is a popular example for a pretty long time you know Uber uses it euro Ola uses it a lot of companies use it. Of course, Elton uses it to them increase their demand forecasting how effective their demand forecasting is, they are able to predict it with the help of machine learning based system, Amazon and eBay used to set depending on the products they cut it down, that's why you would find certain companies coming up there are certain jokes played on social media like how the price of the product has changed, within few seconds, they will there will be means coming out. So, you will see that that's purely based on machine learning based systems the effectiveness of it is slowly increasing. And this is something which is very very important because pricing is one area where marketers have a huge role to play and data driven Decision Making Machine Learning driven systems are helping you out in a great way in this particular area.

And Geo Data telecom it not tell you the offer that you get your friends do not get it is it and the offers that we get if you notice it has been completely personalized for you like based on your data usage based on the calls that you make based on the roaming that you take. Based on all these factors, the itemized billing, if you notice they do a detailed analysis and with the help of machine learning algorithms are able to give exclusive offers for customers. And in fact, I will honestly tell you that a lot of companies telecom companies, which predict very clearly which customer they are going to lose next and they are able to provide specialized offers to retain them. So that's something which has been happening now in telecom market hyper personalization, which used to happen in e commerce. Now it has come to the conventional sector of telecom and you will soon find it in most of the other sectors too. Moving on. So retail, of course, there are a lot of technologies that have been adopted by marketing people in retail machine learning is the one which drives the show behind the scenes. So price optimisation recommendation engines inventory management, talk about any area in marketing, any area which can convert the activities into data which can capture capture data from the activities, you will be able to use AI machine learning along with it to ensure that you have better output or better efficiency or effectiveness coming out of it. The predictive merchandising is something which is creating wonders for retail. It's more like just in time inventory kind of a thing you're able to predict and you're able to ensure that you have the right products available for the customers when they visit the stores. The programmatic advertising is ensuring that every penny that you spend on advertising is a factor Tivoli taken care of, and you make the best ROI out of it. So there are umpteen number of applications for AI machine learning in this space again. So you are going to learn more about this in this course, definitely. Now let's look at the job prospects. So I mean, by this time, you would have realized that he is doing wonders, and yeah, he is creating a lot of buzz in the market. And yeah, is the new electricity, it's going to be the hygiene factor, and anything and everything that we are going to use in future is going to have a tinge of heat on it. Just like the Internet of 1990s, or what has become today, like without internet, can you survive? Like we're talking about internet as the fundamental right today in the society, isn't it just like that AI would also become part and parcel of our lives. If such a demand is going to arise in the market? Are you going to look at it as a kind of, I mean, you should be ahead of the curve, right. That's what we have been taught in our colleges, we have been taught in our corporate experience, that any technology that's going to hit the market, we should be ahead of the curve. And we should be smartly learning something so that we survive for the next 10 years. because things are changing dynamically. Now Yay, as a powerful tool, and marketing as a powerful domain, can you bring both of them together? For able to bring both of them together? Today, if you notice that a lot of job prospects that are available? So people are asking what skill that you're bringing in on top of the marketing knowledge that you're bringing in marketing experience that you have, because market yours conventionally, talk about selling talk about advertising, and very worldly people sitting in the corporate central office who use data extensively for decision making. That is at the centralized level, they take decisions on marketing, based on the data. But today, if you notice, at every level in the organization, they're expecting you to do understand data, collect data, and analyze the data for your own decisions that you take on a day to day basis. So are you picking up these skills? And are you able to effectively put across so marketing plus AI are able to bring both of them together? When you do that there are umpteen number of opportunities that are available in the market. And that's what you see. So as companies gain experience with building more AI systems, the skill needs shift from a focus on year researchers to a desire for business leaders who can understand AI who can understand data who can understand marketing, and how they can marry both the marketing and artificial intelligence, how they can marry both, and come up with the new products, new revenue channels, and new cost optimization techniques which they can apply in their day to day life. That's what people are looking for. Are you building those skills, if you want to build those skills to be successful in this marketing domain, maybe that's where Sarah would add more from the cost point of view. And definitely, this course would be the right choice for if you want to build those skills. Back to you sarana.

Translation RNA, that was a great insight on you know, where the market is heading, as well as the job prospects and few applications as well. I would like to carry the discussion forward in terms of, you know, some of the applications that and we have got a few questions also in terms of certain domains, we'll try to address them as we go along. First, let me talk about AI and retail, I think lectron already mentioned, you know, importance of AI in Amazon. one statistic that's public is, you know, 35% of Amazon's revenue is from the recommendation engine, that the recommendations that they generate, at any point in time, when you actually log into Amazon, or browse through it or search for it, you don't there are 18 CRO products, but never overwhelmed by the choices that you have, they kind of make the experience really smooth, engaging, and the underlying, you know, the technology that drives it is actually the machine learning and recommendation engine to be precise, right? So this is one thing that you can, you know, all of us have experienced because 18 CRO products you can can get overwhelming in no time, but they're able to present it in a way that you know, it's engaging, it's very personalized. And it's actually, you know, right products at the right time. This is just one example. But you will also know that Facebook is actually using a to a great extent where they're able to predict, you know, people you may know and kind of interest that you may have, what are the products, you know, in terms of Amazon, the products that other people have bought, and Netflix also the content that you might like, like a collection or mentioned even the thumbnail could be modified. And it's personalized. And the kind of clips that are played for you before you start the program are actually, you know, customized for each person's interest. So to that level, it's and it's operating at scale. It's not a manual intervention. It's operating at scale, and it's automated to a great extent, right? Anyway. So recommendation engines are at the core of digitization, and they kind of can, you know, drive the revenue up to a great extent. So 33% is 1/3 of revenue from Amazon. So it could be product recommendations, it could be personalized product promotion recommendations. And we're not talking about coupons being sent to people right, it's more about targeted promotions, we'll talk about a little bit more. And finally, logistics and delivery segment also has had a huge impact on because of AI and ml. One example to tie you know, traditional marketing with AML would be this. You know this actually a real life example of you know, a person or family buying certain stuff in Walmart. You can see that they're buying a Diet Pepsi, they're buying an Atkins bar, which is actually a low calorie bar and that's First, it makes sense that you know, somebody is actually losing weight. And slimfast is mainly meant for women and there is at least one female in the household who's actually trying to lose count calories, then you also wonder why they're buying ice cream popsicle, Cheetos, maybe there is a kid, maybe that is a kid in the house. A few weeks later, the same household comes in, and they're buying a few more items. And then you notice that, you know, they're buying baseball cards. So basically, you kind of know now that there is at least one kid in the family, and they're buying dog treats. So maybe they have a dog, right, so with two transactions, you're kind of getting a picture of the entire house, right? So slowly, slowly, you're trying to understand the house a little bit more. Third transaction, the buying official, right? Maybe they're interested in fishing, they're also buying a marine oil, they own a boat, right. So to that extent, with three transactions, we're able to put together a particular picture, which kind of tells you, you know, there is at least a small family with one kid at least. And then there is a dog and they like fishing, and they want they want a boat, right? So imagine what you can do with 100 transactions, what you can do with 150 transactions, the kind of, you know, the image, the DNA of the household that can put together is quite, quite vast and rich, and you may not stop at that the AI will come in where you can actually predict what the household is going to need, at how their life is gonna change over a period of time, when the lifecycle changes are gonna happen, when the milestones are gonna happen. Can you predict it before? And can you reach out to them at the right point at the right price and write products and write promotions also, for that matter, so you kind of understand the entire household, you know, the DNA of the household, where what changes they're going through? What are the lifestyle, lifestyle milestones, they're going through changes they're going through, when they're going through changes in income, you can predict to a great extent, and you can intervene at the right time. And that's actually tying up you know, traditional marketing analytics of understanding a person's household. And then tying to predictive analytics, right? Financial Services, many of you might be still aware, chatbots are quite common in financial services, then you have fraud detection, credit card fraud detection is another place where AML is widely used customer recommendations, what kind of services they would really want, and lead scoring and all those is actually actually being applied in financial services, another, you know, change the product by as algorithmic trading, right. So algorithmic trading has changed the way that the whole market operates. And the future is actually, you know, it's moving towards more more and more percentage of transactions happening with algorithmic trading, trading, driven by AI. And this is all the applications in healthcare as well, someone actually asked a question also about healthcare. So these are actually, you know, the predictions from Accenture, Accenture data, data, kind of a survey data kind of work with the healthcare market, and they find out where they could actually have an impact. You have virtual nursing assistants who could actually and many of you may have heard of even replica, right, who actually provides emotional support with an aide, which promotes human emotional support acts kind of like a award winning program, that

that's brought up a huge change, you have a lot of other applications. So healthcare is another field, where as you know, being adopted to a great extent, for instance, even predicting whether a person is going to, you know, keep an appointment or not, what is the probability that this person is not going to keep an appointment, planning the entire, you know, food necessities in a particular hospital managing better locations, everywhere, you can actually bring in a to great extent, from a healthcare marketing perspective, you can also figure out, you know, what are the kinds of changes, like changes that a particular you know, segment or a particular even at an individual level they're going through, and what kind of recommend they're going to have and, you know, engaging with them at the right time. So that also a can do a huge, economic huge impact. Some of the examples, I won't go through them in detail, but Harley Davidson, this is actually a public data that, you know, our division could use, used in AI to actually increase New York sales by 2008. That's a huge increase in, you know, number of sales, what they use is called predictive analytics, and they try to figure out, you know, who's actually a better lead and follow them, follow up with them in a more, you know, productive manner, and try to convert them and understand the manner, right. So this is exactly what we have in mind for the course. So this slide where we're talking about, you know, we're not trying to make you data scientists. But at the same time, there is a technical component to the course, where you don't need any technical background. But what will make me like, keep you with this, you know, being able to converse with data scientists being able to understand the different programs and algorithms and techniques that are available, and what they're capable of achieving. And you will also be able to implement them on your own with the way that we take the course through. So it's more about, you know, bridging the gap, right. So a person, you know, who like yourself, so who's actually a marketing who's in the marketing domain, or who's actually a business as a lot of business domain expertise, right. And they know the power of a, they'll be able to adapt that power to that particular business domain. That's basically the idea. And that's the main objective of the course. It's not a purely technical course. And it's going to have a lot of applications and management related management applications built into the course.

So that's basically the introduction. Let me talk about the course in some detail now. So you have the course is titled advanced programming, artificial intelligence, powered marketing. Right. And partnered with talentsprint actually offered the center it has partnered with talentsprint to offer this to directors and one of the directors for the program. And my colleague who has a division has another card. And electron will come in for a few sessions, we'll cover take a few topics. We also have pros, a sham lottery Sham from University of Missouri, who will also be covering the concepts of AI and ml, you know, what is the intuition behind each of these algorithms. So there are unreal outside a few industry experts coming in and talking as part of this program. So we have a, you know, kind of few people involved not just one or two offers, there are like four or five roles involved in this particular program, who actually working in this domain. And so just to before we go into the program, just to give you an idea, like, you know how all these are fine, driverless cars, and shipping and shopping are all happening at a very different level, how is this relevant for me? How is this relevant as a person who is dealing with, you know, some business level, you know, decision making, at this point? How is this relevant to me, that's why I have added this slide. So right now, you have historical data, right? So last year, for instance, even if you are in the banking domain, how many people actually reached out and how many people signed up for a loan? So you have the data with you? Right? All we do right now is in India, we started the descriptive analytics, we just tried to summarize and say, This is what has happened, right? In the sense, people from this age group, they are this many people reached out and this many people, maybe 5% 700, people reached out 300 of them converted 400, we were not able to convert. So that's kind of like descriptive what has happened, we use the data only to summarize and say what has happened. And we have strategy teams all over the country, right, in several companies, where they send this report on a monthly basis or on a weekly basis to the management. And all they look at is, you know, what has happened? Is the graph going up? I'm fine. Right. So that's basically where we are in terms of explaining in terms of summarizing the data that we have, there's a huge amount of data that generated on a daily basis. And all we do is, you know, just summarize, at the end of the month, what has happened in last month, that's it. Sometimes we take it further, in some companies that have been involved with, they do take it further to see why did it happen, right? So all these are actually what is currently being done. What we're talking about is taking it one step further and saying we understood what has happened, we try to figure out why why did it happen? Why a particular age group converted a lot more medicine, different age group did not convert for us? Why did it happen? We try to understand, but the third one is, can it predict what will happen using the data that I have? When a new customer comes in? Can I predict what will happen? And can I have a very high accurate prediction, right? So once it predict what will happen, can I make it happen, if someone is not going to convert? How do we make it happen. So that's the way we actually ascend, right? In terms of using the data, the best advantage of having the big data is not just descriptive, and diagnostic, you can predict it. So for instance, if you have a lead coming into your website, or if a lead reaching out to you, and you have a system, which automatically tells you this a hot lead or a warm lead or a cold lead, you can put in the correct amount of effort, right. And if you automate, if it's a web page, and someone is landing on your page, the lead is generated and someone's landing on your page. And when they land on the page, if before and if you know whether this is a hot lead, then you can focus on conversion and the page will load, you can load the page that actually focuses a lot more on conversion at that point in time and close the sale. If it's a warm lead, they are in the dilemma, right? 5050. So can you give them more kind of incentives? Can I give them discounts to get them to convert, right. And the third one is called lead, can I give them more information and educate them a lot more to move them towards the right hand side a little bit more. So that's predictive and prescriptive. If I give the if I give the offer to everybody, people who are a hot lead or anyways going to convert are also going to use that offer. And if we don't do the awkward people in the warm lead role, we're not able to convert will not convert that. So it's all about you know, improving the performance, improving the efficiency and the web, adding predictive insight and making it making things happen. That's basically what we'll be focusing as part of this course. Right, predictive analytics and prescriptive analytics. That's the main focus of this course, objectives of the program. Every company is striving for a data driven approach, everybody has understood that they know data as a data driven approach is what is needed, we can't rely on punches we can't rely on go with just you know, taking decisions and trial and error. So that is why 85% of you know, because of lack of skills, right in this domain, 80% of a projects fail. And that is exactly why we have actually come up with this program. It's mainly for market professionals, marketing and technology come and marrying both like pros netrunner. And mentioned, right. And so here we are offering what we plan to offer is basically to the current and aspiring cxos, who want to actually take the lead in terms of you know, and also keeping up with the changes that's happening in the market and trying to you know, kind of equip themselves with wide the market is going and be relevant, even though in 10 years down the line, 15 years down the line, you're well equipped, and ahead of the curve before others are able to reach up to you. That's the whole idea behind this particular program. And I'm Calcutta, we have a huge host of quantitative talent in the in the in the Institute, and we have a strong analytics program analytics people and talentsprint to this deep knowledge of deep tech industry, we have actually kind of figured out that this is a combination that's going to really be a hit, right. And the first cohort is was actually a hit APM APM actually is currently still going on it's about to you know, the program is about to get over in a month or so. And we've got excellent feedback and you know, some sort of insights from all these participants for the first program, program model, we have divided them into several modules, you have data driven marketing decisions, then fundamentals of AML then we have a small component on HTML using are where you don't need to have any program experience you don't need to have any sort of mathematical background will be tricky more from an application perspective. Then we also have an ml applications and marketing module, and MLM digital and social media marketing, where we'll do a lot of case studies as well. And finally, Futuristic Marketing. And as part of this course, you will also be expected to do a capstone project. So you'll be put into groups and you'll be working on on a project real life project with groups in the group. Right? pedagogy is we're going to use technology, metadata decisions are gonna be conducted online, they're gonna use real life case studies, we're going to have some sort of assignments, if necessary, we'll also have quizzes in class duration is 132 hours, six months, 132 hours, typically, the sessions will be scheduled twice a week, Sundays two to five. And on Tuesday, sorry, there's a spelling mistake that typos Tuesday 6:30pm to 9:48pm. And planets can one campus visit if the government guidelines are low at that point,

course details. So we have the first module where we'll be focusing a little bit more on, you know, digital disruption, how the digital disruption has happened to the marketing domain, and how from digital marketing, we move and move to a different marketing. And we talk about a little bit more about data driven marketing decisions with real life cases on how decisions were taken, and how do you need to use the data. And so that's basically the idea for the first module, where we'll also introduce you to as well, so how the companies are using it, and what are the things to keep in mind, as simple as chatbot, which we think is a you know, given we just deployed a chatbot. And it takes over, it doesn't work that way, as a lot of organization level decisions that you need to take when the handover has to happen to the human, human, human sales professional, and what are the kinds of queries that chatbot can, you know, deal with, all those things will go into detail, and we'll talk about those at a high level, the first module, the second module is where we bring in and we introduce you to the kind of algorithms and we know the most common algorithms and predictive analytics space, and the machine learning algorithms. And we introduce you to support vector machines, decision trees, you don't need to have any sort of mathematical background, or any sort of technical background to be able to understand this, we will be talking about it purely from an intuition perspective. So how do you understand these? What are the intuition behind these algorithms, and how do they work so we can actually understand their capabilities. And next module will actually give you a hands on experience where we will do a demo using our in class, the I'll be taking you through that particular, you know, particular module, and where, you know, you will be able to prepare your own codes and random and see how actually, the responses are coming in how simple it is to actually, you know, deploy an ml algorithm. So when you talk about a ml without actually getting your hands dirty, it might sound like a too complicated element. But when you when you actually get our hands dirty, and we type in a few lines of you know, two or three lines and are able to execute an ml program, then you understand how simple it is to once you understand the intuition behind it, once you understand the right tools to use, that's the basic idea, some of the questions are asking about why we're not using Python, Python or R, it wouldn't really make a difference, it's to give you a hands on experience, we're not trying to make you data scientists, if we're able to understand the logic in our, it's just about understanding the syntax in Python. So it's the same steps that we're going to follow in our as well as in Python, it's, we're using a different syntax in our, you're gonna use a similar or you just have to find the right syntax in Python, which we'll be able to figure out in no time. So basically, we are equipping you with, you know, the steps involved, well, what are the steps in what how do you actually, you know, build some sort of a template we're going to build, so you can apply the template, irrespective of which platform we're going to use, all you need to do is find the right syntax, and you'll be able to Google that and find it will give you the resources, we have a lot of books that will be circulating as part of this program as well, which will cover these aspects. Right. And our another reason why we do that is, you know, when there is a new package that comes a new technology that comes in the first, you know, first technology to actually adopt it and come up with some sort of implementation is are because academics typically prefer working with R and they kind of develop new algorithms, then you will be able to, you know, get your hands dirty, whenever new new algorithms come in, you will be able to use R. So R has its own advantages. I'm not saying Python doesn't have its advantages, but we kind of you know, balance it, don't wait covering her by giving you the steps, these are the steps in what we plan to implement it in Python, you just have to follow the same steps, all you have to do is for you know, write the write the correct syntax. That's it. It's about syntax, right. And the next model will obviously focus on applications and marketing here, we will do a lot of case studies, real life case studies. And we'll also cover time series and forecasts and how to you know how AML actually enhances all of these. So we'll be covering all of those details. And other models where we spend a lot more time would be AML. In the digital and social media marketing space, how to, you know, for instance, we have recommendation systems, how to build recommendation systems, what are the intuition behind them? What is the difference between, let's say smart basket versus Did you forget? So, what are the differences that and what are the differences between customers who bought this also bought or two items that are related to your purchases, so there are several minute minor differences that go in so we talk about all of those differences. We also talk about you know, that how to upsell and cross sell how to score a particular lead and convert them. We talked about all those using case studies and real life data. And finally, we have Dedicated one model to Futuristic Marketing where we talk about blockchain and smart, smart contracts for marketing, internet of Internet of Things AR and VR. And also, you'll obviously be doing capstone projects, we will be presenting your final presentations making final presentations on as part of this module. This basically what we have in mind, we, I know that we have gone over the time limit by 10 minutes, but in case you guys have any questions, please go ahead, and we'll be happy to take it. Are there any questions that you would like to

thank you, Professor, we do have a few questions. So I'm just going to show the questions to you. And then you are Professor efinor. And whoever wants to take them again? Yeah, so the first question is, how is AI useful in b2b marketing and sales?

Yeah. No, no. Do you want to take that question?

Yes, definitely, we can do that. Yes. So, so one thing that I say as I was putting it earlier, irrespective whether it is b2b marketing, or b2c marketing, at the end of the day, we are talking about certain processes and activities, which we do as part of marketing, to reach out to the customer mapping the customer journey we have certain processes to follow, correct. For example, in my consulting business, whenever I try to build a relationship with the customer, we have a Salesforce platform where I keep track of the customer, and the data is captured at every stage. So Will I be able to make the best use of the data that is collected at every stage of the pipeline stage as an opportunity? Or when it moves to a qualified lead? Or at the contract stage? Or after becoming the customer? What is he looking for? What kind of queries are meetings that we have? At every stage? I'm capturing data? And will this data helped me out to analyze what the customer is all about? segment them properly? And then look at how to predict what would be the behavior of the customer? Is he going to give me more business? Or should I start investing in some other area of this particular client? So all these decisions I can take with the help of data, which comes from the analysis with the help of machine learning algorithms and stuff? So always try to look at it as what is the process that you follow? What are the set of activities that you follow, and all the activities are helping you to capture data. And now the data can be used as an asset with the help of machine learning algorithms for decision making. If you look at it from this point of view, in your own organization, you will find umpteen number of applications for AI machine learning process or if you'd like to add you can add to this Yeah.

So one example that I can think of is you know, HubSpot, which is in the online space. We also do the HubSpot case in class as well. As part of this course. HubSpot is a b2b. So it's selling its, you know, services or to other businesses. So HubSpot kind of uses a, to a great extent to score each of these leads, because you have several leads coming in, and to figure out who are the most valuable leads and reach out to them personally, and then you know, focus on conversion. So lead scoring is definitely one domain. So you like the person and also mentioned, so, you know, what are the things that they're about to purchase? What are the changes that can come in? And when do I need to intervene? When am I going to lose this particular customer is the probability of losing this customer. So all those data driven decisions can be driven purely by AI. So irrespective whether it's b2c or b2b, we're talking about predictive analytics. And if you can predict the, you know, predict certain occurrences, certain events with greater accuracy, obviously, you're gonna you as a marketer will be able to use that to a great extent. So the example that I mentioned was hot, cold and warm lead, right. So it's very much very much applicable to the b2b space as well. So it's not just for b2c in the b2b space. Also, you can apply it to a great extent. Yeah. I hope that answers your question. Yeah.

So the next one says, the Program website sales, no programming knowledge is required. But the curriculum appears to need technical knowledge. And this person is confused. It's a little contradictory. So maybe you could sort of clarify what extent of technical knowledge? Yeah.

So like I mentioned, even during the presentation, no technical knowledge is required. Because we will literally take you through even installing the platform prior to installing installing the our studio, which is free, which is available, it's open source anyway. And you don't need any sort of programming background, you don't need any sort of, you know, strong mathematical background, obviously, you know, basic mathematics, we expect everybody to know, but we don't need anyone with a strong mathematical background with the equations and stuff that is not required for this course. We will actually take you through different steps, for instance, we will, you know, I'll actually be personally taking you through the several syntaxes on how to clean up the data. These are the what what do you need to clean up the data for? How do you deal with missing data. So all these things are actually quite intuitive. And we're going to take it more from an intuitive approach rather than a programming approach. That's another reason why we were chosen are in our you can actually take it in an intuitive manner, and you can actually run the code. So no programming language, experience is required. And we'll be taking you from the absolute basics. And at the end of the each session, you will be you'll be having a for instance, if you're covering, let's say, a neural network, right, you will have a particular set of code and you know exactly what the code does. And you will be able to deploy it in everywhere every other place wherever you need. To deploy, write and test to certain things. So that's basically what we're planning to do, basically get you to develop templates, right? So we will be developing templates. And once you get the hang of it, once you attend one session of that sort, where we talked about us to do when we're talking about deploying algorithms in our, you will realize that it's actually not that complicated. You don't need to write pages and pages of code, we entered machine learning algorithms executed in a single line, right. So there are just data cleaning, that actually happens, all the other code is actually basically data cleaning. So which will take you through, so you don't need any programming background at all. It's more a management course, with a particular module that gets you there that makes make sure that you know, you get to get a hands on experience on deploying ml models. That's it.


The third one is, what kind of capstone projects are being done by the participants in the first cohort? Yeah,

we've had a diverse range of, you know, projects that have been done. One, I think, one was in the company in the digital marketing space where, you know, using Google Analytics data and user level, Google user level web analytics data, they're trying to predict the, you know, lead scoring and how to, you know, deploy different landing pages for them. That's one of the projects. Another project is, you know, the mutual fund space, can they actually figure out when a particular person is going to drop out of ASAPs? And what can I help? Or what do I do when I get that information? What if we can predict the probability the person is going to drop out at a particular point? How do we actually ensure that they're going to continue? These are the two things I can think of another project is actually in the I think, in the ZTE space, right and only do a lot of meetings because it's a confidential data, z TV space, they're trying to figure out, you know, what kind of, you know, we do characteristics, can it predict what we do characteristics are actually relevant for a particular demographic and can actually deliver it to them. So that is the viewership time in social media, when they actually share clips. So what kind of clips Should I create and which domain it should be relevant to? So that is another project that I could remember, there are almost eight to 10 companies that are involved in this, but all of them are real life projects, by the way. So these are the examples where Yeah,

the next one is a you spoke about a digital marketing, as part of the program, how much of digital marketing is covered.

As part of the program, see, we will be talking about more about applications of VA in digital and social media marketing, we are not going to make you an expert in paid search marketing, for instance, that's the objective of this course, the objective of this course is to actually figure out what are the applications of HTML in digital and social media market? One example would be like, you know, if somebody lands on your page, right, and you have a lot of data on how many times this person has visited you how, what is the visit duration, and the kind of products that they're looked at? Can you predict the probability of that person converting at this point in time, if I know the probability, can I actually deliver the correct content to them to increase the conversion rate, that's the kind of focus we're going to get into, we won't be talking about you know, how to analyze a paid search data, how to analyze out through search engine optimization, we're not going to cover all of those in digital marketing, we're going to cover a good applications, right of ml in the digital marketing space. Apart from that in the social media space, also, we won't talk in detail about how to make posts and deliver but we'll talk a little bit about when you have the data, how from, you know, insights, Facebook Insights, for that matter, we have posted a lot of content in the past, we have a lot of data on how many people liked it, what kind of promotions that you used? And what kind of conversions you had, can you build a model based on that? And can I come up with the right content to the right person. So that is an application of a MLM the social media marketing space. So we'll be going into those details, but will not be training you and paid search marketing will not be training you and you know, search engine optimization will not be training you on different platforms for social media. So this is more about applying AML in the space. So even if you know the basics, right? So you don't need to be an expert in the marketing space to understand what we're doing here. So you will be able to understand, yeah,

okay. Okay. The next one is I work in a startup? And how will this program benefit me? So it's essentially how would it benefit someone who works for a startup?

startup would be a very, very broad term. So it could be any domain, but irrespective of which domain you are in. So when you understand the value of data, when you understand the value of predictive analytics, when you understand the impact the prescriptive analytics can have. So you are you can, you know, kind of mold the entire organization in a way that's it's data driven, right? At each touch points, what are the kind of data that's going to be important for you how we can leverage the data in the long term. So without going through the post for you to visualize, that would be very, very difficult, right? So once you go through the course, you kind of understand that every piece of data that comes in is quite valuable. And you can actually use the data to make a lot of predictions to understand customers a little better. And to kind of increase the conversion rates a lot more, making the entire business process more efficient. When you go through the course you will understand you'll be able to visualize and see how the data can be utilized in different ways. So as to which startup you're talking about, there'll be an application no matter what.

Yeah. Okay. The next one is an interesting one where AI helps in influencer identification and customer audience selection.

Customer audience selection and influencer definitely yes. So if you look at, you know, influencer selection when you talk about mega influencers, right when you talk about celebrities, there, I don't think AI can play a huge role because we know who the celebrities are and how to choose them. But can you identify micro influences, so what we call as you know, mega micro, you have several categories of influences when you talk about micro influencers, who are normal people who have actually a huge number of following, and you know, who are likely to have an impact on your product if they talk about your product, not necessarily a mega influential, like a celebrity. So when you have 1000s, of people of that sort, can you score them? Who's actually who's likely to be a few, you know, successful micro influencer for you? And can you reach out to them? And can you actually get them? So ranking them, right, a could actually predict if you, if you have the data, we can actually predict how these micro influences whether they can actually have an impact on your business. So identify micro influencers, definitely as macro influences, you obviously know that they have 2 million 5 million followers. And when you're actually posting the content, they want to have an impact, but micro influencers, when your budget is limited when you want to go to normal people, and when you want them to talk about your product, can you identify them? There are several products in the in the market, which actually do that, out of the people talking about a product, who are the topless influences, you can actually automate that today. And you can predict it on your own figuring figuring out who the micro influencers are. When it comes to customer audience selection, I actually mentioned it as part of the social media marketing part where we are the past data on what are the posts you have made? And who's the audience that you have created? If you have the data you can actually write, you know, you can actually predict if I choose this particular kind of segment, what kind of response Am I going to get? So you can try out different audiences. And you can figure out, which is the right content that would match. So you can do a lot with a man in that space.

down to the last two questions, I think, currently, there is a challenge of tracking data across campaigns, especially in multi channel digital campaigns. Is AI going to simplify this process? We are yet to see any major advancement in this situation?

Yeah. Yeah, got it. Thank you. So try. That's a good question. So tracking wise, if you look at you know, tracking across channels, amen. Doesn't really clear on here, right. So when you're talking about tracking, we did have a tracking mechanism. So when you are able to track across channels, for instance, the other example that again, the same example I can think of is HubSpot. HubSpot will be able to track users across different platforms, like on your directly on your website, on Facebook, on Google ads. So it can actually, you know, pull up all this data together and give a complete picture of how many times this person has visited your page, what was the source of visit, the entire customer journey is very clear. So there are tracking mechanisms. And tracking has nothing to do with a ml ml is more about when tracking is done. So you have a lot of data with you. What do you do with the data? Right? How do you predict something? So AML would play a role in predicting a tracking is more like a mechanism, right? So tracking is, you know, you don't really need a artificial intelligence algorithm to track you need a system in place, which kind of logs every single visit every single interaction of the company, and then puts them up in a readable format, that's more of a software process that actually, you know, stores them in a database, what AML can do is take the database updates algorithms and predict a lot of things what you want it to predict, when is this customer going to come, you know, convert what is the lifestyle this person has, what kind of audience segment this person belongs to. So you can actually predict all of those with AML. So AML is, is not responsible for tracking, it's more responsible for once the data can convert this person and the how do we convert effectively? Right. You know,

the last? And the last question is, I think you touched upon this briefly, but please share some insights on the scope of AI in the healthcare marketing segment, especially medical device.

Okay, medical device wise, see, one example that we started out with breast cancer detection, right. So if a girl could actually read, read and detect, you know, detect anomalies or if you train them to read radiographs, and then the entire segment to go through a change. If we talk about third world countries, if we talk about rural areas, remote areas where you have, it's very expensive to get a radiographer inside. So that means you can have a system where the X rays are done. And then it's automatically being read by the machine and it's sent automatic report is sent to someone for validation or it's sent back, right? That kind of changes the dynamics of how the medical devices can actually penetrate the signals. So when you have an AML, so I mentioned about intervention, right? So you have AML, which can actually predict based on the X rays, it can predict what is the issue that is going on, right? And it's being validated by a person in a remote manner, then you kind of you know, have a different market altogether, the entire market goes through a huge change. So you don't want to need to physically have a radiographers or people, you know trained in reading those elements in a particular particular location to do this. So companies like Siemens already have certain projects, which are actually you know, focused on this particular space, apart from medical devices, right, so you can also think about it robotic surgery. So robotic surgery should be much more advanced. But if we think of applications of predictive analytics, can it predict, you know, well, what can be the demand? Now for this particular? What are the demands for this particular customer at each patient level? Can you predict the demands? Can I predict the inflow of patients? Can you predict which of these patients are going to keep their appointments, who are not going to keep their appointments? To that extent, EMR can help over that, but little devices can actually turn around the industry to a great extent. Yeah.

Okay. Thank you. I think we're right in time. So thank you so much, professors have an application. Also, thank you to the audience for joining in. We hope you enjoyed today's webinar. And that's it from us. Take care and stay safe.

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