Held on Monday, May 25, 2020
AI-powered Marketing is gaining more attention because of the insights it provides. This webinar gives a fine understanding of how AI makes marketers’ life easy and how IIM Calcutta and TalentSprint with its AI-Powered Marketing Programme has provided a platform for marketers to embrace the power if AI.
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.
AI - Paving Way for A New Age Marketing
A very good afternoon ladies gentlemen, this is precise information from economic times brand equity.com. And on behalf of I am Calcutta and talentsprint. I welcome you to this live webcast on the power of AI, which is paving the way for New Age marketing and math. You as all of you would agree the marketers objective is to position products to a specific consumer base by understanding requirements. Saying consumer data is abundantly available today is an understatement. The evolution of big data and advanced analytic solutions are made it possible for marketers to build a very clear picture of their target audience than ever before. Ai powered marketing has been gaining more attention among marketers because of the insights it provides. So hence, it's good for decision makers to understand and use the power of AI effectively while making decisions on marketing campaigns, be it budgets channels, identifying opportunities, or creating even a marketing USP regular innovation that machine learning and deep learning has enabled marketers to leverage artificial intelligence to deliver personalized marketing at scale and superior consumer experience. In this webinar, I am Calcutta and with its age in quantitative courses, marketing courses and and analytic programs, will equip participants on how they could kind of undertake a journey in AI and ml powered marketing decisions that will promote business growth, as well as profitability. The key discussions that will the key discussion points that will take place this afternoon include data marketing decisions, digital and social media marketing, ai ml applications and marketing, how to predict sales, how to understand consumers better, how to identify upselling or cross selling opportunities, how to enhance your marketing or to enhance consumer experience and service. Joining me are four esteemed panelists.
If I could kind of just take you through them.
With me is erythro Bhattacharya, a senior director sales at talentsprint. In this role, he heads the sales for all executive programs and platforms at talentsprint for both the b2b as well as retail segments. The next speaker arthro will be joining us for the q&a session that happens post the discussion in the discussion we have Oh, Professor Sham, who's the Director of the Center for sales and customer development csma CD, as they call it, and he is also the Robert J. True last Senior Associate Professor of Marketing at the true landscape College of Business at the University of Missouri Columbia. Dr. Shams research is in AI and machine learning, pricing strategy, sales, management and competitive marketing strategy. Professor Sham has received the outstanding teacher award for Executive MBA teaching at the University of Missouri. He is he has also taught at the IMD Lausanne Switzerland and the Indian School of Business, the Indian Institute of Management Calcutta, Larrick University in Belgium, among the many other He's also done consulting and executive workshops for many companies, including the whose work like McDonald's Corporation MetLife and so on. His research has appeared in major journals such as marketing science, Journal of marketing research, Journal of marketing, International Journal of Research and marketing, quantitative marketing and economics and others. He has also served on the editorial boards of marketing science and the Journal of retailing. The next speaker is lakshminarayanan ci, the Associate Director of analytics and cognitive at Deloitte, India lakshminarayanan ci is the has held senior leadership positions in companies like PwC, eight k mile software, among others. He also acts as a visiting faculty across several national B schools and he has a postgraduate diploma in AI and ml from IIT Bengaluru and also a pgdm in marketing and strategy from IBM luck now. Last but not the least. Now the fourth speaker of today is sherwyn Ajay Kumar, a professor at I am Calcutta.
Prior to I am Calcutta, Sarah when I was fifth I am today Paul.
He holds a degree in mechanical engineering from the Ph. D School of Technology. He has an MBA in corporate finance and e commerce from Cardiff and a doctorate in marketing from Im Ahmedabad. So, over to you Sherwin to kick off proceedings today.
Yeah, thanks a lot pressure to the introduction. So, a very good To all of you guys who have actually logged in. So today as pressures indicated, so we'll be talking a little bit about artificial intelligence and machine learning in, in the marketing domain. So with a specific focus a little bit more focused on applications rather than the technical side of things, right. So to kick start the conversation, I would like to actually show you guys a recent article, which was published in Jan 2020, on first page of Times of India, where, you know, they spoke about how a actually could beat doctors at breast cancer detection. So this actually is to just to show you guys the power of AI and you know, how it's actually becoming more and more prominent in different domains, and how, you know, we are trying to actually utilize or exploit this kind of power in, in the business world as well. So this news article is actually based on a paper that was published at nature, journal Nature. So this journal is an academic journal, peer reviewed journal, which is one of the top journals in the world. And getting published in Nature is not a you know, it's not an easy aspects. So it goes through several revisions and you know, checks and balances before. It's kind of accepted in nature. So it's kind of you know, it's not based on anecdotal evidence. This particular article is actually based on solid scientific evidence. This was actually a research done by a joint research team of Google medical research and medical centers in the US and UK. So what they were actually trying to do was, you know, feed literally mammograms into the system, train the system to actually see what patterns and interpret images. After training the system, they figured out that the AI could actually beat trained, you know, trained radiologists, who actually had the patient's histories and they could do a lot more reading into the Korean mammogram. So but the AI system actually could actually beat or perform outperform these radiologists in terms of cancer detection. So by a margin of about 9.4% reduction in false negatives, that's a huge margin given like, you know, if you take one lakh people, it's about 10,000. You know, false negatives that have been reduced. Anyway, so that's to indicate, you know, the power of AI. So the discussion of For this particular, you know, webinar would be more on applications of AI in the marketing domain. So with that in mind with that introduction, so let me just pass it over to lakshminarayana, who's going to take you through current trends? And you know, where is the market heading? lectionaries over to you.
Thanks, Rona. Good evening, everyone. And thanks a lot for joining this webinar. So before we get deep dive into artificial intelligence applications, we need to understand where the current market is setting out the jobs are changing all the marketing as a function in an organization, it is changing. So that's something we need to understand this deeper, so suddenly, we can go to the next slide. So we'll look at our quick stats. And some of the changes in the way the businesses are operating today. There is a changing landscape. Like for a long time organizations were going through automation as something which was driving their business for some time for the last 1520 years at least, you'd have seen automation in every organization. Now the next level of automation is happening whereby every organization is trying to see whether they can come up with strong data driven decision making. Of course, it has been there for a long time, no doubt about it, many organizations were following it. But with more data being consumed with more data being generated, every organization is trying to see whether they can see patterns in the data, whether they can understand data more, and come up with better decisions in this world. Like, let's say, for example, one year back people would not have known that they would be sitting at home today and doing the work. So how can we adjust ourselves and take decisions in such kind of a vulnerable, ambiguous world. So that's where the data driven decision making becomes more important. And that's something which is changing in the recent past. And automation and personalization. that's those are the new age mantras today. So on one side, you want to automate all the processes and try to get more data out of it to see how you can use it in your decision making. On the other side, from the marketing point of view, and from delivering some services to the customer, you want to personalize everything. Can you make it iper, localized, hyper personalized for a consumer? That's the kind of thought process that runs in the companies today. So anything and everything, what I mean, we want to think from the customer point of view and understand why the customer needs this. If he's buying the product, what could be the thought process behind it, can I find out more about the contextual data that is there and come up with the conclusion and then help the customer out with the right messaging. So such kind of mantras are driving the digital strategies in the organization today. And of course, I need not tell you the big data, advanced analytical solutions open source community. Everyone is coming together today in the last 10 years to bring this transformation that is happening in the market, which is actually helping organizations to adopt AI to adopt new age technologies in different functions. And of course, I the important aspect is if you take business, the one they won't apply to you transformation, the first function they would focus on is usually marketing, because that's a revenue generating firm, revenue center of the organization. And people would like to explore and deliver better service, find out better customer needs, through data that is available to them. So let's look at the cities that have been done by different companies. So PwC says that 70 per 72% view as a business advantage. So data as an asset is something which should avert for a long time. And that's something which is becoming real, more and more real. And in fact, going with a popular joke today, it is like who is driving the digital transformation? Is it your CEO See, cio or the COVID COVID is driving it and the whole acceleration process has happened in a very quicker manner now. And the other service from MIT also says that nine out of 10 companies are already using AI to improve their customer journeys. So I cannot tell you how important it is. People are adopting technology. People are adopting technology for better decision making to drive better solutions for the future. So, this is where the market is slowly adding. So suddenly we can move to the next slide. So, what is important hours for the decision makers, they need to understand the use use the power of AI, they everyone talks about AI today, everyone talks about machine learning, natural language processing, use of digital workforce, all these are very popular terms in the market. But the problem is not many in the market do not have the right picture of how they can use this power of technology in their business, how they can use it in their specific function, right? They understand the technologies were very powerful, but out to get the technology, customize it for their organizations. That's where the there is a bit of an holding that is needed, which we consultants are generally providing in the market. Like, for example, in the cases that I mentioned, the slide deck marketing campaigns, analyzing the budgets, finding the right channels, identifying opportunities through those channels, and creating some USP for your marketing. All these things can be done with the help of data, and can you help Make use of powerful technologies like AI to look at the patterns in the data. That's what is more important. That's where people are looking more identifying more business cases for implementation. Going to the next one.
So yeah, in retail, I'll hand it over. Back to Savannah. So, Sarah, take it forward, we will discuss it again. Yeah. So to bring in, you know, some perspective on how AI is being used in different domains. And so, we thought we would share a few examples of you know, how what are the current trends in in different domains? So, if you look at in retail, I mean, you would come across a everywhere so this one screen which I think all the participants will be aware of, I mean, we'll be well versed with so Amazon right, so Amazon, so we have 18 crore SK use on the portal, but still, we don't feel like you know, so there is overwhelming number of choices or we don't get actually fatigued or we don't lose interest when we actually shop on Amazon. So that's because you know, there's a powerful machine learning algorithm In the background, that's actually making it much more easier for the, for the users to navigate through the, through the products understand, you know, throw really relevant products in front of them, in spite of being trends, or you know, customers are very similar to the profile of the current customer, you know, showing products that are purchased by those customers. So that those are all really, really powerful machine learning algorithms that is running the background. So it may look simple and the offset like you know, so it's very simple, but it's actually a much more complex algorithm that is running bn because you're talking about 18 co products and running at scale. So that's one example of a application retail in retail and e commerce almost, you know, all the leading players in the world are actually leveraging AI to great extent. So not just with retail, even in Facebook, right? So if you just look at the people graph people you may know so it's able to predict, to great extent you know, what we're you know, you might be connected to on throttle suggestions of that sort, what kind of ads you might be interested in what kind of products you might be interested in based on your now you know, based on your interactions on the web. So the Facebook utilizes a, to a great extent, to throw relevant information in front of you. And the other example is actually, of course, Netflix, where, you know, they have a lot of content, but still be able to curate the content showcase, and come up with recommendation systems that are really effective, right? So there is a, there's a very strong recommendation system that's running based on a machine learning algorithm. Right? So these are some examples of how AI is actually being used to create understand profiles and create a lot of recommendations and come up with
just some that are very relevant to the users. So if you look at retail, I think recommendation engines are at the core of digitization and retail at this moment. So you have product recommendations, you have promotion recommendations, and even in logistics and delivery, right? So routings have actually become much more faster and you know, saves a lot of money for the businesses. Walmart even has a you know, innovative retail lab where a complete store is run by an AI where they have, you know, cameras on top of all the aisles, which generate about 1.6 10 bytes of data every second. And there you utilizing the data to understand what are the products that are being picked out, the system is also being used to actually manage the inventory effectively. And they have huge screens and the entry to the store pair, customers are able to select the products using the recommendation systems and then pick it up in a quick manner and then checkout. So a lot of innovations are actually happening in the retail field. Right. So this just to give an example of you know, how a might actually help out and understanding customers better. So there's just one example of a transaction if you look at the screen, from a from a particular family in Walmart, so you see that the families are actually purchasing a Diet Pepsi. So they're also picking up some Atkins bar and some first drink. So probably there is a woman that also is actually counting calories from the slimfast. It's actually targeted at women. So you get to know that you know, there is a woman in the household but you also wonder why there are ice creams popsicles Tito's if the person is actually counting calories, so probably they have a kid so maybe they have a kid in the house. So this is just from one transaction. So the family comes back to Again, let's say after a week or so, and they're actually making this this is these are actually real life transactions from a single family. So we can see that they're actually purchasing some baseball cards. So definitely there is a kid in the house. And they're also purchasing some dog treats. So maybe they have a pet at home. So you get to get a picture of you know, who are out there in the household and what kind of behaviors they might be in. So this is another transaction from the same family, we're able to track them using the registrations or you know, phone numbers that you typically give out. They're buying fishhooks, so maybe they're interested in you know, fishing, they are also buying marine oil. So that means they own a boat. So when you put these transactions together, you get a kind of a clear picture of the household as a whole. So there is this cute little family with a kid and pet as well, as you know, they're interested in she's interested in fishing and they have a boat. And once you get to know the DNA of the entire household, he can actually do a lot more in terms of predicting when the lifestyles are going to change. So are they expecting a second child? Are they going for a mortgage? Are they are they in the market for buying any insurance, so what is the right time to actually approach them for all of that So these are just from three transactions. So you can imagine, when you have hundred transactions, the data that we have right now in terms of, you know, transactional data when you have hundred transactions, so we can actually put together a much richer picture of you know, who's in the also what are their buying habits? What stage in? What milestones have they actually come across in the life? And can you predict the further milestone that are gonna happen? And based on their can you actually send out proper recommendations and proper suggestions to them. And these are some of the examples of you know, how Walmart and other top companies are actually utilizing these transactional data in an innovative way to unleveraged in a in the process to actually better serve customers. So that's the retail space. It's a very exciting space. And we'll be covering a lot about that in the program. Even if you look at the banking sector, right. So, there are a lot of applications of AI for instance, of course, all of you will be aware of chatbot, chatbots and ICC and a lot of other players are actually starting to use chatbots where they are eliminating the you know, the need for customer service and faster responses to the customers. And fraud detection is another area where a ml can actually contribute a lot. In terms of detecting credit card frauds, you know, anti money laundering, AML fraud detection. So that's another area where you know, AI and ml is actually being increasingly used even for churn prediction. So when a customer is likely to win, are you likely to lose a customer. So if you can predict that too, with greater certainty, you can intervene. And you can come up with some sort of a marketing intervention to try to, you know, retain the customer for a longer period. So that's another space where is being used a lot in banking sector, and also in algorithmic trading, right? So you can identify certain patterns and pick out what stocks are actually trading. That's another emerging area where is being increasingly used. And finally, we can talk a little bit about health sector where, you know, there's just a prediction from Accenture that what will be the value of these different services using AI by 2026. So this was actually published in Harvard Business Review. Last year and bad they predicted that surgical robots are going to take over and that be like, you know, at least an increase to $40 billion by 2026. The value of that particular domains, so the value increased due to AI. And you know, there are a lot of other areas where AI and ml could actually contribute healthcare, for instance, even an administrative workflow, for instance, can you predict no shows? If customers are actually coming up with the patients actually making appointments? Can based on the past behavior, can you predict the likelihood of them showing up or not showing up? And can you actually make, you know, make your rosters flexible? And can you plan your workflow within the healthcare system? So there are a lot of applications that are possibility ml, right. So these are just three domains that we wanted to just give you a glimpse of, you know, where, you know, a ml is actually doing a lot in terms of engaging with the customers in terms of enabling the companies to better understand the customers and provide better recommendations and suggestions. Right, so I'll hand it over to Sham to talk a little bit more about few examples and you know, where, where the program actually comes in? And what is the need of the show? over to you.
Thank you, Sara, and Good evening, everyone, and thanks for making time from business. scheduled to come be with us for for an hour of your lives. So I'm just going to take a little while to to basically discuss why is it that we need to be as marketers or people who are interested in the overall domain of business to be involved in AI. So here's Sarah had started off with something about breast cancer detection. And that was in the health space. Here is an article again, this is a peer reviewed journal Harvard Business Review. So you know, when you submit an article, it's not like an anecdotal war story that you can wave your hands. This is a you have to actually provide data to the editors of the journal to convince them that what you're saying is actually true. And if you can kind of kind of see here the number there is surprising when I first saw it, it kind of hit my eye but this is actually a 3,000% increase in sales or sales leads, but for Harley Davidson when they started using AI to kind of target in a with a very razor like fashion, the right consumers the right prospects for their you know, sales leads. This was and this was a company, this was a dealership that was struggling. They were in the baseline of about 20 30% increase, and then they went to a whopping 3,000% increase in the sales leads. Now, can we go to the next one's for Cerner.
So in some sense luxmi and savona have both talked about AI and marketing. And this is where I really am focused on is marketing applications. And there are many applications. We don't want to discuss all of it right now. But there's application design, segmentation, targeting positioning, the typical holy grail of marketing, which is STP. And of course, within all of these you can there is huge applications of iron and I'm personally involved in applications of AI and pricing, because pricing is one of my major research interests as well. So there is applications of AI and machine learning in pricing in product design, promotions, and Saravana was talking about how Do you promote or cross promote or upsell to people, depending on their psychographics, demographics, behavioral characteristics, and all of that can be honored by AI in a way that human beings just cannot by eyeballing receipts upon receipt and have how many receipts Can you even see. But basically an AI system doesn't get fatigued. They don't go through divorces. They don't need to take vacations, so they can keep on looking at receipts after receipts, 10s and billions of them to come up with a pattern to say this is the kind of household that we're dealing with. Like he mentioned, there's a boat and there's a there's a there's fishing involved. So maybe tackling bait and fish charm, all of those things can be sold right now. I was I just want to step back for a second and say, much of AI has been basically when you when you go to a very lazy Google search and look about look at all the people who are, you know, training people in AI and machine learning, you'll see that the vast majority, the overwhelming majority are From computer scientists, statistics, now more and more people are starting to realize that you need to have domain specialists involved in that conversation. It cannot be statisticians, it cannot be only computer scientists because you need to have people who can actually understand the business who understand marketing, who understand finance, who understand banking, who understand retail healthcare, what have you to be involved in the discussion. So we need to have a situation where people can come in and talk about the business case, make the business case happened applications orientation, when they talk about AI and machine learning. And that is why business people in business schools are critical stakeholders in a way that has not been the case in in many instances, right. So can I go to the next slide. So just to talk about this, this is a term that I use when I talk to you know, my students and the corporate workshops that I do, this is a term that I use with There is a ds ds gap. This is the gap between domain specialists and data scientists. Right? So the domain specialists are basically people who've, you know, gotten a master's degree or maybe a PhD degree in statistics, they can actually handle all the data, but they don't understand the date. And they can come up with the recommendations that are kind of nonsensical because they don't understand marketing, or it's they haven't tried to they don't understand finance, they don't understand healthcare. So in some sense, you need you need. So those are the data scientists. So you need the domain specialists, the marketers, the finance people, the healthcare people to talk with the data scientists. And that gap, which is basically the translator it is when you see here, this is this article into towards data science, which is the primary blog for machine learning and AI, which is fine. I'll just take the first quote, it's saying you need a data science translator a communicator. Now what's a translator? The translator is a person who goes between two different things. people speaking two different languages. And here you have the data scientists talking of machine learning AI, but they're basically into the world of programming or statistical modeling. Whereas on the other hand, you have the domain specialists who are in marketing who are in let's say, promotions, who are in sales or in pricing, and they don't talk to each other. So there is a crying need for translators to bridge that gap. Okay. So then a very recent article, and this is Jan 2020, was the second liquid draw your attention to the second one, which says, you adapt to statistics and machine learning courses to have applications in the forefront? Now those applications cannot be given best by statisticians don't applications cannot be given by computer scientists. They have to come from business people and from business schools. Okay, so, in the seminar spirit, I want to add just a little bit more and put some more structure on what we mean by translators in the domain specialist data scientists gap. Can I go to the next slide serrana
The next slide.
Okay. So this is again, a form format that I've come up with when I talk to my, you know, the people who are actually part of this journey with me. Basically, this is for my workshops, I make a distinction. So first of all the MIT article that I mentioned, it made a very critical distinction between front end data scientists and back end data scientists. Now what do I mean by that? The back end data scientists are people who deal with hardware, computing, databases, data storage, and IT infrastructure. We're the front end data scientists. These are the people who deal with analysis, model building machine learning AI. So these are the people who deal with the model building it the way that we think about it. Now there are internal boundary spanners. Because the backend data scientists don't deal as much so you can have an IoT person who hasn't really done Machine Learning on AI. So in some sense the front end data scientists need to reach across the aisle and talk to the back end data scientists, but these are the internal boundary spanners within the company. When I say boundary spanners, these are the people who walk to talk across boundaries, who break up the silos and say we need to talk to a different division to different business unit to a different department within the organization. So, what we are targeting is the front end data scientists who understand machine learning who can who can understand AI, but most importantly understand the applications of AI and machine learning so that they can talk to first of all, the internal you know, group which is the data scientists in the back end. And also there is a domain specialists who are on the other side. They are not internal to the data science group, but they are inside the company. And those are the people in marketing, sales, supply chain finance, accounting and human resources. So these people, the people in the front end data scientists, these are our core audience. They have to talk to also they have To be external boundary spanners. So, they have to be internal boundary spanners talking to the backend data scientists and they have to be external boundary spanners talking to the domain specialists. So, there is a crying need for translators who can become external boundary spanners and also internal boundary spanners and I view this course of course of this kind as basically enabling people to become translators, who can become boundary spanners between the domain specialists externally within the organization and internally in the data science group as well with the back end data scientists. So, that is what I have and that is what I think that we are trying to accomplish in a course such as this, which is completely leading with applications, leading with implementation and not getting lost in the weeds of more than more mathematics techniques and programming, which is what the CSP clinical side statisticians are doing that to you, sir, I wanna
I just had a quick point though.
Thank you so much. Oh, by the way, Sharma is actually also working on a book on AI applications in marketing. And yeah, yeah. Yeah, sorry Xiaomi put it out wonderfully in the presentation. I think I should add from my experience too, like in the industry, what we see on the left, that is the data scientists, we usually call it as a technology world. The one on the right is more about the functional side of things. And initially, the kind of the line that separates the two was very clear and 10 years back, people did not see the need for both of them coming together. But today the market is heading towards a direction where we need everyone to be strong to be a strong techno functional resource. That is you choose a technology you choose a functional area based finance marketing, you choose one area and you need to marry this technology and the function very well. And you need to bring the better business cases for the industry for the company to look at. So that's what I mean a yo brought it out wonderfully and I mean for me to do a postgraduate diploma in AML is something which is of the same reason after an MBA to get the best of both worlds for serving my clients. So thanks for pointing it out. shamba that was a wonderful point. That is Yeah, thanks me. So I was just saying, process Sharma is also writing a book on a applications and marketing with Emerald publishers, which is soon to hit the stands. And we but he is actually literally written the book on a in marketing. Right. So anyway, so we'll talk about few more applications in marketing domain. Before we jump into the cost details and discuss a little bit more about how we have structured the goals and what are the likely takeaways. So I'll request a lecturer to take us through few applications in the marketing domain, and then we can jump into the course from there. Yeah. What would you like me?
Sure. Thank you very much, Sara. So
I will just before we before I start getting into the applications, I just need to tell you a couple of things about the power of gay So what makes it different? Why are we talking about AI so much? And when people say that AI is a new electricity? Is it just a buzz about the new technology? Or is it something really making a change in the market? So you need to understand one aspect what artificial intelligence has brought in. So in our conventional thinking, we used to think about creating a system in such a way that you design the algorithm, and then you pass an input and then get the output from it. You're never worried about the data that is being passed into the algorithm for getting the results. But what AI has done is it has created a shift in the way the algorithms are being processed, that is the way the machines are being coded. Now what has happened is this machine learning artificial intelligence is all about understanding more about data, understanding the patterns in the data, and thereby Can you come up with better results from the data that is available with you? So innovate has created a paradigm shift from what was it was an algorithmic problem earlier? What could not be fit into the algorithm big problem earlier? Is there a way that we can look at it as a prediction problem today? It may not be 100% accurate while predicting it like algorithmic problem, you know that what would be the what would be the result video, pass the data and get the output. But in prediction problems, of course, there is a bit of probability involved, and it may not be 100% accurate. But the point is, every problem can be seen as a prediction problem today. Let's say for example, Walmart example with Sarah was explaining, you see a clear cut data that was one month data from the push from the shopping that has been done at Walmart. Just imagine if you get 60 to 90 months of data, the same monthly data of provisions that you buy the same bills that it gave it and you pass it to a mission. It can clearly see the patterns as Sarah was explaining, Omni kits are there, what is the health ailments? I mean, 10 years by It reminds me of the story that has happened to me 10 years back in my bs code, like one person, I mean, they collected the data they simply put across a month. marketing analytics tool ran on it on 60 months of provisions. And they were able to decode and tell us very clearly from the provisions data, how many people are there in the family, gender, the salary package that normally what is the average income of the family, then the health ailments that they have depending on the products that they purchase? So they could decode everything? And what is the best part of it when you know all these details? Don't you think you can package as a marketeer and do some new offer in the next month exclusively for you? That's what we call it as personalization. Okay, so data patterns, you're looking at it and then giving it so what was something which was not happening earlier, you're able to do it with the help of this new technology, which is solving a lot of problems. That is artificial intelligence. So now with that, I just quickly jump onto this Netflix, I am sure with COVID-19 all of us would have subscribed to this platform. It has become a kind of a mandatory need. I think some companies will offer a free membership soul to these platforms, I guess. So look at the way that Netflix has done it and it's I mean, the popular case study is, there was one series that was completely designed with the help of the data, the patterns of the data of people viewing the series. Can you guess the series? I don't know whether you have watched House of Cards. That's something which has been completely designed, the directed the kind of actors, the kind of scenarios, everything was chosen based on the patterns that emerge from people watching other series. So what do they like? What kind of actors do they enjoy? Which mode which scenarios with what people enjoy? So all these were put together to create the best series? So and recommendations? We already spoke about it, Amazon does it Netflix also does it then personalization of the thumbnails. So for example, the Stranger Things, can you come up with different thumbnails and see which one is attracting more attention from the customer, which one makes the people to click it and watch it? So we can do that? So at the end of the day, all these aspects are more about trying different things out Quickly collect data, find pattern and see whether it is successful or not. So that's the beauty of bringing machine learning to Netflix. So streaming quality adjustment of course, all the companies have brought down the streaming speed No. So, all these are made a difference. So, now we can move to the next slide. Thank you.
Amazing Amazon is the next one which I would like to talk about
Amazon it not tell you AI if somebody is implementing it as the kind of lifeline for the organization, it is nothing but amazing. We are all talking about a driven marketing AI driven specific functions. But if if you ever talk about a driven organizations, then amazing is the one which stands out which sets a role model for everyone actually. So a driven organization is amazing on it, which has set up the trend across like recommendation engine. Alexa, gathering your inputs and placing orders in different websites. I mean, it's on ghost toes. I mean, I'm sure if you have visited us, you would have definitely gone to the zoo. misconstrues just for the sake of experience, and all these things, there is a machine learning AI that is involved which is actually driving the scenarios. So I'm not deep diving, deep diving into the scenarios here for the simple reason you should. I mean these are in incent thesis to you. And of course when you explore and read you should be able to connect to the concepts that we are talking about here. Go into the next one Sara. So dynamic pricing Shannon was the Singapore net. So that's something which is a popular case today. Like Hilton uses machine learning based system to understand the demand and forecast the demand for future and they saw that the pricing would change and amaze one on eBay also uses dynamic pricing the same price you will not find it every day. And the for the products that you purchase. If it is a regular one can I keep keep it up higher price all these things. For example, I have a two year old daughter made out obviously the wife's and everything the guess which I buy is going to be at higher pricing because I buy it in Amazon every year. They may not offer discounts on those products, because they can clearly see a pattern, the till my daughter goes hold, obviously, I'm going to buy baggies, and I'm doing it regularly with Amazon. So they know that I'm not going to shift to some other platform. So there is a pattern emerging everywhere. And yeah, we are all victims of the super dynamic pricing. So surge pricing is something which we all of us use it right as a customer. But on the other side, we have artificial intelligence machine learning, which is driving this dynamic pricing system. Makes sense?
So moving on to the next one.
A telecom companies, I'm sure you'd have seen a pattern like if you're getting the itemized billing. Do you see some recommendations coming out of it? Like some companies for I mean, I use Airtel and in the iteration bear it. I've been the complete bill. I clearly get the recommendation that you're consuming a lot of data. So would you like to have data related packages In your SIM card, I mean for your service. So such kind of analysis is being done with Delta consumption, they just amylase the consumption patterns. And they give exclusive offers for the customers. That's one churn analysis, I think Sarah touched upon it, who is the customer was going to leave Next, we can definitely see a pattern right. If you are going to switch to geo or some other company, the kind of usage in your SIM card will obviously come down. So that pattern will give them give the power to the companies to give exclusive offers to retain the customer. That is retail, Sara touched upon it in detail. And this is one space where technology is used left, right and center. And I'll be honest with you once upon a time, it was really tough to collect the data of our people shop. There used to be a tough farming we used to conduct the market surveys and all these touch points used to be there. But the kind of quality data was always a challenge. But today with the advancement of technology, data gathering as well as data analysis With the help of technologies like artificial intelligence have become much easier now, what kind of merchandising that should be there in different shops? What kind of SKU sell Well, which one will go better? Which one will not in which store all these analysis can be done easily with the help of technologies like AI?
Going to the next one?
So I'm sure now you have understood that AI is very powerful. Yay as a technology and marketing is always attractive, isn't it? At the end of the day marketers are the persons who bring the revenue to the company and everybody loves being a marketeer. But are you a data driven marketer? That's where the new world is heading towards? Are you I mean, you showed some role before 1015 years back was if you're good in communication, you can be a good salesman, isn't it? That's a popular myth. But today, the thanks to all these technologies thanks to the kind of competition that is there in the market, the data driven decision making has become very, very prominent in the market. So you need to go up You need to understand more about the data more about the customers with the help of the data, and then sell accordingly. So look at the kind of job openings that are coming up in the space. So yay, data scientist is considered to be the most sexiest job of the 21st century. The data scientists with the business domain makes it even more attractive, isn't it? So 10 lakh jobs in 2021. That's what analytics insight predicts. And it's a great news for digital marketers. I am not sure how many people only a few are doing digital marketing. If you do digital marketing, you know the importance of data driven marketing, our campaigns are to be executed and in their AI mixer plays a bigger role and you can automate all those campaigns with the help of artificial intelligence and environmental responsibility. All these areas are looking for a specialist with the domain knowledge and Microsoft is investing in EA and this is from the Deloitte report, the most wanted years Most Wanted is which AI skills are adopters most urgently seeking. That is all able to bring the business knowledge with the AI that is a researchers and the business leader, as Shannon was pointing out there is there was always a gap. Can we try to bridge the gap? Can we bring skills which can shift our focus from AI researchers to a desire for business leaders, that will actually make a difference for all of them. So going to the next ones on Yeah, so finally he so I'll take over from here for the program on artificial intelligence. Yeah. So so this is just to give a brief overview of you know, what the program is all about? That we are prime Calcutta is launching with talentsprint. We'll be having election RNN and President Landry shamas visiting faculty in the in the program as well. So I am one of the program directors for this particular program. The other director is actually Professor David shisha who's actually an expert in Internet of Things, augmented reality and virtual reality. We'll be taking few sessions on those topics as well. So just to take through a few of the details, I think the objectives of the program are very clear. We've covered you know, during the discussion phase where, you know, companies are striving for a data driven approach, there is a huge demand for you know, marketing and technology professionals are the ones who actually can actually bridge this gap. And, you know, where there is a lot of need for upskilling in AI. So that's one of the reasons why we actually designed this program and came up with this program, let the shine was very excited about it, and we wanted to be, you know, actively involved in designing the program and to deliver it in India. And, you know, we're really thankful that he's actually played a very active role through the, you know, through the design of this entire program. Right. And, you know, it's the program is targeted as CS pairing to cxos current and aspiring cxos across the different sectors. We are it's not just to a specific one sector. So, we will be covering examples and case studies from different sectors for the entire program. And we have a lot of in house experts in marketing analytics and quantitative analysis. And I am Calcutta so we'll be certificate be covering different topics during the, you know, different from different modules, just to take you through the program model, these are the different modules that we have. So we'll start with, you know, data driven marketing decision. So I'll be taking you through a few of the topics that we're going to cover in each of these modules. So I'll be taking you through each of the modules in a little bit of detail in a while. So the participants will also be going through a capstone project. So I'll be talking through a few details about the capstone project, as well. Right. So the pedagogy is actually interactive sessions, we'll be using technology, and it'll be delivered in a D to D direct to desktop mode. And we'll be using real life case studies. And it'll be a combination of in class quizzes and assignments are using which the participant will be evaluated. duration is over a period of six months. And the total number of hours that we covered in this course will be 120 hours, and shedule. The cool you know, sessions on Sundays and Thursdays, so Sundays, it's 10am to 1pm. So on Thursdays is the evening so it's easier for people who are actually working to take out take out our labor time during the week to work on this
We initially planned for two campus visits. But given the covid scenario, I don't think the first campus visit will be possible. So only at the end of the program, we have tentatively planned a campus visit. So assuming that things will be back to normal, and we will be able to do a campus visit at the time, so participants allowed to fly down to I am Calcutta, if the situation, you know, permits that is, that's what we tentatively planned. So you there'll be three or four days of, you know, closing sessions in the campus, after which the certificates will be distributed. And that's what we have in mind as of now. Right. So the course details coming to the first module, data driven marketing decision, so we'll be talking about few apply, you know, data driven marketing application. So, starting with segmenting, targeting and positioning, and, you know, customer centricity. So what role does data play and how bad does AI and ml fit in in different aspects of customer centricity? Understanding consumer habits, you know, how do you understand consumer habits from different, you know, transactional data sets and how do you educate the consumers on products that are innovative products that are coming into the market? What are the best ways to reach out to reach out to the consumers and providers a and ml fit in, in this particular role, right. And we'll also talk a little bit about branding and pricing strategy. So just to cover the basics of marketing, and just to give you a preview of, you know, bad a could be applied, you know, in all these different topics. That's what we have in mind for the first module is data driven marketing decisions in the second module, fundamentals of AI and ml where it'll be covered by personalization. So Sam, can you take them over briefly on what you have in mind for this particular model.
So this part basically is, in some sense, the very broad 30,000 feet view of the key concepts of AI and machine learning as it applies to marketing. So the key word here, it's an applications oriented introduction to AI and ml. So we are not going to get lost in the weeds of doing more and more programming or more and more mathematical modeling, but basically keeping overall concepts in mind. So you can become Good translator talking across the aisle to other people who haven't taken a course such as this. And you can tell them, how is it being applied, what kind of data data sources are being used, and what kind of interpretations and you know, insights can be developed from machine learning and AI. So the whole aspect, the whole task, the whole focus intensely, will be on applications, interpretation, visualization, and implementation. And we'll talk about the usual suspects of AI and machine learning, building with neural networks, support vector machine decision trees, we'll talk about that in detail. But overall, the takeaway for you guys should be this is going to be overall key concept of AI and machine learning as applied to marketing and other domains like sales, pricing, including healthcare, retail, and so on and so forth. So the this is my, you know, this is my goal with this module completely.
Back to you, Cerner.
and the model will mostly be, you know, yeah. And we will be actually there won't be a lot of statistics or there won't be a lot of equations in this module, we try to keep it as simple as possible, you know, and we talk about the intuition behind all of these models, and you know how they are and more application oriented. That's what we have in mind. So even if you don't have a statistics background, even if you don't have a strong mathematical background, that's perfectly fine, you'll still be able to understand everything that's going on in these sessions, because we'll be mostly talking about intuition. So what are the intuition behind each of these different techniques and how they are actually outperforming the traditional models that we have? Right, so that's what will be covered in this particular module. We're going to the next one, so where you know where you get your hands dirty, and you try out a few AI and ml algorithms and using a statistical software which is our again, so this will mostly be you know, application oriented, you don't really need to have any coding background or any sort of mathematical background to be able to, you know, understand what is going on here. So we this particular model will be Covered by me, so far, I'll be taking you through, you know, kind of like a template, where this is what you need to do to run several models, and you can just use those templates and make few changes, make few changes, you know, few edits, and you'll be able to run the run the, you know, algorithms yourself. So, as and when Sean will be covering the intuition behind each of these methods parallely I'll be covering the, you know, the, how to implement that in our and in a very simple way, in a simple manner. Like, you know, some of these models, you just need five or six lines of five or six lines to run the entire model you just need to, and what you'll be able to try it out with different data sets that you might have in your you might might have from your company or from your business that you're running. So you'll be able to do that on your own. We'll be covering this in a very simple manner where you don't need really any any sort of coding experience, right. So that's the module on HTML using our so we have a separate module on digital and social media marketing because of the you know, because most of the digital and social media techniques involve a lot of data analysis. Then a lot of prediction mob, you know, bitmart, beat estimation and other stuff. So that's we've actually kept a separate model for digital and social media mark. Here we'll be talking about, you know how AI and ml can help you with search engine marketing, in terms of, you know, reverse engineering certain aspects on what works on search engines and what doesn't work for your particular domain. In search engines. We'll also be talking about data driven paid search marketing, where you will be able to optimize bids using a an ml. So in terms of and you'll also be able to do some sort of, you know, optimizing your customer acquisition costs, optimizing a paper, click, so all of those will be trying to cover few examples and show a few data sets as well. We also talk about attribution modeling. So we will not be going to detail in terms of how a name will be applied in attribution model. But we'll be giving you glimpses of some data that has been used in the past into identify attribution modeling, and a little bit of social media marketing as well how AI NML can help you with social media, how social media platforms are actually leveraging AI to a greater extent. So we'll be covering that in detail, and we'll talk about you know how come Please can leverage the NFL to enhance their marketing efforts in this space. And finally, the aim of applications in marketing, we have several different domains where we'll be covering, you know how, with few case studies and data sets on you know how to predict sales, how to estimate effectiveness of promotions, can you identify which customers are going to quit and you can actually lower your high value customers, can you predict defaults or credit card defaults. And we'll also be covering segmentation using different techniques in detail. And Market Basket optimization and recommendation systems like similar Tommy's on Netflix out to what is the concept behind it the basic concepts behind Market Basket optimizations, how to come up with recommendation systems for movies for groceries and other supplies. We'll also be doing a lot on customer sentiment analysis, sentiment analysis is actually picking up quite a bit in in the social media space where you would like to predict you know, what is the sentiment in each of those comments that are actually coming out. So when you have a huge set of comments and data that's coming out of verbal data that's coming out of social media. You don't want to go to Do them one by one. So we can actually aggregate them and try to identify the overall sentiment that's coming out of this particular set of comments in your page in your business page or in your campaigns that you're running. We'll also talk about natural language processing applications. And finally, we'll also go to neural networks we'll be covering the basics of neural networks and how these artificial neural networks and convoluted neural networks are actually can be applied in marketing. So that's what we have in mind for AML applications in marketing. The final module will be Futuristic Marketing, where we'll be covering a little bit about So, this will be covered by my co director presentation shaha. So, where he will talk about blockchain and smart contracts for marketing, IoT is augmented reality and also you know, a bots into RPA. So how to enhance customer experience use think bots and RPS and finally marketing the world of Alexa we covered Bill equinor and and where he will talk a little bit more about you know, how to leverage Alexa and how to actually better deliver customer experience. Right. So this is what we have in mind for the program. And as part of the program, the participants will also be required to do a capstone project. We'll be putting them into groups. And they'll have to take a real life
issue and maybe use a secondary data from their own organization on from a partner organization and run through the models that have actually taught them during the course. So we'll obviously be helping them through the entire process. But, you know, they'll get a, you know, they'll get some sort of sense on you know, how to really have experience on running few models if you've taught them a day ml using our module. And they will be run those models and get those results and see and if they implement that's well and good to come up with recommendations at least. So that's what we have in mind for the entire project. Right. So with that, I would like to hand it over to Rashad. So if there any questions or any anything to the participants would like to ask, so we'd be glad to actually answer them. Going forward. Yeah. Thank you, Sherwin. I shall Ahmed laxminarayan. This is a very enlightening session. And thanks for taking out so much time to explain in detail to this audience. So, I, I was in fact contemplating whether I should enroll for the course myself. Though I can't call myself a marketer by any stretch. So Bhatia knows lots of questions from the audience, which also shows that they've been paying a lot of attention. So, first question is what kind of data skills should a person have if they are marketers? For example, should a marketer learn programming for data analysis or should marketing and stats be combined in the academic world itself? What are your thoughts?
So, can I take that?
Yeah, please go ahead. Okay.
Yes, please. Yeah, so
here's my quick take on this. The most important thing for a marketer contemplating using AI and ml is understanding the data itself. That is what gives him or her a leg up in in applying AR because the fact nowadays is with so many resources available online or even in your organization, every company unless it's a very small mom and pop store has a data science group, it might be one person, but it has some kind of a data science or analyst group. They already know how to run simple programs or at least they can they can learn how to run simple programs very easily. So instead of the marketer reinventing the wheel and saying, Let me become a statistician, which is completely the wrong thing to do, the marketer should say, I need to become a better marketer, which means I need to understand the data. If it's let's say, it's again, I go back to something which is very dear to me, which is pricing, just because pricing it hits the bottom line very quickly and immediately. You should understand pricing, you should see what makes the consumers willingness to pay be high or low and what kind of promotions price promotions, you should run. And once you understand your data, you can take it easily across the aisle and this is what I mean by being a translator you can take it across the I sit with the data scientists in your organization and say, can you build a predictive model, but every time you have to be involved with the data scientists, so it's not like it's not like a relay race, we think the baton 400 meters and then forget about it. Once you have, once you've, you know, handed the baton over to the next person, you need to be engaged in the entire process of working with a data scientist. Or if you can learn some statistics, that's fine. But that is definitely not a core requirement for being a data set. You need to understand what data science means you understand you need to understand the data, but you don't have to turn into a statistician or a programmer, for that matter.
Can I please move on to the next question?
Yes, yes, please. Proceed.
So, so so you know, the next question is about we hear about AI in almost all sectors, except oil and gas, which is probably because The security issues. But have, could you please throw some light on AI in the oil and gas sector? That's okay. I'll take that question sironen Sham because I mean, I'm managing some of the oil and gas clients. So I understand the sector there. So I'm bit surprised that the statement has come like oil and gas sector is very quick in adopting AI and all these advanced technologies. In fact, for one of the clients which are doing they're just transforming themselves into a digital organization, digital organization in the sense like AI, machine learning, IoT, all aspects mobility, every aspect of technology, emerging technologies that we're talking about. They are bringing all these technology aspects into their normal operations. And that's something Yes, of course, digital transformation is oil and gas sector was slow to pick it up, but now they are trying to lead the race. They're trying to compete with the healthcare sector, which is considered to be the quicker adopter of early adopter of technology most of the times, and that's one. And the second thing, the popular use case being like, if you'd like to read more, you can read about the predictive maintenance that happens with oil refineries, which they call it as assets. So just read about the predictive maintenance cases, you will understand our machine learning and AI is being brought in to implement in in a particular space, which is actually saving a lot of costs for them, especially when the oil prices down. This is one use case every oil company is trying to implement it for them themselves. Check that out. Yeah, thanks. All right. Cool. The next the next question is how do you see AI enabling b2b marketing?
specifically for you know, midsize tech organizations.
Can I say a little bit about this? Yes.
So yeah, so b2b marketing By the way, my other avatar is in sales. So I direct the sales center here and much of the sales in the Midwest is basically b2b sales. Because Midwest, unlike the coasts, the Midwest is very manufacturing oriented. And if you're many of these manufacturing companies, they're their customers are other companies. It's b2b. So, in b2b marketing, the major impact of AI and machine learning has been in sales, lead forecasting sales pipeline predictions, when is it? When are you likely to close? Closer lead win predictions, predicting the number of wins, depending on the number of leads that you have prospecting, lead qualification running sophisticated AI models to say what is the probability of closing a particular client in a particular amount of time? So the question so they ask questions like what is the probability of closing this sale in three months time? What was the probability of closing the sale in five months, months time Based on that they can do their budgeting and resource allocation. Because if you want to close the sale and realize revenues in three months time is very different than if you're going to close a sale and realize revenue in a year's time. Right? So all of that comes down to pipeline, predictive analytics of the sales pipeline or the sales funnel. And that is a very, very standard b2b application of AI and machine learning. So pipeline analytics basically says pipelines in front of
okay, but the other question is okay, when we go about collecting consumer data, so the question is that is tracking consumer behavior in line with ethics in data collection, and with the new codes enforced? How do you kind of look at this panning out? Yeah, so shampoo and Should I take this question? Yeah, sure. Yeah. So I mean, I mean, you always hear this thing on the newspaper and everywhere that you know, big brother is watching and all the data is being actually recorded of all the consumers and minister in a critical concern. So, what we see as marketers, you know, responsible marketers is, you know, making, making the lives of consumers a little better and easier, you know, so, when you use these techniques to actually make these slides better, and, you know, make the product suggestions a little bit more relevant, rather than abusing the data and you know, trying to get to personal details, so, I think we are completely within that ethical realm. So, in terms of utilizing the data in a responsive manner, so, it again the, the onus comes into the companies, right, you know, to use this big data in a responsible manner, and not, you know, you know, piano that should not be able to do in privacy anyway. So, we have this unique example in target where they had this pregnancy prediction algorithm where they could, based on the, you know, purchases made by female they could actually predict whether You know, whether they're pregnant or not, and pregnant women are actually like a goldmine, right? So they can actually send in a lot of promotions and they could actually capture that. And so what happened was, they were, they were sending these all these coupons about diapers and other stuff to pregnant women and the pregnant woman actually not told that household in within their own home that they're actually pregnant. So they were kind of freaked out. Like, you know, how did the our target come to know about this before they were actually told anyone else so they're not actually told anyone about this in the second trimester. So this was kind of a huge problem for target and they actually shut down the entire program. So it kind of you know, there is a huge invasion of privacy over there, but they did overcome it at a later stage. Like they kind of camouflage the entire, you know, Pregnancy related coupons within completely, you know, unrelated baby stuff. Like you have wine glasses along with diapers, you know, next to diapers and coupons for wine glasses, you know, put next to episodes kind of, you know, that kind of protecting their privacy as well at the same time. They're sending in relevant stuff. So there is a huge gray area here. In order to what extent this data can be used, but the onus is again on the firm's to actually responsibly use it. SHAN, do you want to add a few more? Yeah.
Movement nowadays on ethical AI, or explainable AI, and AI is like a tool like any other tool, it kind of depends on the person using the tool. So I'm pretty sure that there will be safeguards in place either legal over the entire macroeconomic system or legal system of a country or within the legal framework of the company itself to make sure that it's not too invasive. So again, it's it's a tool, and it depends on the people and the motivations of people who are using the tool. So it is a little unfair to put the blame on AI rather than the user of AI and if people are if companies are responsible, business leaders are responsible AI becomes fair and responsible and ethical. But that said, like I said, there is this huge movement in ethical AI and explainable AI so it's no longer a black box, and once things that explainable AI transparent, much of this criticism goes away anyways because the criticism comes when we don't understand something when it's a black box, so people are working towards the end and this is something we'll discuss in the course as well that people are working towards removing the black boxy aspect of AI and machine learning, making it more transparent.
The next question is about you know, career options. What are the kinds of career options that of course in AI will unlock?
Okay, I'll take that person for the job. The career options for for the CO I mean for people who are taking this course. So we are speaking about two bigger areas. The two Godzilla is like the one Godzilla is the marketing which has been giving jobs continuously year on year like, oh, it only increases because we need more salespeople to sell the products on the A float that is there is no doubt about it every year. Even if you look at the top the school's top colleges, the recruitment happens primarily for the sales and the marketing space. So that's one space we can never ignore. The second one is the artificial intelligence which is the new Godzilla that has come into the market now. And everyone is saying that Okay, boss, everyone is seeing it also, like 10 years back, it was a new age technology. But today, people have started adopting it. And everyone is under a peer pressure to adopt and see successful applications in their form. Now bringing the two guzzlers together and then making you understand what they are and giving you that power. That power will actually empower you to get into a lot of new wage jobs that are going to be created. So what this course will offer you is more of the skills, the skills that will help you to navigate through marketing and become a better marketer, as Sean was rightly pointing it out. Because going forward, the the people who are just with the normal sales still may not be able to survive people with the normal market. Still may not be able to survive in the long run, you need the additional data driven decision making still also as part of it. And especially when the data if you're able to see patterns with the help of AI, that is something which is going to equip me for aq for at least the next 10 to 15 years. So one thing you would have noticed any degree that you do a common pattern which I have seen my personal experiences, the validity of the degree is roughly around 10 years, for the next 10 years, what should keep me running in the market. That's what we are, we always think that we always keep our so that's my personal mantra. And I strongly believe that this is something which is going to give you that necessary power to sustain survive and become a leader in the market and distinguish yourself from others in the market. So that's the power you will get. So obviously, the career options are going to be wide enough. And it's an evolving space, and you're going to beat the curve by picking up the skill much ahead of others in the market. That's something which is lucky for you.
Can I just add one script in here
running out of time. So please make it quick because
we are taking of this course is we are basically training the translators, again, going back to this whole translator thing. So as a translator, you can reach across the aisle and talk to other people on the other side. So if you're a data scientist, you can talk to the domain specialist because you now are a translator, and you can actually talk about machine learning in a practical manner. If you're marketer, you can be a translator and talk to the data scientists because now you actually actually understand what the data scientists do and it's no longer like I said a black box we do so in some sense, being a translator you can act with two different constituencies.
Yep, stop. I think we have time for just one last question. In May we could probably even bring array throw in there is a question on you know, what is the pricing for this program and what is it called cover?
Right. So, the pricing for the program is three lakh rupees plus GST you have of the program. It'll cover the entire cost of the program except for the cost of travel and stay in campus. But then you have long term EMI options available. All the details for that are available on the talentsprint website as well as in as part of the Calcutta page. So you can go ahead and find out more information about that post the webinar, we will get a communication from us giving you details about these aspects as well.
That's a gentleman, I think, from us, a huge thank you on behalf of economic times brand equity.com. And it was a very enriching discussion. And I'm sure all the panelists enjoyed it as much as I did. Thank you very much for enriching us in this manner. Thank you. Goodbye.
And thank you, everyone. Bye. Bye, Lakshmi. barsana.
Thanks a lot, Tricia. Thank you, Tom. Like thanks. Soma
Watch the entire interview here https://www.youtube.com/watch?v=Dee6A6chiHw&feature=emb_logo
Note: This video transcript is generated by AI. Therefore, it may not be 100% accurate.