DMA Trailblazer Series with WION | S1E8 - Decoding Data and Values ft. Industry Leaders

As the iconic investigator Sherlock Holmes said, it is a capital mistake to theorise before one has data. And I think our detective would enjoy our next Trailblazers show all about the science of data-driven marketing. And I'm delighted to be joined today by Gul from Versuni, Yatnesh from Greenply, Abhinav from College Deco, and Ashish from Home Credit India. Gentlemen, welcome to Trailblazers. Now to get the show going, we're going to start with our joy-on of data. Number one, Gul from Versuni. Gul, when my producer presented me with this topic, the science of data-driven marketing, it doesn't sound very exciting, right? I was worried we'd lose viewers. Make it exciting for our school.

I personally believe that, you know, when I was growing up, it said, you know, marketing is all about science and art, okay? And data is making it more scientific. So we are losing on the art. So for me, and you will see that I am a firm believer of, you know, we used to say that, you know, I take decisions from my gut. Now what is gut? Gut is also accumulation of data, which is coming from your life experiences, what you have observed, what you have been taught, what you have been told. So for me, it is absolutely clear that data was always there. Data is now more available, accessible. And then you can make data to liberate yourself rather than, you know, slow you down. In Versuni and other brands you've worked with, what do you do with data?

How do you even define data? So I think, as I said, you know, what is data? Then why do you, first of all, why do you need data, right? Because at the end of the day, if you are very clear about why, then you know what and how, correct? So why do you need data? You need data to actually land some of your hypothesis with a little bit of proof, right? From all my life experience and all that was data for me. And the human processor is in work, you know, for ages, right? Now we have processors which actually help us aggregate that data more. Make it more, I would say, usable for a larger audience. And it is easier for us now to scale things. So that's the difference. Or that's the benefit that has come with a lot more data available.

But I think decision making, even today, okay, has to be a mix of both. How we use it in Versuni today. We actually enjoy data. And when I say we enjoy data is because we have a philosophy of experimenting a lot, right? And when you have that philosophy, what you do is, with the help of data, you keep on experimenting, but you learn very quickly. And when I say learn quickly, it's like you fail faster. In case if you are failing faster, you learn from it, you come back, you restart, and then you are up for something else. That is how data is helping. You have a lot of experience of the analog age and the digital age. So perhaps that makes you better able to combine the art and the science.

Do you find that with the younger generation of marketeers who are pure digital natives, and we will get to Abhinav in a second to talk about that type of business. Are they any less able to apply the art to the science? Or do you find that they are so data driven, they may not see the data wood from the art trees? I do not completely agree with you on that. And I am actually inspired by a lot of young marketeers in my own organization. I see that they make a balance between both. Provided the direction that you provide to them is clear to them. I think it is an age where you bring more clarity, you know, you have better results. And in terms of both art and science.

And they actually, in our organization, at least that's my experience till date with the young marketeers, they are smart. And if you empower them to say use data, because if you are clear about why and what and how, then they are actually making data as their biggest enabler. But they are not leaving the art behind. Gull, you have been a fantastic guest to start the show. Thank you so much for appearing on Trailblazers. And now may I welcome Yatnesh at the marketing helm of Greenply. I see you are nice and warm with your scarf, Yatnesh, to cope with the Delhi winter. Yes, I think that is from the data. I am coming from the market side, which is not that chill or winter. I checked the data. The data says put a scarf on. Put a scarf, be protected.

And that's why I came here. But when I think about Greenply, right, big ticket items, interiors, sort of a big purchase, maybe fewer customers. It feels less data intensive or data feels less relevant. Is that wrong? No, I think it's very interesting. You have to see, we represent the category where the category lives into the consumer heart. But same time, consumer uses the mind also. So it has both the data and you have to say all the facts in terms of the aesthetic is consumer is taking. Yeah, there is not much synthetic data is available. So we have to depend upon some of the real data or observed data. We have our own sources to collect this data. And it helps us to decide some of the new product innovation.

It helps us to decide some of the customer services also. Just very recent case study if I have to give it to you. During the pandemic time, everyone was started working from home. So we thought it as a product, we have to come out with the one product which can help consumers to divide their home spaces. But when we check the data, what we find consumer wanted to create a certain spaces in their home, which can give them the more lifestyle and leisure experience. So what was the product development we were doing? It was against the data which the consumer was reflecting to us. So I think we also using the data. There is a less syndicate data, but more the real time or observed data which we are using it. Well, that's very interesting, isn't it?

Because like, like, just for example, if I have to interrupt you, it is. Can you guess it what the consumer was looking to create at their home during the pandemic time? Freedom. What kind of the interior space they were looking for their home? Tell me. The maximum consumer looking the more the bar at their home. The second level is the Puja house, the temple creation. And we were thinking the consumer was looking for more the work from home. They're looking more for the workstation. But this is the maximum thing consumer trying to create post the pandemic in their home. But how do you synthesize on a practical level the synthetic versus the real? How do you go about that?

You see, in the traditional world, the brand was trying to connect and communicate consumer with all the possible medium. And now in this new world, where the consumer has using more and more web, and web has given the very complexity of the choice to the consumer. So they are continuously exploring, evaluating. On this, there is a certain set of the data available from the synthetic world. You can say the computer generated data, what they are searching, what they are exploring. But same time, there is some real time observed data is also available, which we captured from the marketplace, which we captured from some of our own sources. This tells us how they are evaluating it.

So between their exploration and evaluation, you have to come out with the solution, which is the more intuitive for them, which is more inclusive for them. So that's the way the synthetic and as well as the real time data is helping for us to take the decision. Okay, so when you're talking to young marketeers, what do you tell them they need to do to be great at data interpretation? Yeah, there are the certain biases comes when you take it the more real time of the observed data. But once you mix it up with the synthetic data, those biases get neutralized also. So you have to use it, you have to analyze it. And sometime analysis give you the pivot to the new business things also. I was recently seeing the one of the startup presentation.

They were analyzing the data of the scanning of the sea. From there that startup entered into the phishing startup business. So sometime when you do the analyzing synthetic versus the real time data, you pivot to the new business also. The same way the marketer has to see, they have to mix up both the data and they have to come out with their own observations also. Data can do a lot of damage, right? And you know, we're all of us aware of the impact of data on kids, on mental health, of all those things. Where do you stand on that? And where do you as a brand do your bit to prevent data doing damage? So if I have to say the data is one of the most important tool currently into the marketer's hand.

It's bring the unique opportunity, but same time it comes with the certain caveat also. So we have to as a marketer, we have to use it the consent and control of the data in a very smarter way. Sometime the productist characteristics which we're trying to market, it can become the detrimental to the brand also. Or maybe this is not good for the society. So as a marketer, we need to practice it and we need to see, there are the regulatory are there. But we need to see how can we should not invade the privacy of the consumer or how we cannot make it the harmful for the audience in the name of the inclusive marketing. So that's the caution which as a marketer we need to see while using this data. Yeah, it's a responsibility. Yes, it's a responsibility.

Thank you so much for joining us on Trailblazers. Hugely delighted to be joined by a man who's using data to make the right college decisions. Abhinav, welcome to Trailblazers. Along with choosing your partner, you know, having children, things like choosing college, they're kind of right up there in terms of decisions. But historically, the amount of data actually being used is this big, right? Yeah, I think you've touched upon a very, very nerve, you know, nerve guard over here in India. Education is a highly prestigious price possession in this country. You want to get married? Be a graduate. You want to apply to a government job? And this and the dichotomy is that only 27.4 percent of Indians in the right age are actually applying to college.

So the cross enrollment ratio is only 27.4 percent. So what we're doing right now is actually using data to be able to predict which will be the right college for you based on psychometric tests as well as questionnaires that we roll out to the kids when they reach out to us for counseling. Now, if I think about choosing a college, albeit my choice is very binary, it feels very complicated. It's emotional. It's social. It's academic. How on earth can data or the algorithms that you use make at least improve that decision, let's say? It may not make it for you. And I think that the key word there is improve and enhance.

We understand which cities or villages kids are choosing which course and which stream and looking to go to which part of the country. So a massive amount of migration happens in India for higher education. Now, in that context, can I help predict that this kid with these kind of marks with this financial background has a propensity to migrate to a certain kind of city and would be more comfortable being in a certain kind of stream of course in a college of this kind. So we're able to use data analytics and now data science rather to be able to predict that.

And once we've done so, we offer the child the choice of being able to look at the options available on the table and thereafter guide the children and the parents as well in making the more informed choices. Abhinav, tell me, how do you use data to get a better grip on the emotional drivers of your customers? Very interesting question, actually. So over the last seven or eight years that the organization has existed, you've created enough sort of data points and variables on which that emotional decision making also lies. So we've understood that availability of finance while looking rational is actually an emotional decision. We're able to predict basis monthly household income.

What is the kind of streams and basis the grade that the kid has got or the kind of streams that they would be happy with. But we're also using a psychometric analysis to figure out the psychological reasons behind why a certain career or a stream would be the right fit for a child. And what's also important to understand in making the emotional become more measurable is that we use every possible tool when the child comes on to the page the first time. So right from using a hot jar or clarity, et cetera, et cetera, to figure out what all are the pieces of content that the child is interested in. So what are we serving today? India has got about 54,000 colleges. The United States has got about 6,000. The UK has got about 3,500, 4,000.

So in a multitude of choices, the choice actually becomes tougher. So we offer the information about all 40,000, 50,000 colleges that exist in this country from their exams and grades and et cetera, et cetera, and what would it take to get in there. But when the child is glancing through that information at the back end, we're also reading where is the child spending the most time. So I can say that, you know, content is king, you know, but actually data is God. It's helping me understand what content needs to be the king on the page. You've been a fantastic guest. Thank you so much. It's been fantastic chatting up with you as well, Jasper. Lovely talking to you. Thank you, sir. We're delighted to be joined by Ashish from Home Credit India.

Before we get into data, let's start the other way around. Where do values feature in your business? Because you wouldn't be who you are if you didn't have them. I think one needs to understand that while whatever you might want to think about and whatever the stories might tell you, lending is a very, very important business. It's a very ethical business. At the core of it, ethics has to drive it. Compassion has to be layered around it. And as a brand, and I can talk about my brand, we are driven by our brand purpose of saying Zindagi hit loosely gets translated to life as a celebration. It's about empowering people to extract more of their life now. And it's a very interesting thing, isn't it?

Because, you know, the act of lending, which is a form of risk management, you know, it's very data driven. It's very deterministic. But in the end, it'll come down to why do you trust one business versus another, which is a whole bunch of kind of non-data things, right? For a customer to be able to decide from one business to another is about the trust factor you can generate. And that trust in a brand comes from things where underlining fact can be data, but the storytelling has to be very, very strong. And where you can invoke the emotional part. Data needs to be used to weave your storytelling. Data needs to be used to make those decisions. Try and understand, I get almost about 30,000 to 35,000 applicants applying for loans every day. Every day.

And this is 24 hours. Sounds easy. But the equation gets much more complicated. If I were to tell you that 60% of this comes between three hours of six to nine, which are the business hours, between six to nine p.m. Most of the people will apply. And you need to make decision about what limit, what interest rate, and how much does a person get for how long tenure? Well, let's get into the weeds of this, right? So 20, 30,000 people come in, right? X of them are ratified automatically, presumably. Y are rejected. How do you use data to handle the remaining Z where you're trying to figure out credit worthiness and the rest of it? How do you actually use data at that point?

The entire game is in data there because of from the, see, if you look at lending business at the core of it, any lending business is about while at the front it might look like you're giving out money, which is the easiest business to be into. You give me one trillion dollars and I can distribute in next 30 seconds. The real business is to be able to give money to people who will return it back. Am I right? So the decisioning is about putting people at a scale of probability with the probability telling you that what is the probability of money coming back within the given time. It is also important because of then you are enabling a person to live a dream life rather than putting them into debt trap.

Do you need in Home Credit India, Ashish, marketeers who are fundamentally different to the marketeers that Ghul needs or Abenav needs? Do you need more people who are more data scientists than marketeer? I think I don't need different kind of marketers because of marketing at a core. The fundamental of marketing remains same. It's about people, it's about your customers, it's about making their life easy, it's about the experience you deliver. And the storytelling and all of that. The data here all one needs and any business in today's time needs people who can understand data. There is a very old age saying about data in the storytelling that not many truths you will find which do not have our data associated.

But no lie has ever come without a number attached to it. Well that's very interesting isn't it because we're living in the kind of post and pre possibly Trumpian age, right? Where things like fake information, manipulation of information, all that kind of stuff has become absolutely prevalent. And that's where data does damage. How do you stop that happening Ashish in your brand? I think some of the things that we do very very specific is one certainly comes about at an individual level. It's about data privacy and guarding our consumers, educating them very much about the best practices in the industry as well as at the product level, at the individual level.

So we have a complete content play out there, whereas we push out content to our consumers helping them understand their basics better. Our category flag is about trust. And you cannot win into the category if you are not able to educate your customer rightly. If you are not able to provide them the right information. If you are not able to give them information when they need it the most. And these are some of the things which all of us do. In past 15-20 years the world has changed very very drastically. Earlier there was a dearth of data. Today there is a, I think we are living in an over. We're drowning. We are drowning in data completely. So it is not about big data. It is about that crucial, specific, small data which you need.

Which can help you build better connections to your customers, can help you to help your customer lead a better life. And if you are able to use that small data, I think you have done your job. I can't end this show, Ashish, without talking about AI. Is it your forecast in a couple of years time or even a couple of months time, my producer is going to replace me with a laptop? I mean, they can probably, but then they will not have viewers. Then the viewers will be bots, I think. They can do that. If they are producing a show for bots, I am sure they can do it for the laptop. I like the things you say, Ashish. Please tell my producer the same. You have been an absolutely fantastic guest. Thanks, Jasper, and same to you. Thanks for having me.

So what is data? Data as such is neither good nor bad. And what we have learnt is that fundamentally data is connected to people. And people, as has always been the case, respond to stories and storytelling, which is the oldest part and the most valuable part of marketing. We have had an incredible conversation with some extraordinary guests. I am Jasper Reid, and you have been watching Trailblazer. A joint DMA Asia. We on initiative.