Data data everywhere, but not an iota of insight! You’ve gathered a ton of data, but what do you do with it? How do you turn your data into useful information? This episode is a masterclass on converting data into marketing insights, and what kind of muscle and genes a modern marketeer needs to develop. Listen on as Vani decodes the value of Data for winning the battle of customer love.
01:40 – Different kinds of data.
06:30 – How to use data intelligently?
14:00 – First-party data is your best bet for success.
17:00 – Simple practical ways to use data to drive growth.
Read the complete transcript below:-
So I wanna ask you two questions Suresh, number one is, In what form does the data exist with organizations typically, large and small? And second is how does one use data, even if it is basic data, how does one use data to actually produce insights that lead to business impact?
01:40 Suresh Shankar
Thanks, Vani. I think this is a great question. So let me try the first one, what data exists today? Today, if you look at Vani, the whole world, the most valuable piece of real estate is the little brick that about 6 billion people on the planet carry around in their hands, right? And that is a vast emitter of humongous amounts of data. Likewise, I think even if you take an enterprise and you look at an enterprise and its systems, whether its systems are distribution systems or whether they are ERP systems or whatever else. Every one of these things is generating a huge amount of data about every single thing that’s happening in literally every single second in your company. The problem of course, is always in this thing is that, as you said, it’s your second question which is how do you start to use the data? And my answer is that I like to divide it into three parts. The first is a macro trend of what I see and the second one is I’ll go into what I call the new marketing gene that we all need, right? The thing that needs to drive us as marketers. This is a podcast for marketers. I think, the two big macro trends that are happening, first thing that’s really looking at it is that if you look at the holy grail that we’ve all sought as consumer marketers over the last many decades, right from Philip Kotler and his original book on this and I think the whole idea was that we’ve always wanted to understand our customers. We wanted to know exactly why they are doing, what drives them, what are the motivators they have. What’s the choices that they make? How do they make those choices? How do we actually create packaging of propositions that appeal to those choices? And we have sought increasingly to narrow the focus into smaller and smaller segments from mass marketing to highly segmented marketing. And the Holy Grail money has always been this idea of marketing to segments of one. Can I know each consumer so well that I know exactly what she’s thinking about and what she will buy and what are the messages that will get her to choose my brand over something else? So the good news is the tools that are available today, the amount of data that’s available, is really leading up very much to that point when it’s possible that kind of a thing is possible. The second macro 10, I think, comes with a little bit of a twist, which is that as an enterprise today, the voice of the customer or the preferences of the customer is already with you. Most enterprises have these voices for the customer. The only thing is it’s hidden deep in the data that you already have about this customer. We all talk about data as oil, right? You have a lot of data about the customer in every company, right? And the key question really is, the twist is, how do you actually go and get the signal from the noise? How do you actually take that voice that is hidden deep and translate it into a language? That we can all understand as marketers, right? And that’s, I think, two big things. And that brings me to the second point I wanted to make, therefore, if this is the case, how do we as marketers, what genes do we need? I think the first thing is that we all need as marketers to learn, to understand data and there are different kinds of things that we have with data, right? We have data that’s inside the enterprise or what you call internal data. There’s external data, which is data that’s flying outside the enterprise. And especially if you take the last 15 years, there’s been an inordinate focus on the data outside the enterprise, what we call third party data, right? The amount of stuff that’s lying. This has been something that Google and Facebook have really mastered the art of making us use, which is I know where the consumer is, I know whether the thing is I’m dropping a cookie on them or a UID. I’m collecting an infinite amount of data and social media and I’m gonna use that to help you target better. Why are we not looking at the amount of data that enterprises already have collected or have access to, which is sitting inside them what you call first party data. And not only is this I think neglected area, I think it’s gonna be an area that’s gonna explode in importance in the next, it’s actually already underway, but I think in the next few years it’ll be gonna be massive. And every company is going to turn towards this whole, how do I exploit first party data simply because of what’s happening? The concerns about data privacy, Apple’s iOS changes, which make it impossible to track you across apps, Facebook’s advertising revenue has dropped because of that. So what’s happening is your first party data is going to become the critical raw material for you to understand your customer. And this is a gene, a muscle that we need to build. Also, I think when we look at data, we all tend to say, hey, listen, can I collect vast amounts of data? You always focus on collection of data. And every CEO and CXO you meet says, oh, my data is an asset. I owe a million customers, and I have so much data. You ask them another question, what value are you getting from the data? What’s the return on your data asset? And the answers are never that clear. And in that case then you know you have the data asset, but you’re not utilizing it. It becomes to me, a wasted asset. And this happens because our marketing muscles and genes have been trained towards an excessive focus on collecting data and ensuring the cleanliness of this rather than focus on generating value from the data. That is a habit, right? It’s a habit. You gotta have the habit of saying, I can take this data and refine it and process it and that’s really a muscle or a gene that we all need to have as far as data is concerned.
No. So how does one make use of it? Give us some simple case studies, if our listeners had to start doing business differently starting tomorrow using the data that they have sitting in the system, what would you recommend they start doing? Stuff that they could do by themselves?
06:42 Suresh Shankar
I’m gonna give you an example that I personally like a lot. Now, the numbers I quote here may be slightly out of date, but Spotify has about 200 million users and they have about 200 million songs. Now, just think about it. Do you use Spotify? Let’s say we are two of the 200 million users, we have 200 million songs. That’s 40 quadrillion possible combinations. Think about it, 40 quadrillion possible combinations, right? But just the thing is, Spotify can start from the same place. Let’s assume you and I listen to five songs that are the same, yet they will find a way to show you a different set of songs than they show me. They will find a way to show you a different set of moods on a Monday versus a Wednesday versus a weekend. They will find a way to take you on a journey through that personalized playlist, which seems so relevant for you. That is a superb use of data saying, I have all this data, I collected it all. I have found a way to take this data, make it into a platform, analyze, organize the data, make it something that’s valuable and then give it in the form of a superior customer experience that’s really relevant for every single one of those 200 million users differently. So to me, this is the best example of what I believe the new world of marketing led by data and providing relevance to consumers is.
07: 50 Vani
Fabulous. Yes, absolutely. But do all organizations have access? I mean, what form does the data exist in organizations? Do they have access to as much data and B, do they have the intelligence or the way with all to be able to make use of data the way Spotify does?
08:06 Suresh Shankar
Absolutely, and that’s a great question and I think many organizations have huge amounts of data. There are 45,000 banks in the world, believe it or not. There are 5 billion users that they expect to have for wallets over the next few years. Today, there are 20 billion instruments in the form of credit card, debit card and wallets. There are probably a trillion transactions, UPI alone has a billion transactions a day or something. So imagine that’s data when that UPI transaction happens as a buyer, a seller, a payment, a timestamp, a date, a location, all of it is data, right? Let me tell you the thing about this, less than half percent of this data that is being generated is actually being used to analyze, And so that’s, I think, the scale of what we’re talking about.So let me take you to Myanmar when we go there. It is 2017 we got a call from the CEO of this bank called KBC Bank. KBC Bank is the largest bank in the country. Think of it like it’s a privately owned bank like HDFC, but it’s like a monster, right? It’s like the State Bank of India, right? Got 500 branches, 18,000 employees. It literally is the largest corporation in the country. This is 2017 and the new CEO went in there basically when he called us and said, hey, we wanna do something, 40% of the adults have a bank account, 40% of the homes have electricity. There is no supply chain. There’s already two or three telcos that’ve launched. It’s a cashflow economy. That’s the situation we are starting with, right? Now, here’s the good news, as a marketer, Vani. Smartphone penetration, even at that time, was over a hundred percent. The urban download rates of apps like Facebook and all that were as high as places like Singapore. You had a clear market leader because you have the biggest distribution network in terms of branches, ATMs, et cetera, and the management team out there had a really big heart because they said, my vision is for this country to have a hundred percent financial inclusion. It’s about 70 million people. So you know, if you take about 20, 30 million households, they said, can every household actually have this? So that is the vision. So now how do you go about doing this is the question, right? It’s a data dark market. There isn’t data available and I’m picking that because the question you ask me, how do you work when you don’t have data? And when you’re not Amazon or Netflix. So we actually had the idea then working with the CEO of saying, can we build like a, you know what the CEO now calls it? James Web of Telescopes, right? We had the Hubble and now we have the James Web, right? So he said, can you build like the James Webb of Telescopes, a super app wallet that has hundred percent financial inclusion that can enable eCommerce that can do e-credit at a local level that can mobilize the employees. He clearly said we can’t do it alone, so you’re gonna work with a bunch of tech partners. And here’s what we built out over the last five years and I’m gonna start with what we built and quickly go to what we achieved. So we built out what you call a consumer wallet and nothing different from Paytm or anybody else who does that, right? By the way, it doesn’t have UPI, right? India has UPI, there’s no UPI in Myanmar. So somebody has to build that network. So we built out a paytm like app. You built out an app for agents to come on board and sign up. You built out on merchants and all that to come on and sign up with a QR code. So you had to know exactly what Paytm and Google Pay did. It is fully mobile. You know, nothing of the old web thinking and you had to get full KYC. KYC is becoming more and more important and when Paytm started, it didn’t have it. Now everybody’s saying KYC is important. With biometric onboarding that could happen within a day. We divided, we said, how do I actually take a large country and make this work and make this available for everybody, everywhere? So we had these two or three apps that we built. One for the consumer, one for the merchant, one for the employee. We divided the country into 400 plus geospatial territories. What is the biggest potential liability we have? 18,000 employees. On September 29th we had 18,000 employees. On September 30th those 18,000 employees were then enabled by their own super app and wallet, meaning they had a wallet and they had the super app that they could go on and on October one, it became the single largest sales force they had in the country that was available in Myanmar. We found every merchant, and we plotted them on a map. We tried to get some indications from the economic thing from various sets of data about how do you put that on a map? And then we started telling the employees who lived in that area or worked in that area, just the merchants, you need to go and acquire and put that QR code sticker. We then told every employee, we basically worked with every employee to say, can you onboard a hundred customers? And the employee becomes a person who gets the biometric and the KYC done. Every quarter we work with the employees to say, what can you do to become the best salesperson for the bank using data because we give them data about customers, about friends or whatever else and they use that to onboard people. They use that to onboard merchants. We use the data of the customer to tell the merchant, how many customers are there in your area? Whom can you target with what kind of offer? We use the data of the merchants to tell the customer, what can you find that is close to you that can, we know right now, on the third Wednesday you’re always trying to do this. What kind of things that you have to do and what’s this led to? I’ve got a long story short, I could go on about this. Today they have 10 million customers, nearly close to 10 million customers, 350,000 merchants. They have about 90 million transactions a month or 3 million transactions a day, which is small from an Indian standard, but you must realize they’re 120 at the size of India. We’ve had over a billion transactions so far, but here’s the amazing thing. It is a profitable super app compared to every other app that’s burning money and 30% of the GDP of the country goes through one app, 30% of the GDP. And it’s a self-fulfilling virtual cycle because the more merchants you add, they’re actually being able to find customers to sell stuff to. The more customers you have, those customers are able to find the merchants they wanna go and buy stuff. And the employees are beginning to now become like the person fueling this whole thing. So it’s an amazing thing where in a data dark country with a non savvy employee base, we have actually managed to create magic with data.
Fabulous. Fabulous. This is a huge case study. Lovely. Now tell us about organizations Suresh, say young organizations or startups, so one is I have an app. The app sits on my consumer’s mobile and so I get first party data from that app. Now I have to intelligently use that data. I have to figure out which part of that data I can use, intelligently use it. What about startups that may not have an app? Would you be able to offer some insights? So two parts to the question. One is, I have an app, even if that app sits with 5,000 consumers. How can I use insights from that to make some difference to my business starting tomorrow? That’s number one. Number two is if I don’t have an app and let’s say I’m speaking with my consumers via Telecom service that is also the data that exists within the system. Would that also be within the purview of what you can offer insights on?
14:58 Suresh Shankar
So let me take those questions one by one. I think the first is when you’re a company and let’s say you have 5,000 or 10,000 customers and you have some data about them. One of the big challenges that I find is that every company is in order, focused on acquisition and saying, I would rather have a million customers and get very low monthly active users as opposed to, I think saying I have 50,000 customers, but they’re all very active on using my app. The best unit of data, an atomic unit on which you can operate is that block of 5,000 customers that you already have. If you have the data on the app, if you know why they’re buying your product, when they’re buying your product, how much money they’re spending, how much time they’re spending browsing through the app, and let’s say it’s a commerce app, right? And what you’re doing is a huge amount of insight and voice of the customer that can tell you. I know so much about these 5,000 customers that I can do two things with this data. One is, can I get these 5,000 customers to spend more time, attention, money with me, and use my product more frequently? But equally, now that I know there are 5,000 users and I’m dividing them into different kinds of users within that, how do I use that information to go out there and target? And get the next 5,000 and now then it becomes an atomic unit that you keep replicating at scale and each 5,000 person atomic unit becomes a little bit more granular in terms of your understanding. Just think about it as if you’re going from a territory setting up a distribution network at Pepsi, right? You wanna go to one thing and say, I know a hundred retailers, they will stock me. Then I go to the next one. And say, hey, I learned something from the first hundred retailers and their stocking patterns. I’m gonna go and do it again for the next hundred. So that atomic unit is what I think the company needs to adopt and say, let me do something with these 5,000 people. And that is information that is already in their app but I suspect that in most organizations, like I said, we are saying, hey, we’ve got the data, but it’s not important. I need to get to 10,000 and 20,000 and 50,000 customers. We don’t take the view that those 5,000 customers may have the key to the next 5,000 that we want to get. Does that ring a bell for you?
Yes, hundred percent.
16:52 Suresh Shankar
Now, let me come to the other part where you said what happens if you are dependent on others. Your question is, what happens if I’m actually using another platform to sell? This is a much harder question because I think, one of the things about platforms like Facebook and now Instagram, increasingly and TikTok in other parts of the world is that you know, our Google through search is that they have offered us this promise of like I can help you reach millions of customers in a highly targeted fashion. And to some extent, it’s great for small businesses because I think clearly you can’t get that kind of access to those customer bases. But my view, Vani, is that this is a dangerous path to trade because every time you go there and you start to use these platforms more and more to do something, we get the short term results that we want, but it makes the platforms even more powerful. And what’s happening is that soon we lose the ability to actually do something with the data that we get about the customer. I can advertise, I can get that thing, I get the click, but I’m not forced to go back and get more and more expensive advertising time and I think you know this better than me. The advertising return on those ads is now coming down as the targeting gets less and less granular and therefore more and more expensive. So while I think in starting, you don’t have a choice but to use the platform, I would still say that the moment you get a thousand, 2000 customers are coming in, you’ve got to find a way to take the data that you have and use that to create, hey, let me get the next 2000 and 5,000 and keep growing like that. And the third question was, what about people who are sitting in contact centers? What about people who are calling and all that and how do you do that? Now there’s great news on this front actually and I think this is why the biggest breakthroughs are happening in terms of AI technology. The other bigger breakthrough that’s happening is in the area of unstructured data and unstructured data typically refers to either image, data or words. And today what you have is, for example, you can take a call center conversation, you can convert that into a transcript. For example, this podcast, we can run a transcript of it, right? We can pretty much run it through an attribute extraction machine or algorithm that can find out all the things, not just the word cloud, but something deeper. The words that you’re using, what does it say about you as a consumer? But interestingly, we are now getting to a stage where even the conversation and the tone, the tonality, we can actually have AI platforms that are being built today as we speak, that can look at the tone of the conversation and say, is this person angry or not? And this becomes information that can be also fit to the person having the conversation saying the customer is getting irate, or the customer is whatever it is, the customer has come. So you can even, I won’t say we can measure emotion, but we are starting to get algorithms that can understand what is going on in the customer’s emotions. I gotta go back to that idea I said right at the beginning, which is if you are a marketing, if you wanna develop a marketing, a new gene or a new muscle, you’ve got to look at data very differently and say, that is the core of providing hyper personalized experiences, which I think is what every marketing person’s life is all about, right? How can we provide those hyper personal experiences?
Absolutely. Absolutely. Like you said, personalization at scale has become the new buzz. Tell me, for all of the data that does exist in various forms for young entrepreneurs or companies that may not be able to immediately get the help of data specialists like yourself, what would you recommend they do, immediately with the data at hand to make some tangible difference to business.
20:21 Suresh Shankar
The simplest answer I would give is instead of going out and recruiting 10 more sales people and saying, I gotta spend more money on acquiring the next 10 customers, I would say if your existing customers are generating any data, spend a quarter of the time and a quarter of the money trying to create this atomic unit of saying, I understand these thousand customers better. I would simply say this is a must do. If you’re selling in a B2B thing, you know which enterprises are using your data and why they’re using it and what features they have. If you’re a B2C app, you know that a thousand people are using your app to buy something or of some kind. They’re coming from somewhere. They’re shopping for something, they have a certain journey they’re making and it doesn’t cost a lot because you know there are tools available that can give you this information today. It’s about whether you want to spend the time, your time and attention to actually go in and say, I want to understand this atomic unit, and I want to find out why they’re doing what my best customers are doing. Once I know that, let me see. And what my worst customers are doing and what differentiates the two. And now you have these two cohorts of data.
21:20 Suresh Shankar
I would actually say understanding this atomic unit of your best customer and your worst customer and saying, what’s the difference that I can drive for my next set of customers that I acquire is the single best thing that a startup person can do.
It’s so simple, and you are right. It’s basically using data for the simple fundamentals that we learned probably in the first class of marketing and MBA and yet it seems to be stuff that a lot of us as entrepreneurs still don’t do.
21:44 Suresh Shankar
But, I won’t blame the entrepreneur, it’s a tough life. You’re doing a hundred things. Talent is hard to get, money is hard to raise and I think the other problem that we have with data is that as a field, we have tended to confuse the issue and there are so many points about which data, why you’re doing this, et cetera. That there is what I call more noise than signal.
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