 Can you all hear me? Can you all see me? Well, this was the one we used to in the pro-covid times the virtual calls, right? Thank you Look, I think it was a great couple of sessions one thing that was common That we all learn it's about data was the common thing that we all learned How much of the importance that was there? including from the opening Sessions to Marvin and then a punker and everything I think I want to welcome all the fellow panelists here Mr. Animesh Ravi and our garage. Thank you for being with me. I think one of the that the topic for today is Using data the forefront of marketing strategy I think in coming back to our topic While technology plays really big a low bigger Play in developing a successful marketing strategy but with always on smartphones and computers and Connected devices and the data it generates for market ears the challenges have never been greater Okay, and and for a market year and every Conversation starts and ends with data Okay, I think to deal with a lot of Uncertainty complexity and the change We generally normally lean into right technology right people And the right process. I think we learned that in the last session also But there is actually always a bigger elephant in the room is the robust data strategy, right? I think the first thing that I wanted to really understand for my panelist here to How big is our important role a robust data strategy is in some of the decision-making that you do Hi, good morning everybody, this is Animesh. I'm from ITC Spent a lot of time in actually sales and distribution and then moved on to technology and analytics So a lot of what I'll share with you is based on my experiences in how Sales and distribution happens in FMC G I represent ITC limited and as you all know, we are a large FMC G Organization with huge reach across the country a lot of our investments actually went into Making sure we get the right granular data, which meant a lot of investments that we did in terms of You know systems and processes at our distributors at our frontline sales team The entire focus of our investments was making sure that we get the right granular data at the required frequency We have large number of Field force which visits the markets on a daily basis close to 40,000 Frontline salesmen who are there in the market. So ITC as an organization has invested heavily in Enabling them through digital tools Each one of our front line sales person has got a personalized app On a mobile smartphone, which collects all the information that's required by us and on a real-time basis We have access to what's happening in the market in terms of sales That being our primary system as far as the data collection is concerned Over a period of time. We have also invested heavily on consumer data, which meant Information collected across social media across our brand activations that we do Across, you know, third-party data that also we engage with We have also over the last five years invested in D2C because we realize that it's important to get You know first-party information With a with a platform so we have created ITC eStore Which is operating in many of the metros and large towns Which serves as a primary information as far as the trends and consumer Behavior is concerned So all of that put together Our strategy has been to integrate all the data and we were invested in cloud technology We are putting together all our data Harmonizing them making sure that they are ready for any analytics And of course that investment has taken us at least three to four years because that's where you make sure that you have the right quality of Data and all analytics then becomes possible on top of it That's been our journey and our strategies, you know constantly to make sure the quality of Information the frequency at which it is expected. They're all of highest order so if I see Just one line on my background is I come from the consumer tech world Flipkart and before that it's been about a decade in Silicon Valley and consumer tech as well and So I think see we are what you would call a digital native It's a term that's used so for us if I look at it I'll talk not just about as a market here, but from a consumer company consumer tech company, right? See For us literally every decision everything the whole business is run primarily with data There is no other way to even run our business even consumer preferences what we understand about our consumer We have around 500 million Which is about a third of India's population is our sort of the user base so you have a very good section of the entire India and In that almost all consumer preference are revealed so we do use the external market research kind of approaches but more to understand at very high level because really consumer revelation of consumer preference happens in the platform itself and So if I look at it almost everything we understand is through data more or less, right? It's whether what sort of assortment where carry what sort of Sellers we bring on the platform what sort of products we bring out of the platform all of these for example Revealed by our consumers through search through browse to filters they use so on so forth so Fundamentally, I would say end-to-end our entire value chain right looking at Which consumer to means what do consumers want what selection to bring on board what sellers to bring on board what sort of Quality requirements are there what is the speed of delivery that's required or pretty much everything is revealed through data For example delivery speed you would look at which Product requires higher delivery speed which requires lower delivery speed you can look at Your promise and the conversion on your platform and you can say what those are what's that consumers are giving so these are Absolutely revealed preferences, which means that you actually understand what the consumer is trying to tell you Hi, I'm a nagraj. I am from Madison world, so I'll be talking from an advertising agency and a marketing sort of view So actually for us Everything is about consumer journey and we primarily use the data to define the consumer journey and also to understand the consumer journey And by consumer journey what we mean is it starts anywhere from the how the consumer views your Brands perception to the final purchase and along the way. You know, what are the touchpoints that he gets influenced and How best we can influence that's how we Use data and we work as an agency. We don't own our own data We work with marketers like itc and flipkart and what we have seen is actually they represent the two Extrements so companies like flipkart are very data heavy and they are you know, the challenges are different and Companies like itc who are in FMC g while they are huge spenders in advertising their data in terms of you know First party data is a little bit of a challenge. So the way we handle that is very different. But the common problem that we And as in the marketing fraternity we try to do is how much I'll put it in three buckets The first bucket is how much incremental sale or in incremental revenue I can get because I have access to high quality data The second is optimization. How much can I do more for less or you know do more for the same? And third which is the most interesting and that's where I think most of the marketers should focus Which is how do how well do I get the product market fit? Because if you get product market fit and I don't think in the past We ever had such a rich data, you know We used to go and do market research that most of the time I had just done one Experiment where you know one ad which was never shown on TV when we went and asked them Where did you last see like a 60% of them said they saw on TV and we know that ad was never aired on TV. So, you know When you do a market research, especially Without measuring their behavior. It's not good today. We get very high quality Customer behavior data. I know either it is through search volumes as a simple as one or through purchases as you know Final leg and that has helped marketers understand market fit market product fit Better and you know as a saying goes if you are a dog food manufacturer And if the dog loves the food, you know, you don't have anything else to worry So the same way, you know, whatever if they if you can get the product to the consumer and using data We are able to do that at least we are able to give those insights which help marketers get there That's a great point. I think The beauty of this combination that we have here is one is Extremely complexity and he may spoke about in terms of the offline Reliance and then the and then the group level and then Ravi spoke about really good No matter how much the user base is the speed of which that you want to use that data is an important and of course now grass sum it up by really Saying then how how conservative approach that you need to take really by working with both the world's right to good point Like why will we understand this data strategy? I think one of the Biggest we've been here in this or a many number of years the the different data types that's available and How how is this after the cookie less world how important it is and respectful it is to own Respect our own data Versus really relying on an acquired or in our source data that's comes to our desk next I know I'm really important thing that I wanted to cover up is run the first party data strategy, right? So be it offline and online really wanted to understand how important it is How are you embracing that for yourself in your respective businesses? Maybe I can start As a traditional FMCG company, it's very difficult to get FMCG first party data because a lot of your sales, especially in Categories that we operate in happens through the offline stores And that's a known challenge and therefore a lot has been done over a period of time to actually complement Information with the information available from offline sources What therefore we have been doing is to rely on Consumer touch points which are also opportunities for collection of first party data So things like building on activation data We do a lot of activations in the offline stores and those become our first source of information about consumers So that that's something that we have been compiling over a period of time So the activation becomes the first source In case of ITC fortunately we also have a business in hotels and that is a very very great source of consumer data So what we have been doing is to think across different businesses within ITC How can we leverage that information for FMCG businesses and therefore if there is a thought which is being worked upon to look at Any business of ITC collects information about consumers? It is an asset you know which can be leveraged across different businesses of ITC so we are creating a data lake Which comprises information captured from different businesses of ITC and that being used as as an asset across the organization Recently as I had also mentioned earlier that we started with ITC e-store and While it doesn't give you a whole volume of information, but what it definitely tells us is about trends Preferences patterns Consumer behavior in certain geographies and these are also lead indicators for us So that's the way given our context of FMCG operations. That's the way we are dealing with the first-party data See in flipkart I think you mentioned it Santosh. First party data is not the problem What is a problem which is a big challenge is how to act on it in near real time? So you're talking about petabytes of data acting on it near real-time, right? so you have things like NA is equal to one kind of marketing where you're essentially doing personalized recommendations based on what you bought last week last month So on so forth and on the other hand, you're also doing cohort level personalization It could be geo based gender based it could be affluence based so on so forth All of these have to come together at the point when you are on the app browsing So that is one big challenge The other thing maybe I'll spend a little bit time on is where is the world going? Okay, so if with the world today, right? We have invested heavily in Applications of generative AI for example, right? So if you look at things there are Approaches by which for example based on Your brow means the entire custom consumers base browse history today our fashion design happens on a computer Somebody in my team actually builds designs for curties for saris and so on so forth through approach called sable diffusion Generative AI approach by which you are actually building designs that are going to sell well based on Millions and millions of search browse purchase data history. It's a generative AI technology which is Used again similarly large language models can be built to understand much more seek The search itself will evolve pretty soon see search has been a big source of Understanding consumer based on what they reveal as what they need, but even that will get disrupted in the next five years or so which generative AI with large language models where you can have much more Conversational way of looking at things in retail and as that comes on board into e-commerce This is something we are building. I'm sure other e-commerce companies would be building something as well And what happens with that is? With that you will start Sort of building very very targeted way of understanding what the consumers want the revealed preference See historically marketing used to be understanding means questionnaire surveys things like that where it's very very difficult Means obviously there are approaches to get revealed preferences. I think revealed preference getting revealed preference has become easier and easier with these kind of E-commerce and things like that where consumers are there searching for example Nobody knows about the world more than a search engine or a e-commerce platform on what consume e-commerce platform more from a product Perspective search engine more from all kinds of intense perspective and so on First split party data From an advertising community is the buzzword for last four years because every year the cookie was supposed to die and It has taken four years and still in the ICU, but it has not died because I think Google has to You know to take out the ventilator Whatever I mean jokes apart We have the last four years most of the marketers have Understood the importance of first-party data and they know whatever Personalization which have given tremendous results when you personalize an ad and or a communication It gives obviously a very high ROI. So the advertisers understand the importance of first party data Again comparing the two panelists what we also sees in the FMCG CPG sort of a business The richness of first party data is really little difficult in the sense, you know there. What happens is how do I connect? Many data source. How do I do a fusion so that you know from a small data? I get a larger data pool too. So, you know there the discussions are more on enrichment and enrichment is a big Problem because you know we have two big guys, especially in from the advertising world the meta and the Google who contributes Nearly 70% of digital business and they don't share that data. They don't talk to each other so evolved marketers are now talking about You know, how do we do data control them and data clean rooms these years and enrich the first-party data Whatever little they have they want to do it the second segment segment where they get good data is the retail business whether you know It's the type of let's say jewelry retailer or you know fast-food chains. They get very rich data And they may not be as rich as let's say the third data set which is pure E-commerce player either is a D2C But interestingly what we see is all the three the net has taken off quite well So even a traditional FMCG business there are large a lot of companies where Online contribution to their revenues nearly 15 to 18 percent So you are able to get the data and the more the moment it crosses a certain amount of threshold It starts mirroring the same behavior as you would see in the retail segment So I think if a brand gets 20 25 percent of its business through online whether it's through Amazon or flipkata Whatever then by and large the behavior becomes very similar to what happens in the retail one if it's only three percent Then there's a huge difference. So you can't generally so that's one way we use the first-party data the retail business is actually all about you know, it's very easy to enrich because you get the first-party data quite rich there and for the final players like Digital first-person obviously they have too much of first-party data in which they have to share it with and have the lesser privilege of the two In terms of you know sharing their data in whatever without Compromising the privacy which is where the data clean rooms and all have taken off Yeah, it's interesting. Isn't it whether it's an offline world or online world? There's a ocean of data available That's where I think the data strategy and then the type of data strategy, right when you get these two right I think the biggest thing is now when you have a lot of data available You start really doing something building a strategy over it running a lot of think campaigns and things over it Now it really comes to measuring things, right? That's where we actually get focused on So when I look at that generally you know if I If I commonly hear the opens or clicks are really old time of measurements right now So I'm really keen to understand especially, you know from from your perspective and image But if I look at you know what Ravi spoke about a ton of data a lot of things that available and I'm really You know curious to learn more down to the products are being developed based on the behaviors and etc So what metrics do you normally use? Outside that to actually start measure and then I want to hear from Ravi also how they've innovated beyond opens and clicks Question is on what metrics we use. Yeah. Yeah, absolutely So a lot of analytics that we do as far as our sales systems are concerned are towards increasing You know depth and range In the market so very direct metrics which we talk about from a sales FMCG sales point of view is What's the incremental lines or the range that has been throughputed? What's the incremental sales that have been generated based on the analytics, which is running at the back end? More and more as we are developing analytical solutions for our marketing mix So we are already having solutions which are giving us the right allocation Between so it's the typical market mix modeling work, you know, that's going on right now So there of course you're able to measure through these models. What is the ROI of of your investments? So if you spent a rupee on Consumer promotions, what's the incremental sales you generated so metrics like those which are standard metrics of ROI? of course, we also track very closely the brand health measures like the mind measures You know recall spawned and You're able to now with the help of these modeling techniques able to isolate the impact of these individual investments Whether it is in media or it is in consumer promotions or it's in trade promotions What is each of them attributing to your overall sales? so a lot of these are very standard but direct impact on sales and we really are very focused on our You know revenue and cost parameters Every each of these operating metrics will translate into some sort of a financial outcome And that's that's what we finally measure at the end of it I think yeah, I Think there I think it's a lot of things are common because I think eventually Finally the outcome is the outcome. It doesn't matter whether you're offline online what you are, right? So key things are if you look at short term it is at a session level or a Session level see the most granular is session level conversion session level Revenues and so on then maybe next level is the cross session So for example, if you're buying a TV, you don't come one time and buy it you keep coming a few times and Buy it so what happens cross session then what happens in every month M1 we call it M1 repeat How often do you repeat then going to we have a RFMD framework recency frequency? Monetary value and diversity diversity here means we are a horizontal platform our success is defined by people buying Clothes from us Diapers from us TVs from us mobiles from us so on so forth so diversity is very important and the and Obviously the CLTV customer lifetime value right and these are our objectives much like any FMCG any retailer They are very similar metrics where I think we can bring in some level of differentiation is around Being able to pull all the data together and become very predictive about what it drives these metrics and how do you be very? Deliberate about moving some of these metrics and this is where some of the new technologies have become very very useful In particularly in machine learning AI and so on See as You know I used to work in a analytics specialist companies before so as a service so what I have observed is most marketers They can use data Either to get incremental sales by that what I mean is if that if not for that data that sales would not have happened Or you know that revenue stream would not have been opened up For example take ITC also if not for e comma certain of their revenue That's an incremental to what they were doing and probably Then the second is optimization Where you know because of availability of richer data. How do I optimize? Oh, he was saying about market mix so previously market mix used to be you know a little bit of science and art because Of the lack of data. So there used to be a lot of leapfrogs We used to have some benchmarks and therefore we used to come and say this is how you need to allocate today it is becoming more of Science which is what it should be because we have a very richer data sets and And we will be able to quantify it that much better and also using very robust machine learning algorithms even prediction to a large extent especially if you have a very Sound data, let's say in a retail or in a e-commerce platform We are able to predict it with fair amount of accuracy. So if you change your mix from let's say high on TV to high on digital This is what you would get and by and large those models hold good when we do that thing so to Matrix that most marketers need to adopt is what is the incremental? sales or incremental revenue Which are genuinely incremental that is outside of not using the data would not have got that and how well I Optimize I think those are the brought to families of Matrix that most marketers should are adopting Okay, no, I think the good learning out of this is I think what I've said that is Whether it's actually offline or online pretty much metrics remain the same, right? So it's actually Going to be ROI like any mesh mentioned everywhere or it could be across different behaviors and everything It's a it's a good point. I think let's let's wrap it up with what just one piece of advice in terms of Really looking at now we have the data strategy, right? No, we know what data target or do we measure? Just a last one last advice and what you advise you know it could other CDOs that are CMOs in terms of a Differentiative factor that you can bring in whether in terms of a technology or it could unification anything at all any advice there are for the I don't the differentiation that you want to bring in Just a quick one line and since we are out of time is that I think and we converged right if you look at a Sort of whether it's ITC whether it's flipkart the outcomes are the same So I think keep your focus on the outcomes technology data everything else is a means to an end and Don't means you should get excited about new technology, but not so excited that you forget what the outcome really is That's the only thing else. So I think my Observation is also when the market is very clear What should be the outcome or is very clear about what is the problem then data can do real wonders? So marketers should spend a lot more time in identifying. What's the problem? They are trying to solve or what is the opportunity they are chasing but they should be very Being able to define the outcome in one line. I want so-and-so to happen I mean unless you're able to put it in one line Then there's not much you will get out of data. So the big thing is to be very sharp and Clear on the outcome that you're expecting Just to add to these perspectives In my experience, I think one important aspect for us to keep in mind is that while Technology and data is quite well sorted. It's the change management and user adoption which typically gets missed out and You know, I think there's a lot of investment that needs to go in that those aspects There are many ways to do it right from engaging the actual stakeholders and end users at the early stage of the project itself And then minimum taking them along the entire journey of transformation Linking their KPIs to the outcomes that you expect following up with a lot of communication I think a whole lot of focus needs to go on change change management and user adoption So that's the only addition to the points being raised here Thank you. Thanks again for today and I'm sure the advice as well to see it outcome is pretty strong word Then now thank you