 So good afternoon everyone. I hope you are all keeping safe and doing well. It's a pleasure to be part of this pitch CMO summit, and I would like to extend my thanks to Exchange for Media team for the invitation and also for this opportunity to share my views and learnings on the subject marketing in a data first world. But before I throw light on how Maruti Suzuki has embarked on this journey, I would like to share some facts and figures to establish how are we living in a data first world today? The International Data Corporation, the IDC predicted in 2018 that the world would generate 175 zeta bytes of data in 2025, up from 33 zeta bytes in 2018. That's a CAGR of 26 percent. Also, the famous British mathematician Clive Humby, first used the phrase in 2006, data is the new oil. He added a word of caution with it and saying, it is valuable, but if it is unrefined, it cannot really be used. So friends, data must be broken down, analyzed for it to have value, activities and strategies, and that benefit the business in several areas need to be planned by data-driven decision-making. Of course, followed by what the previous speaker said about gut. Ultimately, it is data-driven decision-making. In the same 2018 study by IDC, also noted that organizations that have invested trillions of dollars to modernize their business, 70 percent of these initiatives fail because they prioritize technology investments without building a data culture to support it. Therefore, for organizations to have a sustainable working model, it is essential to have both technological investments and supporting data culture. Looking at these facts, the first question that comes to mind is, how can an organization adapt or be ready for the data-first world? Organizations need to make data-driven decision-making the norm. Create a culture that encourages critical thinking and curiosity. Transforming how an organization takes decision may not be an easy task, but incorporating data and analytics in decision-making cycle will definitely lead to better decision-making. An organization realizes the full value of their data only when everyone, whether he's a business analyst or a sales manager or a human resource professional, he's empowered to make better decision with data every day. This complex system and business of today, decisions that are not well informed by data can turn out to be not just inconsistent, but also arbitrarily inaccurate as well. This happens not because of lack of intent or knowledge or skill, but because of assumptions or because of biases, because of habits. The recent advances in artificial intelligence, big data, control experimentation, and systems thinking are ushering in a radical evolution in decision-making. We at Marty Suzuki, being a leader in the automobile industry, have also taken some crucial steps to be ready for this domain. We understand that data-driven marketing is very important aspect to improve revenues for auto retailers. At Marty Suzuki, we have observed that customers follow a digital buying process, experiencing both physical interaction at the dealerships and also interact with our digital touchpoints. This brings in a huge opportunity at every touchpoint to not just know more about the customer, but also offer them a personalized experience using analytical tools and new technology solutions. For example, we have more than 6,000 assets in our digital ecosystem, comprising both of websites and also social media. We are utilizing a social media listening tool, the sprinkler across 4,000 plus social media pages for monitoring and listening purposes, and a digital software site core for content management and personalization capability on 2,000 plus websites. There are also multiple in-house systems, which we have deployed to empower the dealer partners for capturing offline data as well. Using all this platform, over the last few years, we at Marty Suzuki have been able to establish a data-first ecosystem that encompasses data from the customer across all interaction points, and allows flexibility to read through customer buying patterns and so on. Though having a huge data arrangement is not sufficient, it is very critical that we accurately harness the insights from the same on an ever-evolving basis to meet our end goals. Let me now take you through our journey to explain how we have evolved in our data-driven marketing approach. Our first step was to build our own first-party datasets. Friends, this is critical and looking at the future. What we understood was a third-party data will not be available to do marketing post the GDPR compliance once it's rolled out in India. One of the most important steps we have taken to take care of the first-party data in our existing dealer management system, which is the DMS. Using all the first-party data that is available with Marty Suzuki, we have created what we call SVOC, a single view of customer to understand customers' attributes and actions, helping to create more accurate segments and resolve their identity across different devices and identifiers. All of these add up to target effectively across marketing channels. We boarded a big data platform. It's called the CDP for managing our first-party data in 2019. To stitch its structure, first we adopted an integrated CRM system in 2018, created our own SVOC, which has got ingested into CDP for further unification with unstructured data like cookies for better targeting and higher marketing returns. Let me now talk a bit about our DMS. Our SVOC and CDP followed by some other innovations and projects. First, I'd like to talk about the dealer management system. Our dealer management system are designed not only to record customer transaction data. Remember, we have more than 2.2 crore consumers who have bought vehicles from us so far. But also to publish reports on daily, weekly, monthly basis which empower the on-ground dealer staff and our regional sales team to take customer first decision and analyze buying patterns both by geography and by demography. Using data analytics, we have developed a lead-scoring model which categorizes leads according to the probability to convert, and we are using about 22 data parameters for that. This helps our dealer sales executives to prioritize customer inquiries driving to higher conversions. By ingesting SVOC data with the DMS, we have also been able to create customer search and also upsell and cross-sell opportunities for the dealer. Dealer is now able to read through customers historical transactions thereby suggesting the next best product for them and to recommend additional purchases from allied businesses like insurance, service, extended warranty, and accessories. We are using an integration layer. It's called MuleSoft that helps in data transformation and enables usage of data across systems for announced customers experience. This is also important I feel because many organizations have legacy data, legacy systems, and so on of the biggest impediments to quickly transfer to a new system is actually the integration of the past data into the new one. This integration layer tool which we are using the MuleSoft is very important from our perspective. So single view of customer is the other thing which I spoke of and all our businesses, they had massive volumes of data recorded, but they were stored in silos, making it very difficult to see how a customer consumes our offerings. Single view of customer logic was developed to aggregate all transactions of a customer into a single record. Hence, making it possible to understand how a particular customer moves in this journey. For example, a customer who gets his car, service as a Maruti service station, renews his insurance through a Maruti insurance, buys accessories from Maruti Suzuki accessories website, can now be recognized as the same person, and can be suggested to download, for example, the Maruti Suzuki Rewards app, or enroll into a loyalty program to reap its benefits. Hence, further engaging, and I think the objective here is to more engagement, and also to improve the satisfaction levels. SVOC has made possible for us to understand a customer through its journey, and to understand their behavior when interacting with Maruti Suzuki. It also helps in data analytics, and developing machine learning models, with the help of which we can provide a much better experience to the consumers. So let me talk about customer data platform, but that's the key to the entire ecosystem which we have built in the last couple of years. Our customer data platform gets data ingested from offline as well as online sources like digital assets, all apps of Maruti Suzuki, dealer website, DMS, SVOC as I explained just a few minutes back. This data from all sources is unified in a master table, and further used to create customer segments for targeting and for personalization use cases on digital assets. This helps with media optimization as well, better spends on media monies, personalization opportunities, and of course the next best action for each of our consumer. As I mentioned earlier, Maruti Suzuki has a vast digital setup comprising of brand website as well as 2000 plus dealer websites as part of our hyper-local program. Now imagine the volumes of data that is being generated by these assets. Using CDP, we can map the journey of the customer from top of the funnel to the bottom, also vice versa, giving opportunity to target them with customized creatives. Additionally, using options like programmatic advertising on digital, which is primarily driven by customer actions and behaviors on the internet, there is a plethora of marketing solutions where these systems can be integrated with each other to drive new campaigns as well. So while Maruti Suzuki has been a pioneer in leading multiple industry-first initiative, I will now like to briefly talk about a few more data-driven projects that have been instrumental in our success. Here I would like to talk about first the marketing cloud. With the objective of putting our customers first through our marketing team, you utilize the integrated CRM for sending targeted multi-channel communications, including SMS, email, push notifications, and the Maruti Suzuki rewards app. The audience of communication campaigns are filtered from our database through analytical processes as well as business experience, so that the right customer gets the right information at the right time. With the help of our database, we can find the customers who are most likely to avail of the benefits of our offering, like the Maruti Suzuki driving school, the MSDS. It's a scheme to target young adults to promote safe driving habits amongst young generation, which also, while we also acquire them as prospects for new car sales, in that sense it's also a marketing tool. The content of the communication is carefully crafted to the preference of its audience along with the language of the message and the time at which it is sent to the customer. This helps us to engage consumers and improve customer satisfaction as well as CLV, the customer lifetime value. So we don't just send promotional content as some marketers do, but also we collect feedback through surveys and send alert communication. For example, in case of extreme weather or a natural disaster, we do send alerts to our customers so that their cars are both safe, thereby building a much closer relationship with our customers with the help of data-driven marketing communications. Another example is our loyalty program team conducts various communication campaigns to increase customer transactions and engagements. Objective of this campaigns is to increase transactions enrollments, referral inquiries, increasing engagements, and so on. In 2021-22, over 24,000 new cars referrals were generated through the marketing campaigns. In 2020-23, we are targeting to double this number. So let me now talk of ACRM or analytical CRM. That's a retention campaign to contact existing pool of customers in stage of their customer journey where they may consider buying an additional or a replacement car. To target this right set of customers in this campaign, a machine learning algorithm was developed, which learns from MSI's historical data, finds the set of customers who may be looking to buy an additional or replacement car. This campaign has been instrumental in generating over one lakh prospects in 2021-22 financial year with nearly 3 percent conversion to sales from our customer base. A very recent example which you may have read because I have spoken about it, there was a WagonR consumer who bought the WagonR in 2005. In our database in the DMS, we have all his record about his different transaction, his service visits, his even his family details, etc., etc. Very recently, our system, I mean it's not something that we track manually, but our algorithm suggested that his daughter might be graduating, and hence maybe a possibility of her buying a car. She studies in the, she did economics in this Matri College. Sure enough, we approached that consumer and he was very surprised. We were of course surprised ourselves when we first came to know, and that daughter of his has bought the new WagonR, which was launched just a few months back, three, four months back. Here's an example where in any other case, we would not have approached the customer, we didn't know it, but our database about the consumer threw up that possibility. That's one example which has helped us. It's part of the single view of customer that we have built our database. We have regional office campaigns as well. Several campaigns are carried out to communicate promotion offers on regional level. These campaigns are often specific to few models. Now as opposed to ACRM, which does not advertise any model or feature as per the requirement from regional office. So these campaigns also help us inform target customers of our latest cars and their features, while subtly prompting them to register an inquiry or book a test drive by simply clicking the link in the message and selecting appropriate action. We utilize personalized content that includes customer name and their preferred language to better connect with audience and improve open and click rates in such campaigns. In fact, we have a launch tomorrow of our SUV, the new Brezza and already through this campaign, we have so far got I think close to today morning, I was checking about 42,000 bookings already through this personalized campaign. So we hope by tomorrow we should touch around 50,000 bookings for our even before we launch the product. Other thing is a speech analytics. Speech analytics is an advanced analytic tool to analyze the recorded calls made by customers at corporate contact centers to provide us with actionable feedback on customer sentiments, identify performance and quality of our agents at different vendor parties, top reasons for calling, repeat calling, form rating based segments of agents, and deploy them in the right agencies and right queues in expanding the knowledge base. A very important thing which I think all of you must be aware of is what is called the net promoter score. NPS is now globally used market research metric surveyed with two questions to measure the likelihood of a consumer recommending a company, a product or a service to a friend or colleague after a business cycle. This questionnaire has a scale and an open-ended question. High-scoring customers are called promoters, as you all know, low-scorings are called detractors and middle-scorings ones are the passives. Now data from NPS is used to obtain a bird's-eye view about customers' perspective on a brand basis their interaction with a particular touch point and to provide direction for the business to work and improve. Internally, business use cases are analyzed in which correlation is drawn on the scores obtained and its impact on business, which helps us take decisions in the right direction. For example, promoters, which are the high-score customers, are more likely to purchase a Maruti vehicle post their driving school training than detractors. That's an interesting insight and we have seen our driving schools actually helping us, not just in terms of building our brand, but also building prospects for our future purchases. Now there are also a few projects which I like to mention here we are working on relating to artificial intelligence and machine learning-based solutions, which should enable us to strengthen our algorithms further. First is Suzuki Connect. Many of you would have probably experienced Suzuki Connect. It's something which is now used extensively in cars. It's a device which has got lots of sensor data coming about the car, and that car is transmitted to a cloud. On the cloud you have an analysis, which can be very helpful for many things. For example, you could have predictive analytics which enable the system to read through driver behavior patterns, thereby promoting road safety, safer environment for buyers. You could, for example, know how many times a person is accelerating or going too fast, or exceeding speed limits, how many times he's braking. So you could predict, for example, that in a normal car if the brakes are to be replaced after let's say 20,000 kilometers, there are some drivers who use the brakes so often, their brake pads have to be changed within 10,000 kilometers, because they brake so often. So if you know that, you can approach the customer, it's good for customer, because then he can be reasonably sure his brakes are in good condition, and sometimes our consumers are surprised when we tell them that your brakes have worn out, and that's because we know the usage of that vehicle through Suzuki Connect. There is a project we are doing on Rettina, and I think some of you may be familiar. It's a Rettina movement measurement. It's a functionality which will allow drowsiness detection to monitor the alertness of a driver. So while you are driving, there is a camera which monitors the Rettina movement, transmits the data to the cloud, and the algorithm tells us that so-and-so is driving in so-and-so place and is drowsy. So you can actually alert the driver that better to stop the car and rest for a while. But the drowsiness detection can be a very good potential for safety. Many of the accidents which happen is because the driver is drowsy. You may be knowing that of course. The third is capturing the mood of the customer. Now, this is interesting. While customers step into the showrooms, there is a AI-based solution. We are trying it in one of our showrooms. We are doing a POC. It will help us understand how they are browsing through the outlet using different signals and predict the likeliness of them buying a specific model. So today, we utilize this data to plan, to display location of car models in the showroom. It's a very exciting data to study because you can actually capture the mood of the customer. Sometimes the family walks in, you are monitoring their movement of AI. You have the mood board if I can call them for each. Then you realize that the decision-making is actually the wife. While the guy is asking the question, there is something in the body language which makes you make, which the algorithm throws up and says, listen, this guy is asking questions, but he's not the decision maker. Actually, the decision maker is the wife. Now, you may say decision-making is always the wife, but that may not be true always. We always believed in cars. It's the guy who asking the question who is making the decision. It could be the other way, of course. So that is being done. Sometimes you also know how much he wants to buy the car, which means that it gives you a negotiation when it comes to discount management. So it's a tool which dealers can use to know how much the consumer is looking quickly to finish the deal. Pricing engine for selling a car, selling your old car with Marty Suzuki true value is now becoming easier, but the day as customers can use the pricing engine to predict an estimated value of their old car. With this, we are able to bring in more competitive pricing, convenience, decision-making capabilities for the customers. And I would urge any of you who's wanting to upgrade from your old car to a new one, a new Marty car, of course. You can try this pricing engine. You can see how quickly and how accurately it comes to that pricing. Other examples of predictive analytics that we are working on are demand forecasting, consumer scheme optimization, voice boards on incoming customer calls, interactive chat boards and website and so on. Of course, Suzuki Connect, as you know, I'm using that Suzuki Connect. I told you it's a treasure of data. It can be utilized in many ways. I, for example, when I give my, I have two daughters. When my daughters take the car, I sometimes worry how fast they are driving. This is a, whether the fuel is enough if they are getting late at night or whatever. So I keep telling them, like my older daughter, I keep telling her, why are you driving so fast? And then, of course, they tell us that this is, listen, this goes against privacy because you are monitoring individual data. So I tell them, as most marketers do, that actually it's not an individual I'm monitoring. It is the cohort of daughters. Of course, I have two of them. This is the cohort of daughters, which I am monitoring. But I think I've not yet got a lawsuit of this me, but I continue to use, I'd say, great possibility for the monitoring of driving, not the lawsuit. I'm talking of the monitoring. The third is also something which we all need to plan, which is that while I've talked about all this thing but based largely on first-party data, there are other steps which we are taking in the direction to ensure that we are future ready. For example, getting active on second-party data types, tying up with various other first-party data providers like mobile service providers, financial institutions, two-wheeler companies, petroleum companies. We are working with them with the following checks. One, the checks are important. The second-party data provider has to implement the basics of constant management, like any good platform does, and also that there is a clear-cut mechanism for scaling up their data under regulatory guidelines. And I think it's important for all marketers here exploring the cookie-less world of addressability. And in addition to this first-party and the second-party data that I have just talked about, we are also exploring other substitutes which are like using third-party data, like the wall gardens, tying up with wall gardens like Facebook, with Amazon to use their first-party data. That is the data that they collect on their platform. And also contextual targeting. We are, from the learning of the first-party data, we can advertise on websites or apps or websites that are relevant to or to interest or characteristics of its audience, like MSI, sports, general entertainment, OTTs, news websites, they're all very relevant. Utilizing alliances or cohorts, publishers in European and American markets, as you know, have started to form alliances. Similar situation will soon come in India. Big advertisers will have to pick and choose similar alliances or in fact form our own alliances. And also the, as you know, the fingerprinting, as we call it, which is to build a probabilistic model by using users' metadata to build a targetable profile. And we are already using CDP to build such models. So the above steps, we believe, have also made Maruti Suzuki future ready for a cookie-less world. Once Google does away with the third-party cookies, which is expected that in the Chrome browser, that should end by 2023 and to suggest to summarize what I have discussed so far. I like to say that Maruti Suzuki has been able to reach a certain level of maturity on this data-driven journey. But it will continue to progress as we get more and more data on customer interactions, feedback, and performances of our campaigns. The end goal is, of course, always be evolving with whatever the consumer requires. And today, we feel empowered and more secure to have built a robust and futuristic data ecosystem that will ensure that we continue to serve our customers this step ahead. But as I conclude, I would like to quote what J. Byer said, the future of marketing isn't big data. It is the big understanding. So we must all embark on this journey to strengthen the capability of the organization to understand the data better and better and better from a consumer's perspective. Thank you very much.