Transcript
Speaker: Welcome to Winning with Data -Driven Marketing Podcast. This podcast is brought to you by WASD .ai Market Research. I'm Julie, your host in this podcast, and in every single episode, we talk to industry leaders, marketers, and growth experts in Asia about how to use data to enhance the ROI in their marketing activities.
Speaker: We bring you real case studies while giving you background on how these leaders built their career to where they are today. Joining me today is Ranga Somanatad. He's a marketing and communication expert over 20 years of experience with help.
Speaker: a lot of clients from the likes of P &G, Samsung, Johnson, Visa, and Citibank to grow and scale. He's currently the co -founder and curator of R -Squared Global Ventures. He's previously the CEO of Omnicom Media Group across Singapore and Malaysia, and before that, the COO and chairman of Starkom Media Best Group. So you can see from his profile, this is a person who you want to listen to as he's a staunch love experience for you to share. Now I would like to welcome to you Hi, Ranga.
Speaker: Hi, how are you? Julie, good to see you. Good. Thank you so much for jumping into our podcast today. My pleasure. Thanks for having me. So in the past year, you have started R -Square Global Ventures. Can you tell our audience a little bit more about R -Square?
Speaker: Sure. R -Squared Global Ventures was set up to operate the intersection of startups, large corporates and investors. And what we typically do is help the startups with their growth agenda.
Speaker: We help large corporates with their innovation pipe. And when we do this, we end up having a virtuous cycle of growth and innovation, which investors are interested in. And we introduce these startups to investors who ensure that they get the right startups to invest in and have the IROI. So operating at the intersection of startups, large corporates and investors. That's what we do.
Speaker: I can see from your profile, your entire career experience has been in marketing. What makes you pursue a career in marketing and being so convicted in this space?
Speaker: I wish it was that much of a design world. There is an element of default and going with the flow too. I did my graduation in statistics and even the choices of subjects, how I ended up doing statistics was that of attrition rather than selection.
Speaker: In India, I did my Bachelor of Science where I started off taking math, statistics and economics as my subjects. In the second year, I dropped economics because the college wouldn't offer economics in second year.
Speaker: And then the third year I dropped maths because I found it more difficult than statistics. So I enjoyed doing statistics. So I graduated in statistics. And then again, the sentiment is graduation alone. Lord liked your job. So you go and do your post -graduation.
Speaker: And I wasn't that keen to do masters in statistics, because that would have been even more difficult. So I said, OK, what is the thing that will appear to me? And I realized that an MBA would be a good thing. So I did an MBA in marketing. So the combination of graduation and stats and post -graduation in marketing kind of helped me to funnel myself into advertising.
Speaker: I got plays on campus with Rakhya Gray, which later on went on to become Gray and Media Car. And because of my background in statistics and then MBA in marketing, I was assigned to work on media team on PMT business and that's how my journey started. So it's a bit of a selection and attrition and it's been a fun ride since then.
Speaker: Wow. We're going to talk a lot more about statistics and advertising. And what are some of the, so would you say because of your background in statistics, it actually prepares you much more easily for, you know, addressing the data driven part in the advertising space? I would wish to think like that, but I think it's more over time.
Speaker: I have met a lot of people who have not necessarily majored in statistics, but purely on the basis of their passion for the subject, they've actually embraced data analytics at a later stage in their career. So if you're a beginner in the space and you don't have the academic training, it's pretty
Speaker: okay for you to embark on this journey because there are enough training materials out there for you to self -learn. And a lot of things that we do in advertising is applied statistics. You're applying and interpreting rather than going through the science and the principles of statistics. Of course, it helps if you understand how these theorems work.
Speaker: But what is more important is are you able to interpret data? Are you able to analyze data from the context of ones you use? So for me, it was an advantage, but that doesn't mean it's going to be a disadvantage for somebody who has not. Kacha, can you tell us a little bit about what are the unique insights that you have learned over the years straight that help to drive effective advertising strategies that you have used?
Speaker: And if there's any success story that you can share, that would be amazing. See, it all starts with the business problem that you're trying to solve. It doesn't start because I want to apply statistics. If that's the starting point, then more often than not, you get into analysis paralysis because you don't know what you're trying to find. So any analytics project, any data science project starts with the problem that you're trying to solve with the business.
Speaker: And when you start that way, you're able to frame hypothesis properly, then you're able to structure the analytics framework properly, choose the right kind of techniques to analyze the data properly, and then whatever outcomes come, you iterate a few times before you see that it makes sense.
Speaker: We've done a lot of market mix modeling where purists come in and take all of the data, do multiple runs, come back and make recommendations purely based on what the statistic says. We realize that those findings are not applicable or not usable because it's too theoretical.
Speaker: When a practitioner of marketing, a practitioner of media, collaborates with a market -based modeling expert, you are able to frame the right kind of hypothesis. You are able to restructure the data to see how the results look. It is almost like cooking, where your
Speaker: putting in the right ingredients to end up with the right outcome, right? And you have to put that in a sequence. You got to state that data in the analytics so that you're able to see the layered effects. So that's what the art of analytics comes from. And that's where your understanding of the market, the understanding of the consumer, even simple things like seasonality, understanding seasonality, understanding crystal times and
Speaker: If somebody is going to be taking a trip back home, what is the implications of that on user behavior? All of those things get layered and all of those don't come in data form. It comes in some kind of an insight. How do you layer that in your analytics and look at a big picture becomes an important
Speaker: So you asked me about what are the successful applications of data analytics. I think for me, one of the favorites was the work that we did when I was at Omnicom Media Group.
Speaker: and my team at OMD Singapore did this work for McDonald's where the business problem was that they were launching online orders and delivery service, right? But then the delivery was being managed by the retail outlets.
Speaker: And there were peak times where the online orders as well as footfalls in the retail store would be coming in at the same time. So the kitchen capacity was not able to service the online delivery and our retail demand. So that was a business problem. And we had a very smart data scientist, a very good strategist, instead of giving up saying it's not a marketing problem, it's not a media problem.
Speaker: How if I mirror the capacity information with search volumes and reroute traffic based on that, right? So first we went to the client, the team went to the client, asked them are they able to share with us the capacity data by each McDonald's restaurant in Singapore. And he gave us on a minute by minute basis how the order is coming in.
Speaker: And the client was very innovative and ahead of their times, and they were very keen to provide that support. Then we went and negotiated and discussed with the Google team to say, hey, can you break down the search volumes by the McDonald's restaurant geographic zones, localization based rather than their own breakdown of the country. And with a few conversations with various teams, including their engineering team, they managed to give us that information.
Speaker: We had the kitchen data and then on a real time basis, we were able to match volumes of search and demand on ground and our messaging as well as the offer tied in synchronized with the capacity. When there was low capacity, we were betting aggressively. When there was high capacity, we were offering in, able to stay on for a longer time. So that kind of balanced out the demand and supply and it was a,
Speaker: Brilliant marketing effectiveness solution, right? And even it was recognized at Khan, it was recognized at Festival of Media Global as one of the best -in -class data -driven marketing solutions.
Speaker: But the icing on the cake was even their global CFO said, these are the kind of innovations that we would like to see in their earnings call. So I think that is one of the best practices I have come across. I'm very extremely proud of the work that my team did, extremely proud of the collaborative mindset
Speaker: our partners had, our clients had to try something different. And we came up with a solution. So to me, data -driven solution looks like that. It starts with the business problem. You see if their data is available, otherwise go and figure out where to get it, how to get it. And it's a process. Once you get it, how do you line it up?
Speaker: We couldn't have managed this manually. It had to be code driven. Somebody had to write a code to synchronize volume of search and capacity. And that kind of automated the entire search bidding process. So to us, it was a very meaningful solution that was created and we created a very happy client. We had a happy team and becoming famous was a good side benefit.
Speaker: I hope that kind of gives you a good example. This is amazing. I like the fact that how when you start actually giving the context of the scenario, my first impression was also to say, oh, is this a marketing problem? It sounds like an operation problem. But like you say, it is really interesting how the marketing actually softens in a meaningful way.
Speaker: Um, you, you talk just now, uh, on this note, you also introduced to our audience market, market mix modeling. Um, do you see that in general, uh, market mix modeling is only something like say bigger, bigger companies are able to apply or, and can you also tell us a little bit more for those audience who have never, not quite, uh, not quite familiar with this market mix modeling. Can you tell us a little bit more about it?
Speaker: Sure. See, at the end of the day, what we do in marketing is we use all the resources on hand.
Speaker: and deploy it with the desired outcome to drive more sales. With that in mind, we want to know what are those variables that we put in, whether it is investment behind distribution, investment behind marketing, investment behind pricing, investment behind gift promotions. We do a lot of those things to get the attention of the customer and get them to buy our solution.
Speaker: Now, trying to figure out which of those elements of inputs are contributing to the sales is what market big modeling does broadly. Now, for that to have analysis to happen, you need to line up all of that data. You need to have your pricing data. You need to have your distribution data. You need to have your sales data. You need to have your competitive media weights data. All of that go in as your input variable, right?
Speaker: And then you put it through a modeling process, statistical process, at a very simple level, it's regression modeling. And there are enough software tools that help you do that. You don't get overwhelmed when you hear these statistical doubts. Then once you put that into a framework, you get the process done, you're able to look at which of these input variables have the highest impact on sales, right?
Speaker: Now, sometimes you might not get to see the direct impact on sales, but you will start seeing impact on intermediary variables, brand awareness, brand preference, so on and so forth. So you are able to then look at the various input variables and see the impact on sales. Now to your question, can anybody do it or only brands who are big and have a lot of budget can do it?
Speaker: Now, the answer to that is two -prong. One is everybody can do it because everybody has got input data, right? You know your distribution data, you know your pricing strategy data, you know your marketing spends data. All of the input data is something that you control. As long as you're organized and you capture that in a, even as simple as an Excel sheet, that's a good starting point. Now,
Speaker: Whether all organizations can do it, whether big company or small company, to me, the answer is not a function of scale. It's a function of mindset. Are you a data -driven organization? Do you want to take decisions that you can actually hold yourself accountable to? The organization culture is driven by data. You will be able to pull this off.
Speaker: If your organization culture, if folks in the organization are a little bit more driven by gut, by experience, even gut comes from experience, right? Then a data becomes an excuse of why you shouldn't do something rather than why you should do something. So going back and reflecting on your organization culture all the way from top
Speaker: of the organization leadership to functional leaders to see if we want to be data driven, we want to take decisions and hold ourselves accountable to those decisions by looking at data and outcomes. Then your organization will have more success when we actually do market mix modeling or any analytics.
Speaker: Having said that the last 10 years or so, or even 15 years in Southeast Asia, we were on a growth trajectory as businesses. Most businesses tended to have a positive cycle in sales and growth. And it kind of became exponential during the pandemic period.
Speaker: Coming off the pandemic period, there is normalization and potentially headwinds that businesses are seeing due to macroeconomic environments, geopolitics, supply chains, so on and so forth. Now, when the businesses were on an upward trajectory, just being in the market gave you growth.
Speaker: So unless you were looking at efficient growth, you didn't really have to look at data to tell you how to just being present available and having a very basic marketing presence gave you a upward trajectory.
Speaker: But over the next few quarters and few years, there will be course connection. There will be softening in some sectors. There will be headwinds in some sectors till the time the macroeconomic as well as geopolitical headwinds stabilize. With that happening, when you're not able to figure out which element of your input is giving you the maximum impact,
Speaker: you will end up having a much more tougher business situation.
Speaker: In contrast from the past 15 years to how the next three years probably look, you didn't need to do a market mix modeling to say, I can grow. And you didn't really worry about which element of my input was giving maximum impact. But as we go into a little bit more constrained driven marketing and business environment over the next say 24 months, 36 months, it's important for you to understand which variable that you're inputting is giving maximum bang for the buck.
Speaker: And therefore, deploying a market mix modeling end of a solution will give you much better insights on which lever to push up, which lever to bring down, and therefore you're getting maximum ROI. And in the Western markets, when the markets were soft, they were not growing as exponentially as strongly as in Southeast Asia.
Speaker: They have started using MMM, market mix modeling for a few years now. Because when the markets have plateaued, you got to squeeze the blood out of your marketing efforts and therefore you try to figure out which one to really push. I think in the next three years, we probably will be in that environment.
Speaker: across the region and it's important for you to understand what you're doing, which part of what you're doing is working the most for your business and therefore you can control those decisions. It was a long answer Julia, I hope it kind of gave me some confidence.
Speaker: Oh, I love the context. In fact, I think you answered a question that I always hear. A lot of times when talking about, say, other than using MMM or other than using other data driven strategies, right? Some companies say, I've been doing so well in the past.
Speaker: Why would I need to do an additional service like this or additional strategy thought like this? And you just answer it. But if you're pursuing efficient growth, I like this word, efficient growth, then actually doing this is important. Do you see what are the few challenges or what are the top challenges that you see company face even when they try to apply MMM?
Speaker: Uh, and if you, if there is any case studies that we can learn from it, that will be great as well on how you resolve that. So we typically attend extreme, um, see, it's a very early stage for many companies in the state board to use some deep statistical techniques to actually get the output.
Speaker: MarketPix modeling is one tool that people use, attribution modeling techniques that people use, especially if you're a fully digital organization where entire operation end -to -end is online. You use a lot more attribution work than MarketPix.
Speaker: The challenges that marketers face is the lining up of the data, collection of the data, storing of the data, retrieving of the data.
Speaker: reporting of the data and interpreting that, right? Each one of those stages requires a little bit more conscious effort who ensure that you're getting the right information placed in the right place, analyze the right way, interpret the right
Speaker: Now, again, I'll give you an example of how it used to be before and how it is today. In the past, before the digital era, 2007, 2008, where we were still in the early days of digital data in marketing, a lot of analytics used to happen with historic data.
Speaker: So we used to work with time series in the sense that you would get sales data two months later, used to get ad spends data again a month later, your reach frequency data a week later from when the events actually happened. So you're dealing with a lot of past data and you're lining up those past data.
Speaker: and trying to figure out the interplay between reach, share of voice, share of spends, distribution, pricing to sales. So a lot of, uh, fast data we used to bring together and analyze. And the speed at which that data came was very clear. It will come in a week's time or a fortnight's time or a month's time or a quarterly basis. Whatever was the cadence. It was very clear.
Speaker: With the onset of data coming from digital environment, the characteristic of data changed. It started coming real time. It started going fast and it started coming from multiple directions.
Speaker: So suddenly the data, which we were used to dealing with, which was past beta also started building a characteristic of past data. So we ended up having past data, past data, and bringing all of that together required a little bit more rigor in data management, right? That was the big, that was becoming the challenge for most markets.
Speaker: If you don't line up your historic data, you don't line up your real -time data in the right form, the outcomes that you get is going to be a lot of noise, a lot of rubbish. So that requires marketers or data analysis analysts to
Speaker: bring in an additional skill set, which is that of coding, that of artificial intelligence, machine learning, so that the software was able to analyze, synchronize and organize data much faster, much real time than humans could be. Right?
Speaker: So especially in the last three, four years, the organization of data with the support of AI, machine learning becomes a very critical element for you to be successful. If you still try to do everything manually, like you did 10 years ago, you're going to struggle to actually come up with any meaningful office.
Speaker: because you're dealing with historic data, which is very probabilistic. And you're looking at real time data, which is very deterministic. And then you're trying to bring all of that together, synchronize it, and then predict the future. You will need machine learning and AI to help you to deal with the volume of data to deal with the complexity. So.
Speaker: If as a marketing organization, you're at an early stage, then what I would recommend is you start organizing your data. You start at least reporting your past data properly, bring it all together and try to make sense of what you have done and what's that doing to the business.
Speaker: If you're in an intermediary stage, then start looking at dashboarding, capturing all of the data into some place, visualizing it in a cleaner manner, integrate analog data and digital data together.
Speaker: start looking at it together as a connector. Then if you are in the advanced stage, then you need to bring in, in addition to very simple techniques that you use in statistics in Excel, start using platforms which are AI and machine learning driven so that it's able to deal with the
Speaker: Also in terms of culture, you start with telling people that data is important at an early stage and start showing brand health measures.
Speaker: At an intermediary level, you start doing some level of predictive work to say, this is what this investment is going to translate to. And at an advanced level, you start optimizing real time, your inputs and outcomes, so that you're able to dial up, dial down almost a live basis. And the nitro boost of that advanced level is where the system starts doing it for you. And you can just monitor if it's doing Riker.
Speaker: Thank you for sharing different advices for different types of companies. I think the audience will definitely be happy because they will find one in the three options that you just mentioned. It does look like in order to pull this off, there is a lot of different types of skill set that is required.
Speaker: Not just on the typical communication side of things in terms of marketing. How would you structure a marketing team? Leveraging what kind of talent or skill set that are able to leverage this kind of strategy?
Speaker: See, first is we have to start thinking beyond our own function, but I have a wider perspective because the way the finance organization collects data, the way the sales organization collects data, the research organization collects data.
Speaker: Our customer service department collects data, they're all in different forms. So as a data analyst, you need to have a wider perspective of what all data the organization is creating, how it is being captured, right? If I were to rephrase data into interactions, what is essentially happening between a company and its user is interaction. Either the company initiated interaction or the customer initiated interaction. We all are coming into the organization as data streams.
Speaker: Identifying which function in my organization is triggering that interaction if we are initiating it. What are the ways in which it is going out? What are the characteristics of those interactions? What is the cadence of those interactions? All of those things, the data analyst or the marketing specialist who's working on data leads to understand.
Speaker: The second element is when the customer is triggering a risk interaction with the company. Likewise, what are the things that the customer is triggering? User is triggering. How are they triggering from what sources they are triggering? And what is the characteristic of that source? How predictive is that source? How deterministic is that source? Understanding those interactions become very
Speaker: So if I were to just pull a number of the air, you probably will end up having about 40 to 50 unique interactions that the brand triggers towards the user and the user triggers towards it. So being able to understand those interactions become an important element for a marketing specialist who is trying to interpret
Speaker: the relationship of these interactions and how those relationships translate to a business outcome, which in this case would be safe. So wider perspective of what's happening is one element. Understanding the characteristics of this important is an important element.
Speaker: Having some element of coding experience is an element that you need to build. You might not have it in -house, then use partners. You know, this is a fast growing space and you might not have all the capability in. So for you to be successful, don't be afraid to be resourceful. Seek help, right? There are tech organizations, platforms, analytics specialists, and marketing services partners who can collaborate with you to make it.
Speaker: Right, so be resourceful is a very important part of this because you're not going to have all the capabilities and how to be able to build this, right? And then at the very fundamental level, I have an ambition, right? That I want to be a data -driven marketer that puts account to be a data center. So that goes back to culture. So a wider perspective, resourceful and having a culture
Speaker: To be accountable are the key elements I think you will need to have in the marketing organization as we embrace data -driven marketing. Thank you. Now we also spoke a little bit about ML, AI. What are the trends that you are seeing or things that will change in the coming few years because of AI in the advertising space?
Speaker: Or maybe it doesn't actually will change, but there is a myth that it will change, but it actually will stay the same. I'm curious about your thoughts. See, there are a few conversations around what AI can do. A big part of the AI conversation today is around generative AI and how is that going to do marketing?
Speaker: I think any kind of the reason why all of this are relevant and important by where to start from that point of view is because the amount of data that is being captured today amongst all these interactions that brands and users are having.
Speaker: it's become tending to infinity, right? That volume is tending to infinity. Those micro events of those interactions are happening at such a scale that it's humanely impossible to organize that and analyze that interpretation.
Speaker: So if you want to be on top of all these interactions and assess which of those interactions have the strongest relationship to fail that you want to leverage, there is no other option, but to embrace machine learning. Now, if I were to then take it to, so that is the answer which comes within the data analytics.
Speaker: But overall impact of generative AI on marketing services is an important question that people are asking. And I think how generative AI will impact is make the process faster, more efficient. And people who are truly capable are then able to invest their time in building that idea and which will be super company, right?
Speaker: Generative AI is not going to create an innovation for you because it is based on all the things that happened in the past. Whatever its recommendation is going to have a history, right? Most innovations that create path -breaking solutions and accelerated growth.
Speaker: It's going to come from the fuzziness. It's not coming. It's not sitting in the binary one or zero. It is somewhere sitting in the fuzzy and the ability for gen AI to operate in the fuzzy is still limited. And it will, the moment there is a precedence to that fuzzy, that becomes one or zero. And that becomes predictive for the AI to then use to actually recommend something till the time the
Speaker: The Y is answered by intuition. The Y is answered by an observation that is already not being captured. The human spirit will keep the animation ahead of what a generative AI is. So to me, generative AI is a great accessory, great asset for somebody highly competent to take all of that grunt work, use that insight and project an idea which has never been done, right?
Speaker: If I were to exaggerate on the other side, it will surely make the media current. Now, if I'm operating purely on.
Speaker: And that data is what I'm presenting as an idea without necessarily making the leap into a meaningful iteration. That's a generative way I can do. And you don't need a mediocre resource to actually give you some solutions, right? So I believe what this will do for the industry.
Speaker: It will separate the wheat from the shaft, as they say, they will basically have the good quality folks become better. So the good will become great and that average will get churned out of the system. So that's a good thing, right? People don't have to pretend to be an expert when they're not, because if I'm a marketer who's using Gen AI to actually create solutions.
Speaker: If I've been using an average service provider, I can do that myself, right? But I'm actually using a very path -breaking, innovative partner who was able to take all of this data and leapfrog into something that's never been imagined, that Gen AI can't replace. And that kind of a solution is what would give exponential growth to work.
Speaker: You work with a lot of different companies and I can see that a lot of companies probably have different success metrics or how do they define success in their marketing as an organization. How do you see this as a walk over time or is there a pattern to how a company define what is the right success metrics when it comes to their marketing?
Speaker: still remains a struggle for a lot of marketers to define success metrics. A lot of conversations in the organization is around what should be the metrics and that's a evolving conversation.
Speaker: It becomes a little bit more sophisticated again in a fully digital brand where all the inputs and their outcomes are happening in the online ecosystem. It becomes a little bit more brittle the process when it is offline to online to offline kind of where we not having a full view of those interactions when they go off the online to offline.
Speaker: So there are two kinds of measures, which I would actually bucket any kind of outcomes to one is called mind measures, and the other is operational measures. The one needs to measure both the mind measure and operational measure mind measures are all the things that customers think about you.
Speaker: whether they love you, whether they hate you, whether they like a service that you give, whether they want something else, all of the things that they have in their mind, it gets captured in brand affinity studies, brand awareness studies, spontaneous awareness data.
Speaker: attributing the product efficacy to the right brand, all of those things are sitting in a consumer's mind. And you need to measure that to see which of your messages are sticking and those messages converting to business, right?
Speaker: So those are mind measures, anything that people are thinking in the mind. So you need to figure that out. And that typically happens through a panel study. You run a panel, you ask these questions and you get that information back and they are probabilistic data. You can't do, you know, for you to be efficient in marketing. You don't need to go and do a census. You do a panel, you then run an estimate on it and see how this 1000 people questionnaire reflects on the overall population.
Speaker: or the target audience, right? So that's a mind measure that you use a panel to run. The other is a deterministic operational measure. Operational measures are more often than not sitting within the organization, which is, what is my distribution data? How many markets, how many stores am I presented? What sizes of units that I'm presented? What SKUs that I'm presented?
Speaker: Um, and then, um, uh, what does the offtake data, that is how much sales is going on, um, from the factory. So you collecting the data from your own source, see how much I have shipped. Um, and then you get a retail outlet data to say, how many people in the retail environment have bought that from the shelves, right? So those are hardcore operational measures of, uh,
Speaker: So you need to layer the mind measure through panel and which is more likely probabilistic and operational measure, which internal variables are more deterministic. And when you look at retail media data that probably as a mix of deterministic and powerful.
Speaker: So these are the two broad buckets that one needs to operate with and see how they come together, uh, in terms of, uh, interplay between mind measure to operational measure. And, uh, from there, the impact on business. So it's, uh, that synchronization of the data stream analyzes analysis of the data stream that creates the magic.
Speaker: I like how you're back at him in the mind measures and the operational measures. I think in the past, I heard a lot of companies a lot more actually focusing on operational measures because they can see that our eye tying very deeply into that. But mind measures sometimes is less, it's a little bit more weak. See, there is a saying in marketing and advertising that consumers make up, they decide with their heart and rationalize with their mind.
Speaker: And when they decide with their heart, you don't have any deterministic data as to what are those triggers that made them decide, right? And when you actually run brand affinity studies, usage and attitude studies, you understand the why behind what. So that's when mind measure studies become important.
Speaker: If you are optimizing purely on operational measures, that is, how am I pushing my distribution? How am I pushing my pricing? How am I pushing my, you know, off -take data, so on and so forth.
Speaker: You get the what, what's really happening in the business. And that's fine. As long as in the growth environment, that kind of gives you decent enough for a lever to actually play around with. But it doesn't give you an answer on why people are taking your product off the shelf.
Speaker: Why are they buying it? Why they repeat buying it? So on and so forth. So mind measures become therefore the right approach. Measuring the mind measures become the right approach to figure out how to push and improve your operation. And for that, you need to be able to work with the
Speaker: Cause consumer response data, right? And you're asking the question and you're getting a response. And that is where, where the quality of mind measures become good or bad. If you run a, you become very greedy.
Speaker: And you run a two hour questionnaire trying to understand what the customer thinks about you. You're only going to get a tired customer responding in a very high level of fatigue. So you got to be very smart about then choosy about unselective about what do you want to really understand? So keep your questionnaires to a very short length, max 25 minutes, 30 minutes. Even that is a stretch. Who's going to sit and answer you for 30 minutes.
Speaker: Ideally, you can pull it off in 10 minutes, which means that you need to really spend a lot of time understanding what I really want to understand, sharpen that will be very easy to respond. That kind of will improve the quality of the responses. So having a right panel partner, having a right questionnaire design, I will give you that
Speaker: strategic advantage, monies, mind missions, right? That also will give you the why behind the watch, which is the operation. Thank you. This has been amazing conversations, Ranga. I have last few very quick questions for you. First one, what are some of the most important skills that a marketer should have, or what advice would you give to them if someone would like to pursue a career in marketing?
Speaker: Three things or two things. First is to have empathy, right? Want to know what's really going on in your customer's mind, in your team's mind, in your organizational, functional mind. And putting yourself in other shoes, a fundamental human need, it becomes even more exaggerated in the marketing.
Speaker: Because in marketing, you're trying to win over the hearts and minds of your customer. And unless you are empathetic towards them, unless you're listening to them, you don't know what you're selling to them. It's relevant. So empathy becomes one key.
Speaker: The second element is to be a little bit more risk -taking and not be risk -overs. What do I mean by that? A lot of times you're persuading another person with a lot of logic and that is a comparative advantage. Everybody has access to the same logic data.
Speaker: Your competitor has the same logic data, you have the same logic data. So when you go to a customer and say, hey, use my shampoo because you got to get more confident because I'm going to have a good day, the competitor is going to see the same thing because they also have the same data. For you to be able to innovate,
Speaker: You need for you to be able to build a competitive advantage. You need to operate on in the space that is not evident for everybody. And that's requires you to go beyond comfort zone, try something that you never tried before, run experiments around it in a successful scale. So risk taking, experimenting is the second level of attribute, the good market. Right. And, um,
Speaker: if I were to add a third level to a marketers thing is
Speaker: Put yourself at the top of the table of the organization, right? What do I mean by that is a lot of the conversations tend to become very efficiency driven, especially if it is driven in the context of financial planning. It is driven from the context of operational efficiency, because the easiest thing to cut in a organizational operational expense expenditure is a marketing budget.
Speaker: And the reason why it is easiest to cut is because we have been shockingly unaccountable for our actions, right? We get away by saying 50 % of what we do, we cannot explain. And that is not acceptable anymore.
Speaker: You have to be accountable. You have to make yourself come to the table in an organization and say, if I'm spending this much in the marketing space, this is the impact on the business. That means that you are bringing data to the table and not judgment. You're bringing qualified insights powered by data to the table rather than an opinion. Right. So.
Speaker: That is a super critical element for you to then come back to a board level and push the right agenda so that as a business, you are able to create the right marketing solutions, have the right communication strategies, and win customers and build a competitive advantage. I believe in the last two decades, the function of marketing is eroded, essentially because we have not owned data analytics. We have not held our students accountable for our equipment.
Speaker: We have used judgments more than data. So bringing in that element to our marketing flair is going to bring us back onto the table and it's on us to basically leverage this very powerful capability.
Speaker: Because I also, as much as I say that we have not used, we have the most competent to use it also, right? And of all the departments in the organization, we have a perspective of external, internal and all stakeholders. We can bring this together and actually create the right competitive advantage for the company. My second question is, what is the one marketing book or marketing resources that you would recommend?
Speaker: One of my close friends and marketing experts, Prashant, has written a book made in the future. I recommend that as prerequisite for all marketing professionals in the region. Please do read it. It speaks very simply about what are the various dynamics in marketing, what has changed, what do we need to do to win, and very compellingly. To me, that's one of the updated books written for the region by somebody in the region.
Speaker: Awesome. One final question. So where can people find you if they want to reach out to you and learn more about what you're up to? I'm on LinkedIn and you can reach out to me on LinkedIn. It's the place that I respond to. I monitor pretty often. So please go ahead and reach out to me on LinkedIn.
Speaker: Amazing. Ranga, thank you so much for being here. It is a very delightful and insightful conversation that we have. Thank you once again.
Speaker: Thank you, Julie. It was a pleasure to chat with you as always. And I'm super excited to see how we can help marketers in this region get the best out of data analytics to power their business. So look forward to hearing from you, all the audience who are listening to the podcast to also reach out to me on LinkedIn. So any specific questions that you want me to respond to, I'm more than happy to support you.
Speaker: Thanks a lot. We'll be getting this opportunity. Appreciate it. Thank you so much for listening. If you find this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or Google Podcasts. Also, please consider giving us a rating or leaving us a review because this really can help other listeners to find the podcasts. You can find all the episodes or learn more about this podcast at was .ai. See you in the next episode.
Speaker: Bye!
