In this episode of the Consumer Insights Podcast, Thor is joined by Bipul Markan, Head of Research and Insights MENA at Assembly Global.

The world of insights is continuously expanding. But what does that mean for insights professionals? And what skills do you need to stay ahead as the industry evolves?

In this episode, we dive deep into these topics with Bipul Markan, Head of Research and Insights MENA at Assembly Global. He encourages insights leaders to broaden their capabilities, embrace new tools, and foster a culture of learning within their teams to navigate the shifts in consumer behavior and the world of insights.

We also discuss: 

• How synthetic data offers valuable opportunities in market research, especially for experimentation and data augmentation.
• How to supplement traditional research methods with newer methodologies.
• Why advanced analytics are worth the investment.

You can access all episodes of the Consumer Insights Podcast on Apple, Spotify, or Spreaker. Below, you'll find a lightly edited transcript of this episode.

Thor:

Hello everyone and welcome to the Consumer Insights podcast. Today, I'm excited to have an incredible insights leader joining me for what I know will be an enlightening conversation. I'm thrilled to introduce today's guest, Bipul Markan, Head of Research and Insights MENA at Assembly Global. Bipul has worked with some of the world's leading brands, and market research agencies. He's built a global career, lived and worked in India, Europe, and the Middle East. And he's passionate about driving business growth and enhancing customer experience by developing innovative solutions and generating impactful insights. Thank you so much for joining me, Bipul.

Bipul Markan:

Thank you for having me. It's really exciting. I'm looking forward to talking to you about all things consumer insights.

Bipul's Introduction

Thor:

And we are very excited to have you on the show. So in order to get started, I'd love for you to tell us about yourself. So take a couple of minutes, tell us about yourself, tell us about your role and how you got to where you are today, how did it all begin?

Bipul Markan:

Great. Sure. Again, very much thrilled to be here. I would say I've listened to a number of Consumer Insights episodes on Stravito and I've absolutely enjoyed listening to the conversations you have had with other industry leaders. It's been enlightening as a journey to even listen to those conversations.

Now, in regards to my introduction, you've covered most of it, I think really well. But to give all the listeners a very brief overview about myself, more so adding to what you have already said.

Yes, I've been in the world of consumer insights for coming close to 15 years. So we're seeing very diverse set of brands across FMCG, Telco, financial institutions, consumer labels, retail, you name the category and I would have worked with some brand in that category.

And in this journey of becoming a consumer insights professional, or rather I call it more of a consumer insights advocate than just a professional, what I've really learned and something I've found fascinating is the power of brand. And this is something I'm really passionate about, so you're gonna hear this a lot during our conversation today. Arguably, in the current times of very high cost of living, you could say that price would play a more important role than anything else in defining consumer brand choice. But some of the validation work that I have seen throughout my journey of learning about customer insights has shown that businesses that prioritize long-term brand building during times of high inflation or recession, reap greater advantage than those just taking the root of price discounts and promotions. So yeah, over the years, the work I've done with international and local brands has been very much centered around uncovering or rather I would say quantifying the impact of the investments, the initiatives on this intangible but hugely valuable asset, what we call as brand.

Very recently, I managed brand tracking and brand positioning projects for some of the leading home improvement and FMCG brands in the world, have had an incredible experience evaluating repositioning of an international Scotch brand, leveraged analytics in predicting what to expect in brand health and market share shifts for a telco brand.

Define an insight

Thor:

Well, thank you so much for sharing that, Bipul. I think there are so many experiences I'd like to spend time unpacking. And I think each episode has its own flavor. But we try to stick to a couple of questions that we try to always get different opinions on. And one of those is the definition of an insight. So how would you define an insight? And while you give us that definition, please highlight how you view that that definition has changed over the course of your career.

Bipul Markan:

Sure, that's actually a good question to start our discussion because I was thinking about the insights journey I've had and I felt like in our industry, we use the word insight regularly and I won't say everyone does it, but we do tend to think, we tend to not think about it much.

We use it for everything or anything that we see from the data. And at times we tend to call a fact an insight.

Now, if I really have to take sort of a step back and, you know, think about insights at a very basic level. What is it? It's any information that changes how I would think about my consumers, brand or competitors. It's that deeper and intuitive understanding of what already exists. So it's really about uncovering, you know, the whys, if I really have to speak in layman terms. Now, just to bring it to life, you know, with an example; let's say the data suggests that consumers are having a more fast -paced life. They are having breakfast on the go. There has been an increase in the consumption of cereal bars. And this is all very interesting findings, which if you would talk to anyone in the consumer insights world, or if you would have spoken to, let's say, 10 years back, would have been seen as, my god, this is amazing. We didn't see this coming. But the thing is, these are all facts. Insights would be using these facts as a direction to explore, what product could meet consumers' needs and expectations based in these sort of occasions, what could be the new category disruption, what could be the new target audience we could approach.

So these are the insights for which you need deeper understanding of consumers and you need that deeper understanding of the facts which we see in day in, day out when we are looking at some of the data. And when it comes to how the definition of insights has changed over the years. I think it has been a long journey, at least for me over the last 15 years. I feel like we as researchers or even marketeers have become evolved and leveraging data sets to get answers to our questions. So one thing which has changed profoundly in our industry is the approach to how we do the exploration for insights.

If I think about 15 years back when I started my career, we were very much focused on what I call as a data post-mortem. What happened and what can we learn from it? It's still very much relevant, a way of looking at the data. We still do it. And there's no harm in it because some of the fundamental human behaviors don't change. And that's why we shouldn't ignore years and years of customer insight learnings we have gathered. But what has changed is how we use it. Now, what the businesses are likely to consider an insight is, what could happen next? We are seeing some very dramatic changes in the marketplace, in the consumer behavior. And that is where the question that is on everyone's mind is, what does it mean? What could happen next because of this? And nine out of, I would say, 10 conversations I've had during my time have been very much around, what do you think would happen next? And that's where the power of analytics comes in.

Whether that's about simulating impact of media mix on brand, how you're using certain touch points will have an impact on particular brand objectives and on commercial goals like sales, or whether it is about uncovering emerging category trends using digital analytics, or even understanding of which image perceptions could be targeted, could be used to drive greater long -term brand equity gains. The possibilities are absolutely endless. And this is where I feel insights has a huge role to play to deliver that business impact by forecasting, by predicting the trends. That's where the definition of insights is really changing.

What is synthetic data?

Thor:

I love that. And if we, when we kind of started thinking about this episode, I think we, we thought that we would address one topic first, which is the synthetic data debate. And again, when we were preparing for this episode, you shared that you think people overlook the value that synthetic data can provide. And this is something I would love to dig into. But before we do, could you perhaps give those in the audience who are maybe less familiar with this topic a quick refresher? So first of all, what do you mean when you say synthetic data?

Bipul Markan:

Sure, I know this is a topic which is gaining importance and some more traction recently.

It's a topic which has been debated a lot in the industry right now. Many say it's not reliable, it's not ideal to use synthetic data for market research purposes.

But I also know that it's a belief which is changing every day, and something I see or I feel that in the conversations I've been having with a lot of the people within the market research industry or even marketing folk. So yeah, just to sort of take a step back, as you said, let's try to first unpack what is synthetic data. Now, synthetic data is created using algorithms and statistical methods. It tries to simulate realistic data patterns and relationships.

Given we are living in the world of AI, AI generates human-style responses to questions based on the data sets available. It could be based on mining the knowledge that's available on the web, or it could be created based on existing market research data, which obviously involves using advanced machine learning techniques, which helps augment smaller data sets. Now, essentially, it's more like instead of using survey responses, instead of asking a real human panelist, we are using AI to be a proxy for human responses from service. So that's what synthetic data is.

Thor:

And where do you see that it can contribute to the world of market research? And ideally give us a couple of concrete examples.

Bipul Markan:

Sure. I think synthetic data, it's still in very nascent stage when it comes to its application. I know like this piece of research which has been published by a company called Markets and Markets who have sort of predicted that the global synthetic data generation market will grow from not 0.3 billion dollars in 2023 to almost like 2 billion plus dollars by 2028. So, it clearly shows that there's a lot of application that's going to come our way in the world of market research. And I personally think that synthetic data holds promise as a valuable tool in market research. It can facilitate innovative analysis. It can work quite useful with some fast turnaround, quick decision tests, especially when we are living in the world of zero-based budgeting, we want to save cost. It could be quite useful and it can also offer many other opportunities and enhancements. So I've been thinking about some of the examples here because I've been talking to so many people about how we can leverage synthetic data.

And one of the things which really sort of comes out in every single conversation I have is experimentation and testing. And that's very much DNA of the consumer insights industry. We want to experiment, we want to test, we want to see how A leads to B leads to C. And market researchers can use synthetic data to model various scenarios and hypothetical situations. And I'll give an example. I know some of the agencies, market research agencies are already doing this. Let's say you're about to launch a new ad with very high stakes. Traditional methods, you could use them. There's so many available in the market. But they will offer insights based on past trends. You will have benchmarks available. But with synthetic data, you will have the option to simulate a lot of possible future scenarios. And that can also help you gauge possible outcomes. So it really helps you create hypothetical market consumer reactions. It could help you anticipate challenges. It could help you test strategies.

Of course, you have to take it with a pinch of salt that it is still in nascent stages, but definitely there is an application which supports what we already do around experimentation and testing. Now, another big benefit or application I see of synthetic data is data augmentation. So if you would speak to anyone in the insights industry, people would tend to agree that it's not always easy to find data representing all the different audiences, all the different groups we want to research. And that is where synthetic data can be used to augment real world data sets, especially when the available data is limited or it lacks diversity. So let's say there's a study where responses from a particular demographic are underrepresented. So rather than restarting the whole data collection process, which obviously can be daunting and costly affair, synthetic data can help fill those gaps. And researchers can achieve a more holistic, balanced view of market landscape by generating data that mirrors, let's say, the missing or the underrepresented segments.

Thor:

I love that. And I think there are of course limitations, but if you had to, you know, kind of incorporate what you just told us with the limitations and distill what you've shared into some very concrete action items, what would the first three steps you would recommend to someone looking into incorporating synthetic data into their insights work? What would those steps be?

Bipul Markan:

I would actually go back to what I just said, experimentation and testing. Use the iterative approach. It's not a one-time or a one-size-fits-all approach that can work with synthetic data. And we also have to accept the fact that when you think about the human brains, our brains and emotions are highly complex. So it will require a lot of experimentation to really understand whether AI can get close to replicating the results of human surveys. I know there's a lot of work which has been done where they have been able to sort of, you know, compare the responses from the synthetic data and from the real panelists and there have been conflicting results. In some cases it works, in other cases it doesn't, which actually takes me to the second point that try to test it, for a simple research first, which is more generic in nature than the one that requires greater emotional reflection.

And I know has done some work, particularly in this area, in testing the synthetic sample, which showed that when asked about practical issues where the knowledge is very much available in the public domain more easily to AI, the answers from synthetic sample are similar to those provided by the human panelists. But when you ask more nuanced questions, which require greater emotional reflection, and that's where you tend to see greater differences between the results.

So I think those would be two things. And just one more would be around check the source behind the synthetic data. There's a lot of discussion happening in the industry right now. What is the? Origin of the conclusions that we are making from the synthetic data. What that means is, is it based on existing market research data sets or is it derived from open sources? If it is the latter, I would be over cautious with deriving any hard conclusions without validating them with a primary research.

Thor:

I think that's such good advice. Switching gears for a second and going into the topic of analytics, I know you're also a strong proponent of the benefits of investing in advanced analytics. From your perspective, what constitutes advanced analytics in this day and age? And what are some of the big reasons insights leaders should actually spend time and invest in them?

Bipul Markan:

Sure. That's my sweet spot is what I would say. No, it's a very relevant question, particularly in the context of connecting the dots between multiple data sets we deal with in this day and age of insights. Now, what is advanced analytics? And I tend to always go back to the definition of what it means, and then I reflect on why we should be doing it or not doing it. Now, advanced analytics is the automated examination of data or content using some sophisticated techniques and tools, and the different names, different agencies use for it. But what it helps us is it typically goes beyond using traditional methods, and it helps us discover deeper insights, helps us make predictions, it helps us sort of generate more actionable recommendation. Now, if I sort of link it back to what we were talking about, what insights means, and how I define insights, we define... This is exactly what we are trying to do in the new world of insights.

We want to discover deeper insights, make predictions, and generate actionable recommendations. And that is what advanced analytics really helps us do. It just makes it even a stronger reason why we should consider including advanced analytics in the suite of tools and techniques that we should be leveraging and answering different business questions and really going back to the business and providing them with a data-backed insightful recommendation. Now, of course, sorry. Now, I was just going to say there can be obviously different stages or types of advanced analytics. And it depends on it what stage of this journey and insights person is within their organization, whether they are looking at doing something which is more predictive or whether they want to use something which is more diagnostic, you know, really understanding what happened, understanding those patterns, the past events, and then really trying to understand or investigating the reason behind those patterns. So you could be at a different stage of implementing advanced analytics in your insights journey, but it definitely has a huge role to play.

Thor:

Now I assume that a lot of people in the audience, this, what you just said will resonate a great deal with a lot of people in the audience. But I think, is there some, is there, if someone in the audience would want to push their organization to do this type of investment into advanced analytics, would you have any advice on how they can get started?

Bipul Markan:

Yeah, sure. I think the first thing that I would say is focus on foundational projects and not easy wins. And I'll give you a couple of examples from my past experience on this, because it can be really tempting to choose an easy project to prove the power of analytics and just to do something which looks shiny, but that may not help land the right impact in the business. So it's key to focus on foundational programs that will make doing analytics easier in the long run. So for example, if you have to run a market mix model, don't think about it from the lens of media impact alone. Consider what other data sets could be linked into it in the long run, which will make it easier for you to refresh the market mix model every year or every ordinary year so that you can really show the senior stakeholders a pathway towards adding continuous value to the business. Now, this is where a piece of work which I had done, it was for a retail brand who wanted to understand relationship between media, brand perceptions, and sales.

And the objective was to answer questions like, how do brand equity and media investment impact commercial results? What is the impact of paid media channels on certain brand perceptions? As well as what are the key brand levers for future messaging which could help build differentiation for this brand? And the analysis involved application of advanced analytics to isolate the impact of media investment on certain brand levers. And then it was linked to sales. But then on the onset itself, it was very clearly looked at as this is not a piece where we want to just share something with the business today and they're going to see some good numbers and they're going to get fascinated with it and everyone is going to forget about it. This was done with the intention to create a model which the business can use year on year, go back and check what is the relationship that exists between these different data sets and why we should be investing behind ABC to be able to drive sales in the long term. I think that long term view is really important, which was the driving force behind really putting in a lot of thought behind creating the foundations for this research before taking it to the business that you know why you need to do.

Thor:

I think that's such good advice. And I think what's really a red thread for this conversation is just how, you know, different ways of working and how we need to upgrade our stack of tools. And if we tie it to some of the things you shared in the preparation for this episode is that you think that insights professionals need to stop over relying on traditional research methods. Could you tell us what you see as being, first of all, traditional research methods? Where are they when you talk about this? And then why do you think that there is that tendency to over -rely on them?

Bipul Markan:

I mean, when I say traditional research methods, I mean, what I think or what I consider that is more of surveys and focus groups. And that's the bread and butter of insights industry. I won't say that we should not do that. These are fundamental and their role shouldn't be discounted, of course. But what I meant is that insights professionals should be open to embrace, you know, a diverse range of data sources and methodologies to gain the deeper insights into consumer behavior. Now it could be social listening, it could be search trends, it could be conversational AI bot interviews, a completely different methodology to what we are using in vast majority of the surveys that we run in the market research world, or even using behavioral analysis. Consumers don't always do what they say. In fact, a lot of the work that I'm seeing in the industry right now is indicating that the say-do gap is widening what consumers say they don't always do so that you can't just rely on one source of research to make some very strategic sort of decisions around how you want to grow your brand in the long term. And there is a tendency to over rely on the traditional methods because I mean, one very valid reason that comes to mind is we have tried it before. We know how it works. It's proven. There's validation available there. So there's less risk in it. And that's the reason people tend to go back to the fundamentals. And yes, we should do that. But as I said earlier, it's really about augmenting it with some of the more advanced methodologies and data sources to gain that deeper insights into the consumer behavior.

Thor:

I love that. And I think a lot of today's discussion highlights how much the world of insights is expanding. Are there other ways you see the world of insights expanding? And if so, what might they be?

Bipul Markan:

If I think about my time being on the agency side and even more recently, I am seeing that while the world of insights is expanding, in a way it is becoming more central to the business strategy plans. If you really look at what is happening around us in the world of insights, we're increasingly able to measure what has been unmeasurable and we are doing it faster. We are in a world where consumer experiences and products are being customized to the extent we never thought it would be possible. The behavioral research methods are on the rise and this has been now been augmented with GenAI, which is obviously enabling us to connect the dots better and faster. Now these advancements are enabling richer, more accurate quicker analysis and as a result insights, which is ultimately feeding into better and faster decision-making. Now, not that insight wasn't valued before. I mean, we were doing a lot of very fascinating and interesting things that have been doing over the years, but I see that insights is becoming more integrated and given, you know, what we are facing as a challenge in the industry, there is synthetic data, there is constant disruption across categories. And which means there is greater emphasis or need of insights to step up and provide the business with an understanding of these unpredictable shifts and in a way help predict them what we call as unpredictable today. And in a way it puts greater emphasis on the need of understanding even consumers better and more frequently.

Thor:

I mean, I couldn't agree with you more. Now, I think you've shared some really insightful learnings with us today, but there's, there's so many. So if you had to summarize, what's the one big takeaway you want listeners to get from this episode?

Bipul Markan:

Given we have talked a lot about some of the new things which are impacting the insights industry from synthetic data to the sort of growth in the advanced analytics, the role it plays, I think the most important point here is that as anyone in the insights industry needs to understand that they will have to continue to learn new skills. They will have to broaden and deepen their capabilities to be able to prepare not only themselves, but also their teams, their organization better for the shifts which we are seeing and we'll see more of in the coming months and years. It's really about, I mean, we are in the industry of knowledge, we are in the industry of constant learning and we need to make sure that we lead by example, we really showcase to our stakeholders and as an agency to our clients that we are learning faster than things are happening, which is in itself a huge challenge to tackle.

Insights lunch date

Thor:

So the last question that I have for you today, people, is a question I love to ask and I have to ask, which is who in the world of insights would you love to have lunch with?

Bipul Markan:

Given I'm a huge fan of marketing effectiveness and ROI measurement, how A impacts B, that's how I tend to summarize it.

I tend to follow Les Bennett and Peter Field because they've done some real groundbreaking work in the world of effectiveness measurement.

If I had the opportunity, I definitely would like to have lunch with them and just talk all the things effectiveness. I'm not sure how much they would like to talk work over lunch, but that would be my dream.

Thor:

I believe that would be a very interesting conversation and I would probably eavesdrop on it. Now, this has been such an incredible conversation, Bipul. Your perspective on insights is truly remarkable and I think we can all learn from it. Before we end today's episode, I'd love to return to some of the moments of our conversation that have really stuck with me.

And when I asked you about the definition of an insight, you told me that at a very basic level, an insight is any information that will change your view or your understanding on our customers, our brand and our competitors. In layman's terms, what helps you uncover the wise. When we discussed synthetic data, you reminded us that it's still in a very nascent stage. It has a lot of potential applications for the world of market research. And it holds promise. As an example, it could facilitate and innovate the analysis supporting quick decision tests in a context of zero-based budgeting. It can help simulate a lot of potential future scenarios and many more things. And lastly, you reminded all of us within the insights industry that we will need to learn new skills. We will need to broaden our capabilities. Not only you, but your teams and organizations. And we all need to lead by example. Now I know that I've learned a lot from talking to you today and I'm sure our audience has as well. So thank you for joining me.

Bipul Markan:

It's a pleasure to be here. Thank you for having me on the show.