Paul Morrison
So hello and welcome to Retail and Consumer Pulse brought to you by WNS. In this series, we unpack the world of retail and consumer goods, exploring the latest strategies, innovations and trends. And to help us, each episode features an industry expert or leader sharing their views on the way ahead.
My name is Paul Morrison, and I lead the WNS retail consumer practice in Europe and for today's session, we'll be looking into the pulsating world of retail and consumer analytics. So, I'm delighted to be joined by Gautam Singh, who is global head of data analytics and AI at WNS. Hi Gautam, thanks for joining us today.
Gautam Singh
Hello, Paul. Thanks for having me.
Paul Morrison
Very welcome. And I guess to sort of kick us off. You've been working in analytics for over two decades. Perhaps you could tell us a little bit about your journey and your current focus.
Gautam Singh
Sure, Paul. So, I'm actually an engineer by background- mechanical engineer, and I've been in the industry, perhaps I shouldn't admit this, but it's been 30 years now. I ended up post engineering in the US, and after a business degree, I got into management consulting. And that really sparked my interest in analytics, where I got involved in a number of opportunities going back to 1998 or thereabouts in the United States.
And some very interesting use cases were, in those days, we would play around with Excel spreadsheets and do stuff and you know, we do very differently today. But it definitely sparked my interest in that space. And then, I've been in there ever since having continued as a management consultant for 10 years, and then set up my own analytics company, which I incubated and was CEO for 18 years before WNS has acquired us after a very successful 18 year stint. I spent the last two years at WNS, integrating my business with WNS’ Analytics business. And now I'm heading up the combined entity.
Paul Morrison
That's great. So, you’re ideal guide for our conversation today, and let's just level set the conversation for those less familiar with the topic than yourself. And I guess I would, if I were to set out in one sentence what analytics is and I'll be interested as if we challenge this in the conversation. It's really all about the application of tools and techniques, technology to generating valuable insights from data. In a way, it sounds quite simple.
What strikes me, working in the retail and consumer goods space, is how broad this is, how many use cases there are, how quickly it's evolving and how it continues to become central to these businesses in retail and consumer goods. So, from my perspective, it's a really key question in an industry where there's loads of competition, there's loads of margin, there's loads of volatility. So, it's a broad, very broad area to look at. Maybe if I could put the mic to you with the question - What is the point of analytics? You know, what is it really driving towards and what organization is trying to get with it?
Gautam Singh
Yeah, I think, ultimately, it comes down to two things that analytics is trying to do. It’s either trying to or it's probably trying to do both. It's trying to make organizations and workflows more efficient. In other words, if we can use the power of analytics to leverage data to do things more efficiently, then absolutely that should be done and the second aspect of what analytics is influencing is to be more effective. And effectiveness is in the sense of providing intelligence from data through the power of analytics to help make better decisions and then deliver greater value. So, both sides of the coin are impacted by analytics and really what's driving all of this in today's day and age is the explosion in the amount of data that we have to play with, both structured and unstructured.
Paul Morrison
Hmm.
Gautam Singh
And equally importantly, the growth in compute power that allows us to actually play with that data and make sense from it.
Paul Morrison
So, that's really interesting and I think that is a key question. Key point is why is analytics continuing to move up the agenda and become more and more central as you pointed back - you talk about data, you talk about compute power. I think, many of the organizations I've worked with in the analytics space, many of them have got a long history, a long journey to this space. I think back to some of the origins in the 90s you talk about, about yourself and more basic analytics than more descriptive, more backward looking.
I think about the move up the chain to more diagnostic questions answered, trying to answer the why as opposed to what happened. And then in the last 10-15 years, more of a focus on predictive and real time analytics and sort of changing what's possible. So, it does seem to be that it's much more powerful and much more data rich than I had ever before. Is that how you see it?
Gautam Singh
Well, absolutely. And frankly, it's the other dimension to look at from an analytics perspective is – historically, it was very narrow. In other words, we were using statistical methods to find trend in data and that was then being used to make decisions. And in that sense, it was quite narrow. It is now much wider, especially with the advent of artificial intelligence in Gen AI, where we can take not just a narrow look at things, but include data from other sources or other areas, so in other words, structured and unstructured data. And not only look and use statistical methods, but use machine learning, which is probably closest to how the human brain works. And thereby, take into account many more parameters in our analysis than we could do historically.
And that allows us to be far more powerful in both the efficiency and effectiveness sides of the coin that I mentioned earlier. In terms of the power of analytics to deliver intelligence, to be more effective and more efficient.
Paul Morrison
That's great. And I think it's a key question on AI, how that is changing, changing what's possible. I don't think it's possible to summarize it quickly. I mean that is AI and analytics, there they are overlapping capabilities. Where do you draw the line or how do you organize these two very complex ideas?
Gautam Singh
Good. So, the simplest, first of all, it's not that complex. I know there's a lot of talk about all of this stuff, but it's not actually that complex. Analytics is, if you like the superset, and it covers all kinds of analysis, from data both qualitative and quantitative. So, in other words, numeric data and text data, for lack of a better word for qualitative data. But you can now also include image, video, audio, et cetera.
So, when you look at all this data and you analyze it, that's analytics. What AI is – it’s a subset of the methodologies or the tools that analytics uses, where AI is essentially an extension of machine learning, where we are starting to use neural network type analysis capabilities and methodologies to conduct the analysis as opposed to statistical methods, which is what we used to do historically. Which is a subset. So statistical analytics is still valid and it's still useful in many cases.
But for others, we can now move on to machine learning and data science where neural network type methodologies are being used. And then, further subset of AI is Gen AI which is another development that has happened where we not only are able to use machine learning to do the analysis, but we are able to use these techniques to generate new things that didn't exist in the past. Hence, Gen AI and all of this is very, very relevant and useful in the in the consumer goods space in terms of how it's being, how it can be leveraged to be more effective and efficient as a CPG company.
Paul Morrison
Mm hmm.
Yeah, that's great to demystify it and set it out in straightforward terms like that. That's really helpful. So, maybe, we could turn a little bit to, if we think about retail and consumer goods, obviously two very different, two very broad industries. How do we think about the range of activities that a company can generate value through with analytics? How would you summarize the spectrum? Maybe there's one or two examples or stories you want to pull out?
Gautam Singh
Sure. So, let's start with retail. Retail is ultimately selling to you and me, to the end consumer. And therefore, if you look at the number of interactions where data is being generated within the retail value chain, it's just huge. So historically, again it used to be point of sale data. When you go into a shop, you buy something, you leave a historical trace of your activity at that retail store. What you bought, how much you spent - perhaps some level of who you are through loyalty cards, etcetera, which is there even, you know, 20-25 years ago.
However, if you look at the retail experience today, you start by surfing the Internet. You get input from social media channels through your peers and through social media groups that you may be part of. You go in store and you look at stuff, et cetera, and then of course you got mobile as well. So, if you look at this omni-channel experience, before you actually buy something, the amount of data that a company or potentially a retailer has in order to really get on top of understanding who you are as a customer and how they can position their products and services to you is exponentially greater than it was when we were only playing with point of sale data. So, I'm just trying to create the size of the opportunity. The second thing that all of this is driving.
Gautam Singh
All of all, the second thing that's been created as a consequence of all of this is that the power of being able to collect this data, analyze it and therefore, hyper-personalize what products and services can be put in front of you and me when we want to buy something is also exponentially larger. You can treat each customer as a unique individual that can be presented a unique proposition, whereas historically you would advertise on TV or whatever and you're addressing a very large customer segment.
Equally.
Now you can very micromanage it or make hyper-personalize it to almost any individual. That's the power of, that's the size of the opportunity. And the answer to address this opportunity is rise in analytics and analytics is highly, highly dependent on the fuel that powers analytics, which is data.
So the biggest challenge and opportunity is to get on top of all of this data, be able to collect it, and assimilate it so that it can be analyzed through the power of the tools that are now available. And that's the starting point. So, the fuel for analytics is data. The data is in desperate places. As I mentioned, it's on mobile, it's on the web, it's on point to sale data. It's on social media. This all needs to be harnessed, synthesized and made ready for analysis before you can get anything out of it.
Paul Morrison
Mm hmm.
Absolutely. And that's retail example, Yep.
Paul Morrison
OK. And if we move across retail, I guess there's all sorts of other opportunities around the, you know, operations around store management, around shopper analysis, around assortment planning. So, there's a whole series of practical analytical questions or insights that can be generated as well.
Gautam Singh
Absolutely. So, if I just take that same paradigm of the amount of data that you can collect of a customer's experience, then when you look at the serving of products and services to those customer side of things, in other words, what do we show on the website or on the shop floor?
Our ability to use the power of analytics to further fine tune how and what we place where and what products and services we put on our shelves is again significantly improvable.
So, for example, let's just take assortment planning because we have all this data now available. We can be far more precise in - which set of products that was your assortment has the highest probability and greatest demand, and therefore influence what you put on which shelf, bottom shelf versus top shelf? How many of them to stack behind from an inventory perspective? How fast is it likely to move, et cetera?
All of that is highly, is far more predictable than it perhaps was in the past. To an extent, you can actually prescribe rather than just predict. So, in other words, you can be prescriptive in terms of what you are going to be able to sell by the power of analytics. That's how far advanced or how far nuanced operations can get to in this new world available.
Paul Morrison
Hmm.
And because of the changes you're talking about and you mentioned personalization, then the level of customization that is available to an individual shopper is now far beyond what was possible when the concept was first floated 20 plus years ago. It's a reality now that a retailer with millions of weekly shoppers can understand them as an individual and that wasn't possible before till quite recently.
Gautam Singh
That's correct, yes.
Paul Morrison
So, that really helps. Let's look at the CPG space. I guess, a number of the techniques we might use with retailers could be shared with CPG, around personalization, around digital analytics, let's say, but obviously there's a whole focus on supply chain, different focus on supply chain that exists for a CPG company in terms of what analytics can deliver. What's your sort of headline and thoughts on the CPG analytics opportunity?
Gautam Singh
CPG company, they are one step removed from the end consumer. So historically they were very dependent on the retailer to market and sell their products. In other words, if a retailer put their products on the top shelf, you know, the customer was more likely to see it and potentially buy it, et cetera.
Now, what CPG companies are able to do again, leverage the power of analytics and perhaps be able to influence a customer directly as well through social media and their own marketing efforts. They are far more powerful in influencing the retailer in terms of where and how and what the retailer needs to do with their products. So, that's one aspect.
But the more interesting change is CPG companies are now in many, many, many cases, and you'll see more of it going direct to consumer. So, they're bypassing the retailer and building a direct relationship with their end consumer, which historically they didn't necessarily control to the same extent that they can now. So, the biggest of retailers will, you can get on to their website and buy things like your shoes, which historically would go through a Nike retailer or you know some kind of a Sports Direct or equivalent retail outlet. So, going direct to consumer is a trend that we are seeing in this intermediating a retailer for a CPG company to go directly to their consumer, to the end consumer.
Paul Morrison
Mm hmm.
Hmm.
Gautam Singh
Again, that's all being driven through the power of data and analytics that allows them to be able to directly interface and directly is with a consumer without going through a retail chain in the middle, whether online or offline.
Paul Morrison
Yes, that's really interesting change in the relationship there and the sort of the blending theatre of e-commerce between them. I guess another interesting aerial dynamic that's massively data fueled is around retail media networks and the extent to which that is bringing retailers and consumer goods companies into some sort of new collaborative ways of working that are both consumer goods and retailer driven. Do you have a view on the retail space?
Gautam Singh
Yeah, I think it's, yeah, absolutely. I think the driver to all of this is reducing margins because it's becoming far more easy for the consumer to be able to compare prices, compare products, be influenced by what's being said on social media, et cetera.
Margins in this industry, whether it's retail or CPG are being really, really challenged. And hence, it's driving the retailer in the CPG companies to also collaborate to take out wherever cost can be taken out in order to be more competitive and in order to create some margin. So, that's where the drive, that's where I guess the underlying levers are coming from in order to push this collaboration to take place. So that's point #1 the, the second thing I'd say is that the consumer is getting far more powerful than he or she historically was.
Paul Morrison
Yeah.
Gautam Singh
In the sense again that the retailer, the retail outlet is where you needed to go to look at what's available and be able to choose from you know, what's on the shelves. That is now become something that is no longer restricted to a retailer, due to the high speed you can get on the Internet or your mobile phone or whatever, and look at products and services from across the board. Compare them for both quality and price, and the influence on which one to buy based on what's going on from a far wider set of influencers, you know.
Paul Morrison
Absolutely. There was a conversation on this podcast a few weeks ago with Richard Lim, CEO of Retail Economics, and we were talking about retail driven AI and the idea that up to this point, a lot of the AI investments have been driven, you know, by the retailer. But the growth of platforms there to enable the shopper to get value is starting to change that.
So, tip balance. That's definitely an interesting key thing that we'll keep our eyes on. I'm wondering if there are any other disruptive trends that that you can see in the space that's changing the way that analytics is done in these sectors that you're tracking?
Gautam Singh
Yeah, I think, the one you know we've been talking about all of the reasons why margins are tighter, and it's harder for retail and CPG to compete in this new world that we live in. But I actually see it as a much bigger opportunity as well.
And what I mean by that is just as much as analytics is or let me explain the new world that we live in is enabling the consumer to be far more effective in what they want and what price they want it, or want to pay for whatever they're buying. At the same time, this retailer and the CPG companies have also got the power of analytics to hyper-personalize. And if they are truly able to hyper-personalize, build a truly loyal customer base, they are actually able to increase their margins, you know, reduce them and that comes from that hyper-personalization concept. So, you know, if I'm going to be able to buy something that is really, really tailored to what I really want, I'm willing to pay more for it.
And that's the Holy Grail for retail CPG in terms of their consumer. So, I think a lot of the world is focused on efficiency through analytics and what can we do to take out cost and improve our supply chain and so on. But I think there is an equally large opportunity to uplift revenue and margin through the power of analytics, and that's what's actually even more exciting for me and me at WNS in terms of how we are working with our clients. Look at revenue growth and look at consumer loyalty and consumer segment analysis, and therefore hyper-personalization in order to drive both revenue and profit.
Paul Morrison
Absolutely.
Gautam Singh
While still treating the customer very happy, I should add.
Paul Morrison
Absolutely. No, that's interesting. I think the point about revenue is key. And I know that in many of our client relationships, that's the paramount outcome or lever that's been targeted through analytics and investment in technology. There was one case study recently with a retailer where it was using, connecting marketing data to preferences and personalizing, enabling the personalization of communications and marketing messages to the consumer base. And that was generating a very strong uptick in click-through rates and ultimately revenue. So, it's a very, very strong and live lever.
Let's just turn quickly to, I guess the point there that we've flagged around; WNS is obviously highly active in the analytic space and has been for many years. And we've got many long standing customer relationships in the space. I think if I look at the CPG sector, 7 out of 10 of the world's largest and leading CPG players are WNS customers.
Do you see the task of delivering is changing the skills required or the capabilities required, significantly changing in how value is extracted or is it simply more and it's faster paced than before?
Gautam Singh
I think overall there's a huge opportunity, and then there is a shortage of supply. So, I think the benefit for WNS is that we have the scale and the experience to be able to do more for our clients than they could do for themselves on their own. So, that's the first point I'd make. The second point I'd make is this is a very fast evolving space. So, both the technology and the analytics tool set is dynamically improving by the month and by the year and part of the task for us is to remain on top of the latest and greatest and what can drive value for our customers.
So, we do invest quite a bit on experimenting in our own internal R&D to play with the latest and greatest technologies out there or analytics tools out there. At the same time, it is important that we sort of co-create with our clients. So, a lot of times, the opportunity for our clients is based on what they don't know and what we don't know until we get into the room together and sort of explore. And that's the sort of route we take when we are trying to get the most out of out of analytics for our clients. That's point #2 and then point #3 I'd say, which is the most important, is that ultimately, data is the fuel here. And a lot of times, you know, we start a conversation saying we need to use Gen AI, what can we do with Gen AI to drive value, and we end up starting with getting the data in the right place, and that's a very different problem to leveraging Gen AI.
And so, I think, there's a lot of hype around analytics or AI and Gen AI. And that sort of hides from the fact that a lot of times what we need to do is just get the data in the right place, and then perhaps very often, we need simple regression analysis or statistical analysis to drive value. And you don't necessarily need an AI or Gen AI to bring that value to the table.
Paul Morrison
Mm hmm mm hmm.
Gautam Singh
So, I think that we need to get past the hype, look at what use case is and work backwards from there to see what we need to do from an analytics perspective to bring value to the table. Yeah. And that's it. That's important sort of intervention when, you know, we are encouraging and working with the clients to go through.
Paul Morrison
That makes sense. Get the foundations and get the fundamentals in place. Does that mean that the way of executing on analytics opportunity? Does that mean that there's a sort of foundational or enabling phase and then we are into period of projects, maybe developing a center of excellence?
There's a maturity curve after that foundation is in place, how do you see about Gen AI?
Gautam Singh
So, even here, I see a difference in how many companies are doing versus what perhaps a few companies are actually leading the way, and let me explain that. So yes, exactly as you said, a lot of companies are saying, OK, we need to build our data foundation and then we'll drive analytics from there. And in that spirit, we are setting up centers of excellence. We are setting up chief data officers or chief data analytics teams as a central function to go and put all of this data, get the data engine done to build the data lakes and the data warehouses in order to drive analytics.
Absolutely right. We need to do that. However, that takes time and that's a journey in itself, which may take three to five years to go right, by which time the technology moves on, and we're going to start again. So my view on WNS is slightly different in that, yes, we may support you in building your data foundation on a long journey that we may be on, but we don't need to wait for that to start bringing value to the table. And what I mean by that is we can pick up specific use cases and let's just say for example getting your pricing right.
For your set of products and pricing analytics would then work backwards and saying OK, in order to get my pricing done right, what data do I need? And instead of building the entire data lake and the data engineering around the entire foundation, we build a data pond which addresses the pricing need of that particular use cases need. And that way, you can start delivering value on a much more controlled and smaller scale.
Paul Morrison
Hmm.
Gautam Singh
As long as you're making sure that the pond building of the data site is in line with the strategy and a road map to build a lake. In other words, you don't have to rebuild the lake when you've got all these data ponds that you built, but instead your data ponds become a data lake when they're connected together. So, that's one theme that I would and I am encouraging my clients to think about.
They're related bit that I'm seeing is that as clients start building these CoEs, they took power away from the businesses where analytics use cases were being needed. And basically, if the commercial organization needed to get pricing right, they had to go to the central CoE and say, OK, can you help us with analytics for getting pricing? That is changing now as companies are realizing that frankly in today's day and age, we don't need to go to a central function, only in order to deliver value and the power is shifting back to the businesses to be allowed to go and do what they need to do.
To address the individual use cases through the sponsor of building a data pond, as long as you're consistent with the corporate strategy on the underlying data lake that you know. And that is a very interesting trend to see power first move to a central CoE and now power going back to the businesses at least for many areas.
Paul Morrison
Mm hmm.
That's interesting. And so there's a couple of really important points there around the practicalities of approaching and organizing. You talked about the data pond and picking off and targeting an opportunity that is discreet and manageable and provides good ROI. And then the piece around the organization and where a CoE should live or you know how the organization fee should be structured. What about this question of workflows – you mentioned workflows a few times. I suppose that the question is analytics can create great insights, but then what? What next? What's your thoughts on how analytics and workflows are connected and should be connected?
Gautam Singh
OK, so that's another important, interesting topic. So my point of view is actually very clear on this. Historically and frankly, even today, a lot of companies look at analytics as something to add on top.
So in other words, let me take an example. I have a supply chain workflow. What can I do it with analytics to do something on top that tries efficiency or effectiveness into on top of my workflow? But the real trick is, and the real value comes when analytics is built into the workflow. So, it's not sitting on top, it's integrated into it. And what that means is the workflow becomes synonymous with the analytics. It's not an additional nice to have, but it's actually completely embedded in it.
To use a simple example of trying to explain this visually - it's like analytics used to be the cherry that used to sit on top of vanilla cake and the vanilla cake is what we used to make. Now analytics is a cherry that made a tastier or better than it was as a pure vanilla cake.
Paul Morrison
Excellent.
Gautam Singh
The way I think I analytics needs to be seen is to not have a cherry on top of the cake, but almost have a Black Forest gateau or a Black Forest cake, where the cherry and the cake are completely integrated. We can't separate the cherry from the cake. It's a sauce. It's in the cake, and actually, it makes the cake tastier and better. And that's how I see analytics and workflows, really integrated in order for them with today's technology and today's advances in order to make the workflows more effective and more efficient. So, by the way, in that analogy, the Black Forest cake tastes better and costs less than the vanilla cake with the cherry on top.
Paul Morrison
All right. And I do really like the, I like the analogy. How many organizations today do you think have got that recipe and are organized in that way? This is an emerging trend that you see.
Gautam Singh
I think there are leaders and laggards in this space. It's down to the thinking and the mindset internally and how they're organised in order to make that happen. And a lot of it requires an understanding of how analytics can drive binding. Is it a nice to have? Is it the cherry on the top or is it truly something we should be integrating into our workflow? And it's top down driven. If the leadership is aligned to integrating analytics into what everything and anything that analytics can turbocharge, then it gets integrated in where it's more lip service to say, OK, I'm doing something to show that analytics has been incorporated. It typically is a cherry on top. In this case, it gets, you know, it gets discarded or it's not really bringing anybody to the table.
Paul Morrison
Oh, that's right. And technology, presumably is part of that. So you see the flow of insights to the Salesforce or to the operations team through core platforms through a service now or a HubSpot or a Salesforce is that, is that how you see it, the actual mechanism for providing that workflow?
Gautam Singh
Yeah, I see technology as the enabler, not the answer. So, technology is what allows us to leverage the power of analytics. It is not the end answer in itself.
And hence, I go back to the original point in terms of what's the end goal? The end goal is to be more efficient and more effective. And that comes from being able to analyze the data through analysis or analytics and be able to drive better decisions like hyper-personalization, like finding areas for revenue growth, like finding areas to optimize your inventory or your supply chains, et cetera. But in order to do that, you need the technology. Technology also provides the compute power and the compute infrastructure to be able to make analytics more effective. That's how I see technology. It's the enabler.
Paul Morrison
Hmm. Yeah. You don't see a near future where more autonomous systems eat into that decision making and make it more hyper-automated. Or do you think? Yeah.
Gautam Singh
No, I do see, I think the disruption that we are going to see in our scene is in workflow being disrupted and replaced, if you like, by product - in product, meaning a combination of technology and analytics built on top of it. However, as happened when you know similar disruptions happened in the past, what it allows the human to do is to be able to sit on top of all of this and work up the value chain in terms of what value can be can be derived from this. It is not necessarily replacing the human, it's we defining what the human does.
Well, in my book, the combination of this underlying technology with analytics and the human redefines the workflow to make the workflow more effective and more efficient. It's the same example, if you know, the vanilla cake looks very different when it's a Black Forest.
It is actually tastier and as I mentioned, not more expensive in this new world that we live in. But it is different. It's been reimagined, it's been redefined in terms of, you know, what the cake was originally. And that's the way we need to look at things. There will, of course, therefore, be certain things that are completely productized and replaced with a product answer. But there are many new workflow being created or new versions of the workflow being created when you look at how they might make the most of that underlying product, when I add a human expert on top.
Paul Morrison
These cake analogies are making me hungry. So, that's really, really helpful there to think through that changing balance between technology and talent and workflows. As the ideal analytics capabilities being put together, what are the other really important things to get right or that we often see challenges with?
Gautam Singh
I think it comes down to workflow. It is very important to think workflow and think about how that workflow can be reimagined in the current context of technology advances and analytics advances. And also keep in mind that there are many new sources of data which is ultimately the fuel here that need to be combined in order to turbocharge or make your workflow or reimagine your workflow in this regard. And I don't think it's anything more complicated than that. If you think workflow, and we think reimagination, and we understand how analytics, data and technology come together, we're in a good place.
Paul Morrison
That's a great steer. I look at the clock. We are already over time, so I'll just have, if I may, one final question and it's really looking forward from where we are today, you know what other disruptors or changes do you think you see in the market for analytics in retail and consumer goods? Anything that's going to continue to change the story that you were telling here?
Gautam Singh
Yeah, I think things are going to change and they're going to change much faster than we did historically, but change is not necessarily bad. Change is typically good and ultimately definitely good. So I'm an optimist. I'm not scared of Gen AI or AI. I'm actually very positive about what it's going to do for us as a human race, but it will change things. We will do things differently and work differently and behave differently due to all of the powers of being unleashed through technology and analytics come together. What that's gonna look like is anybody's guess.
My only sort of view on that is – it is going to be, that change is going to come and it's going to come fast, but it's going to be good for everybody.
Paul Morrison
That's a great time to end on. I'm an optimist as well. I share that view and I think working with you and the team on projects in this space, it is super exciting what is happening and the value that's been created. So, definitely endorse that optimism. So time is one again. I just have to say, Thanks Gautam for a great discussion. I really, really enjoyed that. I really look forward to our next conversation, where we can catch up on the very latest. So, thank you.
Gautam Singh
Actually, Paul, it was very nice to talk to you as well. Thank you.
Paul Morrison
Superb and thanks to you, our listeners for joining Retail and Consumer pulse today. If you've enjoyed the show please like and follow us and please stay tuned for our next episode. Thanks and goodbye.
Gautam Singh
Thank you.