KludeIn I Acquisition : Regulation FD Presentation - Form 8-K

Welcome investors to The Absolute Return Podcast. Your source for stock market analysis, global macro musings and hedge fund investment strategies, your hosts, Julian Klymochko, and Michael Kesslering aim to bring you the knowledge and analysis you need to become a more intelligent and wealthier investor. This episode is brought to you by Accelerate Financial Technologies. Accelerate because performance matters. Find out more at accelerateshares.com.

Julian Klymochko: Welcoming Anil to the show from Near. Anil, how are you doing today?

Anil Mathews: Good. It's a pleasure having you. A pleasure speaking to you again.

Julian Klymochko: It's great to have you on the show, excited to get into all things Near, but prior to that, you've spent the past 22 years as a serial entrepreneur. So, to start. Basic question that many people discuss are entrepreneurs born or made in your opinion?

Anil Matthews: [Laugh], that's a very interesting question. I believe that they both born and made. I met many successful entrepreneurs who are born to do this and many successful entrepreneurs who have made as well. And I think a lot of it boils down to the choices you make. Kind of how you implement the learnings in your life to, you know, being an entrepreneur because you know, different businesses might need different types of skills and levels of persistence. But I think everything boils down to the ability to take risks and garner a great team around you. And I think you know, so the answer to your question would be, I've seen both you know, if you're not born to do this, you can basically be made to do this as well.

Julian Klymochko: Certainly, a hotly debated topic, but that makes a lot of sense. Now, prior to founding Near you had a number of other ventures over more than a decade, can you walk us through some of these startups that you launched prior to founding Near?

Anil Matthews: Yeah. I've been an entrepreneur for most of my life I would say. I started my first company in 99. And, and then, you know, this is my third company by now, but all of these companies were deeply rooted in tech. So that was a common theme. And you know, myself, I like to do products and I'm a tech guy, so everything that I created I use my strengths and of course the first one is the most difficult because you have a lot of things to learn. You need to figure out a lot of ways on how not to do things, but eventually I think by the time you're on your third strength, you know, a lot on what mistakes might be costly, what is probably the right approach. And that's why I think when I started Near in 2012, by then, you know, we had a template on how to do this and I think that's where the success speaks for itself.

Michael Kesslering: And specifically, what has kept you coming back to the enterprise software sector? What makes that sector or vertical special in your mind?

Anil Matthews: I think it's also like I mentioned, it was a lot of learnings I had personally was around this sector and for us, I think the value that we could create as a team was a bunch of specialists who were all from the similar industry but knew what challenges enterprises are facing. And we always believe that we have a better solution than what they're doing internally today to sort of address some of those challenges.

Julian Klymochko: Now, as an entrepreneur in the enterprise software sector, and you founded a number of startups focused in this space, what was your background? Just so our listeners know, like, were you a programmer? Were you a technical founder? Were you on the, you know, on the product?

Anil Matthews: Yeah, my first company. By education. I am an engineer, electrical engineer but my first company, you know, I was doing little bit of programming. So, I started, you know, coding myself but then slowly move into more sales-oriented function and eventually sort of managing things. So that was my progression, but I think understanding coding and technology helped me work with other technologies and sort of design things in a way that was more efficient and sort of, I could contribute to each and every product, even today, I contribute to all the products that we make so that really helped, yeah.

Julian Klymochko: That's interesting. Yeah, I'm also an electrical engineer by training, not by vocation, because always been focused on the business and finance side, but I digress. Focusing on Near, after you've had these other startups, you said you founded the company in 2012. Where did the idea come from and what were you trying to accomplish?

Anil Matthews: Yeah, I think this was when I was out of my second company, you know, I was trying to create a completely different offering. I thought I I'll take a stab at, you know, creating a consumer-focused app, but then I needed to build that app successfully. I needed a service that could be an API or could be, you know, sort of a platform that I could latch into, which would give me I would say people's, you know, trends around people's moment or football trends as you would say, but I couldn't find that at that time, this was 2012, late, you know, 2011. I couldn't find that. And so, we said, okay, if we create a concept around this which we could use for ourselves, but it could be more useful for others as well. And eventually I sort of let go of the original idea and realized that this could be a lot more powerful because a lot of other you know, brands and enterprises might find this useful. So, we completely changed and pivot to sort of creating this platform instead of the app itself. That's how the whole thing originated.

Julian Klymochko: And if we rewind 10 years ago, 2012, you talk about consumer apps and mobile device data. That whole era was really coming, just a fruition then with the iPhone and the app store was really in its first few years there. So that makes a lot of sense. Digging into Near business model, you have data and data intelligence solutions. I was wondering how do these help businesses. Like what would your typical customer do with it?

Anil Matthews: Yeah, I think just to explain that a little better, if I take a step back. So, what we do is today we have you know, in simple terms, people's behavior around places. So, it's a massive data universe that we own and operate, which allows us in a privacy compliant way to look at this you know, people's moment across places and which we then use to understand what could be their behavior, not just in the physical world, but in the digital world as well. And all this is stitched and connected wire unified ID. So, we have around 1.6 billion active user IDs at any given month. And which means it's those many unique IDs that we have in our system and each ID would have. It won't have any personally identifiable information. So, we won't know who this person is, but it'll have signals.

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Again, a lot of the signals are consent based signals from the digital world in terms of what apps this user could be using, what websites they might be visiting, what devices they carry and so on, but also physical world attributes in terms of what is a brand preference in the real world, how far do they go to get groceries or gas sort of, you know, where do they typically, which area do they live in and where do they go for work and all these aspects. So, this allows us to provide this information, this intelligence, as we call it to brands and enterprises, who needs to understand the consumer journey, because especially what has happened is, during the pandemic and post pandemic, consumer journey has completely changed. And a lot of this understanding that brands had about this consumer journey and sort of their behavior is completely, you know, I would say gone to a [Inaudible 00:8:36] and now they need to relook at, okay, so how do I engage with my consumers or my customers? And we help them with that. So, we take our platform, the license of platform, and then look at, okay, I know few things about my customer or my consumer, depending on the industry, but I need to know a little bit more now, what do they do when they walk out my door? Where do they go? How many times they go to competitor, where do they come from? How time spend there? And all these questions, they want answer which we provide to them.

Julian Klymochko: Now, in terms of providing your clients with these data sets that these vast data sets that you're assembling, how are they useful to those clients? Like what specific data? I don't know if you can provide insights into specific use cases.

Anil Matthews: Let, me take an example. So maybe, you know, probably help you answer your question. One of our customer is one of the largest media companies out there. Now they have, you know, they typically work with large websites and apps, which is very informational or entertaining in nature. So, before we came into picture, they have this large website where you would go to read news about, you know fashion or news about celebrities and things like that, or information and article about this. But the challenge they faced is that you're not logging into read these things. So, they don't know who you are and when you move from their website (A) to their website (B) they never had an idea that you're the same user because these both are disconnected. Which means, and their key revenue model was to basically monetize this data itself.

So, they would create profiles of users based on their digital world interactions of what you're reading, what you're liking, what you're watching. And they would create profiles of users, which they would then monetize by advertisements. The challenge with this was that because this is so disconnected, they had limited ability to sort of wrangle with this data. Also, they're not depreciating themselves in the market. [Inaudible 00:10:58] came into picture. The first thing we did is. We used our patent to technology to connect this siloed properties together and connect all of them to the single unique key that I just discussed about earlier. So now that's connected to single unique key. When you hop from website (A) to (B) to, you know, C, T, E, F, they would know that you're the same user. And most importantly, we would now be able to tell them these users, not only digital world behavior, but physical world, like I was telling.

So, all of a sudden, as an example, before us, they were creating buckets of people's behavior. Let's say, it's say it's called auto enthusiast. Based on you read something about a car in one of their magazines, online magazine, or you like to watch the video and this bucket is what they would sell to auto companies. But since we came into picture now because we can tell, Near can tell them how many of their users were seen in an actual auto dealership. They can take that data to the same auto company and tell them. Now we have a higher intent to purchase these users who are seen in the real dealership, have a higher intent to purchase because of that. You need to pay us more and they were paying them 30% more. So typically, in effect, what happened is durable to actually increase your data ease using our technology and our property without the need of actually increasing the user base itself. And that is a big impact. As an example, I can tell you of using a technology

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Julian Klymochko: That makes a lot of sense in terms of the value proposition to your customers now, in terms of your data intelligence solutions, could you tell us a bit about the market, total addressable market in terms of size and also.

Anil Matthews: Yeah.

Julian Klymochko: What is out there in terms of competition?

Anil Matthews: Yeah, just to address to your sort of question, if you draw a quadrant and on one axis, if you look at companies that are local to global.

Julian Klymochko: Mm-hmm.

Anil Matthews: And other access, you look at companies that are point solutions to, let's say, aggregators to full stack solution. Near would sit on the top because we don't have a competition that is truly global in nature, providing full stack offering that we provide because and by full side, what I mean is, basically what we are allowing is. We are looking at enterprises who's big or small sitting on first party data, but they're not able to derive any meaningful value out of this. And there are three key reasons for that based on all our expertise and experience. And the first reason is, that most of these enterprises, the data is in silos.

Like I explained about this example, but even if you're a retailer, you know, your data is in, you know, there's some data which is an app, there's some CRM data, there's some POS data. So, data is all over different formats store differently and things like that. So that's first challenge. The second challenge is that most of this data is a poor quality, is half paid. There's missing information, missing addresses, you know, missing understanding of the consumers and the third, which we all think is a trivial issue, but it isn't. Is, most of these enterprises also don't have the right data skills because they're not data companies themselves. And so, when we come into picture, Near. We are trying to address these challenges that enterprise are facing on how do we maximize value out of this data? So, we are able to provide a full stack solution, which allows them to stitch this silo data and enrich it with deeper understanding from both the physical and the digital world. Derive intelligence in terms of deeper insights and analytics and help them act and measure this data's efficacy all in a single hosted platform.

So that full stack. Now coming to your question about competition, that full stack offering at a global scale doesn't exist. What we come across is either point solutions. Some of them being one of the piece that I just mentioned or companies, which is very local in nature, just doing it in, you know, let's say Australia, but not in Singapore, but doing in Singapore, but not in UK. One of the reason companies or customers like working with us is because we are very global in nature that allows them, them being global, to work with us across different regions and have a single vendor. So, they like that aspect as well. So that's where we stand. The total tam for what we offer is around 23 billion, which is because of the nature of you know, where we are today in terms of competition, we believe it's highly hard penetrated and are store.

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Julian Klymochko: Now, in terms of the application of your technology, would you say that in the primary use would be lead generation for your customers to get for their sales process?

Anil Matthews: So, there are two products, one of which I touched upon and the other one. So basically, because if you remember, what I had just mentioned earlier i our call is that we are trying to look at people's behavior around places.

Julian Klymochko: Yeah.

Julian Klymochko: Which means from a platform perspective, the platform is a very generic platform, which does this at a large-scale day in day out. You can drive different kinds of, you know, uses and create different products out of it. So, we created two core products, one, which is looking at things from a lens of people. And the other one, which is looking at things from a lens of places. One of our product, which is especially designed for insights around places is used by some of the largest restaurants in the world, restaurant chains in the world, real estate companies, retailers in the world to look at where to open the next store, because we want to understand where is the catchment area of our customers, how are competition sparing?

If I open a store here, how will the people be? Where will they come from? Where will they go after? So, this is all this data able to decide using our data from a place perspective. So, supply chain optimization, route planning, site selection. These are the use cases of this product, which is designed based on you know, places. I would take a different cut of the same data and look at things from a people's perspective. That is what I just described to you in the earlier use case about how we are able to sort of provide you know, sort of insights on people's moment around the physical world and enhance this data yield for our customer. So, you could look at both ways. So that's why we simplify it and call ourselves a data intelligence platform, because what we are typically doing is we are looking at how do we assimilate all this data together, drive intelligence, and provide this in a platform on a fast basis to our customers.

Michael Kesslering: Thanks for that. It's quite clear the value proposition for your customers that use the platform. What would you say is the benefit for end consumers with your product?

Anil Matthews: So, typically, I mean, what they're doing is, we are not directly, you know, sort of connected to the consumers because all our data sources are coming from partners, which could be telcos in some countries, which are Wi-Fi providers in some other regions, but majorly app data, but that's through partners, not directly, you know, by owning any app or any sort of STK setup. This means that all this data is first consensual. So the consensus is obtained by our partners on our behalf, but also that value that they get that they're receiving on the other side is if it's an app ecosystem, the app itself, right? Where the app is able to provide their services free, because they're able to monetize the data and not rely heavily on ads or any other mechanism or subscription mechanism, which they need to use to sort of generate revenues and similarly for other you know, ecosystems that I just described.

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Julian Klymochko: So as the business has grown over the past decade, you're now reaching a major milestone, which is going public. And this going public transaction includes a 95 million pipe financing, certainly in a pretty tough market for, you know, startups, hyper growth companies going public. I was wondering why did you choose to go public? And what is the use of proceeds with the capital raise that you're doing with this transaction?

Anil Matthews: Sure. So, one is, you know, we look at going public as a financing event, not as an exit event. And we have raised around 134 million to date from some marque investors like Sequoia Capital, and JP Morgan's of the world. We are looking at our next phase, you know, our last decade has been more about building the foundation, sort of creating this gold mine of a platform. And then we are just scratched the surface with these two products that I mentioned, but I think it has, you know, humongous potential on what it could do and going public gives us the credibility. So, it's primarily for the credibility that would help us open larger opportunities, even in countries that we could take that credibility to beyond North America. And so, that I would say the first sort of big advantage when looking at us as a public company, but also the currency, which will be our financing event, like I said, this is what we are excited about as a company where, you know, sort of, we would be able to get access to some of the largest retailers or banks across the globe as a result of being a public company.

And it's a huge milestone, a massive event for a company to become publicly listed. Now, looking at the equity market in the U.S., obviously software and technology stocks have proliferated. They now account for roughly 37% of the S&P 500. So certainly, as an investor evaluates the SaaS landscape, there are a myriad of stocks that they could take a look at, in your opinion, what are some of the reasons why an investor should look at Near?

Anil Matthews: Yeah, I think that's a very good question. First, I would say very, very, fairly valued at the based on the current market trends. And so, it is an opportunity that is, you know, going to grow very aggressively from here because of the new ammunitions that we have in hand. And the way we look at new ammunition is obviously the public credibility, but also some key new offerings that we are launching from our platform. We are also sort of acquired two companies in the past. So, one of the user funds is to see how we can strengthen our inorganic path as well, especially in regions that we need to strengthen our presence or sort of, you know, the slices of data ready to strengthen our mode. So, I think a combination of this, we are a real company with real revenues, you know, running this for almost a decade now. Unlike other, I would say a lot of other offerings that you would see which many times is based on future growth. We are already you know, sort of at a certain milestone. And from here, you know, we see, I would say significant growth in the next three to five years. And I think that is a journey. A lot of our investors want to hop onto. And I would say that's probably the most attractive piece of looking at Near as a public company as well.

Julian Klymochko: And with this entrepreneurial journey that you've been on. Third startup on the precipice of taking it public, as you reflect on that, what do you think is the number one most important attribute for an entrepreneur to have? And what are some of the key factors that have made you successful in your career?

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Anil Matthews: Yeah, you know, from a trade perspective, I would say attention to detail is probably the most important trait I would see as part of a successful entrepreneur, but also the ability to attract smart brains around use, you know, because I think I wouldn't have been here if it wasn't for really a lot of smart people around each function, whether it is finance or tech or product, or, you know, or ops. And I think I'm really, I would say, you know, blessed from that point of view where I could garner this smart people around me, you know, building something so amazing. So, I think the two traits would be attention to detail and ability to attract smart people.

Julian Klymochko: That's really great to hear. Now Anil, one last question before letting you go, what time of the day do you wake up and when do you think you're most productive?

Anil Matthews: That's a very tricky question because we have offices across the globe. So, you know, we have an office in Australia, in Sydney which starts first, and we have office in Los Angeles and the other side, right. So, it depends on some days of the week, but I do wake up pretty early to kickoff the day from the Eastern part of the world and a bit late to sort of end the day at the Western part of the world. So, it's not ideal, but that's become a habit kind of thing now.

Julian Klymochko: Yeah, sounds like you're not allowed to get any sleep covering.

Anil Matthews: [Laugh].

Julian Klymochko: All parts of the world, but hopefully you're well rested these days and wish you the best of luck.

Anil Matthews: Thank you.

Julian Klymochko: With the going public transaction of Near. We'll be following the situation. It's an exciting story. And thank you for coming on the show.

Anil Matthews: Thank you. It was a pleasure. Thank you for having me. Thank you so much.

Julian Klymochko: All right. Thanks. Bye everybody.

Anil Matthews: Thank you. Thank you. Bye.

Thanks for tuning in to the Absolute Return Podcast. This episode was brought to you by Accelerate Financial Technologies. Accelerate, because performance matters. Find out more at www.AccelerateShares.com. The views expressed in this podcast to the personal views of the participants and do not reflect the views of Accelerate. No aspect of this podcast constitutes investment legal or tax advice. Opinions expressed in this podcast should not be viewed as a recommendation or solicitation of an offer to buy or sell any securities or investment strategies. The information and opinions in this podcast are based on current market conditions and may fluctuate and change in the future. No representation or warranty expressed or implied is made on behalf of Accelerate as to the accuracy or completeness of the information contained in this podcast. Accelerate does not accept any liability for any direct indirect or consequential loss or damage suffered by any person as a result relying on all or any part of this podcast and any liability is expressly disclaimed.

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