Sprinklr, Inc. (CXM) Earnings Call Transcript & Summary

Good morning and welcome to Sprinklr's 2023 Investor Day. My name is Eric Scro and I'm the VP of Finance here at Sprinklr. It's great to be with you today from the New York Stock Exchange. Thank you for joining us. We have a full agenda planned for you today, as you could see up on screen. Before we begin, I want to remind everyone that today's event will be webcast and recorded for future playback. Information pertaining to our forward-looking statements as well as a reconciliation of our GAAP to non-GAAP financial results will be available on our Investor Relations website. Before we start, we have a short video. Please enjoy. [Presentation]

Thank you. And ladies and gentlemen, please now welcome to the stage, Sprinklr's Founder and CEO Ragy Thomas.

All right. I got some props and stuff here. It's so good to see all of you in person. You all look kind of same as you look on Zoom. I think I've met most of you on Zoom and it's good to be back here and see you all in person. So welcome to our first -- very first Investor Day. And I think -- if you think about the best gifts that anybody can give anyone else, I think time and attention are the top 2, at least in my mind. And so I want to thank you for giving us your time and attention today. I have 20 minutes or so. And my goal is to communicate 3 things to you. One is, what is it that we sell. And how did we get here? And kind of where are we going with this? Two, is to help you understand that we believe that Sprinklr is the fastest way -- is the music intentional? Okay. I can hear some music. I don't know. Can you? Okay. That's good. Sprinklr is the fastest way you can get AI across your front office. And I'll tell you why that is important. And lastly, I want to talk to you a little bit about the ethos of what Sprinklr is and what we believe in, outside of what we're trying to build. So look, we came out as a public company in a very crowded, noisy market. And I believe that we didn't have a chance to articulate what is it that we really do and have the investors really understand it. And I believe that if you understand the problem and you understand how we're approaching it, the solution, I believe for the discerning long-term investor, we are creating a very compelling opportunity and that's what we hope to accomplish today. I have with me a giant poster that we made, okay? You guys are familiar with LUMAscape? On this -- on the top, we just added some customer-facing functions. Customer service is one. You've got marketing, you got research and insights, you got sales and engagement. So if you are a large company, let's say, you're Microsoft, you're Samsung. And I don't know, how many contact centers do you think Microsoft has around the world? And you're a guy, let's say, in Philippines, putting together a contact center. How many RFPs do you think you have to do to put the stack together? That's just this. That number is [ 13 ]. If you want to get your marketing stack going, how many RFPs do you think you have to do, even if you have Salesforce and Adobe. That number is probably 5 or 10. There are 63 subcategories here, 63, 6-3. And you might think, well, somebody can do 40 RFPs every 2 years. The number is not 63. Microsoft Dynamics in North America is going to do 25 RFPs. In Europe, some -- most of them have a different CRM system. If you are Diageo, with 200 brands in 185 countries where you're spending money in every market for all of these things, everyone is doing their own thing. What is the problem when everyone is doing the whole thing? Do you agree that in the front office you've got to see who you we're speaking to. Who is buying Xbox, is the same guy who has an Office 365 subscription? If I go from -- moved from New York to Toronto, can I see that it's the same person. If I switch from phone to live chat, can I see that it's the same person? If I can't, how do I get the same content, if I learn something in Brazil, that's wrong with my product, the tickets are going up, what does it take for me to get it into my call center in Toronto or in Jersey, if you're Samsung or in Korea. Do you understand this is a big problem? We live this problem. If you're a large company, if you're any one of the 43,000 companies that we target, this is a very real problem for you. You're spending somewhere between I don't know, depending on who you are, $20 million to $80 million on your contact center, maybe even more. If you are a large brand like a Diageo, you're spending somewhere between $500 million and $1.5 billion on marketing, media and content planning. And it is in disarray. That's the context in which we exist. If you understand large companies and you understand the landscape, I don't have to explain to you what the problem is. It's a freaking nightmare. I don't care how many big companies are in this space. So this is who we are, for 13 years, we've been building this company. We are -- I have to say AI, we are an AI-powered company, platform for large global enterprises across front office functions on over 30 different channels today. What do we sell? What are people buying from us? Many of you know that we have multiple customers, not one, multiple customers and growing number of companies that pay us over $10 million a year. And every year, they buy more. Most quarters, they buy more. What are they buying? I'll tell you, Sprinklr Service is a fairly complete unified CCaaS solution, complete CCaaS solution. And I'll explain why a social media origin company is able to do this really well. I want you to understand it. But we offer 30 different products inside the contact center, everything from workforce management and knowledge base and community, to ticketing, to e-mail, to chat but all in one unified platform, one unified platform, the stitching together is not your problem. Okay? That's what HDFC bought. HDFC is a top 10 bank. They just completed an acquisition. I think they're in top 5. 70 million customers. We're not talking about, "Hey, here's 5 seats and do digital customer care." We [ replaced ] close to 15,000 contact center agents, what they log into and they were logging into 6, 7 platforms. I met the CEO of HDFC. And he said this was one of the best things they had done. And I met agents who literally walked up and thanked us because they were like doing this before. I don't know about your neck but that's not good for your [indiscernible]. Does it make sense? Sprinklr Marketing is a comprehensive workflow orchestration and reporting solution that has advertising built in for over like, I don't know, 15, 20 channels. We don't aspire to do what Trade Desk does or Google does or Facebook does. We come in as an overlay layer. So if you're Siemens and you've got so many business units operating in so many markets. And you want to run a global campaign. Where do you originate it? You originate it in Sprinklr. You create a campaign brief, break it into market briefs, then you disseminate in the market, you orchestrate your content strategy, then you create your derivatives, you translate it. We don't do what Adobe does. We're not producing it. But the orchestration, versioning, translation, you need that, how else do I run a global campaign, if I want to run it in 30 languages. Where does my global editorial calendar reside? That's in Sprinklr. And then we take this meta data, we propagate it into this individual channel. So we pull the reporting data back because if you're Diageo, then you know whether you should spend more money in Mexico or in Brazil. You know whether you should spend more money on Facebook or WhatsApp. You know whether you should spend more money for a regional campaign or a global campaign. You know what worked last year. You know which holiday campaign worked last year. You know what piece of content, if you Cartier, you know what piece of content worked on Facebook in the U.S., so you can use that in Canada. What is that worth for you? If we can optimize, make that content work 30% better media dollars, 10% better and you're spending $1 billion. What would you pay for an orchestration system like that? Insights, forever, I thought the category got hijacked by survey companies. Customer experience management is very different from customer feedback management. When you're talking about feedback, you've got real-time unsolicited feedback and you got after the fact solicited feedback. I have nothing again surveys. I believe you have to understand the power of what people are talking about. And you have to understand and translate that into insight and you should drive your products. If you're Google, if you're Apple, if you're Microsoft, if you are any company that sells a product but most of them are becoming online and connected, you've got to take that feedback and prioritize what to fix. We provide you the insights from public external data that's legally available to you in a compliant, privacy-friendly, consumer-first way. And that's driving business strategy for large companies. If you're Samsung, you're learning what features should I highlight in my advertisement. And also learning what's wrong with my microwave or my laptop. Does it make sense? And that's how it connects back. And then we got the bread-and-butter social. And you're going to hear, for the first time, my man Manish is going to break out product numbers for you and I think you'll be pleasantly surprised. Each one of these product suites are -- there are 30 products between these. When you buy, you can buy a suite, you can start with the product. Service has 13, Marketing has 3, Insights got 7 and Social has 8. But beauty of Sprinklr is that everything is built holistically from the ground up. We acquired 13 companies in our 14-year history. Do you know how many code bases we threw out, 100%. We threw out 100% of the source code 100% of the time, 13 times. Everything was built from the ground up because that's what's going to take to build a giant in this space, unified data model, a truly resilient omnichannel architecture. People say omnichannel and they confuse it with multichannel. If you support e-mail then you support chat because you acquired 2 companies, great, you support 2 channels. But if someone goes from e-mail to chat, you can't connect the 2 without asking the customer for the context again. And that, in 2023, I think it's kind of super lame. When you create an AI model to understand an adverse event, you should use the same thing in your contact center? Shouldn't you -- or should you have to deploy -- buy technology, again, makes no sense. You've got to have the UI built in a way that to add a new product -- practically, we're cranking out products in 3 months. We took on the contact center space that's 40 years old and we did it in 3.5 -- 13 years of building a platform. This was built as a platform. And to understand why a company like ours that's 13 years old can take on the contact center, you need to understand this origin story. Some of you might know, I did 3 companies before this. And they were in e-mail marketing. So in late 1999, in 2000, I was building e-mail marketing technology. And the world was using direct mail at that time. You remember, some of you may not but if you're old enough like I am, you'll remember going out, picking up your mail and you have the stack of mailers, you sort through it. There'd be coupons, there'd be offers, there'd be statements, right? You don't get that much mail because [indiscernible] all of that businesses have gone out of -- like completely out of business, an industry got collapsed when e-mail came by. I remember, I used to go to CMOs and say, take your direct mail budget, move it to e-mail and they would say to me, it's too personal. And we all had a Hotmail and AOL addresses. And I would say, no, you're going to see consumers are going there. And that's the insight that led me to start Sprinklr, put my money, fund it myself. That's where the confidence is coming from. Because at one point, when I left Epsilon Interactive, we were the largest e-mail marketing company in the world. I'm a product person. I was the CTO of my first 3 start-ups, okay? We wrote code. I used to drive down to Weehawken, 3 a.m. in the morning and swap hard disks that's what Backup and DR commodity service meant. That was cloud back then. But what we learned was for e-mail, you need campaign management, right? What we learned was, even then you need to plan campaigns, so planning. You need asset management, you need the ability to publish. You need the ability to respond when people do. You get the idea. And then you needed automation, you needed reporting. I rebuilt that technology 4 times on our way to becoming the largest e-mail company. 4 times. We took every bit of learning I had, that was version 1 of Sprinklr. We are in version [ 90 ]. Along the way, I met my CTO, who is building application service. We built SaaS before SaaS was sexy. We did cloud before cloud was sexy. We coined the phrase front office before that was sexy. We started using AI before that was sexy. He was building app servers, and we just really -- the genesis of Sprinklr was from a beautiful place. Does it makes sense? So the fact is, we've got 30 products that's built on a homogeneous beautiful architecture. So it doesn't matter, whether you're Coca-Cola, you're Samsung, doesn't matter, where you're bringing on 100 business units. Doesn't matter whether these business units operate in 50 countries. The last problem I didn't solve in e-mail was a single instance architecture. We solved that in version 1 of Sprinklr. So you think a start-up that can sell very well can take on Sprinklr. Think again. Think again. This was conceived as a platform and the insight, which we started publishing in social media, we started responding -- all of those planning -- but this is why it took us 3 years to become the leader. But once we became the leader, it's just too hard to catch up, right? And then what we learned quickly was, man, we had content and campaign and other things. We got the world's first and best omnichannel communication engine. We had a clean start, no legacy, expanded that sucking in petabytes of data and there's no way to understand it. This is why we started on AI. This is why our prospectus had 5 pages of AI. AI is not a buzzword for us. And we added other channels because customers said -- customers were saying to us, "Well, if I'm so efficient and Whatsapp can -- why can't I do it in chat." And that's what we built. So what you're seeing -- and has shown us in the last several years, voice has gotten digitized. Telephony, which the Genesys, the Avayas, the Ciscos spent so much time getting good at, is now a commodity. So a company like us that has built the app stack with the AI, with the console, supervisor consoles, quality assurance, AI, routing, everything, digitized voice, translate, transcribed in real time, apply the same logic, boom, here in the contact center. And you're not doing 300 agents in the corner, you're taking on the mother lode when we added voice. And this is why our contact center solution is 12 years old. This is why it's future backwards. This is why it's not easy to replicate what we've built. And where we are going is, taking each one of our product suites, our contact center product suite is taking on a 40-year-old legacy contact center industry. If you don't believe me, ask the customers who are piloting, bought and tested it. We're going to have a couple of them talk today. Our Insights product, we're going to add surveys and so that's going to go along and become the customer feedback management platform. Our marketing suite will add support for other channels. So we'll become the definitive dashboard for the CMO. We're not interested in the details. We interested in having a singular governance layer, AI layer, automation layer, analytics layer. We don't believe you have to buy an integration company and a data lake. We work with Microsoft, Google and AWS, right? And that's where -- and our social will evolve to become sales and engagement. Does it make sense? That's what we're trying to build, okay? And there's 1 hidden thing that most people, I don't think fully comprehend. What do you think is in a CRM system? The heart of a CRM system is basically transactional data. Did you buy something, did you call the call center? What do you think is in a CDP? Behavioral data. Did you come to the website, did you open an e-mail, right? This is after the fact, it's relational data, that role that data looks like this. I can take all the data that's in my Salesforce system, and I can put it in Google Cloud and pay $200 a month. They just thought it was a big problem, not anymore. What I want to help you understand is, the enormous amount of data that's out there today, 4 billion people are online. I can look you up online. I know where you went to school. I know who your friends are. I know who you work with. I probably can find out when you got married, what your kids look like, how long can we ignore that data, right? And that is what we call as external, that's CXM data. We are the purveyors, it's not -- you can use it. You don't own it. You don't need to own it, to use it. And it's conversational. It's unstructured. It's in hundreds of languages. This is the technology we've been building for 13 years, very unsexy but very hard core. So makes sense. Right. So there are 3 core differentiators that we have. First is the fact that we are a platform, truly omnichannel, centralized governance, unified front office architecture. All of that but 1 single instance platform with all these capabilities; 2, before AI was sexy. And I'm sure some of this, as ChatGPT's volume goes down, we will forget that this was a buzzword. But there are companies that are AI-based and companies who are not and you will learn why Sprinklr is going to win. And AI is about 3 things, guys. It's not an OpenAI integration. AI is about; a, models. Do you have engineers who can create, optimize and tweak models? Two, do you have data that you can train it on? We've been training for 5 years, at some point, we had hundreds of consultants annotating and training. And third is the feedback annotation, feedback loop. When -- in Sprinklr, when our AI says, your message is not on brand and you don't choose it. We give you a smart response as a contact center agent and you don't choose it, that feedback goes into Sprinklr right back and makes AI better. And lastly, as an enterprise company, we sell to the largest global companies who know how to buy enterprise technology from the very beginning. Security audits, there are people from banks here, 13 of the 14 largest banks are our customers. Pharmaceutical companies, CPG companies, 7 of the 8 largest CPG companies are our customers. All 10 of the 10 biggest brands in the world according to Forbes are Sprinklr's customers. About 85 of the top 100 are -- we focus on revenue. We start with increased quantifying growth, taking out costs, managing risk and that's how these customers renew. And there's a growth [indiscernible]. You can start with one product but then you learn and I'm going to tell you what the Sprinklr philosophy is, ethos is but you'll learn that we obsess about customers. I did 300 meetings last year. And every customer I meet, I give them my cellphone number. And I say, "Call me if you're ever, ever upset. If you think we b********d, you call me." If you want to buy, our team will take care of it. My CTO flies over. We have, [AK], one of my engineers will be here. We've flown him with a one-way ticket to one of our customers, early on and we told him don't come back until the customer is happy. If you know other enterprise software companies that do that, buy their stock, right? But they learn it. We're not 100%. We're hiring -- sometimes it's not perfect. We'll find out, we'll fix it. The ethos of this company is very, very, very different and very deep. And we focus on value. And if you do that and you put these workflows in place, you have cosmetic companies in France, they go, "Oh, this is great. It's working amazing for France. Why isn't the U.K. doing it?" Why is my other business unit -- because the power of this shows up and then you go from market to market, business unit to business and then you go, what other product can they buy. And on an average, we release 300 to 600 features every quarter. Today, we have a product payload of over 25 -- maybe $25 million to $50 million of product payload we can sell somebody. Now obviously, you're going to look at numbers and say, go to market, something will crack. We're putting the same obsession onto it. I don't know whether it's going to take 2 quarters or 20 quarters but we'll be here until it's done. So 43,000 companies are in our target market. We started out trying to solve it for the biggest companies, the most complex problems. We figured that's easy to make things simple once you handle the complexity, which I think we have. We focused on companies over $1 billion as we got started. And then we realized that companies under $1 billion with the potential to get over $1 billion, have the same problem. So we just extended down but we're not going after small companies. If we pick a customer and we now have customers who have $100 million in revenue that's paying us north of $1 million. If you can talk to SimpliSafe, one of our customers. You can see the power of unification, how soon you need it. Well, if you're not growing, you don't think you're going to be a big company, you shouldn't buy Sprinklr. There are plenty of SMB solutions you can go -- you don't really understand -- you don't have the problems we're solving. But when we do, if you're a company that's spending $50 million on content and media, if you're a company with over 50 customer service agents, if you're a company whose product road map depends on what customers like, then I think you need Sprinklr. I think this 43,000 companies need Sprinklr. We're 1,000 down, 42,000 to go. That's the way we think about it. Around the world, because our customers are, most of them are global implementations, a partner ecosystem that's developing as we got into the CCaaS space that's taken a life of its own. We have 12 verticals that are a priority that makeup of 80%, 5 that we've prioritized. So that's really our story. That's who we are. That's how we got here. I wish I could say all this at IPO. I think the people who understood didn't believe us. And we're not the company that you look quarter-over-quarter. Like, how do they do and then try to extrapolate back. We're a company you look every 5 years and go, how did I miss that? That's what we want to build. That's what we wake up doing every day. Sprinklr AI, it's the fastest way to deploy AI across your front office. You want to get AI to your agent in a point solution, go buy an AI point solution. If you realize that, i.e., whenever somebody complains about luggage, you need to understand the airport. And once you understand the airport, you need the time, is it at the origin destination, there's a lot of AI that we've built for every industry. But once you do that, you go, I want to pick it up from a blog. I want to get it in my contact center. I want to know it in my marketing. I want to know -- my competitors are doing it. How do you get it across the world, across markets, across business units? How do you get it across functions if you don't have Sprinklr? Help me understand, how do you do that? I don't know how. That's why we're building this. Across the front office for every customer role, if you want to get AI and our AI is not -- we didn't start 6 months ago. We've integrated -- we have a lot of respect for OpenAI, amazing. Generative AI is going to give our AI wings. We're already seeing that adoption is up. It becomes a lot more real. But we have built thousands of models, many -- for most industries, many for our customers, personalized. We make over 10 billion predictions a day, over 100 million data points, in over 100 languages. In many cases, we achieved -- we're able to achieve near human accuracies, some of the best AI companies in the world are our customers. Now we're not saying we have the best AI. Let me be very clear. That's not what I'm saying. We're saying for conversational purposes, for the front office, we've trained our data, our AI models on more data than most people have and it's more accurate when trained with our models. And we can prove it and that's why these customers, some who have their own AI choose to use the Sprinklr AI. And then, look, there are companies that have an OpenAI integration and have announced a AI strategy. There are companies that have introduced a AI product and there are companies who deeply embedded AI into how they think. And I'm sure, Google is here today. Tesla, they are few companies that have just got it in their DNA and we're one. And this is what I would love to close with. I was reading the book on Amazon, which is fantastic. People look at Jeff and go, well, he [indiscernible] around for breakfast and that's how he built a great company. Look at what they did when they were starting out. Look at the period, I don't know how many years there's, people thought they would go bankrupt. That's what I want you to look at. I wanted to look at the years where Tesla went almost bankrupt. I want to look at the conviction in the team's eyes, collective eyes, when they go through that. When you think I am building the biggest selection on the planet, the cheapest prices on the planet and I am obsessed about customers, first principles. That's what I want you to think about when you think Sprinklr. We are building a new category. I think it's going to be the future. The evolution of what CRM is going to be. CRM is going to be there forever. We connect to CRMs, be very clear. We're not trying to replace CRMs. We're trying to connect to it and we are trying to create an operating system, a connected platform at the edge where you meet the customer in the brand, in an ad. I'm not doing this to be cool, okay. I think it's rappelling and making some noise. And so obsessing about customers, it's in our DNA. We're trying to create the world's most loved enterprise software company. Most loved in enterprise is a rarity. People buy enterprise software because they have to, not because they love and we want to change that. It's not very hard. If you look at the giant enterprise software companies and the customers I talk to, love is not the word that comes up with enterprise software and we want to change that. We want to make it really personal and human. And so we don't -- the vision is very clear. It's like we've plugged that vision into our GPS. We know where we are going. But we make a left turn here or a right turn here is driven by what customers say. We have a product development process. Started with 3 customers, make it work for them. They're happy, rolled it out to a few more, before we rolled it out to everyone. That's how we get that right. That's how I know we don't go wrong. We don't do free [indiscernible]. People pay because the product -- this category is just emerging. And some of you have started lumping front office software together. Analysts, the Gartners and Forresters beginning to do it. I think we're going to do this sooner or later. I'd like to see that start now. And for us, customer obsession starts when, after the sale, right, checking in, making sure they get value, they're consuming the software. And we have a process. We don't do NPS. The reason we don't do NPS is we don't want to know whether you'd recommend this. We ask you, every customer, every quarter, every month, depending on the size of the customer, we ask them, how happy are you with Sprinklr. That's it. If you give us a 10, it means you're so happy that you're telling your kids about Sprinklr. They're like [indiscernible] I don't know why you're bothering me with this stuff. And 0 means you fired us and we didn't know it. And if you don't give us 10, we ask you what are the 3 things we can fix, 3 things. We get it prioritized, goes into a product development process, every week regionally, it gets escalated. Early days at Sprinklr, I would say, there's only 1 meeting you cannot miss. At Sprinklr, it's called CDAP, where you wouldn't miss it. And I'd say, if you're attending your own funeral, you can miss the meeting, otherwise, you're on. If your customer is unhappy you're on. I'm not -- I don't get upset at most things. Most of you know that I only curse out of excitement. But if a customer is unhappy and you're not jumping up and down to fix it, I will get upset. That's baked into Sprinklr, that's what I'll close with. We're trying to create the world's most loved enterprise software company. Our mission is to enable every organization on the planet to make customers happier. How do you make anyone happier? You have to be where they are. You have to listen to them, you have to understand what matters to them. You have to do work across everything you can to make them happy. And that's what we do. Happiness cannot be bought. But you can have your best day in your life and the worst day in your life and someone can try to make you happier, that's what happier for us. And our strategy to do that is to create this category. It's very ambitious. I won't fault you if you said it's too hard to do. You should but keep checking on us every few years because I think I got the world's best team on it and we're not going to quit. Thank you so much for your time. I'm excited to be here. Amazing to see you all in person.

Next up, we have Sprinklr's Chief Technology Officer, Pavitar Singh.

Thank you, Eric. Thank you, Ragy, for that session. It's always a hard act follow Ragy. I'll try. Now today, I'm going to talk about 3 things. First, I'm going to spend -- dig deeper into our architecture, how we have been building these products. Second, I want to spend some time deeper talking about our AI. What we have been doing for the last 7 years and what we intend to do over the next few years. Then I want to talk about our unified CCaaS platform. Now let me remind -- when I met Ragy, almost 11.5 years back, these are the 3 things he told me that, Pavitar, in this platform, there are 3 things I'm expecting you to build natively. First is omnichannel. And the reason omnichannel is important is, let me take us through a journey. So remember, when we go maybe 30, 40 years back, voice came and we were communicating using voice with each other and then brands followed and started using the technology. And then they bought voice service software for contact service. They bought sales software to do voice. Then e-mail came along as a channel. Then companies started providing e-mail solutions for service, for marketing. Then web came and the live chat came as a channel, then social came. And when social came, it was 30 other channels, it was just not 1 channel. And these channels will keep coming, [indiscernible]. It took 5 days to reach 100 million users adoption, right? And almost every brand is adopting the channel as we know of. And brands are going to expect solutions in order to work on those channels. Now you cannot keep up -- we cannot have 1 new startup, [indiscernible] for 1 new channel to solve 1 front office function. It's unmanageable. It's unmanageable to deploy. It's unmanageable for customers because then when they will switch channels, they'll need to repeat themselves. And even today, 2023, when we call any contact center, and by any chance we disconnect that call, we call them again, we have to restart, right? It's 2023. Why is that happening? We believe the fundamental reason is, it's not being architected right. And that's what we are trying to address. Second thing, we wanted to fix in our platform and built like that was omni functional. See, when brands needed to scale, when enterprises needed to scale, it made perfect sense to do a functional approach. Let me scale my marketing teams. I will have a marketing head. I'll build a marketing function. Let me scale my sales team. Let me scale my customer service team. But guess what, customers or brand do not see like that, today I want to talk to marketing or tomorrow, I'm going to talk to sales, no, they see 1 brand. They want to have 1 consistent brand experience across all your functions. You need 1 platform to do that. The third part was, when you're a large company, you're going to operate in multiple geographies. Now guess what, in every geography, you cannot buy 1 point solution for 1 channel, then you need 100 different e-mail technologies just to do customer service, 100 e-mail technologies just to do marketing out there, then how are you going to connect, how are they going to share knowledge with each other. It's a mess. And that's what we wanted to address. So when we say 14-year platform, right from day 1, I remember, when we started writing our initial code, I wrote some of our initial code, as always, we are going to build a platform. There is no other way we can go and build 30-plus solutions today and maybe hundreds in future decade as we look forward to because we believe point solutions are not serving our brands fine. Now let me take a deeper approach and let me spend few minutes on unified data model and the reason it's critical. What we did, first piece which we did. And I remember first line of code I wrote was universal profile. I said, it does not matter if a customer reaches out to you on Twitter or LinkedIn or e-mail or voice, it's just 1 customer. You should be able to represent that in universal profile. We were having customer 360 natively embedded in our data model. Universal message, any interaction, which happens when a customer reaches out to a brand, either if they're complaining on any channel, it gets modeled as universal message. And if as a brand you're communicating back that also is a universal message. How many times we have received a marketing communication on e-mail, where if you try to reply, they say, we are not watching this email id, no reply. And I said that doesn't make sense. You're trying to reach them. If they want to talk to -- back to you, why wouldn't you allow that? The challenge was technologies were not -- unable to do that and we have solved that in our unified data layer itself. So by default, we become omnichannel. So if tomorrow, I have to add threads as a channel, all I need to do is take threads APIs and adapt to my data model. That's it, then my rest of product lines are operating on my data model and they seamlessly will work, right. Now, the advantages we get with this approach is natively everything is one. All channels are one, all functions are one. So it's a very simple secret sauce for us. We are natively integrated in our data model. So that's why reason -- Ragy was referring to, we don't need a data lake because we are a system of record for all of that. Now what this allows us to do is, it allows us to build once and deploy across every channel. So if our customers tomorrow adopt threads as a channel, then they don't need to change their workflows because my workflows are not operating on channels, they are operating on my data model. So they will be able to have 1 simple, single governance and workflows on any channel they choose to operate. They'll be able to unify journeys across functions and the customers will be able to seamlessly switch channels and I'll show 1 demo. We want to choose different channels for different kind of interactions and that's a choice we should have as consumers. We shouldn't be forced by our brands with whom we interact, hey, you can only talk to over a call or you can only talk to a line chat, no, that's not how I'm communicating with my family today. I would prefer to talk to you on the channels where I'm already there. And we want to make sure customer 360 is natively part of the platform. Let me quickly hit the demo. So here, we have a bank as our customer. I am going on live chat as a customer. Our bots come upfront. They are having interaction I choose to interact in native language and our bots can do that, "Hey, I want to know my account balance." But I need to authenticate you. So we need to do it in a secure way, we authenticate and we will now go to -- we tell you the account balance. Then you say, "Hey, I want to know my last 3 transactions but I want to know them on WhatsApp." We say, okay, perfectly fine. Let's do that. Then we ask, "Hey, which of your number we should reach out to you? We have multiple numbers." You choose that. You say perfect. We got it. We're going to get back to you on that number immediately. We take that context and bring it to your WhatsApp, hey, do you want these details? I said, yes, okay, here are your last 3 transactions. Then what you observe is, I didn't make one of those transactions. I want to report that. So hey, the second transaction seems odd, I didn't make it. Can I talk to one of your agents? Perfectly fine. We can make that happen. You make a request. Until now, our bots are having this interaction by the way. We are involving humans now only. Request callback, perfect and we get a call. Now, here, you see, we have across channel history available to agent. [Presentation]

Now what we saw in this video was a customer went through almost 4 different channels. right? They started on live chat, they shifted to WhatsApp, they went on to a phone and then they received an e-mail. When agent logged in, they go just 1 experience out there, right? And they were able to do that seamlessly without integrating, without spending any dollars on anything, all happening natively. Now the second thing which we did, and I can spend days talking about unified data model but let me skip that. The second part, we were very clear when we were building our technology was, guess what? Every product has almost 80% same stuff. Every product needs a governance layer, every product needs a reporting layer, every product needs some kind of workflows, some kind of automations. What we realized was why don't we build it once and then we just use it. So we created a set of reusable modules in our platform, which we reuse every time we are building a product. Now what this allows us to do, that is, this allows us to innovate faster. Because now if you'll see our history, every year, we add a lot more newer products than we did last year. So not only we are maintaining all the products we have in the market, plus we are adding more products. And plus, we are doing that at very efficiently as far as our R&D costs are concerned. The reason we are able to do that is to reuse maximum out of our infrastructure. When we talk about CCaaS, the #1 need in CCaaS software, there's -- the reason only few companies have built that, it needs to be super scalable, super resilient, right? You cannot afford even a second latency. In voice infrastructure, you cannot afford any downtime. And that comes from -- as all of these components are battle-hardened for us. We have scaled them over last decade, scalability and resiliency becomes very normal to us. So whenever we launch a new product, by default it's scalable because guess what, when we are serving our customers, they are bringing billion of data points. We are processing Twitter Firehose, Reddit Firehose. We bring a ton of data every day. And when we bring that data with an automation engine on that, we act on that data. There is a reason, Sprinklr was purpose-built for the largest enterprise because no 2 enterprises are same. They have different workflow requirements, different governance and different scale environments. On the same infrastructure and code base, we are serving a customer who's paying us maybe $20,000 and we are serving the same infra a customer who may be paying us north of $10 million. And that comes from our -- this [indiscernible] platform. Let me take one example of that. This specific -- I'm talking about here 5 different products, guided workflows, chatbots, IVR, workflow, journey management as we know of for marketing and service. Guess what, they all are built using just 1 technology that what we call is process engine. And this one, first time we built in 2016. First time we've built this technology for workflow engine applications into our content marketing product line, to orchestrate marketing task when you're running these campaigns. Then when we needed to build our chatbots, we said we're going to use the same technology. We don't need to do anything extra. When we built our IVR, we were using same technology, right? And this allows us to, as I talked about, faster innovation and much more scalable infrastructure for us. Now guess what? Not only we have done that for our back-end platform, we have done that for our user interface also, right? Most of the user interface in enterprise software is quite reusable. There are tables, there are components. What we have done, we have created a library of 400-plus modular reusable components. So guess what, when tomorrow we are building a new product, there's a high probability we're going to use one of those components. And it has 2 advantages. Not only we get it faster at lower cost, our customers who are using already 30 products of ours, when they add 31st product of ours, they don't need to be trained again and again because same components will appear again. So that allows us to continuously reduce the time to value for our customers. And that's the reason I know our CRO, Paul will take us through a journey where our customers start with one suite, adopt second suite, third suite, fourth suite, 15th product, 18th product, easier, every new product they adopt it's faster to get time to value. Now not only we are able to reuse these components for creating new products, a lot of these we're able to on-the-fly configure differently for our enterprises. See there's a difference between when you are creating a solution for an SMB business versus a large enterprise, large enterprises, very different needs. They are very complex in their nature. They are managing billions of dollars of revenue. They cannot operates on standard run of the mill product. You can configure our product so that it feels purpose built for you, while we are not writing a single new line of code for that customer. It's all low-code configuration in which our implementation team, our partners' implementation teams are able to leverage, they just configure it using a visual interface in order to serve those enterprise needs. Now, what I have touched upon till now, I want to make sure we all take away with that, Sprinklr is lot of reusable Lego blocks on our front end, on our back-end architecture, allows us to innovate faster, allows us to innovate efficiently, at the same time, creating better customer experiences. Now as Ragy said, it's truly omnichannel, governance across BAUs and it's unified across all the customer-facing functions. Now let me talk about AI. And I want to thank OpenAI, Generative AI, Google, they have done amazing innovation. And now AI is on top of everyone's mind. We had this big aha moment. I remember when me and Ragy got on a call in 2016 and we said we need to be all in on AI. I said, I don't see the other way, right? We have to make AI as native to our platform as it's oxygen to us, humans, without that we cannot do that, right? Then what we did, we started building 8 layers of our AI. And recently, we have adopted Generative AI because it's, quite frankly, a massive exponential advantage we are getting out of that. We did our own NLP. We even used to do our own basic NLG, natural language generation even before Generative AI. Obviously, Generative AI has taken it to the next level. We did predictive analysis. We did speech analytics, in order to build all our products. Now this AI is central to our platform, goes across the platform. What it allows us to do and I'll show you some demos today, with AI, we are able to reuse across different product lines. So lot of AI when we built for our listening product line, our Insights product, we were right away able to use that in our CCaaS product line. What we built for our marketing product, we were able to reuse. What new things we are building for CCaaS, are able to reuse in our other product lines also. This speeds up our innovation again and reduces our cost. Now when we started doing our AI, we were not doing lot of time series-based predictions because our data was not very structured in nature because we were getting this massive tweets, Reddit, forms, blogs, contact center transcripts, which were like free flowing text. And that was harder problem, I mean, few years back, obviously, AI has accelerated that and now it's a lot more easier problem. But we have spent a lot of time in understanding and creating our AI solutions around that. Now what we have done is, many of our products, which are -- I have listed here, could not have existed without AI. They don't exist. Because when we started building, let me take an example of contact center. When you're running a large contact center, let's say, you have 1,000 agents. How do you make sure that your customers are getting a consistent experience? What typically you did? You hired a quality management team, which sampled few of those interactions. They listened to few calls or they went through those chat transcripts and they manually scored them; a, agent displayed a knowledge of the skill, agent was empathetic and you were manually doing that. But guess what. You can only do 1% or 2% of sampling of them, how do you scale that? So when we created our quality management solution, we said, let's turn it on his head, we're going to do it all AI first. All what we did is, 100% of the calls or chat transcripts or any service interaction, now goes through an AI model because in the slide before, I talked about, we can understand all of the text, we can give it attributes and meta data. And then we trained AI to analyze and score on 30-plus different parameters. We are able to score on empathy. We are able to score on knowledge. We're able to score on did he have a proper opening, proper closure and guess what? So now using power of AI, we expanded the solution capabilities massively from 2% sampling to 100% sampling done in real time. And that's something we've been building for years now and we have rolled out it to all our customers. That's when Ragy said that Sprinklr is the quickest way for you to deploy AI to your complete front office because our products already are AI first, majority of them. Our workforce management, when we do a schedule forecasting, schedule adherence, all AI first, when we talk about our AI conversation, AI -- cannot be done without AI. We analyze not only when we do quality management. Now let's see about reuse we are able to do. When we do contact center insights because as a CEO, you want to know what's wrong with your products, what's wrong with your business and your customers are telling when they're calling you. And we are able to do that using our AI and start informing rest of the C-suite our product lines that what they can do differently so that they don't get that volume of tickets. So almost dedicated AI products we have in our 4 suite of product lines. We have 50-plus features in our products, which are just AI enabled. For example, almost everywhere, we have tried to challenge the existing way of doing stuff. Let me take 1 example. When you're running a contact center, what you do, you have a concept of cues, here is where I'm going to get my premium customers. On this cue, I'm going to get my premium customers who are looking for, let's say, reporting their credit card tab. Here, I'm going to talk about trying to upsell them and you train your agents on certain skills and then you map them to cues. That's how the world worked. Then what we said, is there a better way to do that, even beyond that. What we realized was when we went through contact center transcripts, we figured out few agents are really, really good with dealing with this specific intent of complaint, which is just not top level credit card fraud, which goes maybe 3 levels deeper. And these agents are great in dealing these kind of issues for this kind of audience, this kind of customer profile. They are really good with these demographics for this problem. Now when we had that aha moment, what we did, we created smart assignment. And we said when a new ticket will show up or a new call will come, that will have these attributes, I'm going to pick up these agents. And that all happen at run time and all gets updated. You don't need to manually come and score the agents because QM, quality management is doing automatically. Everyday, they are getting scored and everyday, they are getting better match. Netflix can do a phenomenal job of recommending movies. Same technology can be used to pair the agents also. Once we did that, guess what, its first principle, if you take your most qualified agent for that specific subskill, they're going to perform better. They're going to solve that faster. So your cost is down, plus your customers will be lot more happier when you do that. So our approach to AI is not just, I need AI, we believe AI has transformational capabilities to improve how software was built or each feature can be rethought. And we have rethought many of our features. I remember our guidance to our product teams was, every product need to do 3 big features, every release. And they don't need -- I don't need this as a formality or compliance, we need to make sure you're reimagining that part of the stack -- product stack. And we have done that over last 6 to 7 years and we continue to do that. Now let me talk about Generative AI, phenomenal piece of technology, amazing innovation, which has happened. Our approach there is, we see it in 2 big buckets. There are general purpose large language models. OpenAI, we already integrated. We're already working with Google to integrate Bard AI, releasing it in our future releases. There may be a future another large language model, we don't know. But as and when they will come, we will integrate with them. They are doing a phenomenal job on this scale and will make it available. The other thing we want to do and we already started working on that, we're going to take smaller large language models, which are open source in nature, where we can further train on top of that. So we will have this concept of domain specific or even customer-specific large language models, where we take a base language model and we train it for specific use cases, specific data. See in the end, what happens is, with AI, there are 2 key aspects of AI. You have more targeted data, better it will get trained. And if you have better F1 scores on that AI model, it will -- obviously, that means it has higher accuracies. Higher accuracies leads to better business outcomes because then you can be more confident, then it's going to do a better job choosing the right agent, then it's going to do better job choosing the right ad copy to run that. And we believe we are going to be adopting Generative AI pretty aggressively than any other vendor out there. Now, what we did -- I want to just reinforce our approach to AI is, global level models are great but they're not very accurate. Most accuracy you get with your customer level model. We have created an architecture which allows us to hyper train specific models and continue to host them and fine-tune them. We have done ton of AutoML capabilities where we can discover the right architecture and we can deploy that to 1,000-plus models, which are customer-specific model deployed for us. Just today, I was talking to my team. We were doing a POC for one of the airlines in Korea and we were increasing the accuracy of our AI model. What we found out, like in 4 weeks or 5 weeks, we started with 70-80% accuracies and we were able to take it 90s, right? Because we were able to better feed it, better targeted training data and keen to -- continues to fine-tune. Now you see the business outcomes when you're getting 70% accuracy versus 90% accuracy. And architecture needs to support that. And this -- when we do that, not only we get higher accuracies, we get better stickiness because our customers will continue to use us because it will be very hard to replace because our accuracies will be harder to recreate easily. We have talked about, Generative AI is amazing on language generation, can help us then -- amazing in summarization, something we were struggling to solve without Generative AI, right? And we will spend some more time specifically on that. Similar to normal AI, what we have done is, with Generative AI, we are going all in. We are going in all of our product lines and reimaging, can it supercharge some of our AI features, existing? Can we do some new features which were not possible before. I'll quickly take 1 example. We had this feature of smart replies. What it did, when we got a case, interaction history, we gave it to an AI model and it predicted responses our agents would use. It did basic natural language generation. We were doing that. But what we realized was when we took that response, which our AI generated and we give it to a large Generative AI model it increased the accuracy of that response by a factor of 3x, right? That's the one when Ragy said, it's giving our AI wings, right? So we are not using plain vanilla Generative AI, but we are first running our AI, taking those imports and then running them and giving it a large language model, right, which gives -- which allows our AI to become much more better and some of the features like discovering workflows. We never did that before. But now we are working on that using generative AI. What the specific feature does is you're a contact center, right? Every day, people are solving cases. But guess what? They are solving cases in multiple different ways. You do not know are they -- how they are solving those cases. So using this feature, you can discover that this kind of issue, that's how they solve it, then you are able to create a knowledge base article out of that, you are able to create a playbook out of that and you're able to work on that. We couldn't have done that without generative AI. So we believe, I think next few years, these are the years of AI. These are specifically years of generative AI, and we are all in, right into that. We are verticalizing our AI because we talked about that. So now when we sign up a new airline customer, we can go much faster. The time to value gets compressed and we can take them live on much higher accurate models. We are doing that for financial services. And we're going to keep adding new industry verticals to where we will go pre-practice AI. Let me take a few products and let me walk you through a few of those products actually. As Ragy said, every product persona can benefit from AI, right? And that's what we have done. Let me take an example. Let's go through a customer service agent and just see how they are resolving a customer inquiry. So we are able to use generative AI to summarize that case. Biggest problem in contact centers was, they don't want to read such a long transcript. We all are human. So now AI can do that for us, and we can directly pick up where we left. We can -- these are the smart response we generated. We can go to generative AI and we can ask it to change the one, make it more empathetic and it does it in like a fraction of seconds and we can ascend that. Now what it does is it improves our customer experience seamlessly. We are able to get -- they don't need to remember knowledge-based articles because AI can pick up specific fragments, bring that. They don't need to remember which guided workflow they need to run, AI nudges is able to find that, help them take us through trying. And in the end, what you are observing here is using AI, we are able to make it simpler for customer agents to do their job, right? It was hard. You cannot create the complete transcript, you were making it to be harder for our agents. Can't AI summarize that. This is what customers wanted. This is what we have done. This is what's pending. So Mr. Agent, why don't you go and solve those things. And here is how you do solve those things, it's a graded workflow, how you solely transition dispute. Here are the sequence of steps, is how you do that. And that's how you delight your customers and do it in a much smaller amount of time. When you're running a contact center, average handling time is a very key metric. If you reduce that, your costs go down. And guess what, if you reduce that and at the same time, do a better customer experience, your revenue also goes up, right? Let me take an example of customer service supervisor. 100% of calls are getting analyzed. We are telling why are people calling you. Then we are telling you which contact drivers are having a highest average handling time. So that you can do something about that, right? Earlier, these were dependent on manual dispositions, which agents were doing and that was adding and taking time away from that. We were able to use our listening, put it on contact center data and start discovering hundreds of teams around that, why people are calling. And how happy or unhappy they are, we were able to use the same sentiment technology. We are able to find agents who has lower quality scores. Then what we are able to do is we are going to go deeper into that case at which point they are a negative customer experience. So you can go there, understand that and cost the agent, right? I told about that we score like -- for this case, this agent scored 72, this was very low in positive language. This could really good on some other aspects. So you can go to those specific areas, and then you can coach an agent on that. And this, you can do 100% of the colds, any day, right? And that's how you delight your customers, right, at scale. Now the same technology, as you see, a lot of reusable blogs. In future, we can reuse that to analyze sales interactions also. We can use that to inform what information our sales agents can do better, right? So everything we are building, we are thinking from a lens that it can be reused at some point of time for one of different office sponsors. Let me take example of social media marketer, our bread and butter. Here, our media marketer wants to launch a sustainability campaign, the pickup in ascent. It's about sustainable clothing they use generative AI to help them generate content. All they need to do is give a basic prompt, what kind of content you're looking. I want to do post around eco-friendly clothes, about a discount for a specific audience segment. And within seconds, we'll start getting the recommendations of some content they can get started with. Just the amount of time it can cut from their day-to-day work, right? And then they can choose to use that, or they can choose to use a few things from that. But what we are doing is we are making AI available in the day-to-day workflows. So they don't need to go to another tool to use AI, they can do it as they are doing their job, right? And they can do it across channels, they can do on Twitter, e-mail, blog, anywhere, actually, right? And here they chose a content, they're going to schedule the content and there also will find various applications of AI, where AI will recommend this is the best time to schedule this content. We call it smart scheduling. So not only we are mixing generate AI, we are mixing our own AI. And in the end, there are multiple AI models are being applied in their workflow to help them do their job much better, right? Like here, we are going to use smart scheduling and say, guess what, this is how you should publish, you don't need to decide. But if you want to change the time, here is the second best time there. You can do that, right? And within clicks, it didn't take more than a few minutes to produce a new content schedule at the best possible time of all using AI. So this way, our customers can get more better returns. I know I'm about time and it's going to take 5, okay. So let me touch upon CCaaS. Why did we need to build CCaaS? And as Ragy said, for us, it was not really a big deal. We had most of the payload already there. All we needed to do was add voice as a channel and then build a few of these products, which was very easier for us because we're able to reuse a lot. But here's the problem statement, this 40-year-old industry, what it has created is this point solace in chaos. We need to buy 15 different solutions. You need a different solution to do voice, different to do e-mail, chat, then you need to buy a chatbot, you need to buy a voice bot, you need a quality management, you need an agent desktop, guess what, it's not sustainable. What we found out was, you end up with then multiple point solutions, guess what? They individually cost a lot more combined. You need to integrate them together that again costs a lot more. Then you need to maintain them in order to have consistent workflow. It's a lot high TCO. Okay. I get it. We are spending a lot more money, better. There must be some results. Then we can justify spending that kind of money. Guess what? Guess how high is agent entries and contact center industry, they don't want to work. Highest Agent entries in this industry? Because why? Because technology is failing them. There are manual processes, systems are not connected. How can you connect 15 solutions consistently to all your technology which you are using? It's harder. It's very costly. You get lower efficiencies. You need to learn multiple systems, there are information silos, obviously, you will leave. So guess what? I get it. We're spending a lot of money and our employees are not happy. I hope customers are happier because there's no other way to justify that amount of investment. Guess what? They are also unhappy, right? They have to repeat information, right, every time. They have to own hold lines, if you call -- if I call a contact center now, there's a chance I will be on hold for 10 minutes, 15 minutes, 20 minutes and they'll keep playing a random music to me, right, and say, you are very important to us. We are going to get back to you in 17 minutes. I said thank you. In today's world, I -- truly you're validating that for me. Missed upsells. Guess what? Why am I calling you, I'm having a bad experience. If you recover that experience, you are like to do more business with me. How many times when brands had done an amazing service recovery, we have bought more. But we are failing that due to technological silos around that. I get it because that's how channels came in picture. They all were not born magically one day. They evolved over the last couple of decades. So I get it, technology evolve like that. But today, when we were building our CCaaS platform, we have that hindsight. It's a multichannel world. Customers want a different experience, agents want a different experience. Then that's how we built our CCaaS platform. All channels, all applications in one. Makes sense. Guess what, customers are happier. Employees are happier, and it costs less. That's so simple for us to comprehend that, the whole course, lower employee attrition that also adds to course by the way because you have to train new employees, if you have to keep hiring and then customers are happier. Okay, I get that. But now you'll say, how you've done it at scale. But your largest skill when you're talking about CCaaS. I'll get to that. And then I'll just reinforce. We have done AI first in our CCaaS. Every part of our CCaaS product lines, we are supercharged with AI. I talked about routing. I talked about agent resistance, self-service. So this way, we are able to create ton of ROI. When we do that, when we unify CCaaS and we do AI first, guess with, our customers, they're able to reduce average handling time by 15%. If you do that, this is one of our customer examples, guess what, your contact center cost is down by 15%. Today, when you're running a large contact center, you are still spending a lot of money on your human cost. Maybe more than 90% may be human cost, maybe less than single digit will be technology cost. And any improvement you have in that spend directly is the ROI you're able to create and you are able to make a strong business case for that transformation. We are able to reduce first response time by 19%. We've achieved that with some of our customers, better customer experience. Another leading electronics company, we were able to deflect more cases from voice to digital. Digital cases cost less than solving a voice. So if you are a solution, if you're only doing one channel, if you're only doing voice as a technology vendor, do you have an incentive to move customers on digital, if you're not doing digital for that brand. No, right? Incentives are not aligned. That's the reason we still see today lot more brands are heavily dependent on voice channel for customer service. Incentives are not aligned with technology and customers. As we do all our channels, it doesn't matter to us. You use Voice, WhatsApp, Facebook, digital, Tomorrow Threads, we don't really care because we are powering end-to-end. So we are not risking losing any revenue, and that's the best interest of our customers. We are able to do better CCaaS and NPS. So when we do finance and all of you are from finance, a lot of these might seem competing priorities. Hey, I'm going to reduce cost. Will that have a better customer experience? Will there be a trade-off, but know, using a unified-CCaaS, which is AI-first powered every part of the ecosystem, you will reduce cost but at the same time have a better NPS and you'll end up having better revenue. What we have done is we are deploying it at scale. This is one of the examples. This is monthly scale where we're able to use WhatsApp and SMS for self service, we're able to use social, 50 agents. We're able to do e-mail, almost 1.5 million-plus interactions in a month. We're able to do outbound sales goals for 8,000-plus agents, almost 11 million calls every month. We are able to do another 7 million inbound interactions with almost 2,500 plus agents, we are able to do this with 12,000 agents with 24 million interactions a month. Almost 300 million a year. And all complete unified take is deployed across channels, across AI, all the functions deployed. And once you do that, immediately, you get ROI. We'll have some of our customers today talk about those. We believe these ROIs are inherent in our approach to product in our approach to our solution. I know we have a video, but we may skip that. Yes. Okay. We have 6 minutes video on our unified CCaaS solution. If you guys are good with that, let me play that. [Presentation]

Thank you. On that, I think I want to pass to Eric. But before I do that, I just want to summarize what we talked about is we have taken a platform first approach to build our technology, right? What we have done is we have embedded AI in all our products. And what we are able to do is we are able to truly disrupt CCaaS market using our approach to solving that problem. Thank you.

Thank you, Pavitar. Okay. That concludes the first half of our show today for our event. We're going to take a 5-minute break. So please help yourselves in the back, get refreshments, use the restrooms. We're going to start promptly at 11:30. So for those on the webcast, we'll be back in 5 minutes. Thank you. We'll be resuming with Paul Ohls, our Chief Revenue Officer. [Break]

Do you want to start on that.

Yes, yes, yes. Where is the click? Thank you.

Paul. Okay, everyone, if you could please take your seats. Okay. For the second portion of the day, I'd like to welcome up Paul Ohls, Chief Revenue Officer for Sprinklr.

Thanks, Eric. Good morning, everybody. I know we're running a bit behind here, and I don't want to cut into the time for the stars of the show, which is our customers and our lovely CFO, I know he needs to have his time as well. But just to introduce myself, Paul Ohls, I run our customer-facing teams, both pre-sales and post-sales I wanted to take some time today to kind of walk you through our history and how we got to this point with a big focus on where we're going from here and scaling out those things that have historically worked for us in our current state. Let me start with our global footprint. So we've got a global presence of 25 offices in 17 countries. We started in the U.S., then expanded to Europe. But I would say the highest growth that we're seeing is really coming from the Middle East, APJI regions. I think a big part of that is the rapid adoption of modern channels within those areas, seeing about a 25% to 30% higher rate of adoption for modern channels within those within those geographies and a higher expectation of consumers to be served in an omnichannel type of way. Ragy walked through the 43,000 ideal customer profile accounts that we've identified. I want to take a moment to talk a bit about the go-to-market strategy that we're applying against all of these. So we started clearly in what we call Vector 1, the Global 2000. Starting last year, we made a concerted attempt to move into Vector 2. Now while these companies are $250 million to $1 billion, they don't have the size and scale of where we started. These are the future Ubers. These are the future Airbnbs. Ragy gave you an example. I'll give you another one, just to give you an idea for the opportunity we have in the Vector 2 segment. We have a customer that is paying us $2 million a year recurring subscription ARR that has about 2,500 employees. So you see there's a big opportunity here in Vector 2. Now Vector 3, we're not applying direct sales resources as we are in Vector 1 and Vector 2. Vector 1 is where we started, field-based sales, calling on the biggest companies in the world. Insight sales approach for Vector 2. We don't have a concerted sales approach to Vector 3 nor do we have a concerted partner approach there. What we are doing is the self-service capabilities. So Pavitar talked about, we started with the most complex in the world. How do we simplify that so that high-growth companies, where you're not applying direct sales resources can actually come in and adopt your products, configure them, set themselves up. But what we're finding, and there's a reason this goes all the way up the top is Vector 2 and Vector 1 customers are also coming in. It's removing friction in the sales process. They're able to trial our products before we formally engage. So of the 43,000, they really fall into 12 verticals. There's a few that we've already started on our journey of operationalizing and we're going to continue this process. But it's under the theme of making it easier to sell that you've heard us talk about. What does that mean? Sales teams having kind of a major and a minor in the vertical approach of which accounts they're assigned to both customers and prospects, creating marketing materials, aligned to these vertical stories, enablement for both direct sellers and partners, product and packaging around defined use cases. So for instance, if I am a Bank of Montreal and I've got financial advisers that need to go out and prospect for clients. How do I do that in a compliant way in a heavily regulated environment like financial services. Have that bundle of products, it's all the products we talked about bundled in a way that solves that verticalized use case. If I'm a Roche, I'm in the pharmaceutical industry. How do I leverage the set of products in a bundled use case that helps me go identify and engage on adverse events that I'm looking at across my patient base, my customer base. Or if I'm the government of Qatar and I want to provide a better citizen experience and use the same kind of technology that the Samsungs of the world use, but I want to turn that towards my citizens to save money, treat them in an omnichannel way, how do we bundle that up in an offering for governments. And finally, we've established a value realization framework. Pavitar gave you some examples, business case-based presales and did we deliver on what we said we were going to do in a post-sales value realization framework that Ragy touched on in our CDAP process. So let me take you through how we've evolved through the years in the personas we engage and the entry points that we've kind of traditionally had. So started obviously, as a way for big companies to publish and engage across this huge amount of growing social channels. And then we started this ability to not only capture that social data, but expand that out with at the time was listening, but it's expanded well beyond just social listening, right? It was review sites, it was blog post, it was contacts in our transcripts. And then how do we take that information and route it to the right people in the company, whether it was a product team, whether it was a set of agents in the contact center that we're assigned for social customer care or, in some cases, legal and compliance. We had to start building that capability. And these were the early days of AI for us. Just to kind of connect the dots on Pavitar's 2016 conversation he talked about, the volume and velocity that was coming forced us to say, we have to figure out what's engageable and non-engageable. That was the first problem. What's nonsense and what can we actually do something with? What's the sentiment? What's the intent? You have to start building the vertical side of this because if I work at Red Bull or a gaming company, and I hear the word sick, that's a good thing or so I'm told. If I work for a restaurant, fast food, I hear the word sick. That's not such a good thing. Financial services return, good thing, retail return, not such a great thing, okay. Once we started capturing this feedback, we also figured out, you can use it to create content that you're going to put out in the marketing and take it the last mile and actually advertise and increase your return on ad spend all the way through to the end, which is when we started engaging with the CMO. I mentioned the routing. I mentioned the AI. Pavitar and Ragy talked about this. You're doing this for me over here. You've got 50 agents in the corner doing social customer care. You got a great interface, the AIs on point. You're able to get things to the right people. Can you take over chat for me? Can you take over SMS? Can you take over e-mail. Fast forward, we're now at the point of voice dealing with COOs or kind of Head of Contacts in our operations. And where we are today because we've reached the point where you've got multiple front office teams on Sprinklr, you got data flowing across here. You got customers paying us $10 million by the nature of the size of the relationships and the strategic side of things we're now dealing with the C-suite. And let me walk you through a couple of real-world examples here. I'm going to take you on a few customer journeys, just to give you an idea. Top 3 technology company started with us with a small geo-based social team. We then moved into Sprinklr Insights, kind of the early days of Insights, but this is also one of the customers that we started tuning our AI around because they had such a volume coming in about themselves, about their competitors, about their products, pricing strategies. They had 100 people whose job it was to read this information, tag it and assign it out. They said, that does not seem efficient. How can we use AI to solve that problem. Those people were redeployed to more strategic roles. Just here, we took out about 10-point tools. Ragy talked about where we expand new users, new geos, business units and, of course, new products. You start to see in year 5 and 6, a few new products coming in, big jump in users. I'll fast forward here. This is now a $15 million a year. Everything you're going to see here is ARR, subscription ARR. $15 million a year ARR, $60 million total contract value customer for us. We're not even in the main part of the contact center yet. Mark my words, we will be, but we're not today. Just to give you an idea of the payload in the core set of the products. Another one, beverage company, top 4 beverage company started with a little bigger relationship about 5 years ago. The CDO here mentioned that she was spending $1.5 billion with a [ B ] per year in what she called the content supply chain. 100-plus countries. Each one has their own agency, getting reporting back in different formats, PowerPoint, Excel, had no idea where to fund, where to spend money. We've now got an 8x relationship from where we had from the beginning here with a goal of having a unified kind of marketing operating system, paid owned, earned, all in one place. I now know where to allocate my spending on a significant amount of spend. Last one I'll share with you. Top 3 beauty cosmetics company actually started with Insights. This company identified through the Insight Sprinklr was bringing in that there was a niche out there that they could exploit for products that had to do with travel specifically. People complaining about dry skin on airplanes, right? Got time to market on a product targeted at travelers using Sprinklr Insights. Fast forward CDO said, my goal is that every consumer that wants to engage with us, we will engage at 100%. So they started using Sprinklr to drive that with the end goal in mind of where we are today, it's about a 20x journey is 25,000 beauty consultants that used to be in the store are now able to do this in a digital way as a key part of their direct to consumer strategy. So this is what we've done on the upsell side, what I'm driving, what our goal is with my team is, how do we now scale that out in a way that makes us highly efficient with the theme of making it easier to sell. So let's talk about 3 things that we're doing here to drive that easier to sell and scale this out. So the first one is we had such a focus and such an ability to upsell within our installed base, we haven't had people solely dedicated to just bringing more folks in so we can create that virtuous cycle. So dedicated new logo sales team, Vector 1, Vector 2, sole purpose of acquiring new customers. Second one is to build the muscle within the go-to-market teams and post-sale as well of specialists within the contact center space. So these -- their role here is to partner with their counterparts in the field across all of our geos in helping engage in that world of the contact center. I'm pleased to say we're ahead of schedule here. We've got the team in place, and we're already engaging here. So having an impact for us. Finally, we need to mobilize our partners to act as a demand engine. Partner program in the past was a little ad hock and informal. I'll walk you through here in the next slide. what the partner ecosystem looks like and how we're doing this. But the goal here is to have a very clear 3-way value exchange. How it helps us, how it helps our partners and of course, how it helps our customers. The goal though is if you get this right, partners become a lead engine for you and a pipeline engine for you. High level on the partner ecosystem, your strategic GSIs. This becomes critically important in the contact center, but we've been working with the Accenture and Deloitte well before we went full CCaaS as well. But you see we're expanding that to those where they have the ability to resell Sprinklr. In some cases, there's partners that prefer a referral model. And then there's, of course, those that want the ability to do the services for Phase I, Phase II, Phase III. The hyperscalers, we've had long-term relationships with. We use a lot of data, the platform uses a lot of data, great for consumption in the hyperscalers. We've got technology partners here where our application, in some cases, may have some pieces embedded like OpenAI, more common. We're living side by side with some of these other platforms. Data is flowing back and forth. We work together strategically on opportunities. Global agency holding companies, massive for us in marketing in advertising. Once again, a bit informal in the past. We have some partnerships coming here that we're super excited about. And then finally, the channels themselves. So a lot of co-development happening here, working with the channels to not only support multiple customers, but also in an innovation type of mode of what more we can do together with a big emphasis in a lot of these, not only in kind of the traditional advertising, which is where they get a huge amount of the revenues, but a lot of interest here in what can happen in the world of customer service and the contact center as well. So don't take my word for it. I wanted to take a moment now to introduce our customer panel. And Eric, would you do that? Or shall I do that? I'll take it away. All right. Let's have -- who's coming up. There he is, Arun. Arun, come up and please introduce our panel, right?

I am Arun Pattabhiraman, [indiscernible] at Sprinklr. At Sprinklr, we have the privilege of helping some of the world's most iconic brands, deliver extraordinary customer experiences, working across every major customer-facing function. And oftentimes, these experiences are delivered not just through technology, but also through the efforts of passionate leaders who are working behind the scenes to make these projects come to life. One such iconic brand that we've had the privilege of working with for several years now is Google. And today, we have Alena Johnson, Head of Community and Social Operations at Google's users and devices and services teams here with us to share with us the journey that Google has had with Sprinklr May I welcome Alena on stage, please.

Good to see you.

Good to see you. So Alena, maybe we can kick it off by you giving a quick introduction about yourself, the role that you play at Google and some of the biggest challenges and priorities that you have at the top of your agenda.

Sure. So as you mentioned, I lead the devices and services, social and community operations team at Google. And really, our goal is to create radically helpful experiences for our customers on social and community channels. And when we say social, and we've heard it a lot today, we're not just talking about one thing, right? There are multiple Google brands. We support customers in multiple languages. And we're really everywhere our customers are. So that amounts to hundreds of different touch points in which we're interacting with our customers. And really, our priorities are on all of those touch points to create very, very high-quality, customer-focused, efficient experiences and also then to be the voice of the customer to our partners. So really for us, when we think about IT priorities, Obviously, social analytics, social listening, AI, ML are extremely important to us in order to be able to deliver upon those goals.

Great. And I know Google has been working with Sprinklr for over 9 years now, and it all started with Google leveraging Sprinklr as the de facto social media management platform across the company. But over the years, I think the number of use cases that Google uses us for has exploded. And today, YouTube, Google Cloud and even the gUP team, which is Google users and partners team has been using Sprinklr. Could you share some insights on how the use cases have evolved over the years as you started exploring the Sprinklr platform?

Yes. So our partnership in 2014. So we go way back, 9 years is a long time, right? All outbound publishing is done through Sprinklr today at Google. And even some of our largest divisions are using Sprinklr for social support which is really exciting. Today, my team specifically uses Sprinklr -- first Sprinklr Insights, social listening, a key value for us there is real time and being able to see all of conversation across those different touch points and aggregate it in such a way that we can deliver the de facto voice of the customer to our partners. And it's just been in terms of the evolution, right? We didn't start where we are right now. I think Paul took us through kind of what some customer journeys look like with Sprinklr. And it's been really similar for us and just continued to grow year-over-year. And really, we see Sprinklr as a strategic partner for us when it comes to social.

Great. And Google is a technology behemoth, and you guys power some of the most popular apps and websites in the world. What made you pick Sprinklr over some of the other alternatives that were available in the market?

So Sprinklr is super strong. There are a number of things that set it apart. I'll talk first about the AI because that's really where my team is thinking in right now. But it's very difficult sometimes to get that real-time data, right? And you're only as good as quickly as you can respond to your customers, and that's really where that high-quality efficiency and customer-focused operations comes in for us. And so being able to see everything in real time and maybe I'm nerding out a little bit here, but a very beautiful way through data visualization in a way that really connects with our product teams when we're sharing that information with what our customers are saying, real-time customer feedback. That's super powerful and makes my job, my team's job much easier. I'll also say when it comes to AI and different automation that we're able to do through Sprinklr, some of our data, and I would say a good amount of our inbound data can oftentimes be span, especially for teams like YouTube. And so what we had previously done in the past was manually triaging all of that data. And now with Sprinklr through automation, we're able to do that extremely quickly, which helps to improve service levels, improve customer satisfaction. We can reach more customers that need us highly, highly powerful stuff through Sprinklr and I'll say lastly, incredibly important to us is security, being able to meet all security, data privacy, compliance needs and Sprinklr reaches our very high bar that we have with that, such that we're able to even form deeper integrations with our first-party customer care tools, and that allows us to have an omnichannel cohesive customer experience, which, at the end of the day, that is one of the most powerful things that you can do in customer operations.

Fantastic. And I know I'm speaking to a leader in AI, but how does AI fit into your overall business strategy? And specifically, how are you leveraging Sprinklr AI to bring that vision to life?

So AI has always been really important to our business as it's been important to yours, so we share that value. For us, all of our different teams within Google, whether you're in marketing, support, analytics, we are leveraging AI today and continuing to look at new use cases like through Sprinklr Insights, Sprinklr Marketing, Sprinklr Service, they are all different ways that we're using AI, whether it's through those real-time insights and using MLPs or even through that automation and triaging.

Great. And shifting gears to customer service. For a massive brand like Google, you're serving hundreds and thousands of customers across different geographies, different channels and in different languages. How is Google currently leveraging Sprinklr service for customer service and social customer support?

Yes. So as I mentioned, some of our largest divisions at Google are using Sprinklr for social support today. One is our users and partners team. They recently moved over to Sprinklr and they're interacting with customers in 13 different languages. And just within the past 6 months, there's a 10% increase in agent productivity through things like Sprinklr AI and automation. So early days with that new team coming on to Sprinklr service, but it's been very encouraging, highly powerful and definitely, our teams are looking to that in terms of those results.

Great. And I know you're a power user of all 4 product suites of Sprinklr, Sprinklr Insights, Marketing, Service and Social. Where do you think our partnership is headed.

So I mean it kind of goes back to that question of how have we evolved, right? Like I think the key thing within that is like we've continued to be strategic business partners together when we're looking at social support, marketing, analytics, operations. And so I see that continuing, right, having that strategic partnership together. But I'll also say, like, as a leader within social, social is constantly changing. The fact that Sprinklr is able to keep pace proactively with where social is moving and thus able to meet us where we need to be in order to interact with our customers, it's only going to help us reach more customers together. So, yes.

Well, thank you so much, Alena, for sharing the Sprinklr Google journey with all of us today. And thank you so much for taking the time out and coming out to New York and to be with us here today. Thank you.

Thanks, Arun.

So the next customer is actually a large retailer from the Middle East and African region. Unfortunately, Ali from Alshaya, who leads customer service operations at Alshaya was not able to make it in person because of visa issues, but he was kind enough to help me record an interview with him last week. I want to play that story here on this forum. Just to give you a quick view of how Sprinklr service is transforming the contact center operations at Alshaya, which like I said, is a leading retailer brand operating over 3,000 stores across 40-plus brands in Middle East and Africa. So may I request for the video to be played, Eric? So first of all, Ali, thank you so much for doing this for us and sharing your journey with Sprinklr with the audience here today. You represent Alshaya, one of the biggest retailers and brand franchise operators in Middle East and Africa. But for the benefit of this audience, could you introduce your company, your role and some of the biggest challenges that you were looking to solve when you pick Sprinklr.

Sure. It's my pleasure. So Alshaya is, like I said, a retailer. We offer Franco franchises here in Middle East and North Africa. We have the 60 brands, international brands, there from state from the U.K. global brands across several different sectors, apparel, wellness, beauty, hospitality, so various different brands across different sectors operating in this region here. We've also got in other regions outside of the MENA region, but I guess, I look after MENA region. What I'm responsible for is the customer service to across this region for all of our brands across all of our markets.

Great. And I know you were using one of the biggest names in the CCaaS space before you migrate it to the Sprinklr platform. And I know that Alshaya, as you articulated is a pretty big brand in the region, operating over 3,000-plus stores and probably 40-plus brands. What's your experience been when it comes to Sprinklr helping you manage the scale and complexity.

Sure. We went for a proper RFP process where we had 7 different vendors presenting to us the solutions. Also, we're gaging problem statement. They came back presented to us potential solutions for us as an organization. Sprinklr stood out because of all of the AI they use because of what we were able to see the transformation which our agents and most importantly as well, our customers will see based on the interface, which you have based on the system capabilities. We were very much a fragmented systems which we use. So Voice was on a platform. LiveChat was a different vendor. Ticketing was on a different vendor as well. The agent -- it was very painful for our agents in our contact centers to be able to respond to a customer. So we were doing it. But again, they have to use 15 different systems, which were integrated into one overall platform. So if you were to phone us room to complain or have a query about your order, we would have to look into a certain system to be able to respond to that, too. One agent would have had up to 15 different log-ins for different systems to be able to just respond to a customer. So what Sprinklr was able to do is integrate all of these existing systems into one with one single interface for our agents to use. So it's the huge figure for us as an organization for me and my department is to improve the life of our colleagues and our customers, making it an effortless journey for them as well. And this is what Sprinklr was able to do for us. They were able to unify all of our systems using the latest technology, using advanced technology and also the team that were presenting to us, we're very flexible as well. Whenever we had a challenge or whenever we had a concern, they were able to come back with a solution that really worked well for us as an organization.

Now one of the things that every business is scoping with is the disruption that is being caused by AI. And AI has become an integral part of every business strategy and customer service operations is no exception. So just curious to understand how AI fits into Alshaya overall strategy and your experience working with Sprinklrs AI platform to solve some of the business problems you just articulated.

Yes. So it's the word on the street at the moment as in AI. So everybody is talking about wherever you read online mentioning AI, et cetera. So what we've seen so far with Sprinklr, it's been really positive. We're able to quality check automatically the calls that our agents are having to the ability to turn a disgruntled customer into a -- from a demerit to a promoter. And you can see that on the call for the use of AI. So on the recording, you can see where it was red, turning into amber, turning to green at the end of the call. What it's also going to enable us to do the agent assist as well. So assisting our agents with potentially ask this question or that question, et cetera. So we like even easier reducing the call duration as well. And most importantly, delighting our customers. So it's been very positive safe. The more we build on that base, easier and better is going to get for our customers and our colleagues.

You recently implemented 6 to 8 weeks ago, the Sprinklr platform. Any early metrics you can share around business impact that you're seeing or any feedback you're hearing from your employees or customers after migrating to the new platform.

Yes. So quick wins has been the average handle time. That's reduced. Pre-Sprinklr, it was at 4.5 minutes. It's just under 4 minutes at the moment. And that's not the full -- we haven't rolled out the full packages that we've taken from Sprinklr. So they're still in development, and we're adding them on as and when they're ready. So or we've got a great road map, and that's at the moment, being implemented in chunks. So we've seen good improvements so far in the channel time. Also, the ease of use for our colleagues has been notable. So they are much happy as we are speaking to them on the contact center. You asked the feedback has been very positive. How we are finding it again, easy to use would make their lives easier. We've made them happier as well because they're not having to go into multiple systems, do things manually, customers have also been delighted as well actually, when we speak to certain customers and our feedback at the end of the call, we've seen that they're pleasantly surprised that we're able now to identify who they are. We're not having to ask them for their order number, and we're not having to manually input several different pieces of information. Obviously, at the end of our call, we mentioned the voice of the customer or the NPS. With that seen so far, steady improvements as well, which has been a real eye opener and great to see. So far, it's been positive.

That's fantastic. And Ali, I understand that your vision in your role at Alshaya is to be able to craft effortless agent and customer experience. And you've just started the journey with Sprinklr. So I'm just curious to understand if you could share what the road map looks like, what the future looks like in terms of expanding the use cases on the Sprinklr platform to be able to bring this vision to life.

Sure. So like you said, what we wanted to do with the platform is to empower the clinics in customer service and other functions because we're not only using Sprinklr or the platform for customer service, we're going give -- we've already given access to our colleagues and logistics to our colleagues in finance because when a customer turns in, they're not only turning in to ask about where is my order, they are asking different questions which relate to different functions in the organization. We've given them more access to make ease of use. We're able to track. So they get access onto the platform. We're no longer using e-mails or excel sheets and send the ticket to the customer -- sorry, to the colleague in logistics colleagues in e-com operations is all via this one platform, we're able to track it, ensure it's an SLA. So again, it's delivering that customer apps across all touch points would ease. So what we wanted is omnichannel touch this operating model to leverage all of our customer interactions and drive customer advocacy at scale as well using a much more intelligent relationship management system. We're also going to be embracing the world of messaging and chat. So next, what's coming is the chat bots. We've recently launched telephony on to chats, but also chat bots is what's coming down the line in the road map as well. Again, more -- what we also want to offer our customers is really easy, intuitive self-service and automation services as well. So they're not having to phone or come to us for a query. They can log into our website and able to get answers to their queries with ease, be on our apps, be on our website. also embracing more social media channels to offer help and support as well. And again, most importantly, is that consistent and outstanding experience for all of our customer service channels as well. And then given our colleagues the full complete view of customer interactions and customer or community engagement all into one platform as well.

I must say that it's a very powerful vision and we hope to be able to partner with you very closely to co-innovate and bring that vision to life for all your customers, including your agents and internal employees. Thank you so much, Ali, for sharing that journey with everyone here today, and we really appreciate the opportunity of giving us to transform customer experience at Alshaya, and we look forward to growing this partnership and bringing your powerful vision to life.

Thank you. I look forward to what's yet to come for colleagues and customers.

Thank you. Well, with that, I think I'd like to call upon our Chief Financial Officer; Manish Sarin, perhaps for the most awaited session of the morning this today. So please welcome Manish on stage.

Good morning, everybody. I can already see the anticipation in all of you. Walk us through some numbers and I will. But quite honestly, how amazing was it to hear from Ragy, the vision around unified customer experience management. And I think Pavitar's demos around how AI underpins our entire product portfolio, the entire platform is really compelling. Paul walked us through the journey for a lot of customers as they started small and over a period of time through multiple products, multiple users, multiple geographies, multiple business units. They are big accounts for us now. But the place I'd like to start with is consistent execution for us ever since we went public. And I think that might have been lost in a little bit of the noise over the last few years. It's going to help us level set what we have done since we went public. And it's also going to provide me a framework to come back and talk about what this business can do over the next few years which I'll capture in our long-term financial model towards the end of my presentation. So with that, let's focus on execution in the next couple of slides. So as I said earlier, we've really been focused on execution as a frame of reference when we went public, subscription revenue was growing 22%. Every year since we've grown faster, last year's subscription revenue grew 28%, and that was a compound annual growth rate of 25% over the last 3 years. I've said repeatedly in all our earnings calls, that the 2 key metrics that provide you an indicator of what is happening in the business, our RPO and CRPO. And if you look at both of those metrics, they have shown consistent, strong performance over the last 3 years. Total RPO growing 32% and CRPO growing 28% as a CAGR over the last 3 years. And what a difference a year makes? So not only have we been focused on top line execution, we've also been maniacally focused on improving the bottom line. And this was well before it became a fad on Wall Street. We knew we had to deliver compelling performance on both counts, top line and bottom line. So if you look at non-GAAP operating income, that improved by $42 million, going from negative $36 million in FY '22 to positive $6 million last year. The turnaround in free cash flow was even more dramatic. We went from negative $45 million in FY '22, an improvement of $55 million going to positive $10 million last year. But let me quickly remind you, we also had a onetime litigation settlement last year for $12 million in cash, which is included in these numbers. So said differently, if I took out the onetime litigation settlement, free cash flow last year would have been $22 million, almost 4% of revenues, and that would have been an improvement of $67 million just in the span of 1 year. So now that I've been here a little over a year, and I've had the pleasure of meeting many of our investors. I've spoken to a lot of people on Wall Street who cover us. And there have been lots of questions around what drives the business. And I will cover some of those in the next few slides. But I wanted to be clear that this is a onetime disclosure, we will not be updating this either quarterly or annually, but I will go through that here in a bit. But the question that everybody has been asking is, can you break out your revenue by the product suites? So let's start there and because you asked, here it is. So let me first take a minute explaining how this is constructed. Our Insights product is built on top of social listening. So for ease of reference, we've compiled the 2 together here. So this includes Social and Insights as one revenue bucket, then Marketing and then Service at the very top over the last 3 years. Three key takeaways as you stare at these numbers. First, Sprinklr Service, which is our unified CCaaS product was over $100 million in subscription revenues in FY '23. So let me make sure I say it one more time because I had to run my eyes as well once we did the numbers. Sprinklr Service is over $100 million in subscription revenues in FY '23, growing at a CAGR of 50% over the last 3 years. In a different market environment, different set of circumstances, Sprinklr Service would be a separate publicly listed company. Second takeaway. All product suites are growing at a very healthy rate. I bring this out because many times, investors sort of have some concerns that is it the case that one product suite is making up for declining growth or tepid growth in another product suite. And that isn't the case here. And thirdly, Social and Insights combined, $332 million in subscription revenues last year, 60% of our overall revenue stream in terms of subscription revenues and growing at a very healthy 19%. And I want to bring this out because we've been an innovator in this market. We've been in it for the last 12 years, and we still see incredible opportunity to grow that piece of business. So the next question that I would usually get is you're looking to build a multibillion dollar business, you would need to add more logos into the machine. Paul earlier on covered our ideal customer profile, 43,000 accounts that we can potentially go after, how we have bucketed them in Vector 1 and Vector 2. So let me give you a little bit more color around new logo acquisition. So we added 231 new logos in FY '20. That grew at a CAGR of 18%. And last year, we added 377 new logos. But what is remarkable is, as Paul was saying, 60% of the new logos that we added last year were with Vector 2. These are mid-market accounts that we believe have the potential to be multimillion dollar revenue opportunities for us. And I will cover that here in a little bit. So now adding new logos is fine. But what does that mean in terms of new sales of product suites. So let's take a look there. When you couple new logo acquisition with an ability to sell multiple products in the initial sale that is very compelling. So let's look at the data. This is for the year FY '23. Let's walk through the pie chart, 16% of the time in the initial sale, we were selling 1 product suite. 49% of the time, we were selling 2 product suites 27% of the time, 3 suites and 8% of the time, all 4 product suites. So said differently, 84% of the time in the initial sale we were successful in selling more than 1 product suite. Now you might ask, why is that relevant? It's relevant other than the obvious reason that it increases the ARR per account. 2 big takeaways. Firstly, it increases the stickiness of the customer because they are now buying multiple products from Sprinklr. But I think even more importantly, once customers begin to standardize on the Sprinklr platform, they get more and more comfortable into buying multiple products. Pavitar walked you through more than 30 different products that sit under these 4 product suites. And they then begin to buy into Ragy's vision that there is a unique unified customer experience management opportunity and we are obviously the beneficiary of that over a period of time. So we covered new logo acquisition motion, which has picked up steam. And obviously, with the dedicated new logo team, we expected to pick up even more steam in future years. We covered how we are able to sell multiple products in the initial sale. How about the rest of the installed base. So let's take a look. So as you look at -- this is directly from our public filings, snapshot in time at the end of FY '20, we had 953 total customers, which grew at a CAGR of 14% to 1,428 at the end of last year. But what's more compelling is look at the number of customers that now have all 4 product suites. That went from 105 to 228 , that is a CAGR of 30%. And what that tells you is, again, not only are we successful in selling multiple suites in the initial sale. Once these customers are part of the Sprinklr family, we have become adept at selling them more products during this period of time. So they keep buying more. And that's how if you look at how we disclose earnings, we always talk about almost 2/3 of new business comes from selling into the installed base, and that is consistent quarter-over-quarter. But the next question would be, well, how about the customers who buy only 1 product suite? Let's take a look at that. So that has remained rather consistent over the last 3 years. So again, 152 customers at the end of FY '20 to 143 now. So these are customers that have only 1 product suite. Now of course, it isn't the same customer. We've been obviously adding new customers that have bought 1 product suite. And the prior years, as customers bought 1, being part of us, we've managed to upsell them more during this period of time. But as we dug into this, there is an even more interesting story to tell around customers that have only 1 product suite. So let's take a look. So let me walk you through this data. So as we look at customers that have only 1 product suite in FY '20, 65% of those customers that had only 1 product suite had bought Social or Insights. 19% had Service. If I fast forward 3 years, look at the proportion of those customers that have Sprinklr Service. And what this is telling you is we are becoming successful at starting that customer in our unified CCaaS platform. As we have developed a much broader product set in unified CCaaS, we are getting invited to the table for a lot of these RFPs that wasn't the case in years gone past. So again, just to wrap up the customer journey, accelerating new logo acquisition, the ability to sell multiple products in the initial sale a demonstrated ability to upsell accounts once they're part of the Sprinklr family. And now it demonstrated ability to sell starting with Sprinklr Service before we are able to sell more products into that account. So now let me break this out in a slightly different fashion, which I think will capture what Paul was referring to earlier. Sprinklr has always been known as a company that caters to the high end of the market, premium customers. And that is a -- that's a true statement. But I think we'd be erroneous in drawing a conclusion that for us to get meaningful revenue from an account, they need to be a Fortune 500 customer. So let me walk you through what this data is showing. At the end of FY '20, and this is publicly reported, but not the breakout, we had 49 customers that paid us over $1 million in revenue. If you break that 49, 29 of those were Fortune 500 companies and only 20 of them were non-Fortune 500 companies. But all 49 of them were paying us more than $1 million in revenue. Fast forward 3 years. The Fortune 500 number has obviously grown. So at the end of last year, we had 43 Fortune 500 companies that paid us over $1 million. Now to be very clear, we have more than 180 Fortune 500 accounts. So over the next several years, I fully expect many of those would start showing up in the 1 million-plus cohorts. But what is even more compelling is look at what has happened to the non-Fortune 500 accounts. The ones that are meaningful revenue contributors for us, that has grown at a CAGR of 48%. So at the end of last year, the equation had flipped. There were more known Fortune 500 companies that were paying us more than $1 million in revenue. And I think this goes back to the point that Paul was alluding to earlier, which is as we have really accelerated our new logo acquisition in Vector 2, a lot of those accounts have the opportunity to be massive revenue generators for us. Now this is obviously all on a customer count basis. If you ask me the question, what does this look like on an ARR basis? That 14% would look more like 22%, and the 48% would be more like 56%. So said differently, the same dynamic would hold true, which is non-Fortune 500 companies have become meaningful revenue contributors for us. I wanted to do a little thought experiment with all of you. So we said, okay, so that's great that we are looking at the 1 million-plus cohort. Let's look at our entire installed base. And let's assume for a minute, we get no new logo. What does that installed base opportunity look like? So let's take a look. So I'll walk you through what this slide is. In the column, you see the number of customers buy the product, who has 1 product suite, 2, 3 down to 4. So I'll go slowly. 143 accounts have 1 product suite. They, on average, pay us $100,000 in ARR. And that is a number in the pyramid. That's that number. All the way down to, as I walked you through 228 accounts that have all 4 product suites and pay us on an average $1.2 million. And again, just for that experiment sake, we said, what if we didn't add any new logos, and we were given our demonstrated ability to upsell existing accounts, looked at these 143 and managed to convert them as an example into 4 suite accounts. That would be an incremental ARR of $1.1 million, which is $1.2 million less than $100,000, times 143 gives you $158 million in ARR. And if you do that across every product suite going from 2 to 4 and 3 to 4, that gives you an incremental ARR opportunity of $1.2 billion just in the installed base. And I wanted to point this out because if I look at the existing ARR in the business, and it's roughly $650 million, we have the opportunity to get this business to be almost a $2 billion in ARR business without adding any new logos. Now of course, we all know that isn't going to be the case I've already walked you through how we are now accelerating our new logo acquisition. In that initial sale, we were able to sell multiple products right out of the gate. And even if we aren't, we then have a demonstrated ability to upsell those accounts in their journey with Sprinklr. So no matter which way you look at it, there is a tremendous opportunity here, both in terms of new accounts as well as the existing customers in the book of business. Before I go to the long-term financial model, I did want to reiterate our Q2 and FY '24 guidance. This is no different than what we gave out on June 5. So I won't go through the numbers, but I am reiterating guidance for Q2 and the full fiscal year. So let's look at our long-term financial model. Two sides of the equation. We look at both revenue drivers as well as efficiency drivers. The key things on both sides. First and foremost, platform and product expansion, particularly given the unified CCaaS offering we have. Second, as Paul was alluding to earlier, our ability to add new logos, and now we have a dedicated team doing it and obviously selling multiple products in the initial sale. And then thirdly, an ability to leverage the partner ecosystem. So Sprinklr historically has been a direct sales model. We didn't have much of a partner sort of channel working for us. And now we are putting a lot of muscle behind developing it not just for delivery of professional services, but also as a lead gen and revenue generation engine for us. On the efficiency side, if you look at our subscription gross margins, last quarter, they were 83%, which is best in class as you compare us to our peer group. And we feel very confident, given what Pavitar and his team have built, given the way we bring these products to market, the way we deploy it, that we have incremental opportunity as we gain more scale to improve our subscription gross margins even further. I've said on the last 2 earnings calls that our professional services business is also going to morph. I fully believe in the next couple of years, we will be shifting more and more to manage this more and more to CCaaS CCAs delivery, all of which carry higher margin. And thirdly, like we've shown over the last 4 to 6 quarters, we will continue to demonstrate even more leverage in sales and marketing. So with all of that said, here's the big reveal. So let's talk about subscription revenues. We debated internally and said FY '27, which is 3 years from now and 5 years from the IPO seems like the right time frame. What we are comfortable doing is, at this point, setting $1 billion in subscription revenue as floor for FY '27. Now that would compute to roughly an approximate 16% CAGR between now and then. And just so that we are clear, FY '27 is really calendar year 2026 that ends in January 2027. So it's really is 3 years from now. So what is baked into these numbers? What we have taken into account is the current environment persists. I have no way of projecting is there going to be a recession? Is there not going to be a recession, there is any number of pundits you find on CNBC talking about what's going to happen next. I didn't feel it was a right forum for me to make one of those statements. So we feel, given what we know right now, if the current environment persists, we are comfortable setting $1 billion in subscription revenue as floor. Said differently, should the environment improve, should sales cycles become shorter, all of that would be upside to these numbers. Now you'll notice I am not specifically talking about professional services revenue here. And I think that goes back to the earlier point that I made that we fully expect that professional services mix to morph quite considerably over the next few years. We believe it's going to become a lot more targeted, a lot of it driven by CCaaS delivery and managed services, but the quantum of it may not be very much different than where we are today. So I think if you look at the broader picture, it seems to be a much smaller proportion of overall revenues than where we are today. And it just felt like in terms of setting the stage subscription revenue is what we need to be measured against, and we are comfortable putting down $1 billion as the floor for subscription revenue 3 years from now. Now let's take a look at profitability and free cash flow. So before we dig in, I wanted to just make sure we all understand our rule of 40 is defined as revenue growth plus non-GAAP operating margin. So we've been very clear on that point, but I wanted to make sure all of us are on the same page. On a rule of 40, we were at 27% last year. And we feel fairly comfortable given where we are and all the improvements we are making in terms of operations, we should be a rule of 40 in 3 years' time. So back to what I said, at least $1 billion in subscription revenue and rule of 40 FY '27. Let's talk about non-GAAP free cash flow. I covered earlier last year, we were $22 million on an adjusted free cash flow basis, which is 4% of revenues. Again, we feel comfortable putting down that we would be in and around the 20% as a percentage of revenue for free cash flow. And just to make sure I'm even more clear, we're going to set a floor of $200 million in terms of free cash flow in FY '27. Now on all the earnings calls, I've been fairly clear that for us, free cash flow margin trails operating margin, partly because the billing duration for us has remained low. So that's what this assumes that free cash flow margin will trail operating margin, at least by a couple of points. Now having spoken about where -- what we're going to achieve from a top line perspective as well as where free cash flow and operating income will come out. The natural question becomes what do we do as we become free cash flow positive. So very quickly, as we look at capital allocation, first and foremost, we will always look to efficiently reinvest back in the business. Whether it's new product development, whether it's R&D, whether it's additional go-to-market initiatives, that for us is paramount because as Ragy said earlier, we see an immense opportunity in front of us, and we have a pole position in defining what this unified customer experience management market becomes. Secondly, we've done some tuck-in acquisitions in the past. And we are in an industry that is highly, highly fragmented. But again, we have a desire to be #1 or #2 in all of our markets. So again, for the right set of conditions, we might be open to strategic acquisitions. But again, to be very clear, we don't need any M&A to keep growing. There is plenty of opportunity, as I showed earlier, both in the installed base as well as in the market for us to keep growing. But for the right set of conditions, we'd be open to M&A. And thirdly would be opportunistic share repurchases. When the stock price was low a couple of quarters ago, investors would ask us if we would turn around and buyback stock. And we were always of the mindset that we wanted to increase float, so we could attract newer investors into the shares. As I look out over the next few years, as some of the pre-IPO investors begin to distribute their shares into the market, as we get more newer investors into the mix, we might be open to share repurchases, but it feels like the wrong set of things to do given the immense opportunity in front of us in growing the business. So all of that said, I know I'm the last speaker, so I'll try to quickly wrap it up and open for Q&A. So as you walk away today, I want you to walk away with 4 key takeaways. Takeaway #1. Sprinklr is a platform that is purpose-built for the enterprise, and we have shown and demonstrated success in land and expand go-to-market. Second, we are the fastest and most effective way to get AI capabilities across the front office. Thirdly, we have become a major disruptor in front office software and are now a key player in the CCaaS market. And finally, from here on out, we will be delivering durable revenue growth coupled with a rule of 40, which is increasing profitability and free cash flow in the medium term. So thank you. I know these were my prepared remarks. Thank you for spending your morning with us. Thank you for being a part of the journey as we build Sprinklr. With that, we're going to open it up for Q&A.

Okay. So we're going to start Q&A right now. We have 2 of our colleagues in the back, [Shayan] and Nicole who have microphones. So if you have a question, please raise your hand. Just state your name and fire away. We're going to update the stage with a few more chairs. Okay. Let's get things started. Parker, we'll go to Parker first, Nicole, if you can, right over here.

Yes. Parker Lane at Stifel. My question is the 43,000 logo opportunity you guys outlined, I was curious if you could talk about the share that comes from vectors 2 and vectors 3. And on vector 3 that's more product-led growth and self-service. What's the profile of the customer that would choose Sprinklr in that category today versus some of the more mid-market or SMB tools?

Okay. So vector 1 is large complex global companies. And vector 2 and 3, what we are trying to spot are companies that are likely to get there. And what we're finding is, especially in the contact center business, think about a smaller company, the $100 million, $200 million or $300 million, that has like 500 customer service agents because they are in the consumer business, even 250 agents. Once you cross 10, 20, 30, 40 agents, you tend to need all the sophistication that large contact centers have because you have to round robin calls. You have to -- agents begin to get like different skills, you need AI, you need efficiency in workforce management, and what we find is the sweet spot of the market, now that we're in the contact center that is not served very well by the older first generation companies that are out there who are whale hunting for bigger contact center ships. So we find that, if you're 10 people, you can use like a ticketing system in 1 channel, but once you get a certain amount of sophistication, you need the unified platform. But if you go try and do RFPs and put it together, you just -- you can't justify spending that much money on a system integrator and custom development. So the contact center opportunity is really what is allowing us to kind of come to a slightly different segment than from a marketing or our traditional insights business wouldn't be a great customer for us. The last thing I'd say is we're also trying to spot companies that are growing quickly. First, I'd say, 10 years because we were so focused on large companies, we didn't focus on companies that were fast growing. And so many of you have portfolio companies that are growing sometimes 50%, and these are the guys who are going to need the complexity and sophistication in solutions. And so we want to get them early. So you don't have to do a replacement and you can get started the right way. So that's what is driving this selection. The 43,000 is -- it's -- remember, we talked about it at IPO, we're beginning to get savvy with go-to-market. And I think in the last few years, the first time we have actually done a targetless identification, understood the vertical markets, the use cases, really got to like a named company list that allows us to create a foundation for good go-to-market operations.

Yes. Let's stay here. Matt.

Matt VanVliet from BTIG. I guess on the contact center side of things, how critical is replacing an existing voice deployment versus going in and sort of doing everything else around that. And then the mix of customers you have today, how many are using voice versus that being a big cross-sell opportunity?