Good morning, folks. We're just about to get started. If you could please take your seats. Okay, 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 can 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. Isn't it frustrating when you build stacks upon stacks of point solutions, after all that, your teams still aren't in sync, your customers still aren't as happy as they should be? Sure, today's customers demand a lot. 75% expect you to respond within 5 minutes, when they have a bad experience, 95% will tell the world about it. That's tough. You know what's tougher? The outdated way so many brands approach customer experience. About 50% of CX leaders report their customer data does not flow seamlessly from department to department, as a result, doesn't give their teams a critical, unified view of their customer. This leads to over $10 billion lost every year on overlapping SaaS and too many customers getting lost entirely. What if you could stop struggling in those silos and come together? Get all that data from all those channels inn a single, unified source of truth. Point solutions are not the solution. A unified platform is. Sprinklr is a true unified platform with everything your enterprise needs to learn about your customers, understand the marketplace, and drive business growth across 30+ channels, every major vertical, on 4 fully integrated product suites. AI-powered for the contextual insights you need in the moment you need it, for the most personalized customer experiences at scale. Make your customers and teams happier with the one true unified customer experience management platform, Sprinklr. Thank you. 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. Welcome to our first, very first Investor Day. You know, I think if you think about the best gifts that anybody can give anyone else, I think time and attention are the top two, at least in my mind. 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? How did we get here? 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 the music. I don't know. Can you? Okay, that's good. Sprinklr is the fastest way you can get AI across your front office. I'll tell you why that is important. Lastly, I wanna talk to you a little bit about the ethos of what Sprinklr is and what we believe in, you know, outside of what we're trying to build. Look, we came out as a public company in a very crowded, noisy market. 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. 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 got marketing, you got research and insights, you got sales and engagement. If you are a large company, let's say you're Microsoft, you're Samsung, and, you know, I don't know, how many contact centers do you think Microsoft has around the world? 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 this 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. You might think, well, you know, 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, most of them have a different CRM system. If you're at Diageo, with 200 brands in 185 countries, where you're spending money in every market for all of these things, everyone's doing their own thing. What is the problem when everyone's doing the whole thing? Do you agree that in the front office, you got to see who you're speaking to? Who is buying Xbox? It's the same guy who has an Office 365 subscription. If I move 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, my 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-$80 million on your contact center, maybe 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 the space. 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. I'll explain why a social media origin company is able to do this really well. I want you to understand that. We offer 13 different products inside the contact center. Everything from workforce management and knowledge base and community, to ticketing, to email, 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 five seats and do digital customer care." We replace close to 15,000 contact center agents, what they log into, and they're logging into six, seven platforms. I met the CEO of HDFC, look, he said this was one of the best things they had done. 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 well-being. 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 The Trade Desk does or Google does or Facebook does. We come in as an overlay layer. 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 markets, 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. The orchestration, versioning, translation, you need that. How else do I run a global campaign if I don't want to run it in 30 languages? Where does my global editorial calendar reside? That's in Sprinklr. We take this metadata, we propagate it into these individual channels, 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're 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, you know, 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 against surveys. I believe you have to understand the power of what people are talking about, you have to understand and translate that into insights, 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, most of them are becoming online and connected, you 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, that's driving business strategy for large companies. If you're Samsung, you're learning what features should I highlight in my advertisement? You're also learning what's wrong with my microwave or my laptop. Does it make sense? That's how it connects back. We got a 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. There are 30 products between these, you can buy a suite. You can start with the product. Survey says 13, marketing is 3, insights got 7, and social has 8. 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 it's going to take to build a giant in this space. Unified data model, a truly resilient omni-channel architecture. People say omni-channel, they confuse it with multi-channel. If you support email, and you support chat because you acquired 2 companies, great! You support 2 channels. If someone goes from email to chat, you can't connect the 2 without asking the customer for the context again. That, in 2023, I think, is kind of super lame. When you create an AI model to understand an adverse event, you should use the same thing in a contact center, shouldn't you? Should you have to deploy buy technology again? Makes no sense. You got to have the UI built in a way that to add a new product practically, but we're cranking out products, you know, in 3 months. We took on the contact center space that's 14 years old, and we did it in 3 and a half. 13 years of building a platform. This was built as a platform. 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 three companies before this. They were in email marketing. In late 1999, in 2000, I was building email marketing technology. 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. You have a stack of mail. You sort through it. They'd be coupons, they'd be offers, they'd be statements, right? You don't get that much mail to Valpak, all of that. Businesses have gone out of, like, completely out of business. Industry could collapse when email came by. I remember I used to go to CMOs and say, "Take your direct mail budget, move it to email." They would say to me, "It's too personal." We all had Hotmail and AOL addresses. I would say, "No, you can see consumers are going there." That's the insight that led me to start Sprinklr. Put my money, fund it myself. That's where the confidence coming from, because at one point, when I left Epsilon Interactive, we were the largest email marketing company in the world. I'm a product person. I was the CTO of my first 3 startups, okay? We wrote code. I used to drive down to Weehawken, 3 A.M. in the morning, and swap hard disk. That's what backup and DR and commodity service meant. That was cloud back then. What we learned was, for email, you need campaign management, right? What we learned was email, you need to plan campaigns. Planning. You need asset management. You need the ability to publish. You need the ability to respond when people do. You get the idea. Then you needed automation, you needed reporting. I rebuilt that technology 4 times on our way to becoming the largest email company. 4 times. We took every bit of learning I had. That was version 1 of Sprinklr. We are in version 19. Along the way, I met my CTO, who was building application servers. 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 apps, so it was... We just really, the genesis of Sprinklr was from a beautiful place. Does it make sense? You, it doesn't matter whether you're Coca-Cola, you're Samsung, it doesn't matter whether you're bringing on 100 business units, it doesn't matter whether each business unit is operating 50 countries. The last problem I didn't solve in email was a single instance architecture. We solved that in version one of Sprinklr. You think a startup that can sell very well can take on Sprinklr? Think again. Think again. This was conceived as a platform, the insight was we started publishing in social media, we started responding, but all of those planning. This is why it took us three years to become the leader. Once we became the leader, it was just too hard to catch up, right? 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, we're sucking in petabytes of data. Man, 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, we added other channels because customers were saying to us, "Well, if I'm so efficient at WhatsApp, then why can I not do it in chat?" That's what we built. What you're seeing, sure enough, in the last several years, voice has gotten digitized. Telephony, which the Genesys, the Avaya, the Cisco spent so much time getting good at, is now a commodity. A company like us, that build the app stack with the AI, with the console, supervisor consoles, quality assurance, AI, routing, everything, digitize voice, translate, transcribe in real time, apply the same logic, boom, you're in the contact center. You're not doing 300 agents in the corner. You're taking on the motherload when we added voice. 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. 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 Gonna have a couple of them talk today. Our insights product, we're gonna add surveys, so that's gonna 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're 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. You know, we work with Microsoft, Google, and AWS, right? That's where. Our social will evolve to become sales and engagement. Does that make sense? That's what we're trying to build, okay? There's one 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 email? Right? This is after the fact. It's relational data. 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. Data storage 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? That is what we call as external, that's CXM data. We are the progression. You can't use it. You don't own it. You don't need to own it to use it. 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 hardcore. Does it make sense? Right, so there are three core differentiators that we have. First, it's the fact that we are a platform, truly omni-channel, centralized governance, unified front office architecture, all of that, but one single instance platform with all these capabilities. Two, 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. There are companies that are AI-based and companies who are not, and you will learn why Sprinklr is gonna win. AI is about three things, guys. It's not an OpenAI integration. AI is about, A, models. Do you have engineers who can create, optimize, and tweak models? 2, do you have data that you can train it on? We've been training for five years. At some point, we had hundreds of consultants annotating and training. 3 is the feedback annotation, feedback loop. When you're 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, the feedback goes into Sprinklr right back and makes the AI better. Lastly, as an enterprise company, we sell to the largest global companies who know how to buy enterprise technology from the very beginning. Security audit, there are people from banks here. 13 of the 14 largest banks are our customers. Pharmaceutical companies, CPG companies, 7 of the 8 largest CPGs companies are our customers. All 10 of the 10 biggest brands in the world, according to Forbes, are Sprinklr customers. About 85 of the top 100 are. We focus on revenue. We start with increasing, quantifying growth, taking out costs, managing risk, that's how these customers renew. There's a growth fiber. You can start with 1 product, but then you learn, and I'm gonna tell you what the Sprinklr philosophy ethos is, but you learn that we obsess about customers. I did 300 meetings last year. Every customer I meet, I give them my cell phone number, and I say, "Call me if you're ever upset. If you think we bullshitted you, call me. If you wanna buy, my team will take care of it." My CTO flies over. We have AK, one of my engineers, should be here. We've flown him with a 1-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? They learn it. We're not 100%. You know, we're hiring, sometimes it's not perfect, but we find out, we fix it. The ethos of this company is very, very, very different and very deep, and we focus on value. As you do that, and you put these workflows in place, you know. We have a cosmetic company in France. They go, "Oh, this is great. It's working amazing for France. Why isn't the UK doing it? Why is my other business unit...?" 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? On average, we release 300 to 600 features every quarter. Today, we have a product payload of over $25 million, maybe $25 million-$50 million of product payload we can sell somebody. Obviously, you're gonna 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 gonna take 2 quarters or 20, we'll be here until it's done. 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. We just extended down, but we're not going after small companies. You know, if we pick a customer, and we now have customers of $100 million in revenue, that's paying us north of $1 million. If you can, talk to SimpliSafe, one of our customers, and see the power of unification, how soon you need it. If you're not growing, you don't think you're gonna be a big company, you shouldn't buy Sprinklr. There are plenty of petty SMB solutions you can go buy. You don't really understand it or the problems we're solving. 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 roadmap depends on what customers like, then I think you need Sprinklr. I think these 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 on a life of its own. We have 12 verticals that are a priority, that make up 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. We're not the company that you look quarter over quarter, and go, "How did it do?" 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 wanna 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 wanna get AI to your agent in a point solution, right? Go buy an AI point solution. If you realize that, Whenever somebody complains about luggage, you need to understand the airport, and once you understand the airport, you need to the time. Is it at the origin, destination? There's a lot of AI that we've built for every industry. Once you do that, you go, "I wanna pick it up from a blog. I wanna get it in my contact center. I wanna know it in my marketing. I wanna 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 wanna get AI, and our AI is not... We didn't start six months ago. We have a lot of respect for OpenAI, amazing. Generative AI is gonna 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're able to achieve near human accuracies. Some of the best AI companies in the world are our customers. 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. We can prove it, and that's why these customers, some who have their own AI, choose to use the Sprinklr AI. Look, there are companies that have an OpenAI integration and have announced the AI strategy. There are companies that introduce an AI product, and there are companies who deeply embedded AI into how they think. I'm sure Google is here today, Tesla, there are a few companies that have just got it in their DNA, and we're one. This is what I would love to close with. You know, I was reading the book on Amazon, which is fantastic. People look at Jeff and go, "Well, he putters around before breakfast, and that's how we build a great company." Look at what they did when they were starting out. Look at the period, I don't know how many years this. People thought they would go bankrupt. That's what I want you to look at. I want you to look at the years where Tesla went almost bankrupt. I wanna look at the conviction in the team's eyes, collective eyes, when they go through that. When you think I'm building the biggest selection on the planet, cheapest prices on the planet, I'm an obsessive of our 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 gonna be the future, the evolution of what CRM is gonna be. CRM is gonna 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 connective 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 rattling and making some noise. Obsessing about customers is 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. We wanna change that. It's not very hard. You look at the giant enterprise software companies and the customers I talk to, you know, love is not the word that comes up with enterprise software, and we wanna change that. We wanna make it really personal and human. The vision is very clear. It's like we've plugged our vision into our GPS. We know where we are going, but we make a left turn here, a right turn here. It's driven by what customers say. We have a product development process. Start with three customers, make it work for them. They're happy. Roll it out to a few more before we roll 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. People pay because they, the product... This category is just emerging, and some of you have started lumping front office software together. Analysts, Gartner, and Forrester are beginning to do it. I think we're gonna do it sooner or later. I'd like to see that start now. For us, customer obsession starts when after the sale, right? Checking in, making sure they get value, they're consuming the software. We have a process. We don't do NPS. The reason we don't do NPS is we don't wanna know whether you'd recommend us. We ask you, every customer, every quarter, every month, like, 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 going, "Mommy, I don't know why you're bothering me with this stuff." Zero means you fired us, and we didn't know it. If you don't give us a 10, we ask you, "What are the three things we can fix?" Three things. We get to prioritize, goes into a product development process. Every week, regionally, it gets escalated. Early days of Sprinklr, I would say, there's only one meeting you cannot miss in Sprinklr. It's called CDAP. Well, you could miss it. 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 don't get upset at most things. Most of you know that I only curse out of excitement. 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. That's what we do. Happiness cannot be bought, but you can have the best day in your life and the worst day in your life, and someone can try to make you happier. That's the word, happier, for us. 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 gonna 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 to follow, Ragy. I'll try. Now, today I'm gonna talk about three things. First, I wanna expand a bit 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 seven years, and what we intend to do over the next few years. I want to talk about our unified CX platform. Now, I mean, when I met Ragy, almost 11.5 years back, these are the three things he told me: that, "Pavitar, in this platform, there are three things I'm expecting you to build natively. First is omnichannel," The reason omnichannel is important is. Let me take us through a journey. Remember when we go maybe 30, 40 years back, voice came, we were communicating using voice with each other, brands followed and started using that technology. They bought voice service software for contact service. They bought sales software to do voice. Email came along as a channel. Companies started providing email solutions for service, for marketing. Web came, live chat came as a channel, social came, when social came, it was 30 other channels. It was just not one channel. These channels will keep coming. Recent proof being Threads. You know, it took 5 days to reach 100 million users adoption, right? Almost every brand is adopting that channel, as we know of. Brands are gonna expect solutions in order to work on those channels. Now, you cannot keep up. You cannot keep up. We cannot have one new startup emerge for one new channel to solve one 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. 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 have tried to address. Second thing we wanted to fix in our platform and build 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. Guess what? Customers or brand do not see like that. "Hey, today, I'm gonna talk to marketing, or tomorrow I'm gonna talk to sales." They see one brand. They want to have one consistent brand experience across all your functions. You need one platform to do that. The third part was, when you're a large company, you're gonna operate in multiple geographies. Guess what? In every geography, you cannot buy one point solution for one channel. You need 100 different email technology just to do customer service. 100 email technologies just to do marketing out there. How are you gonna connect? How are you gonna share knowledge with each other? It's a mess, and that's what we wanted to address. When we say 14-year platform, right from day one, I remember when we started writing our initial code, I wrote some of our initial code. I was always, "We are gonna build a platform." There's 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 right. Now, let me take a deeper approach and let me spend a 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 email or Wise, it's just one 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 or any channel, it gets modeled as a universal message. If, as a brand, you're communicating back, that also is a universal message. You know, how many times we have received a marketing communication on email where if you try to reply, they say, "We are not watching this email ID, no reply." I said, "That doesn't make sense." You are 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. By default, we become omni-channel. 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. My rest of product lines are operating on my data model, and they seamlessly will work. Right. The advantages we get with this approach is natively, everything is one. All channels are one, all functions are one. It's a very simple secret sauce for us. We are natively integrated in our data model. 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. What this allows us to do is, it allows us to build once and deploy across every channel. 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're operating on my data model. They will be able to have one 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 one demo. We want to choose different channels for different kind of interactions, and that's a choice we should have as a 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 live chat." No! That's not how I am communicating with my family today. I would prefer to talk to you on the channels where I'm already there. We want to make sure Customer 360 is natively part of the platform. Let me quickly hit the demo. Here we have a bank as a customer. I am going on live chat as a customer. Our bots become a friend. 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 are now got you. We tell you the account balance. You say, "Hey, I want to know my last three transactions, but I want to know them on WhatsApp." We say, "Okay, perfectly fine. Let's do that." We ask you, "Which of your number we should reach out to you? You may have multiple numbers." You choose that. We 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 three transactions." What you observe is, I didn't make one of those transactions. I want to report that. Say, "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 call back. Perfect, we get a call. Here you see- Hi, my name is Tom, and I see that you've made three transactions. We have across channel history available to agent. Yes, I'm trying a new transaction. Sorry to hear that, sir. Let me raise a ticket for you. Before that, can you please confirm the last 4 digits of your account number? Yeah. It's 4, 6, 7, 5. Perfect. I'll raise a ticket for you, and shortly you will receive a confirmation. If everything checks out, the transaction will be reversed in the next three to four business days. Okay, sounds good. Thank you. Okay, thank you, and have a nice day. What we saw in this video was, a customer went through almost four different channels, right? They started on live chat, they shifted to WhatsApp, they went onto a phone, and then they received an email. When agent logged in, they got just one experience out there, right? They were able to do that seamlessly without integrating, without spending any U.S. dollars on anything. All happening natively. The second thing which we did, 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? We created a set of reusable modules in our platform, which we reuse every time we are building a product. What this allows us to do that is, this allows us to innovate faster, because now, if you will see our history, every year, we add a lot more newer products than we did last year. 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 amount of our infrastructure. When we talk about CCaaS, you know, the number one need in CCaaS software, there's a 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. 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. Whenever we launch a new product, by default, it's scalable. Guess what? When we are serving our customers, they are bringing 1 billion data points. We are processing Twitter firehose, Reddit firehose. We bring ton of data every day. 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 two enterprise are same. They have different workflow requirements, different governance, 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 power platform. Let me take one example of that. This specific, I'm talking about here five 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 one technology. That what we call is process engine. This one, first time we built in 2016. First time we built this technology for workflow engine applications into our content marketing product line to orchestrate marketing tasks when you are running these campaigns. When we needed to build our chatbots, we said, "We are 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? 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. Guess what? When tomorrow we are building a new product, there's a high probability we're going to use one of those components. It has two advantages: not only we get it faster at lower cost, our customers who are using already 30 products of ours, when they adopt 31st product of ours, they don't need to be trained again and again, because same components will appear again. That allows us to continuously reduce the time to value for our customers. That's the reason I know our CRO, Paul, will take us through a journey where our customers start with 1 suite, adopt 2nd suite, 3rd suite, 4th suite, 15th product, 18th product. Easier. Every new product they adopt is faster to get time to value. Not only we are able to reuse these components for creating new products, a lot of these we are able to, on-the-fly, configure differently for our enterprises. See, there's a difference between when you are creating a solution for a SMB business versus a large enterprise. Large enterprise has very different needs. They are very complex in their nature. They are managing billions of dollar of revenue. They cannot operate 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, which our implementation team, our partners' implementation teams, are able to leverage and just configure it using a visual interface in order to serve those enterprise needs. Now, what I have, that's the point till now. I want to make sure we all take away with that. Sprinklr is a 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. As Rajiv said, it's truly omni-channel, governance across BUs, and it's unified across all the customer-facing functions. Let me talk about AI. I want to thank OpenAI, generative AI, Google. They've done amazing innovation. Now AI is on top of everyone's mind. We had this big aha! moment. I remember when me and got on a call in 2016. 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? 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. 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, the AI we are able to reuse across different product lines. A 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 costs. When we started doing our AI, we were not doing a lot of time series-based predictions because our data was not very structured in nature, because we were getting these massive tweets, Reddit, forums, blogs, contact center transcript, 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. We have spent a lot of time in understanding and creating our AI solutions around that. What we have done is, many of our products, I've 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. "Hey, agent displayed a knowledge of the skill, agent was empathetic," and you are manually doing that. Guess what? You can only do 1% or 2% of sampling of that. How do you scale that? When we created our quality management solution, we said, "Let's turn it on its 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 a slide before I talked about, we can understand all of that text, we can give it attributes and metadata, and then we train AI to analyze and score on 30-plus different parameters. We're able to score on empathy, we are able to score on knowledge, we're able to score on did you have a proper opening, proper closure. Guess what? Now, using power of AI, we expand the solution capabilities massively from 2% sampling to 100% sampling done in real time. That's something we've been building for years now, and we have rolled out to all our customers. That's where 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, conversational AI cannot be done without AI. We analyze not only when we do quality management. Now let's see about we use variable 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. We are able to do that using our AI and start informing the rest of the C-suite, our product lines, that what they can do differently so that they don't get those volume of tickets. 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 are running a contact center, what you do? You have a concept of queues. "Hey, here is where I'm gonna get my premium customers. On this queue, I'm gonna get my premium customers who are looking for, let's say, reporting their credit card tapped. Here I'm gonna talk about trying to upsell them." You train your agents on certain skills, and then you map them to queues. That's how the world worked. 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 far 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 three levels deeper. 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 this demographics for this problem. 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, and that will have these attributes, I'm gonna pick up these agents." That all happen at runtime and all gets updated. You don't need to manually come and score the agents because QM, quality management, is doing automatically. Every day they are getting scored, and every day 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? It's first principle. If you take your most qualified agent for that specific sub-skill, they're gonna perform better. They're gonna solve that faster. Your cost is down, plus your customers will be a lot more happier when you do that. 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. We have rethought many of our features. I remember our guidance to our product teams was every product need to do three big features every release, I don't need this as a formality or compliance. We need to make sure you are reimagining that part of the stack, product stack. We have done that over the last six to seven 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 two big buckets. There are general-purpose large language models. OpenAI, we already integrated. We're already working with Google to integrate Bard AI. We'll be releasing it in our future releases. There may be a future, another large language model, we don't know. As and when they will come, we will integrate with them. They are doing a phenomenal job on this scale, and we'll make it available. The other thing we want to do and we already started working on that. We're gonna take smaller large language models, which are open source in nature, where we can further train on top of that. 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. In the end, what happens is with AI, there are two 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, obviously, that means it has higher accuracies. Higher accuracies leads to better business outcomes because then you can be more confident. Then it's gonna do a better job choosing the right agent, then it's gonna do better job choosing the right ad copy to run there. We believe we are gonna be adopting generative AI way aggressively than any other vendor out there. I want to, you know, just reinforce our approach to AI is global-level models are great, but they're not very accurate. Most accuracy you get when you're 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. Today, 1,000+ models, which are customer-specific model, deployed for us. Just today, I was talking to my team, we were doing a POC. one of the airlines in Korea, and we were increasing the accuracy of our AI model. What we found out, like, you know, in 4 weeks or 5 weeks, we started with 78% accuracies, and we were able to take it, you know, 90s, right? Because we were able to better feed it, better targeted training data, and continues to fine-tune. Now, you see the business outcomes when you are getting 70% accuracy versus 90% accuracy. An architecture needs to support that. That's 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 there. Amazing in summarization, something we were struggling to solve without generative AI, right? 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 re-imagining, can it supercharge some of our AI features existing? Can we do some new features which were not possible before? I'll quickly take one 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. 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 Rajiv said it is, it's giving our AI wings, right? We are not using plain vanilla generative AI, but we are first running our AI, taking those inputs, and then running them and giving it a large language model, right? Which allows our AI to become much more better. Some of the features, like, you know, discovering workflows, we never did that before, but now we are working on that using generative AI. What this specific feature does is, you are a contact center, right? Every day, people are solving cases. Guess what? They are solving cases in multiple different ways. You do not know how they are solving those cases. Using this feature, you can discover that, hey, this kind of issue, that's how they solve it. You are able to create a knowledge base article out of that, you are able to create a playbook out of that, and you are able to work on that. We couldn't have done that without generative AI. We believe, I think next a few years, these are the years of AI. These are specifically years of generative AI, we are all in, right, into that. We are verticalizing our AI because we talked about that. Now, when we sign up a new airline customer, we can go much faster. The time to value gets compressed, we can take them live on much higher accurate models. We are doing that for financial services, we are going to keep adding new industry verticals to where we will go prepackaged AI. Let me take few products and let me walk you through a few of those products, actually. As Rajiv said, every product persona can benefit from AI, right? 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. 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. Now AI can do that for us, and we can directly pick up where we left. This was the smart response we generated. We can go to generative AI, we can ask it to change the tone, make it more empathetic, it does it in, like, a fraction of seconds, we can just send that. What it does, it improves our customer experience seamlessly. They don't need to remember knowledge base articles because AIs 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, right? 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 read the complete transcript. You are making it way harder for our agents. Can't AI summarize that? This is what customer wanted. This is what we have done. This is what's pending. Mr. Agent, why don't you go and solve those things? Here is how you do solve those things. Here's a guided workflow, how you solve a transaction dispute. Here are the sequence of steps. Here's how you do that. That's how you delight your customers and do it in a much smaller amount of time. When you are running a contact center, average handling time is a very key metric. If you reduce that, your costs go down. 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? We are telling you which contact drivers are having 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 themes 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 a lower quality scores. What we are able to do is we are gonna go deeper into that case, at which points they had a negative customer experience. You can go there, understand that, and coach the agent. Right. I told about that we score, like, if for this case, this agent scored 72. They scored very low on positive language. They scored really good on some other aspects. You can go to those specific areas, and then you can coach an agent on that. This you can do 100% of the course any day, right? That's how you delight your customers, right, at scale. The same technology, as you see a lot of usual 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? 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 functions. Let me take example of social media marketer, our bread and butter. Here, our media marketer wants to launch a sustainability campaign. They pick up an asset. It's about sustainable clothing. They use generative AI to help them generate content. All they need to do is give a basic prompt in what kind of content you're looking. I want to do a post around eco-friendly clothes, about a discount for a specific audience segment, within seconds, we'll start getting them the recommendations of some content they can get started with. Just see amount of time it can cut from their day-to-day work, right? They can choose to use that, or they can choose to use few things from that. What we are doing is we are making AI available in their 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? They can do it across channels. They can do on Twitter, email, blog, anywhere, actually, right. Here, they chose a content. They're gonna schedule the content, and there also, we'll find various applications of AI, where AI will recommend this is the best time to schedule this content. We call it smart scheduling. Not only we are mixing generative 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 gonna use smart scheduling and say, "Hey, guess what? This is how you should publish. You don't need to decide. But if you want to change the time, here's the second-best time there." You can do that, right? Within clicks, it didn't take, you know, more than a few minutes to produce a new content, schedule at the best possible time, all using AI. This way, our customers can get more better returns. I know I'm about time. Okay, let me touch upon CCaaS. Why did we need to build CCaaS? 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 were way easier for us because we were able to reuse a lot. Here's the problem statement: This 40-year-old industry, what it has created is this point solution chaos. You need to buy 15 different solutions. You need a different solutions to do voice, different to do email, chat, then you need to buy a chatbots, you need to buy voicebots, 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. You need to maintain them in order to have consistent workflow. It's a lot high TCO. Okay, I get it. We're 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 attrition in contact center industry? They don't want to work. Highest agent attrition in this industry. Why? 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. 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 on hold lines. If I call a contact center now, there's a chance I'll be on hold for 10 minutes, 15 minutes, 20 minutes, and they'll keep playing a random music to me, right? They'll 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 are validating that for me." Missed upsells. Guess what? When I'm calling you, I'm having a bad experience. If you recover that experience, you are trying to do more business with me. You know how many times when brands have 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 last couple of decades. I get it. Technology evolved like that. Today, when we were building our CCaaS platform, we have that hindsight. It's a multi-channel world. Customers want a different experience. Agents want a different experience. That's how we built our CCaaS platform. All channels, all applications in one. Make sense? Guess what? Customers are happier, employees are happier, and it costs less. That's so simple for us to comprehend that. Lower costs, lower employee attrition. That also adds to cost, 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. Now you'll say: Have you done it at scale? What's your largest scale when you're talking about CCaaS? I'll get to that. 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 assistance, self-service. This way, we are able to create ton of ROI. When we do that, when we unify CCaaS, and we do AI first, guess what? 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%. You know, today, when you're running a large contact center, you are still spending a lot of money on your human costs. Maybe more than 90% may be human costs, maybe less than single digit will be technology costs. Any improvement you have in that spend, directly is the ROI you are able to create, and you are able to make a strong business case for that transformation. You are able to reduce first response time by 19%. We have 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. If you are a solution, if you are 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 are not doing digital for that brand? No, right? Incentives are not aligned. That's the reason we still see today a lot more brands are heavily dependent on voice as a channel for customer service. Incentives are not aligned with technology and customers. 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 CSAT and NPS You know, when we do finance, and all of you are from finance, you know, a lot of these might seem competing priorities. "Hey, I'm gonna reduce costs. Will that have a better customer experience? Will there be a trade-off?" No, using a unified CCaaS, which is AI first, power every part of the ecosystem, you will reduce costs, but you'll at the same time have a better NPS, and you'll end up having better revenue. What we have done is we have deployed it at scale. This is in one of the examples. This is a monthly scale. We're able to use WhatsApp and SMS for self-service. We're able to use social, 50 agents. We're able to do email, almost 1.5 million+ interactions in a month. We're able to do outbound sales calls for 8,000+ agents, almost 11 million calls every month. We are able to do another 7 million inbound interactions with almost 2,500+ agents. We are able to do this with 12,000 agents with 24 million interactions a month, almost 300 million a year. All complete unified CCaaS deployed across channels, across AI, all the functions deployed. 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. Yeah. Yeah, okay. We have a six minute video on our unified CCaaS solution. If you guys are good with that, let me play that. Today, we will showcase how our global consumer electronics customers are using Sprinklr to unify their customer experience with a high focus on our service product. Theme one focuses on the idea of the best care is no care, where AI-driven insights enable proactive customer engagement and issue resolution. Let's explore common issues in the consumer electronics industry. Companies often rely on anecdotes rather than data to understand their customers. With Sprinklr's AI models, we analyze thousands of customer interactions to effectively identify common issues. The top three concerns here are troubleshooting, billing issues, and needing help with an order. There are 20 more reasons why customers might seek assistance. We analyze 100% of conversations to provide comprehensive support, track trends, train agents, improve a chatbot, and update customers in real time. The second best customer experience is when customers can help themselves. Empowering customers with frictionless self-service tools like customer-facing communities, knowledge base, and bots help to increase customer satisfaction while reducing costs. Here is an example of conversational AI. When the customer reaches out to the brand, Sprinklr's conversational AI can help customers solve their issues themselves. Here you can see the bot is able to understand complex multiple issues at once and even understand language switching midway through the conversation without losing any context. As I fast-forward the conversation, you can see how the bot can handle the end-to-end conversation in a seamless, self-service fashion without any human intervention. If the customer does request a call back for their second issue, Sprinklr seamlessly initiates a call with an agent. The last theme is centered around agent experience. The idea that we can support agents with a single workspace powered by Sprinklr AI, generative AI, and automation to ensure they are empowered to solve inquiries within the very first interaction. I'm now an agent, assigned a new voice case in the omni-channel AI-powered care console. This console enables me to respond and process requests efficiently based on my skills and availability. The customer lifetime value widget provides agents with valuable information from external systems. This helps me efficiently resolve cases and offer personalized assistance without switching between multiple application windows. Sprinklr AI+ boosts agent productivity with advanced generative AI features like real-time case summarization. This allows me to focus on customers without taking extensive notes, but also enables quick conversation review for myself and my supervisor, reducing average handling times and improving first contact resolution. Agents like me can utilize different communication channels, like SMS and email, during calls. Generative AI features like reply assistance enable me to generate or improve content with a single click. This is especially great for allowing agents to maintain professionalism, empathy, and language switching without rerouting, resulting in enhanced customer satisfaction. Now, let's explore how AI can empower agents in real-time, resolving cases efficiently. Let's say the customer is angry and is requesting immediate help. "This is so frustrating. I want to speak to your manager now." AI-driven nudges analyze conversations in real time, offering proactive assistance in the care console. If an agent provides incorrect information, the AI quickly identifies it and provides a course correction nudge. As you can see here, Sprinklr can also predict customer dissatisfaction and prompt supervisor intervention. AI suggests guided workflows, contextual scripts that guide agents through to the resolution of the case. Typically, agents will follow predefined actions connected to external systems, and as an agent, I simply follow the steps on the screen and ask the customer the questions that are displayed. Guided workflows can also be deployed on customers' websites, mobile apps, or communities to assist with customer self-help. Sprinklr AI+-powered knowledge base empowers agents with the right knowledge for case resolution by automatically extracting answers from the knowledge base and presenting it to the agents, reducing the time spent to consume an entire knowledge base article. Sprinklr analyzes all voice and digital conversations, detecting important moments like greetings, escalations, and aggressions. AI assigns a quality score based on each parameter, such as missed upsell opportunities and agent behaviors. Supervisors can easily identify insights like top agents, channel variances, strengths, and areas for improvement. Overall, Sprinklr Service is an omni-channel solution that helps to boost CSAT, revenue, and customer retention whilst reducing support costs. Today, we demonstrated how a global consumer electronics customer utilizes Sprinklr to unify their customer service operations. Thank you. On that, I think I want to pass to Eric. 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. 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, or our event. We're gonna take a 5-minute break. Please help yourselves in the back, get refreshments, use the restrooms. We're gonna start promptly at 11:30. 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. Summer nights, mid-July. When you and I were forever wild. The crazy days, the city lights. The way you'd play with me like a child. Will you still love me when I'm no longer young and beautiful? Will you still love me when I've got nothing but my aching soul? I know you will, I know you will, I know that you will. Will you still love me when I'm no longer beautiful? I've seen the world, lit it up as my stage now. My days, rock 'n' roll. The way you play for me at your show. All the ways I got to know your pretty face and electric soul. Will you still love me when I'm no longer young and beautiful? Will you still love me when I got nothing but my aching soul? I know you will, I know you will, I know that you will. Will you still love me when I'm no longer beautiful? Dear Lord, when I get to heaven, please let me bring my man. When he comes, tell me that you'll let him. Father, tell me if you can. All that grace, all that body, all that face makes me wanna party. He's my sun, he makes me shine like diamonds. Will you still love me when I'm no longer young and beautiful? Will you still love me when I got nothing but my aching soul? I know you will, I know you will, I know that you will. Will you still love me when I'm no longer beautiful? Will you still love me when I'm no longer beautiful? Will you still love me when I'm not young and beautiful? Let's stick it together, oh, let's do it together. Share the soul together. Find our way in together. Put it all together. Push and pull together. When I hold this love together, oh, the less I love the better. The better. The less I love the better. Oh, my love, can't you see yourself by my side? No surprise, it's always you and I every night. Oh, my love, can't you see that you're on my mind? Don't suppose it could convince your love to change his mind. So wonderful. Do you want to start on the. Oh, yeah, yeah. Where's the clicker? Thank you. Right. Paul? Right. Okay, everyone, if you could please take your seats. Yeah. 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. 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. 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. We've got a global presence of 25 offices in 17 countries. We started in the US, then expanded to Europe, but I would say the highest growth that we're seeing is really coming from the Middle East, APJ 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 geos, and a higher expectation of consumers to be served in an omni-channel type of way. Raji 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. We started clearly in what we call Vector One, the Global 2000. Starting last year, we made a concerted attempt to move into Vector Two. While these companies are $250 million-$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 Two segment. We have a customer that is paying us $2 million a year, recurring subscription ARR, that has about 2,500 employees. You see there's a big opportunity here in Vector Two. Vector Three, we're not applying direct sales resources as we are in Vector One and Vector Two. Vector One is where we started, field-based sales, calling on the biggest companies in the world, inside sales approach for Vector Two. We don't have a concerted sales approach to Vector Three, nor do we have a concerted partner approach there. What we are doing is the self-service capabilities. 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? What we're finding, and there's a reason this goes all the way up the top, is Vector Two and Vector One customers are also coming in. It's removing friction in the sales process. They're able to trial our products before we formally engage. 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 this 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. For instance, if I am a Bank of Montreal, and I've got financial advisors 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 this 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? 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 omni-channel way, how do we bundle that up in an offering for governments? Finally, we've established a value realization framework. Pavitar gave you some examples, business case-based pre-sales, 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? 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. Started, obviously, as a way for big companies to publish and engage across this huge amount of growing social channels. 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 posts, it was contact center transcripts. 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 were assigned for social customer care, or in some cases, legal and compliance? We had to start building that capability. 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 gonna 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 Roji 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 AI is 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 email? Fast forward, we're now at the point of voice, dealing with COOs or kinda head of contact center operations. Where we are today, because we've reached the point where you 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. Let me walk you through a couple of real-world examples here. I'm gonna 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 moved into Sprinklr Insights, kind of the early days of Insights. 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. Roji 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 gonna 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+ countries, each one had their own agency. Getting reporting back in different formats, PowerPoints, Excel, had no idea where to find, 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, or 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 Sprinklr Insights. This company identified through the Sprinklr Insights 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%." 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. 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? Let's talk about three things that we're doing here to drive that easier to sell and scale this out. The first one is, we've had such a focus and such an ability to upsell within our install base, we haven't had people solely dedicated to just bringing more folks in so we can create that virtuous cycle. Dedicated new logo sales team, Vector One, Vector Two, 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. 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. 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 hoc and informal. I'll walk you through here in the next slide, what the partner ecosystem looks like and how we're doing this. The goal here is to have a very clear three-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, you know, the Accenture and Deloittes well before we went full CCaaS as well. You see, we're expanding that to those where they have the ability to resell Sprinklr. 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 one, phase two, phase three. 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 and advertising. Once again, a bit informal in the past. We have some partnerships coming here that we're super excited about. Finally, the channels themselves. A lot of co-development happening here, working with the channels to not only support multiple customers, but also in a 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 their revenues, but a lot of interest here on what can happen in the world of customer service and the contact center as well. Don't take my word for it. I wanted to take a moment now to introduce our customer panel. 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. Come up, please introduce our panel. Right. Hi, everyone. I'm Arun Pattabhiraman, CMO 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. 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 Johnston, 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? Hey, Arun. Good to see you! Good to see you. Alina, 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. As you mentioned, I lead the devices and services, social and community operations team at Google. Really, our goal is to create radically helpful experiences for our customers on social and community channels. 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. That amounts to hundreds of different touch points in which we're interacting with our customers. Really, our priorities are on all of those touch points to create very high quality, customer-focused, efficient experiences, and also then to be the voice of the customer to our partners. 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. I know Google has been working with Sprinklr for over nine years now, and it all started with Google leveraging Sprinklr as the de facto social media management platform across the company. 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? Yeah. Our partnership started in 2014. Yeah. we go way back. Nine years is a long time, right? All outbound publishing is done through Sprinklr today at Google, Even some of our largest divisions are using Sprinklr for social support, which is really exciting. Today, my team specifically uses Sprinklr for 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. 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. 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? 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 sinking in right now. It's very difficult sometimes to get that real-time data, right? 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. Being able to see everything in real time in, 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, you know, some of our data, and I would say a good amount of our inbound data can oftentimes be spam, especially for teams like YouTube. 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, improves customer satisfaction. We can reach more customers that need us. Highly powerful stuff through Sprinklr. I'll say, lastly, incredibly important to us is security, being able to meet all security, data privacy, compliance needs. Yeah. 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. Yeah. That allows us to have an omni-channel, 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. I know I'm speaking to a leader in AI, but how does AI fit into your overall business strategy? Specifically, how are you leveraging Sprinklr AI to bring that vision to life? Yeah. AI has always been really important to our business, as it's been important to yours, 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're all different ways that we're using AI, whether it's through those real-time insights and using NLPs or even through that automation and triaging. Great. 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? As I mentioned, some of our largest divisions at Google are using Sprinklr for social support today. One is our Users and Partners team. Yeah. They recently moved over to Sprinklr, and they're interacting with, you know, customers in 13 different languages, and just within the past 6 months, there's been a 10% increase in agent productivity through things like Sprinklr AI and automation. 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. I know you're a power user of all four product suites of Sprinklr: Sprinklr Insights, Marketing, Service, and Social. Where do you think our partnership is headed? I mean, it kind of goes back to that question of, you know, how have we evolved, right? 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. I'll also say, like, as a leader within 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 gonna help us reach more customers together. Well- Thank you. Well, thank you so much, Alena, for sharing the Sprinklr-Google journey with all of us today. Thank you so much for taking the time out and coming out to New York and to be with us here today. All right. Thank you. Thanks, everyone. 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 preview of how Sprinklr Service is transforming the contact center operations at Alshaya, which I, like I said, is a leading retailer brand operating over 3,000 stores across 40+ brands in Middle East and Africa. May I request for the video to be played, Eric? First of all, thank you so much for doing this for us and sharing your journey with Sprinklr with our audience here today. You represent Alshaya, one of the biggest retailers and brand franchise operators in Middle East and Africa. 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 picked Sprinklr? Sure. First of all, it's my pleasure. Alshaya is, like you said, a retailer. We operate a group of franchises here in the Middle East and North Africa. We have over 60 brands, international brands, be it from the States, be it from the UK. Brilliant brands, across several different sectors: apparel, wellness, beauty, hospitality. 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, as Ali, look after the MENA region. What I'm responsible for is the customer service across this region for all of our brands across all of our markets. Great. I know you were using one of the biggest names in the CCaaS space before you migrated to the Sprinklr platform. 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 this scale and complexity? Sure. We went through a proper RFP process, where we had seven different vendors presenting to us the solutions. Obviously, we gave them the 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 used. Voice was on a platform, live chat was on a different vendor, ticketing was on a different vendor as well. It was very painful for our agents in our contact centers to be able to respond to a customer. We were doing it, but again, they had to use 15 different systems which weren't integrated into one overall platform. If you were to phone us a room to complain or have a query about your order, we would have to log in to a certain system to be able to respond to that. One agent would have had up to 15 different logins for different systems to be able to just respond to a customer. What Sprinklr was able to do is integrate all of these 15 systems into one, with one single interface for our agents to use. Obviously, a huge focus 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. 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 were 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 coping with is the disruption that is being caused by AI. AI has become an integral part of every business strategy. Customer service operations is no exception. Just curious to understand how AI fits into Alshaya's overall strategy and your experience working with Sprinklr's AI platform to solve some of the business problems you just articulated. Yeah, sure. It's the word on the street at the moment, isn't it, AI? Everybody's talking about whatever you read online, it's all mentioning AI, et cetera. What we've seen so far with Sprinklr, it's been really positive. We're able to quality check automatically with calls that our agents are having. The ability to turn a disgruntled customer into a, from a detractor to a promoter, and you can see that on the call with the use of AI. On the recording, you can see where it was red, turning into an amber, turning into a green at the end of the call. What it's also gonna enable us to do is the agent assist as well. Assisting our agents with potentially ask this question or that question, et cetera. Making their life even easier, reducing the call duration as well, and also, most importantly, delighting our customers. It's been very positive so far. The more we build on that database, the easier and better it's gonna 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? Yeah. 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. 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. Our roadmap, we've got a great roadmap, and that's at the moment, being implemented in chunks. We've seen good improvements so far in average handle time. Also, the ease of use for our colleagues has been notable, so they are much happier. Speaking to them on the contact center, you ask them, feedback has been very positive. How are you finding it? Again, easy to use. We've made 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 ask for feedback at the end of the calls, 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 calls, we measure 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 it's been great to see. So far it's been a positive. That's fantastic. 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. I'm just curious to understand if you could share what the roadmap 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. like you said, what we wanted to do with the platform is to empower the colleagues in customer service and other functions, because we're not only using Sprinklr or the platform for customer service. We've already given access to our colleagues in logistics, to our colleagues in finance, because when a customer phones in, they're not only phoning in to ask about, "Where is my order?" They are asking the different questions which relates to different functions in the organization. We've given them all access to make ease of use. We're able to track. They've got access onto the platform. We're no longer using emails or Excel sheets to send the ticket to the customer, sorry, to the colleagues in logistics or to the colleagues in e-commerce operations. It's all via this one platform. We're able to track it, ensure it's in SLA. Again, it's delivering that customer happiness across all touch points with ease. What we wanted is one omni-channel, touchless operating model to leverage all of the customer interactions and drive customer advocacy at scale as well, using a much more intelligent relationship management system. We're also gonna be embracing the world of messaging and chat. Next, what's coming is the chatbots. We've recently gone on to telephony, onto chats, but also chatbots is what's coming down the line in the roadmap as well. Again, what we also wanna offer our customers is really easy, intuitive self-service and automation services as well. They're not having to phone us 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 websites. Also embracing the world of social media channels to offer help and support as well. Again, most importantly is that consistent and outstanding experience across all of our customer service channels as well. Then of giving our colleagues the full, complete view of customer interactions and customer or community engagement all into one platform as well. I must say, that'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 you've given 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 both 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. Please welcome Manish on stage. It's over there. Good morning, everybody. Good morning. I can already see the anticipation in all of you. Walk us through some numbers, and I will. Quite honestly, how amazing was it to hear from Ragy, the vision around unified CXM? 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. The place I'd like to start with is consistent execution for us ever since we went public. I think that might have been lost in a little bit of the noise over the last few years. It's gonna 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. With that, let's focus on execution in the next couple slides. 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 three years. I've said repeatedly in all our earnings calls that the two key metrics that provide you an indicator of what is happening in the business are RPO and CRPO. If you look at both of those metrics, they have shown consistent, strong performance over the last three years. Total RPO growing 32% and CRPO growing 28% as a CAGR over the last three years. What a difference a year makes! Not only have we been focused on top-line execution, we have also been maniacally focused on improving the bottom line. 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. If you look at non-GAAP operating income, that improved by $42 million, going from negative $36 million in FY 2022 to positive $6 million last year. The turnaround in free cash flow was even more dramatic. We went from negative $45 million in FY 2022, an improvement of $55 million, going to +$10 million last year. Let me quickly remind you, we also had a one-time litigation settlement last year for $12 million in cash, which is included in these numbers. Said differently, if I took out the one-time 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. Now that I've been here a little over 1 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 covered us, and there have been lots of questions around what drives the business. I will cover some of those in the next few slides, but I wanted to be clear that this is a one-time disclosure. We will not be updating this either quarterly or annually, I will go through that here in a bit. The question that everybody's been asking is: Can you break out your revenue by the product suites? Let's start there, and because you asked, here it is. 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 two together here. This includes social and insights as one revenue bucket, then marketing, and then service at the very top over the last three 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 FY23. Let me make sure I say it one more time, because I had to rub my eyes as well once we did the numbers. Sprinklr Service is over $100 million in subscription revenues in FY23, growing at a CAGR of 50% over the last three 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 a times, investors 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? That isn't the case here. 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%. I wanna 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. The next question that I would usually get is, you're looking to build a multi-billion 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've bucketed them in Vector One and Vector Two. Let me give you a little bit more color around new logo acquisition. We added 231 new logos in FY 20. That grew at a CAGR of 18%, and last year, we added 377 new logos. What is remarkable is, as Paul was saying, 60% of the new logos that we added last year were with Vector Two. 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. Now, adding new logos is fine, but what does that mean in terms of new sales of product suites? 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. 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 one product suite. 49% of the time, we were selling two product suites. 27% of the time, three suites, and 8% of the time, all four product suites. Said differently, 84% of the time, in the initial sale, we were successful in selling more than one product suite. You might ask, Why is that relevant? It's relevant, other than the obvious reason that it increases the ARR per account, two big takeaways. Firstly, it increases the stickiness of the customer because they are now buying multiple products from Sprinklr. 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. We covered new logo acquisition motion, which has picked up steam, and obviously, with the dedicated new logo team, we expect it 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? Let's take a look. 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. What's more compelling is, look at the number of customers that now have all four product suites. That went from 105 to 228. That is a CAGR of 30%. 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. They keep buying more, and that's how... If you look at how we disclose earnings, we always talk about almost two-thirds of new business comes from selling into the installed base, and that is consistent quarter-over-quarter. The next question would be, Well, how about the customers who buy only one product suite? Let's take a look at that. That has remained rather consistent over the last three years. Again, 152 customers at the end of FY 2020, 243 now. These are customers that have only one product suite. Now, of course, it isn't the same customer. We've been obviously adding new customers that have bought one product suite, and in the prior years, as customers bought one, being part of us, we've managed to upsell them more during this period of time. As we dug into this, there is an even more interesting story to tell around customers that have only one product suite. Let's take a look. Let me walk you through this data. As we look at customers that have only one product suite, in FY 20, 65% of those customers that had only one product suite had bought Sprinklr Social or Sprinklr Insights. 19% had Sprinklr Service. If I fast-forward three years, look at the proportion of those customers that have Sprinklr Service. What this is telling you is, we're becoming successful at starting that customer in our unified CCaaS platform. As we have developed a much broader product set in unified CCaaS, we're getting invited to the table for a lot of these RFPs. That wasn't the case in years gone past. 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 a demonstrated ability to sell, starting with Sprinklr Service, before we are able to sell more products into that account. Now let me break this out in a slightly different fashion, which I think will capture what Paul was referring to earlier. You know, Sprinklr's always been known as a company that caters to the high end of the market, premium customers, and that is a true statement. 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. 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. What is even more compelling is 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%. At the end of last year, the equation had flipped. There were more non-Fortune 500 companies that were paying us more than $1 million in revenue. 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 Two, a lot of those accounts have the opportunity to be massive revenue generators for us. This is obviously all on a customer account 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%. 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. We said, "Okay, so that's great that we are looking at the 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? Let's take a look. I'll walk you through what this slide is. In the column, you see the number of customers by the product, who has 1 product suite, 2, 3, down to 4. I'll go slowly. 143 accounts have 1 product suite. They, on average, pay us $100,000 in ARR. That is the number in the pyramid. That's that number. All the way down to, as I walked you through, 228 accounts that have all four product suites and pay us, on an average, $1.2 million. Again, just for thought experiment's sake, we said, "What if we didn't add any new logos?" We were given our demonstrated ability to upsell existing accounts, looked at these 143 and managed to convert them, as an example, into four-suite accounts. That would be an incremental ARR of $1.1 million, which is $1.2 less than $100,000 times 143, gives you $158 million in ARR. 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. 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 are 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. 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 2024 guidance. This is no different than what we gave out on June 5th, so I won't go through the numbers, but I am reiterating guidance for Q2 and the full fiscal year. Let's look at our long-term financial model. 2 sides of the equation. We look at both revenue drivers as well as efficiency drivers. 3 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. Thirdly, an ability to leverage the partner ecosystem. 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're 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. 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 two 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 managed services, more and more to CCaaS delivery, all of which carry higher margin. Thirdly, like we've shown over the last 4-6 quarters, we will continue to demonstrate even more leverage in sales and marketing. With all of that said, here's the big reveal. Let's talk about subscription revenues. We debated internally and said FY 2027, which is three years from now and five 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 2027. That would compute to roughly an approximate 16% CAGR between now and then. Just so that we are clear, FY 2027 is really calendar year 2026, that ends in January 2027, so it's really is three years from now. 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 gonna happen next. I didn't feel it was the right forum for me to make one of those statements. 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. 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 gonna 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. I think if you, if you look at the broader picture, it seems to be a much smaller proportion of overall revenues than where we are today. 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 three years from now. Let's take a look at profitability and free cash flow. 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. 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. 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 three years' time. Back to what I said, at least $1 billion in subscription revenue and Rule of 40, FY 2027. 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. We feel comfortable putting down that we would be in and around the 20% as a per-percentage of revenue for free cash flow. 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 2027. 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. That's what this assumes, that free cash flow margin will trail operating margin at least by 2 points. Having spoken about 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? 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 fragmented. Again, we have a desire to be number one or number two in all of our markets. Again, for the right set of conditions, we might be open to strategic acquisitions. 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 install base as well as in the market, for us to keep growing. For the right set of conditions, we'd be open to M&A. 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 buy back stock. 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. 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. As you walk away today, I want you to walk away with 4 key takeaways. Takeaway number one: Sprinklr is a platform that is purpose-built for the enterprise, and we have shown a 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. 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. 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. Yeah. Okay, we're gonna start Q&A right now. We have two of our colleagues in the back, Cheyenne and Nicole, who have microphones. If you have a question, please raise your hand, just state your name and fire away. We're gonna update the stage with a few more chairs. Bring the... One or two? Okay, let's get things started. Parker, we'll go to Parker first. Nicole, if you can, right over here. Hello. Can you please state your name, firm, and question for the panel? Yeah, 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 Vector Two and Vector Three. On Vector Three, 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. Vector One is large, complex, global companies. Vector Two and Three, what we are trying to spot are companies that are likely to get there. What we're finding, especially in the contact center business, think about a smaller company, it's like $100 million, $200 million or $300 million, that has, like, 500 customer service agents because they are in a consumer business, or 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 know, you have to round robin calls. Agents begin to get, like, different skills, you need AI, you need efficiency, you need workforce management. What we find is a sweet spot of the market, now that we're on 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. We find that if you're 10 people, you can use, like, a ticketing system in one channel, but once you get a certain amount of sophistication, you need the unified platform. If you go try and do RFPs and put it together, you can't justify spending that much money on a system integrator and custom development. 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. In our first, I'd say 10 years, because we were so focused on large companies, we didn't focus on companies that were fast-growing. Many of you have portfolio companies that are growing, you know, sometimes 50%, and these are the guys who are gonna need the complexity and sophistication in solutions. We want to get them early, so you don't have to do a replacement, and you can get started the right way. That's what is driving the selection. The 43,000 is, it's. Now, remember, we talked about at IPO, we're beginning to get savvy with go-to-market. I think in the last few years, the first time we've actually done a target list identification, understood the vertical market, the use cases, really got to, like, a named company list that allows us to create a foundation for good go-to-market operations. Yep. 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? The mix of customers you have today, how many are using voice versus, you know, that being a big cross-sell opportunity? For us, remember, we call it internally, we call it Right to Win, the 52,000 agents. We are also, we have opportunities in the pipeline and have many implementations that are much larger. The idea is to just be optimizing go-to-market. When you have a larger functioning contact center, you don't think you have a problem to solve. We have 12 products we can send you, that you saw in the suite, that just unifies everything else for you. What we've done in the contact center market for the Right to Win accounts, we're going after the full stack. The ones that are bigger, we're happy to kinda unify everything else for you if you don't want to make the contact center voice transformation now. What we're finding, the reason we kind of expedited and focused on the contact center, is there's a large flushing that's coming into the market, right now, over the next several years. It's already started, which is driven by the movement to cloud and consolidation and optimization of data centers, cost pressure. We're able to kinda not just show them how they can save money, so we're doing somewhere, you know, the total cost of ownership, we can bring it down by somewhere between 20% and 50%, while improving NPH, while improving agent satisfaction. That's just a beautiful story for anybody who's looking at it. It's just a question of time before we start appearing in the Gartner and the Forrester and the Magic Quadrant. We've talked to all those analysts. We hosted a summit for them, and the feedback, after kind of understanding what we really do and talking to customers who are actually benefiting from it's just a question of time. It's kind of inevitable. Thank you, Ragy. Arjun, right here. Name, firm, and question for the panel, please. Yep. Thanks. Arjun Bhatia with William Blair. I had one for Ragy or Paul and one for Manish. Sticking to the contact center opportunity, can you just talk about maybe, like, go-to-market a little bit more? Where do you think, from a vertical perspective, you see this refresh coming up, where do you kind of allocate your spend and your resources, where you think there's low-hanging fruit for customers that are coming up for renewal in the contact center? Manish, one for you. When you think about the FY 2027 model, how do you think about the business broken up by those four product lines? Does contact center and Service become the majority of the spend by that time frame? Thank you. Mic's on? Yeah. Okay. To your first question, we've verticalized ourselves where we have customer proof points, number one. We believe that if you're gonna go to market, whether it's contact center or any other products, you need to be able to show some proven success there, right? The ones I showed you earlier is where the focus is as it relates to the contact center. You know, where we're seeing the momentum is in really any of the direct-to-consumer, and that's a broad term, right? It's consumer electronics companies, all the way to financial services companies, is where we're seeing that. I wouldn't, like Ragy was saying, I wouldn't just classify it to one, Vector One or Vector Two either. I mean, the question that was asked earlier. There's a very targeted go-to-market strategy for us with our teams, which is, in some cases, it may not make sense to go position the full replacement, right? How do we wedge ourselves in this world of digital, to Ragy's point, be able to unify everything else until they're ready for voice? What I can tell you is that trend is stronger across one vertical than the other, beyond the ones that we showed you up here. if you don't remember, like telco, financial services, airlines, tech. Yep. Tech, and, you know, those are the sort of ones where we are seeing like pull in the market. Arjun, just so that I understand, your question was for that $1 billion in subscription revenue for FY 2027, how does that break out by the product suites? Yeah. How do you think the business is? I can only make directional commentary. I mean, I think this is looking 3 years out. CCaaS would still be a big revenue driver. It's obviously shown tremendous growth over the last 3 years, and given the opportunity in front of us, and we're not even in the Gartner MQ right now, so I can only imagine that we would get a lot more ad backs once we get in some of these, you know, research reports. I think that will continue to be a big growth driver. Quite honestly, I think there was a school of thought at some point that our historical product suites were sort of more mature, but as you saw from the numbers, they're growing actually at a very healthy clip, even though we've been in this market for 12 years. We do expect that there will be enough traction and enough new opportunity, even in our, let's call it, historical product suites. I think that's the way I'm thinking about it. Arjun, let me also add that it's tempting to say, "Look, you got a product that's at scale, and you have publicly traded companies that are growing much slower, that's valued much higher." It's kind of funny to think about it. Why wouldn't you just focus on that? That's the same trap we didn't fall into way back in 2011, when we could have had a faster-growing social publishing and engagement business in 2011, had we just focused on it. Look at all the companies that did that didn't make pass and grow the way we did. What we see is multiple orbits. You know, first orbit is just getting to CCaaS. Now we're in a legit replacement market. You don't have to fumble, you don't have to find the budget. The ROI is just black and white. There's a push in terms of migration to cloud. Very soon, our differentiation will actually be the fact that we are connecting marketing and customer service. These agents that you are able to now, I don't want to use the word displace, but make more efficient, that capital, that human capital, can be replaced and allocated to selling through the contact center. What we're seeing is these, our flagship clients are beginning to convert that cost center, the cost center that's contact center, the complaint center that's contact center today, and convert them into, like, storefront, like the reception that's the concierge desk and the blend of... like you walk into a store, right? That's, that's the future, and we envision that a unified CXM platform can unlock for you. Thanks, Ragy. Cheyenne, let's come right up here. We have Fiona up in the front table here, please. Thank you. This is Fiona from Morgan Stanley, I'm from Elizabeth's team. My question is for Manish, and it's on the drive towards a productivity-driven sales model versus a headcount-driven model. Can you provide any metrics around the improvement in efficiency over the last 6-12 months, and what does the runway for further expansion and productivity look like? Just to make sure I understand the question, and, Paul, I'll have you chime in, too. You're looking for, as we have said before, we're moving away from just a capacity-driven model to more of a productivity-driven model. You're looking for more statistics around how much productivity has improved since. Is that the question? Yes. Yeah. I don't think we've provided any external data around it, but I think this is more of a mindset more than anything else. What we found is, because we weren't properly enabling our existing sales headcount, we weren't getting them to be more productive. Instead of just randomly adding more salespeople, we have devoted a lot of time and energy in terms of whether it's lead gen, SDR opportunities, marketing campaigns, you know, partner-led motion, all of that in the spirit that the existing sales headcount should become way more efficient than what they were before. What you'll notice is... If you look at our S&M spend, that's come down dramatically because we are able to derive a lot more productivity out of the existing sales rep than we did before. I don't think we have given out any external metrics, and we probably wouldn't, but you can just factor all of this from our S&M spend line in the P&L. Paul, would you add something to that? Yeah. Okay, thank you. Go right here, Brett. Hi, Brett Knoblauch, Cantor Fitzgerald. Watching your demos, you can kind of see that the agent's gradually, you know, being replaced. You know, right now, he's just clicking, he's not typing, he's not doing anything, he's copying and pasting. At what point does AI get rid of the need for these enterprises to actually employ people? Yeah. We were talking a while ago in private. Look, the contact center market is worth $800 billion. The technology spend on the contact center market is just a slight, tiny fraction of that. AI is actually putting, I would say, $400 billion-$600 billion of that in play. We are very, very aware of that. The way we are positioning to play in it is we have 13 products in the suite. Many of those products are not priced based on seat. Look, if you buy a community product, if you buy a chat product, the AI components are not priced based on seat. We don't have a large base of seats that we're cannibalizing to start with, because we're disrupting this space. Right now, we have to kind of make it easy for somebody to switch from a pricing and budget perspective. We do support and entertain the seat-based pricing to start with, our intention is to fully kinda reduce the number of calls you get, number of tickets you get, and then make people so efficient that you don't need it. The way we see things evolving is, if you do this right, you're not trying to cut costs, which is finite. You're actually replacing that effort and resource and energy with selling, and that you get. You convert that into growth drivers, which is the way we see the industry going. This is where our early customers are moving to. We think you get efficiencies that you redeploy to grow, and that's how a holistic, unified CXM platform should work. It is absolutely true, and we don't see humans compete with AI. It's a gradual. You see we're the probably one of the only companies, because it's one unified platform, the bot can do everything it can do and bring the human along. When the human does what it's supposed to do, hand it over to the bot, and it's completely seamless. Maybe just on a follow-up question. Your $1 million customer count with the Fortune 500, I think my math is right, like, 75% of your Fortune 500 customers are below $1 million customers. I guess, what do you have to do to get them into that cohort, and why aren't they in that cohort now? Is it a vendor consolidation issue? Is it they just joined? Can you just help elaborate that? Traditionally, we've not done a lot of top-down, you know, fly 30 consultants, do a workshop, sell a $30 million deal, which some large companies do. Our approach has largely been, "Let's just go show you success, show you how we obsess about you, make you fall in love with us," we grow by delivering value and growing the flywheel we talked about. A lot of things drive that, not just how aggressive we are. It's the customers, it's building the champion. It's just we've been letting it happen organically without pounding through it, which I think is the right long-term strategy, there's opportunities as the flywheel hits. Now we're talking to C-suite, we have something that we call the digital transformation plan. We have the ability to go land and grow a little more intentionally than we are, but the organic thing is just working for us. Thanks, Raji. Nicole, let's go right behind you. Michael, please go ahead. Hey, Michael Turrin from Wells Fargo. A quick one for Paul and Raji. When I think about the go-to-market strategy as you do multi-suite selling, I typically think of CCaaS buyers as separate from the rest of your product suite. Maybe you can help us understand how you converge those two as you move, you know, up into the C-suite to sell. Second, I know there is some baseline to get into the Gartner or various reports for CCaaS. How close are you to those thresholds? You know, when you cut together things in those reports. Thank you. Yeah. I'll start. Look, they are different buyers, especially on the voice side. On the digital sides, we're more blended. On the voice side, it's kind of fairly, many cases, just outsourced, you know, we do understand that's independent. How we bring it together is using our insights capability. Now, all of a sudden, you know, Sprinklr contact center solution has insights on call center transcripts that now go to product, that now go to marketing, and now bubble up to the executives, and there's a natural connection point. For us, we really don't need to connect those two to kind of maintain or accelerate our growth, because what we're doing is we're unifying CCaaS independently. That's a powerful value story. We're bringing all those channels together, so you just have one guided path applies to all channels, one set of AI, and we're unifying the marketing and the engagement side. Don't think of it as just full transformation. You can just unify the CMO stack, you can unify the contact center stack, and then, you know, at some point in the future, you can connect the two. The only thing I'll add is also recall the investment in specialists, too. We have some customers, like the first one I shared, on that journey, where you may have a core account team who is driving the expansion in the marketing, insights, social side of the business, and a CCaaS specialist who's driving a parallel cycle for the first time in the same account, right? That's part of what that specialist investment is, as well. Oh, do you wanna take the Gartner and where we are on seat count? Yeah. I think there are well-defined criteria which are publicly available. I think, as we expand our deployment, You know, when we sign up a customer, we need to be fully ramped up with the agents. I don't have a timeline to give when we'll get and hit those thresholds, because those are also changing. CCaaS also, a lot of these analysts are redefining and bringing more digital also in, because you cannot separate voice and digital from a customer service lens also, right? As we are continuing to expand and grow in our Right to Win segment, we continue to acquire our customers, like Alshaya. We're able to deploy a lot more agents. I don't think we have an internal timeline goal yet, actually. It's not too far off. Yeah. I mean, I think about around 100,000 seats, I think they'll start paying attention. You know, I think the last public number we said is about 75 already. We have a bunch of deployments underway, so we're not looking too far out. Thank you. Next question? Yeah, Alex, over in the corner. Thank you. Thank you. Alex Captain with Cat Rock Capital. A quick question on the fiscal 2027 revenue guidance. Last couple of quarters in a pretty tough macroeconomic environment, you've been annualizing subscription revenue growth in, sort of, in the mid-20s. It's like 6% sequential subscription revenue growth. The guidance has us at 16% for the next few years, and you have some good guys for the growth rate. Like, for example, the service and CCaaS becoming a bigger part of the total and growing faster than the total, as you guys kind of broke out today. Are there any bad guys on the flip side of it that will cause deceleration, that causes you to sort of get to from the kind of mid-20s annualized growth that you're at right now in this environment, to the 16% average that you expect over the next 3 years? Yeah, it's a good question. Just so I understand, your question is around, if I could just use my own numbers, Q1, we grew subscription 24%. Q2, the midpoint of the guide is 20%. Midpoint of the guide for the full year is around 19% subscription growth. Your question is, are we assuming anything would go sideways, which is why it's going to be only 16% three years out? Is that the question? That's the core of the question. If you look at the last couple of quarters, the sequential increases, just to reflect the most current environment, sequential increases have been about 6% a quarter. Yeah ... which is, annualizes to 24%. That's kind of what I'm looking at, and also considering that CCaaS is growing 50% and is now 20%-30% of total revenue. Yeah. Seems like that should be a tailwind to growth at these levels. Yeah, I think all of those comments you're making are correct, which is why I prefaced by saying, for FY 2027, it's $1 billion as the floor. Again, should things pick up and nothing should go sideways in the next few years, we would be well over $1 billion. I just find it difficult to make predictions that are 3 years out. We sort of live in a market where the Fed can't tell you what interest rates are gonna be next month. I shouldn't be held to a much higher standard to say what the revenue is gonna be 3 years out. What we feel comfortable giving out today is a floor of $1 billion in subscription revenue. That's great. If I could sneak one other one. Ragy, you just made that comment about the 75,000 seats getting to 100,000 seats before you'd really start to get noticed. If you take the total seat count and the total market, how big is that relative to your 75,000 seats today, and who are the other kind of like... Where are those seats that you expect to be getting in the near future? I could be off, but I think the last number I saw was with $13 million. Mm-hmm. There's roughly 13 million seats at play, and I think if you look at the larger companies in this space, they have millions of seats, even the ones that are bankrupted and not selling anymore. Then what you find is the companies that have just been doing this are at the half a million mark, so it's kind of fairly distributed and broken out. What you'll also see is large BPO outsourcing companies with hundreds of thousands of seats. There's a lot of homegrown stuff. Whatever way you slice it, we're only scratching the surface. Just beginning. Great. Cheyenne, let's go over to the side here, please. Thank you, Pinjalim Bora, JP Morgan. One question on high-level CCaaS is obviously seems like probably a big driver to get to that $1 billion number. One question I always get is, you know, CCaaS is a pretty competitive, crowded market, and if you look at the marketing material on the websites, it seems pretty much the same all around, right? Yeah. You demonstrated to us investors, at least today, that your platform is pretty differentiated overall. How do you communicate that to a customer who's looking around web, you know, googling around? How does that require a lot of hand-holding, long sales cycles? Like, talk about that kind of motion. We're hoping that you'll go out and write reports, and everyone will read it and start buying. You've seen it, right? There's a question of. Look, I think Arun, I'm gonna let him answer the question, but he's been in his seat for a year. We've been an under-marketed company. The truth is, we've been originally, like product-led and customer obsessed. There is a certain strength in that approach when you're kind of as ambitious as we are. I'll let Arun talk about when are we going to be ranked higher? Well, I think the best way to tell our story is through the voice of our customers. You know, when we sit and hear from our customers why they picked us versus some of the legacy large incumbent players, one of the things that we often hear is that it's often very hard for a lot of companies that started on-prem and focused primarily on voice, to actually expand into providing full suite, omni-channel digital services vis-à-vis someone like us, who was born in digital and now perfected in voice, where we are able to easily expand, thanks to the unified architecture that Pavitar just articulated, that we are able to provide seamless omni-channel experiences, including voice as just one of the other channels. I think that story is getting more and more, I would say, fortified through some of the big brands that are beginning to adopt the CCaaS platform. I think once that story fructifies, and once we start getting recognized by the analysts, I think it becomes far more credible for us to win more customers. That being said, we are making huge investments in improving our digital infrastructure from a marketing perspective. There is huge investments that we are making on content marketing and SEO, et cetera, so that we are discovered more frequently and ranked higher for some of the most popular keywords that people typically land on. We are also working very closely with some of our comparison website, peer review site partners, to make sure that as people search for, these products and software, we are visible and ranked higher. Across digital influencer relationship and analyst relationships, and we have just kickstarted, for instance, a worldwide roadshow of CX-focused events, where we are bringing together a lot of contact center leaders across the world. We've, we have planned it across 20 cities. We are down with 10, and each of those we have about anywhere between 100 to 150 CCaaS leaders all under one roof, hearing the story and seeing the power of the platform through a powerful demo. I think we are doing everything that we can to make sure that we are surfacing more and more as a stronger CCaaS brand. I would always defer to telling the story through the voice of our customers, because we have some really powerful brands beginning to leverage CCaaS as T-Mobile. Just to add there, like, you know, I think we are very comfortable, our customers educating about us. Truth be told, you can write anything on a website, right? Everybody can claim the same story, but when we do our product demos, we differentiate, as Ali from Bonsai talked about. Then when they go into further into the sales cycle, they would like to talk reference customers all the time. We put them in touch with some of the implementations we already done, and they tell their story, and that's how we win on that strength. You saw that 50% CAGR we talked about, I think it's on strength of our platform and strength of our customer obsession, the flywheel Ragy Thomas talked about, and how our customers are actually buying more and advocating for us. There's a lot of noise, and you saw this at IPO, right? How many companies were there? Like, and we got no airtime, no attention. The truth is, and I'm not saying I'm wishing bad economic times, the truth is, some companies are built at the core to be different, and tough times will expose, and time is what will separate out the noise from what is real. That's completely okay. Not to make where we all answer the question, but the other thing, and I talked about it, is sign up for a trial and try it out, and ask any of the other pure play CCaaS players if you can do that. All right? That cuts through the noise faster than anything. Paul, do you want to talk about our channel partners? Because I think that's also unique. Yeah compared to what we were doing before. Yeah, I mean, you know, many other CCaaS companies have channel partner ecosystem and have been doing it actually longer than us. I mean, our focus right now, though, is showing them the differentiation first, because they end up becoming, in many cases, the strategic advisor to the customer, right? They will listen to them to get above the noise as well. If you can invest in educating them, real, tangible differences, things like offering them the ability to give their customers trial instances of this, so they can actually look under the hood and try it out first. Making the investment there, they are a great way to rise above the fluff out there and validate for their clients, "We've done our homework, and this is one we suggest you take a look at. Got it. Thank you. Thank you for the detailed answer. One question on AI, since I'm amazed that we have not heard about AI questions till now. You're talking about customer-centric models. I think very kind of specific to one customer is what I understand. You also talked about verticalized models, which seems like a little bit of a higher level. You know, you probably have to train these models in a very different levels, right? And training models, custom models, are always pretty expensive, from what I can tell in my simple mind, right? One, you know, how are we thinking about the cost of these models from a gross margin standpoint? Maybe also privacy, right, in terms of going to the customer level versus up a little bit on the vertical level. Talk a little bit about that. Manish, on the long-term guidance or medium-term guidance that you have, is there any kind of a pricing packaging of from AI that separately you are kind of assuming? Yeah, let me take the first part of the question. As you rightfully identified, when you do customer-specific model, it can be costly, but it's how you solve it with an architecture. As I mentioned in my demo, we have done a lot of AutoML capabilities. That means a lot of these discoveries of the ideal model, ideal architecture, neural network architecture for specific use case, we can keep on discovering automatically without a lot of manual input. We've created a lot of reusable blocks for our data scientist team, which they're able to do, and many times automate majority of those pieces. The second point, costs are fairly contained because we always take a modular approach, and we always start fine-tuning from an existing industry model. We are not rebuilding a model from scratch. We're gonna take an industry-specific model and add some flavor of customer data, maybe 5% more data, and that's it. It'll start getting more accurate. Second point you mentioned is about privacy. The way we have architected our infrastructure and our technologies, we are logically isolated everywhere. When we train a customer model, we train with that customer data, and that's totally separate, and that gets deployed only for that customer. It's part of our SaaS architecture, which is logically separated at the data layer, at the computational layer, even up to how we deploy our AI models. We take it extremely seriously because when we sell some of these largest brands, privacy and security is number 1 item there. To summarize, we achieve scale using industry models, doing a lot of automations, so we can scale customer models without spending a lot of money. That reflects in our subscription margin profile, and that reflects in our R&D cost base, and I think that, you can validate that we are not spending a lot of money doing that. Manish, anything to add? Maybe I can start, and Ragy, you can add as well. It's a little different for us because AI underpins every part of the product portfolio. To go to the customer and say, "Well, you need to pay additional for AI," sort of doesn't make sense. What allows us to win that customer in the first place is the unique capabilities we bring to bear. I don't think you should assume there is a separate AI package. It is sort of pre-built into the product set. Yeah, let's reframe the question in terms of, you know, if you want to increase revenue for this quarter, this year, you introduce AI product. We're trying to create the next enterprise software giant in the front office. That requires us to make decisions that say, when we have $2 billion, how do we grow it to $3 billion? We think of AI as the way to do that. The idea of AI is not, like, how do we get a quick hit? How do we support revenue? It's how do we use AI to create the company we want that's gonna be seen as what comes after CRM. Thank you. We have time for one more. All good? All right. Excellent. Well, thank you guys all for coming. Thank you for joining us on the webcast. We appreciate your support and interest in Sprinklr, and the deck will be posted on the website. It should be there already. If you have any questions, feel free to contact me, later this week. Thank you. Have a great day. Thank you. Thank you.