Thank you for joining us today for Yext's 2023 Investor Day. Our presentations today will contain forward-looking statements, including statements related to our future financial performance, expectations regarding the growth of our business, our outlook for future periods, our strategy, and estimates of financial and operating metrics, capital expenditures, and other indications of future opportunities. These forward-looking statements are subject to certain risks, uncertainties and assumptions which are discussed in our reports filed with the SEC. We encourage you to review them. Yext is under no obligation to revise any of these financial statements to reflect changes that occur after this presentation. Throughout the day, we also refer to certain metrics, including non-GAAP financial metrics.
Definitions of these metrics and reconciliations of non-GAAP financial measures with the most comparable historical GAAP measures are available in the appendix section of the Yext Investor Day 2023 slide presentation, which is available at investors.yext.com. It is now my pleasure to introduce Mike Walrath, Yext's Chief Executive Officer and Chair of the Board.
Thanks, Nils. Good afternoon, everybody. Welcome to Yext. I'd like to thank you for being with us today. It's great to see so many faces here in New York, as well as those of you on the webcast. We're excited to share a little more about the future of Yext with you today. It's an incredible time at the company. I really look forward to sharing that with you. We find ourselves today at the center of one of the most interesting technology innovation cycles we've seen in a long time. Unprecedented interest in AI, large language models, an ongoing transformation of how enterprises think about digital experiences. Here's what we hope to accomplish today. We'd like to start with a quick look at the last year and some of the fundamental restructuring changes we've made to the company.
We'd like to talk about some of the progress we've made, and I'd like to share a high-level overview of our operating philosophy and plans for the years ahead. After that, I'd like to talk about the highest level, about the massive shifts we are seeing in awareness and focus around AI solutions for the enterprise and where we fit in that evolution. We've got a great program for you. When I'm done, you're gonna hear from key members of our operating team on topics including our product roadmap, unified go-to-market, customer voices, and financial updates. We'll wrap with questions at the end of each section, and we'll be hosting an informal cocktail reception at the end. We've got a beautiful day in New York. I think it's the first really spring-like day we've seen. We'll be taking advantage of our rooftop patio.
We hope that you'll stay for that. By combining our focus today on two things, the enormous market opportunity ahead of us and more granular operations and execution topics, my hope is that you're gonna get a picture of how bright the future is for Yext, but you're also gonna have a really clear understanding about how we're going to attack the opportunity with discipline, transparency, and operational efficiency. Let me share with you some of the progress that we've made this year. We've reduced our overall headcount from about 1,400 full-time employees to about 1,100. We've reduced our executive roles further. We've reduced VP plus roles from about 104 to about 73. This is directly related to becoming a more streamlined organization.
When we look at Q4 of fiscal year 2022 versus Q4 of fiscal year 2023, we saw a 10% of revenue decrease in our sales and marketing expense from 51% in Q4 of 2022 to 41% in Q4 of 2023. We also reduced our total share count from approximately 130 million shares at the end of Q4 2022 to approximately 122 million shares at the end of Q4 2023. We'll get into, dilution and share count related topics as we go on. Finally, we talked a lot this year about our quarterly renewal rate.
We saw a significant improvement from Q1, where we saw a quarterly renewal rate in the low 80s to a high water mark in Q4 for the year where we saw a quarterly renewal rate in the high 80s. We've historically talked about this metric as gross retention, and we're gonna be referring to it as quarterly renewal rate to the extent we refer to it going forward. Darryl's gonna get into a lot more detail on this metric and how we're gonna be talking about it going forward. What we do believe is that this progression is showing us that the is an important indication that the work that we're doing on customer satisfaction and reducing churn is taking hold. The next thing I'd like to do is talk a little bit about how our organization has changed.
Just under 13 months ago, we announced significant management changes, which many of you are aware of, including myself as CEO, Darryl Bond as CFO, Marc Ferrentino in the role of President and COO to unify our product and go-to-market execution efforts. Since then, we've hired new executive leaders that include Raianne Reiss as our Global Chief Marketing Officer, Tom Nielsen as our Global Chief Revenue Officer, and Yvette Martinez-Rea to oversee the newly created corporate development position. We've also retained key leaders with long tenure at Yext, consolidated responsibilities, and created a much more efficient organization. I am totally confident in each member of this team's ability to deliver results for their area of functional responsibility. Most importantly, by consolidating functions and eliminating operation silos, we've created a global unified operating team. The culture of performance and accountability across this group is stronger than it's ever been.
Each of the leaders that you see on this slide has global responsibility for their function. Team is aligned, the team feels responsible to deliver results. Results is exactly how we expect to be judged by our shareholders, employees, customers, and partners. The question we get a lot and the question we like to talk to is obviously going to be growth, and I'm gonna come to that in a moment. Now that we've got the company structured properly from an operational perspective, the natural question is: how do we get it growing again? As I've said many times to many of you, the underlying operating philosophy we've installed here is that growth starts with efficiency and using the data to tell us the best way to re-accelerate the business. How do we get there?
This involves understanding when key metrics like sales productivity, qualified pipeline, ARR retention, and the macro environment are lined up to allow us to re-accelerate growth. We use Rule of 40 as a guide, and we think that the growth opportunity, both revenue and EBITDA growth, are best framed through an understanding that a company can't force revenue growth and remain efficient at the same time. This entire team is focused on returning to revenue growth when the data tells us that we can achieve it efficiently. This approach ensures that we're going to continue to make progress against a Rule of 40 metric in different operating environments, and that we're prepared to accelerate growth as the opportunity to do so becomes clear.
Now I'd like to talk a little bit about the evolution of our platform. We're gonna get into this in a lot more detail as we go through the presentation. I think the history is important here. We started as a CMS to manage content for a company's Listings. We became a CMS for SEO landing pages, followed by a CMS for reviews, a CMS for search experiences. Today, this platform can be a CMS across a variety of digital experiences, including full websites with the upgrade to our Pages offering. We were very early to AI. Some might say too early. We started to develop our search offering in 2017. We wanted to bring something differentiated to the market. At the time, Google had just put out a paper on large-scale language models.
You might have heard of these models like BERT, more recently GPT. There's many others. We decided to base our search technology on these large language models and wanted to understand language and semantic understanding, and build that into our models. Over the last five years, we've gotten very good at creating and training these models. It turns out these models are good for much more than search. The investments we've made over the years have allowed us to embed machine learning and AI capabilities into the core of our platform. For example, we've added AI to our connector framework a few years back. Within the transformation layer, we have the ability to do zero-shot or few-shot models that clean, organize, and enhance the data coming from other systems. We leverage AI in our Listings product to do matching and verification of each listing.
We leverage AI to generate content in our headless CMS we call the Knowledge Graph. We have leveraged AI in our Search product since the very first day. We use many different transformer models to create vector embeddings and extract answers from large documents. Internally, we leverage these large language models within our review response team to increase productivity. Our recently announced Chat offering uses multiple large language models to deliver a great chat experience while allowing the enterprise control and governance over that experience. We're gonna talk about how important that is. What we have seen over the last six months is a sea change in interest in how AI will transform the digital industry. The lightning bolt of energy that was the launch and growth of ChatGPT has captivated the world.
AI is touted as the technology that's gonna change everything. At Yext, we totally believe that's true. It's why we've been working with models like ChatGPT, others, and even our homegrown models for years. The excitement around AI is palpable and justified. However, like most new disruptive technologies, generative models and the advances in AI come with very real risks. As the use cases for AI become more visible, the risks are being better understood, particularly for businesses. I think this works best when we use a real example. I recently asked ChatGPT to write a biography for me. It's a fairly simple thing. This is what it wrote. Actually, at first glance, it looks pretty good. It's impressive. It feels like it could be very accurate. I did found Right Media. It was acquired by Yahoo for $850 million.
I was born in 1975 in Brookfield, Connecticut, not Florida. I did attend a university in Virginia, but it wasn't the University of Virginia. I'm a proud Richmond Spider, although I was a UConn Huskies fan before I was a Richmond Spider. I have an English degree, not a degree in computer science. Brian O'Kelley worked for me at Right Media, but he was not a founder. I would have loved to have started LinkedIn. Had nothing to do with it. Was totally uninvolved in AppNexus. That was Brian O'Kelley. I was never on the 40 Under 40, and I'm no longer qualified. If I asked ChatGPT to write the biography again, it would do it again, and it would do it differently, and it would be a similar mix of fact and fiction. You see the problem, right?
What we're looking at is a statistical model of the probability of the next word. That's it. It's an amazing piece of technology, but once you understand how it works, the parlor trick, while impressive, is a little bit less magical. We can laugh about it today, and it's amusing, but because what does it matter if they get my biography right and if there are some misses in there? It'll get better, right? It'll definitely get better. Put yourself in the shoes of the CEO of a healthcare system or a bank or a chain of donut shops. Can you allow AI to answer the questions of your customers, partners, and employees unsupervised? The answer is clearly no. Let me tell you just one way generative AI will certainly get better.
When a business is able to put the power of these generative technologies to work with a, with a Knowledge Graph-led approach to create a set of authoritative answers curated and managed by the business itself, then we can start talking about delivering perfect answers across the full spectrum of digital experiences using AI. The more fragmented these digital experiences become, the more important it will be for businesses to control how answers to questions are provided. Today, you're gonna see how we're putting the Yext Answers platform, including Knowledge Graph and a flexible language model-agnostic approach to AI content generation to work for our customers. You're gonna learn more about how our technology works, how we're gonna take it to market, and how we're gonna use it to solve some of our customers' stickiest problems.
You've heard us talk for years about the size of this opportunity and the opportunity to bring these technologies to the digital experiences. We're also gonna talk about that, and we're very excited about that opportunity. I wanna finish where we started. We will not operate the business on hopes and dreams. We will continue to take a disciplined approach to how we run our business and bring as much transparency to our partnership with you, our investors, so that you can judge for yourself the opportunity and how we're executing against it. I hope you enjoy the content today. I look forward to taking your questions at the midpoint and again at the end, and I hope I'll see you all at the cocktail event afterwards. I'd like to turn it over to Marc Ferrentino.
Thank you. Thank you, Mike. Thank you all for being here today. I put a jacket on for you guys. I hope you appreciate it. As for those of you who know me, I don't normally wear a jacket, I thought it was the right occasion. I wanna talk a little bit about customer journey and really the evolution over the last few years. It used to be that to make your business digital, all you had to do was build a website and buy some AdWords. That was pretty much it. If you wanted someone to learn about you, the entire customer journey would start with your URL, and the website was the center of your digital experience. Over time, things shifted. Over time, the customer journey fragmented.
A customer interacts with a company over search, mobile, chat, social, messaging, maps, and less and less your website. There is no real true center to the digital experience anymore. All of these new digital touchpoints are just as important as your website now. Many of these touchpoints, like voice and search and chat and messaging, are now driven by innovations in AI. A user, for example, could come in through one channel, then switch to another, then another. A customer, in theory, could engage with all of the channels in a single day because each one is optimized for a different part of their daily life. At this point, your company needs to manage all the experiences across all the digital touchpoints.
While almost every business and the bulk of the experience vendors out there focus on creating experiences on a company's own website, it turns out your brand is everywhere. Customers and prospects are experiencing your brand on third-party sites like Google and Facebook, Bing and Apple, and more. In fact, we ran a study that showed that more than half of your brand interactions are happening off your website or mobile app. You can't just ignore half the journey. User expectations have never been higher. All these different consumer services that we use today, they set the bar for customers and prospects and what their expectations are and how their interactions with your company online will be. Actually, they set the expectations for your employees too and their work environment. ChatGPT, great example. Exhibit A, resetting the expectations of experience once again.
All these great consumer-grade experiences, they all have a few things in common. One is they have incredible search, great search, right? Netflix, great search. Pinterest, great search, right? Great content. They all have great content too. When was the last time you went to Spotify and found a song or an album that didn't have a description or some content along with it? They're all multimodal. Website, mobile, messaging, right? They're leaning into this fragmented journey and meeting the customer where they are. They're all super fast. I'm talking sub 100 ms response time, which for anyone who doesn't know what that means, it's fast. It's really fast, right? Most business experiences are not even close to that. Cutting-edge technology. They all have ML or AI groups inside their organization, and they're implementing those technologies right now.
Except these companies, they also have tens of thousands of employees to make this awesome, to make the experience awesome. They have huge engineering teams. They have scalable infrastructure. They have armies of content people. Of course, they have data science and ML and AI teams. Most businesses do not have these sort of resources to deliver a comparable digital experience to these big guys. They need help. They need a partner to help them level up and to keep up. I've said digital experience. Let's talk about what I mean by digital experience. Let's look at a modern digital experience. Let's take, for example, here, a typical digital experience for a retail business. We have, for example, a prospective customer might find something on Facebook or Instagram or Snap.
They might find a new product that piques their interest. They then read reviews online for that product to see if other customers also thought the product was interesting and if they had a successful purchase. They then go to Google or Bing, right? What do they do? They look for a competitive offer. They probably price shop after that. They then visit the website of the retailer. They look up product information. They maybe read a blog or two. Then they want to try out the product, typically, right? They maybe go to look up a store location, and they'll sign up for an in-store appointment, potentially, which then leads them in-store to the kiosk to sign in and check in for their appointment.
Ultimately, they go home, maybe check the return policy, read an FAQ or two, they purchase the product online. The product arrives, they begin using it, right? That may lead them to follow-up questions, which will lead them to support portals, to support articles, and even to support agents. There is a team of people at the retailer who thinks through this every day, right? They assemble this digital experience. They put this together very thoughtfully and with many different vendors. The entire experience was built on top of this set of digital building blocks. The digital team at the retailer cobbled together all these digital services to build this end-to-end digital experience that we just looked at. They did not use one vendor because there is no one vendor that does it all.
It takes a village of vendors to deliver a proper digital experience. All these building blocks, all these different building blocks here are what the industry calls composable services. You need vendors who subscribe to this architecture in order to plug and play best-of-breed technologies. You can see how hard this is for a business to accomplish this all and do it well. It's not easy. They need help, but help is on the way. I think it goes without saying that AI is going to disrupt every industry. I know we've been saying it for a few years, but with the innovations over the last, call it 12 months, I think everyone can see how the time is finally now. We believe that AI will disrupt the digital experience space. That really is our vision for Yext.
Yext is leveraging AI to completely disrupt the digital experience space. Our vision is to leverage AI and graph technologies to enable every business to build a seamless, multi-channel, consumer-grade digital experience. Sam Altman, CEO of OpenAI, recently said in an interview, "Too much of the processing power of AI is going into using the model as a database instead of using the model as a reasoning engine." We couldn't agree more, and we built our platform with that in mind. We believe that the architecture of the future will consist of a data layer that will store the knowledge about your company, an AI layer that will handle generation, reasoning, and orchestration, and an experience layer where you will fine-tune and deliver each experience against each channel.
You want the inspection and auditability of the data layer, in our case, that data layer is built on graph technology, combine it with the logic generation, orchestration, and reason capabilities of AI to deliver these incredible experiences. We wanna make this available to companies of all sizes. Look, this concept completely turns upside down the traditional way of how experiences are created. For example, a content management system or CMS is not a CMS anymore. You don't wanna think of content as the webpage, right? A CMS is now a representation of all the sum facts about the business and its relationships because the content is now generated by AI for each channel that a user might engage the business on.
In a world where I can generate 100 versions of an article about a topic or a product, in a world where I can generate 100 images to make your product most engaging and appealing, where I can create versions of articles and content that are personalized for each person, what is content? What is content in this world? For example, if a business wants to put an offer out for one of their products or services, to do that, all they need to know is what the offer is. That's pretty much it. The AI can write copy for every channel, webpages, mobile, social, conversational. The offer can then be communicated across any channel. The experience is generated, and it's augmented by the AI. Digital experience platforms are a major pillar of enterprise software. It is foundational to a company's operations.
It's a must-have. Even when there are downturns in the economy or maybe the business itself is having some troubles, they can't turn off their digital experience. It's not possible. We are really excited to be part of this massive category, and Darryl will talk about it a little bit later today and go into some more detail. Yext delivers a platform ultimately that helps a business build incredible consumer-grade AI-first digital experiences, and we call it the Answers Platform. The Answers Platform is a composable API-first digital experience platform that is made up of several composable services that work great together, but also can be used individually with other vendors' products. At the center, we have our headless content management system, which we call the Knowledge Graph. Collecting data and content from across the business is foundational, obviously.
We built out our connector framework, which is a configuration as code ETL tool that can bring in information from anywhere across the enterprise. Once you've collected the data about the company, you can now create and deliver digital experiences anywhere. For example, you can control and deliver a digital experience on third-party platforms like Google, Facebook, Bing, Apple, and many, many more with our Listings product. You can monitor, respond, and generate reviews about your locations, your products, your company, with our Reviews offering. You can build and serve multiple search experiences across any part of your organization with our Search offering. You can build high-performant SEO-optimized websites with our Pages product. Recently, we announced, actually being able...
We announced our Chat product, where you can create compliant AI chatbots, all built with the latest large language models. Our platform overall is built on top of a scalable, secure, and compliant cloud infrastructure that you can deliver experiences globally with low latency. We have enterprise-grade user roles and permissions, an ecosystem of integrations and software partners to make it easy to work with other platforms, at the core is our AI and machine learning foundation, which allows us to build and deploy new models and leverage them across any part of our technology stack. Michael, in a minute, will go into more detail about our AI infrastructure.
Since we are a composable API-first platform that will integrate with any other experience products, such as Adobe CDP or Salesforce's Commerce Cloud, we wanna make sure that consumers will be able to build their digital experience of their dreams. A customer can use as much or as little of our platform as they like. We hope they would use more obviously, the flexibility is an important part of a modern architecture and is customer-friendly. Many vendors, they try to lock you in with one sort of monolithic giant offering that is really not great for the customer long term. You can see when you compare us to existing digital experience vendors, you can see the difference. We help companies deliver consumer-grade experiences with modern AI-based technology. We are built for a multi-channel world.
We know having great search as part of your experience makes all the difference. We are built on a modern web architecture that delivers very, very fast SEO-optimized websites. We believe in open source technologies and languages, we wanna make sure that what you build is actually portable to other platforms. We are built for this amazing new conversational future that we've been talking about for years, and it's finally here. All right. Next, I'd like to dive into how we leverage AI in our products and our model-agnostic approach. I'd like to invite up on stage our Director of Data Science, Michael Misiewicz.
Hello. Thanks, Marc. good afternoon, everyone. My name is Michael, and I lead data science here at Yext. What I'd like to talk to you about today is our strategy and outlook on the amazing breakthroughs that are taking place currently with large language models and how we're integrating these new technologies into our products. The key thing I'd like to communicate with all of you is that it's not really. We've actually been in this game for a while. We're not new to this game. Our Search product actually was built from the ground up starting in 2019 with large language models way before anyone was even talking about GPT. A question to ask is, well, why did we start early with this technology?
Well, that was not long after the August 2018 publication of the BERT paper, which was a landmark paper in this field, and they showed some really key results in that paper that were very important. BERT was demonstrated to be dramatically better at understanding words in context and also retrieving data. These are things you need to do really well to build a really good search experience, and large language models showed us the way, and we were able to dramatically improve the quality of the search experiences by using this technology. There's still more to this. We spent a lot of time trying to figure out what makes a good search and what makes a bad search. Obviously, we wanna make bad searches get better.
One of the factors that we found, no matter what we did, we always found that content was the most important factor in the system. That's why we started using large language models to improve the content in searches. We started to build up the connectors so that we could build better Knowledge Graphs for our clients, also powered by large language models. We also spent a lot of time making sure that we were working on the boring stuff. What do I mean by boring stuff? Well, it's things that never really make the demo, and it ends up being about 90% of the work behind the scenes. I'm talking about things like warehousing, data ingestion, data labeling, model retraining. If you wanna pull off a really powerful algorithm-powered product, you need to get good at this stuff.
That led us to this concept called the virtuous cycle of AI-powered features. One of the things that you might notice about a lot of famous products that are powered by algorithms like, you know, Google Search, ChatGPT, Netflix, Instagram, all of them have this virtuous cycle, which is the more you use them, the better they get, and the more of a strategic moat the company has who makes them. So the cycle starts with good logging, warehousing, and instrumentation. You need to be able to log predictions from your models and then all the interactions that end users end up having with those. In ChatGPT, for example, this is when you thumbs down a bad response. In Netflix, it's when you click on a video they recommended. Instagram, same thing.
Once you have this data that's coming out of this instrumented system, you need to start getting it into the hands of humans to label and retrain and improve the data. You have to use that as the basis for what you do next. We have a great labeling team here at Yext, headed up by a linguistics expert, and she manages all the hard work that's required to write correct labeling guidelines, to hire the annotators, to make sure it's all done correctly and ethically. Once you have that, you can then be ready to deploy your models out into production, into new features, and the cycle repeats it again.
The more and more you use our products like Chat and Search, the better they get and the more of a differentiated data difference we'll have and a moat that'll be strategic for us. How do we actually go about building one of these models? Well, I'd like to walk you through some of the technical and operational details of building a large language model or a product feature powered by them, or in other words, what the data science team actually does on a day-to-day basis. Well, at its core, there are three steps and two artifacts that are produced when you train a model. I'll start with the two artifacts. Every large language model is comprised of two things, a computer program that defines the model and a set of model weights.
All large language models these days are implemented as deep neural networks, and they are written in Python programs which define all of the neurons and how they interact with each other in that artificial neural network. The weights define how strongly they are connected to each other. When you have these two things together and you've obtained the weights through training, you can start to do inference, for example, in a product feature. In the first step of this process, you need to define your problem. Are you working on chat? Are you working on search? Are you working on review response? Are you working on content cleaning? Are you dealing with images? Are you dealing with text? All of these, the answers to all of these questions have a big impact on how you structure your program and the architecture of the network.
That in turn is going to define what you put in your implementation. One thing I wanna point out here is that large generative models aren't necessarily using a new problem framing. The idea of generation has been around for a while, but what has gotten different and better about them is that the architectures are way more complex and capable. The more things change, the more they stay the same. You still need to do a really good job with the nitty-gritty details and execution, warehousing, labeling, that sort of thing. In the second step, you need to get good, clean data to train your model. That's where that virtuous cycle I was just talking about comes back in again and where our labeling team has made a big difference.
They make sure all the data is labeled correctly, ethically, and in a brand-safe manner. We've made big investments here for a big difference. Your third step. Now you are ready to run the training or inference, and you're ready to deploy your feature in production. Building effective infrastructure for using graphics processing units is really important in this area. It's a place where we've focused quite a bit of energy. You've seen in Mike and Marc's slides how important the accuracy of these models can be, especially with generative models. Building product features powered by large language model technology must take this into account. You really need to make sure you're getting the balance right between speed and cost and prediction accuracy. I mentioned these two artifacts, code and weights.
How do you get them? You've got three options here. Option one is that you can build the program and the weights entirely from scratch. This is the most strategic and most differentiated, but also the most challenging and time-consuming. Option two is you can borrow from somebody else. There's lots of people who release open source implementations of many of these models along with the weights, and this is a great middle ground because a lot of the models can be like building blocks, and you can still get a massively differentiated product without having to go through all the effort of doing everything from scratch, even if other people have access to those same weights. The third option is you can buy through an API. Many, many companies are offering access to large language models.
They're highly capable, although you have less options around cost and differentiation, but it's a really great way to get started quickly. Let's talk a little bit about the landscape of what's out there. There's a lot of models that are on the market now, and this mostly represents what's in the buy and borrow categories. This slide is from last month, and it's already out of date from March. As you can see in the upper left corner here, we have an area of this landscape that is pretty well suited for search problems. These models are really accurate at understanding words in context, but they also run really fast and could be useful in a search experience because they can run in the path of a user query.
Down on the sides here, we have the GPTs. These are available through OpenAI in a proprietary manner, this is a lot better if you're doing something like chat because you don't have the same latency requirements, you want a better accuracy here. They're a lot bigger and slower to run, they are well suited for that application. Lastly, in the upper middle here, we have BLOOM, which is a good research model if you're working on problems related to content extraction, or summarization and review response, that's also good if you're not in the critical path. Since it's open source, you can run and retrain it on your own infrastructure. The point I wanna make here is that we are all over this map right now.
The last area, where do we see things coming next? Well, I think the first thing is that it's important to be flexible with many models. I think the space is gonna continue to be commoditized, and just being able to plug into a lot of them is gonna be an important thing to do. Second, I think it is gonna be continued to remain important that we continue to execute correctly in terms of balancing, cost, latency, and accuracy, and choosing the right model for the right job at the right time. Third, we wanna keep building out complex Knowledge Graphs for our clients because it's clear these models are becoming better and better at reasoning, and that opens up a pretty exciting possibility.
Like, just imagine what you could do if you had all of your enterprise data stored in a Knowledge Graph, and then you could use one of these large language models to reason about it. What kind of digital experiences could you build with that? It's pretty cool. I will thank you, and I'm gonna hand it back over to Marc.
Thank you, Michael. All right, now I'd like to show a brief, 10-minute demo, and I can just tell you right now, 10 minutes is not enough time to show all the things that our platform does. We picked out some of the highlights, and we wanted to show you an example of what a customer journey looks like on our platform, and then what does it look like to make or to basically change some of the parts of that journey. Yext is a platform to enable businesses to build modern AI-driven digital experiences. I wanna start off by showing you an example of one of these experiences. For the purpose of demonstration, I'm gonna use a fictional ski shop in Durango, Colorado, called the Ski Warehouse. Let's take a quick tour of that digital experience.
The digital experience for the ski shop starts on third-party sites like Google, Facebook, Bing, Apple, and many more. You can see when we search for skis in Durango, Colorado, that the Ski Warehouse shows up in the results. When I click on the listing, you see information about the store in addition to reviews and social posts for an offer for the Nordica Enforcer skis. All of this is powered by Yext Listings and Reviews. You can also click through to the website. Here's the main website for Ski Warehouse, all built on Yext Pages. It has navigation, shopping cart integration, product search, category pages, and featured products. All of this is powered by Yext Pages and Yext Search. At the bottom, it has something called Guided Search.
This is a wizard-like flow that helps the customer select the right product for them, even if they don't know what they're looking for. Let's go through the ski Guided Search flow now. The flow will ask a series of questions to help narrow down the choice for recommended skis. This experience also has our recently announced large language model chatbot called Yext Chat integrated right into the page. Yext Chat is acting as a shopping assistant that helps the user as they go through their journey. We can ask it questions like, "What are all-mountain skis?" "What's the difference between that and big mountain?" We even ask it, "How late is the Durango location open until?" Pretty good. Let's select all-mountain skis and continue through our Guided Search.
The guide has recommended a number of skis at different prices based on the questions answered. You can also do classic product search to find a product. Let me search for Nordica Enforcer 100s. This is product search, classic product search, where you can sort by price, name, or change the different facets. Let's click into the Enforcers. It will take you to the product page. This page has images, descriptions, specs, related products, and product reviews, all powered by the Yext platform. This site also has support functionality. You can ask questions like, "What is the return policy?" Get a great answer. "Types of ski bindings." I also get a great answer with articles, FAQs, and videos. Everything you saw here was built and delivered on the Yext platform.
All of the content you saw on Google, in Search, on the website, in chat is all stored in our headless content management solution that we call the Knowledge Graph. The entire customer journey is all powered by Yext. Let's take a look at the platform behind this incredible experience. This is the Yext platform. We're in the account for the Ski Warehouse. Everything starts with the Knowledge Graph, our headless CMS. The Knowledge Graph is our core content store where we manage information. You'll notice that we have things like FAQs, things like ski bindings, ski boots, skis, terrain, snowboards, videos. If you click on FAQs here, you'll notice that there is an FAQ around the return policy, which you may remember from the earlier demo. Let's take a look at some of the skis that we have inside of the Knowledge Graph.
Let's do a search for the Nordica Enforcer. Let's update the Nordica Enforcer from 2022 to 2023, and let's update the price from $799 to $899. Now, what you see here is actually all the information that we have in the graph, including the images, the descriptions, the sizes, details, specs, and other related entities such as ability level and terrain. Now, as I updated the information in the Knowledge Graph, and because we're using a single headless CMS to power all these different experiences, all the experiences are gonna update instantly without having to make any more changes. This would flow through to update everything that I showed in the previous example. Let's take a look. Here we have the main website.
As you can see, the Nordica Enforcers have now been updated from $799 to $899, also updated from 2022 to 2023. Let's search for the Nordicas. You can see in search, it's also been updated to reflect the new price and the new year. If we click into the product page and I do a quick refresh, what you'll see is also in the product page, we've updated it from 2022 to 2023 and from $79 to $89. Let's see if it's been updated in chat. How much are the Nordica Enforcer 100? As you can see, it's also been updated here too. With a traditional platform, you'd have to manage search and your website and your chat as totally different systems, generally, they're gonna get out of sync.
As a quick test, you can go to any site on the web and try a search, a chat, and browsing around, and I can be pretty much sure that for most businesses, you're gonna completely find different answers across all those different experiences, let alone keeping it all in sync with third-party experiences like Google, Facebook, and other sites. We also have our Pages product. Pages makes it really easy to turn your content into a full working website. Here's Pages down here, and you can see the commerce site that we were looking at earlier. I can click into the commerce site and let me go into the Deploys tab. This is what an administrator would see when they're working on their website. There are two different ways of editing the visual aspects of that website.
We have a decoupled architecture, this is all about the presentation layer, and the content layer lives in the Knowledge Graph. The presentation layer lives in a GitHub repository. Here's the actual code powering this ski website. You can see I can click in here and click to the source and dive into the templates and even find the template that's powering the product page that we were looking at before. You can see that we're pulling in fields from the Knowledge Graph and then rendering those fields into the UI. This is where a developer would come in, write code, they push it to GitHub. We will automatically deploy via our pipeline and push this to production. You can have different branches, and you can have production links, and there's a lot of capabilities in here to deliver on the enterprise-grade workflows.
The other option you have for writing the template is our Studio product. Studio also sits on top of the exact same GitHub repo, but it's meant for non-developers. It's meant for someone who can have access to the underlying components but can only really move them around and make some minor modifications. Here you see is an example of the ski product page from the demo. I can very easily come in, click on something like the batter, and I can come in and actually make color change. I'll change it to a Yext black, ultimately I can save this. Under the hood, all that we're doing is we're making a code change to the React repository.
This setup allows for both developers and point-and-click users to easily be able to go in and make changes and deploy those changes. I'm now gonna go back into the Knowledge Graph. I am going to create a new blog that is called Telluride in Gem of the West. We will publish it. I'm now gonna fill out the content of this new blog, but I don't really wanna start from scratch. I'm going to actually use a brand-new feature that we just launched called Computed Fields that allows you to generate content inside a field based on rules. In this case, this field is set up to automatically generate a full blog based off of the name of the article.
It's using generative AI, so it's using GPT under the hood to write this blog, and it's trying to write five paragraph blog based on the title. It looks like it's returned a new version of the blog. Let's take a look. Well, it looks like it's got some great information here about Telluride. Not bad. All right. You know what? I wanna put an image right here at the top. Let's do that. Let's open this up, and I've got a Telluride image that I can add. All right. That looks pretty good. All right. Let's take that. Let's save it. I also wanna add a cover photo. Let's add a cover photo there. Okay. All right. Looks pretty good. Now what else do we need? Okay, we need a summary. Summary is pretty important, so let's generate a summary. Okay.
Discover the beauty of Telluride. Pretty good. Subtitle, also very important. Wanna make sure that we are SEO optimized. Metadata, same thing. Let's take a look. Let's generate the metadata based on the contents of the blog. Looks pretty good. Of course, keywords, very important. Maybe we'll change this a little bit to get rid of outdoor adventure. We don't want a keyword stuff here. Okay. Looks pretty good to me. All right. Now we have this new blog about Telluride. It was really easy and quick to generate new content inside of the graph based off of information that we're seeing, based off intents that we're seeing. In this case, I was able to add a new blog using the generator. If I go back to the site, you'll notice that this blog now shows up.
Let me bring up the site, and here we go. You see the new blog. If I ask the question in chat, let's say I ask a question like, "How many skiable acres does Telluride have?" You see I was able to pull out the answer, over 2,000 acres of skiable terrain across two mountains. It pulled the information from the blog. Not only did I use the generative AI to help write the blog post, but you can see how that improves your chat experience as well because it pulled the information from the blog itself. All right. I hope that gave everyone a little 10-minute taste of what's possible with the platform, what we mean by digital experience, the sort of broadness of that definition. We'd like to take it a step further.
Next up on stage, I wanna bring Christian Ward up. He's our chief data officer, and he's gonna talk about all the things that our customers are building on our platform. I think this is a really exciting session.
Excellent. Thank you. Hello. My name is Christian Ward. I'm the Chief Data Officer here at Yext. My role here at Yext is to work very closely with our customers and our partners on their data strategy to optimize their usage of the Yext platform on their journey toward AI-powered digital experiences. Today, the digital experience at all of our customers suffers from two key challenges. The first, called the information overload paradox, states that as we all have access to more information, our ability to process that information as humans actually declines. This is also known as cognitive overload or analysis paralysis. Fundamentally, this means that if I were to Google search something like dinner near me, I'm met with over 5 billion search results, just for dinner.
This content and sensory overload, it's slowing down the customer journey, and it floods the customer with too much information. The second major challenge is the explore versus exploit trade-off. Consumers are typically balancing their time through different mindsets as they engage in any digital experience. The first is exploring their options, learning about a topic, a product, or a service. From there, though, the individual typically shifts their mindset to more of an exploit mindset, meaning to take an action for their own benefit. This is where many digital experiences struggle. Better digital experiences bring the exploit options much closer to the exploration so that people know what to do next. Essentially, digital experiences have overloaded human beings with content while also making it very difficult to know what to do next. This is where our customers leverage the Yext platform.
We address both of these challenges to build better digital experiences. By centralizing the authoritative human-curated content, data, and knowledge of every organization, companies are building on a platform that can leverage AI tools in a multi-model design across all the different digital experiences that they need. Let's look at some of those experiences from our customers. When the state of New Jersey needed to build their COVID-19 information hub, they leveraged the Yext platform to store their knowledge very efficiently. This knowledge is deployed through this website. I'd like to point something out. This is probably the only government website you've ever seen that does not have drop-down navigation. This entire website is designed to engage the citizen to ask a question directly of the state of New Jersey.
Now, inside the platform, the state of New Jersey can analyze all of the questions and engagements from their citizens to identify areas of new questions, concerns, or even opportunities to better the entire citizen experience. For example, by listening to the citizens during the pandemic and every query typed into that search bar, they were able to identify that people had concerns about evictions due to the hardship, the financial hardships caused by the pandemic. They utilized the Yext platform not only to answer the question on their site, but also to publish that, as Marc showed, on a site, on a page that also brings that content directly to the Internet.
Now when someone types something in like, "Can I be evicted in New Jersey if I have COVID issues?" They're actually met with the authoritative answer, the top result, where we're telling them directly from the state of New Jersey, this is the access point for where you can find more information on this. What's great here is once they click this link, it takes them directly back to the state of New Jersey's COVID information hub, where they can ask their next question directly of the state of New Jersey. By centralizing knowledge from the authoritative source, our customers can leverage the Yext platform across the entire digital experience, both on their site and off their site. In healthcare, customers are using the Yext platform to link patient care to physician availability.
In government, we are helping bring clarity to complex processes like the patent process at the United States Patent and Trade Office, directly surfacing the right information from their Knowledge Graph. Digital transformation in industries like finance is all about simplifying the dialogue, cutting down to those lean data inter-interactions with the customer. With First Citizens Bank, their homepage, content, and sites have undergone a massive transformation where customers no longer have to hunt through countless drop-down menus. By leveraging the Yext platform, this digital experience is as simple as asking a question. No matter what the industry or what the customer type, the Yext platform provides the digital experience with an authoritative, curated source of knowledge. Now, here at Yext, we are our own best customer for our platform.
To give you a sense of the power of the platform, let's dive into the actual Knowledge Graph we use here at Yext. The first thing you'll notice in our own Knowledge Graph is we have close to over 160,000 different entities. This is the data where we're storing all of the different content, things like our information and all the linkages between that information. I can see right here our blog posts, our FAQs, our jobs, our locations, our products, our services, even our integrations, all stored as entities with their attributes and the relationships to each other to build those digital experiences. As Marc just demonstrated, if I go just below this section, I can see all the digital experiences that we're also producing from the graph. I can jump to any given website.
For example, internally, we have an agent desktop to assist all of our internal teams with any customer service requests, and this entire system runs on the Yext Knowledge Graph on the platform. I can jump back into the Yext platform and launch into a completely different experience, one that I'm very fond of, which is where the ideas from our developer community are proposed by people that use the platform. They're submitted, stored, and even voted upon inside the Yext platform. Again, we store all that knowledge here on Yext. If that developer has a question in the ideas section, they now can launch our new AI-powered chat. Again, where we're taking AI and AI tools and sitting them on top of the authoritative knowledge that we have curated for this digital experience.
Here's a completely different digital experience over at our Hitchhikers developer portal, where they can search through all of the API documentation on how to build on our platform, as well as go through all of the content in our learning modules. Again, all stored here. From our marketing site to our internal intranet site called Telescope, the Yext platform streamlines the entire process into different experiences, each designed to meet the person directly on their journey wherever they are on their journey. When I jump back to yet another Yext experience, this is our internal knowledge base. I'm reminded that all of the searches, all of the conversations, all of the chat, all that content, the engagements within the journey are being captured and stored in the graph where we're applying our AI research tools to then look at all of the questions.
We can see everything that our customers, our employees are asking us, and that in turn helps generate new knowledge and new experiences. In fact, similar to the way Michael was speaking about in his large language model discussion of the flywheel, there's a business flywheel here for every one of our customers. It's moving them from this process of the classic marketing monologue to a far more personalized digital dialogue. The future of digital experiences are driven by the customer providing direct information to a company via search, chat, or other methods of conversation. This dialogue creates a flywheel effect where each experience helps optimize the knowledge that is available and primes us to build that next best experience. For example, at our customer, Cox Communications, we recently had the pleasure of participating in their digital summit.
We examined different types of questions users were asking through their search experience platform, which is powered by Yext. Cox Communications stores almost 60,000 different entities in their Knowledge Graph. Leveraging our AI-powered cluster analysis, we identified that there were 100 of questions on how to forward phone calls just in the previous two days to the conference. Now, if I ask an AI to write an answer to this question, as was demonstrated before, the results are a little disappointing. If we used a third-party AI-generated content at the conference to show them, and it generated this on how to forward one's business phone, this was the question that was constantly being asked, and it generated the content. Everyone at the conference very quickly realized that this answer, while interesting, is in fact completely wrong.
This is an answer, if you look closely, for remember the 1990s and 2000s office desk phones with the CW Forward All button? That's what it went out and found because it didn't have the authoritative data to answer this properly, in a much more current time frame. Alternatively, inside the Cox Communications Knowledge Graph from Yext, if you ask that same question, you get the exact direct answer from them. To hear more about our Cox Communications partnership with Yext, I'd like to welcome Jim Robinson, the AVP of Digital Operations of Cox Communications, to the stage. Excellent. Good to see you, sir.
Thank you. Thanks for having me.
First, thank you so much for being here and joining us. It was a pleasure to be down with you all a couple of months back. First, to start off, I'd love for you to just tell everyone about yourself and your role and your mandate.
Sure. Those of us that are old enough to remember the dawn of the internet, there was this thing called the Webmaster, and my organization is what a Webmaster looks like in 2023. It's a little bit of a junk drawer. We have UX and a lot of our content, and then obviously, you know, tied to Yext and our partnership, SEO falls under my organization as well.
Excellent. knowing that it's all of these different digital areas, could you tell the audience a little bit about Cox Communications?
Sure. Sure.
Very large organization. We love working with you in this partnership because you do touch so many different parts, different customer types. Tell us about Cox Communications?
Yeah. you know, we are privately held, that means that we kind of fly, I think, below the radar a little bit. We are the third largest cable television provider and fourth largest cable internet provider. We have fairly substantial markets in Phoenix, Las Vegas, Orange County, really across the U.S.
Excellent. We've been in partnership since about 2015. In that process, that eight years, you all have been one of the earliest adopters of each of the different technologies. You know, take me back to as we started working, you know, what was the process for engaging with Yext and how we worked together?
Yeah. You know, unbeknownst to my team at the time, we were leveraging your Listings product quite successfully. That, you know, big companies kind of being what they are, was siloed away kind of with our retail stores, which is, you know, makes perfect logical sense. When I first started with Cox on the digital team, we didn't really have any kind of established SEO discipline or practice. It took us about two years to clean up a lot of our technical SEO stuff, URL structures, and page, you know, crawl budgets and all that good stuff. We had just gotten to the point where we were starting to look to exploit like all of the work that was kind of foundationally done.
At the same time, just kind of coincidentally, we are primarily an Adobe shop. Their tool, Search & Promote, which was kind of their internal site platform, had gone end of life. You know, we had been shopping around for, you know, a new tool, and Yext had started to kind of come up on our radar for that. Your local pages and, or Pages, the local pages that we use, had hit our radar from an SEO perspective. As we started to work with supply chain, just ironically left hand, meet right hand, they're like, "Yeah, we already have a relationship with these guys," and we had called you in to do a demo 'cause Answers had just started to kind of become a.
I think we're even in beta, to be honest.
Yeah.
Yeah. It was just the perfect kind of timing and opportunity, and we needed it, and you guys had had it ready to go, so.
Well, knowing I wanna say that was in 2019 as we started the process, and you utilized Answers both for the Answers Platform for search for the residential or the retail.
Mm
and the business customer. Tell me a little about working within the Yext platform. How you use the platform itself.
Okay
... because we have the Pages, we have the Listings, we have the Search.
Yep.
How do you leverage all those different pieces?
You know, you talked a lot about the Knowledge Graph. I was a little bit disappointed to hear that you guys have 160,000 entries, and we only have 60,000. We're gonna have to do better there 'cause I feel like we should, you know, have at least as many as you guys.
Yeah.
You know, my team is one that supports both lines of business, so we support residential and Cox Business. Obviously, the, you know, as the org goes, there's different teams that focus on that. We really see the Knowledge Graph as kind of the underpinnings of this whole thing. Again, you know, kind of as we got started in this journey, didn't maybe realize the true power of that, you know, just at first glance. As you get more familiar with the product and the tools, you really see that that is kind of the linchpin to this thing. That Knowledge Graph really does power everything from our Listings experience to our Answers experience to our Pages experience.
You know, our focus is, you know, for my team, is primarily on Answers and the Pages. The Knowledge Graph and the capabilities of the tool have allowed us to kind of segment, different responses and different content to, you know, the type of audience that we're addressing. If you think about, you know, the use cases that we have for a non-customer that are searching for, you know, internet service or TV service, those questions are really around price and availability and, you know, maybe what channels and speed. You know, that's a vastly different use case than, you know, what a current customer would have.
The power of the platform, you know, enables us to kind of pivot on several access points around, you know, customer versus non-customer, even locality. A lot of our prices are, you know, different depending on the market. The Knowledge Graph and the tool itself gives us the flexibility to kind of address those use cases and make sure that the content is relevant to the customer and that we're answering their questions.
Excellent. I know I think 2020 we decided to do a case study together...
Yep
... in partnership with Cox. Couple of interesting data points off that you had 51% increase in site search conversion rates, and a 59% decrease in repeat on-site search, meaning they had to refine it or type something else in.
Mm-hmm.
How does that impact the business when you can show percentages like that?
Yeah. I mean, you know, I touched on it a little bit, like not just from a Yext perspective but from a search perspective generally on our site. You know, we have really tried to make strides in relevancy and trying to answer the customer's question. Cox being the brand that it is, it's pretty easy that Google's just gonna promote some content of yours. That doesn't always mean that it's the right content, and I think you see that in those, you know, in those statistics, right? We were serving pages because Cox Communications was in the query, but it wasn't really relevant content to the customer, and so that leads to frustration on their part. They may be getting, you know, content that is not necessarily germane to whatever question they're asking. It's just a bad customer experience.
From our perspective, you know, search, you know, our organic search is some of the best converting traffic that we have from a sales perspective. When you look at the current customer side, a lot of that is about call deflection. We're, you know, making if the customer gets the right answer, and we're giving them the content that they need to solve their problem, you know, that's a call that we don't have to handle. You know, realistically, most people don't want to call anyone, right?
Right
... these days. Like, they just want their question answered. If we can make it simple for them, I think that just holistically the better relevancy is, you know, somewhat of a halo effect for the brand, especially for somebody that's providing kind of a technical service. Like, you want somebody that appears to know what they're talking about. That just adds credibility to us that, yes, we can answer your question, we can get you the information that you need and that you're looking for.
Well, the beauty of that is this is really how trust is built between.
Mm-hmm
... the brand-
Yep
the consumer, which is, as I ask questions. If I get good answers, great. Let's keep the conversation going.
Yes.
Very much like if you walked into a store and talked to a human.
Yeah.
That obviously adds trust to the entire process. When we were at your summit, we talked a lot about what was coming out in the spring release. And I'm thrilled with the spring release with generative AI, with the new Yext Chat system. Knowing that you have this graph.
Mm-hmm
you know, knowledge, what are some of the things you see going forward? How this platform can help in terms of utilizing these new released tools or these new options.
Yeah. I mean, we talked a little bit about this backstage, I feel like the kids that like the indie band before they got popular. You know, we've been talking about this for, you know, three or more years now, always trying to, you know, is it ready yet, is it ready for prime time? I think finally, you know, we are on the cusp of being able to really put production-ready use cases out, we've got some things that, you know, we've been working on. From, you know, from my perspective, being the operations guy, a lot of my world is consumed with efficiency and scale. You know, internally and just human nature, like, people think of innovation like, you know, Meta and the VR glasses and things like that because that is cool and hip.
There are real, you know, bottom-line use cases to being able to scale this type of stuff. For us it's difficult because we know the value of personalization, we know the value of one-to-one marketing, we know the value of these segments, but the reality is, you know, there is a return on investment equation in terms of like what my team can support and for those use cases. AI really just kinda tosses that out the window. You know, I can, I can scale now almost infinitely.
Obviously, there's some business concerns and, you know, Michael, I think did a good job earlier of showing like, "Hey, this thing is not always 100% right." We're gonna work our way into that and get comfortable with it, but I think the product that you guys have, the way that it kind of creates somewhat of a walled garden gives us that safety net-
Yes
... that you're not just out there, you know, just showing everything that's on the internet. That, those capabilities, I think, really open up a just, you know, a whole new universe for us in terms of being able to scale personalized content. I mean, that's just the easy button. I think, again, we're on the cusp of, you know, the early days of the internet, where everybody was just. You know, your mind couldn't even work fast enough to put all the use cases together. We see opportunities in, you know, quality score with paid search landing pages and, you know, the list goes on. There's a lot of stuff that we can do here, and I don't even think we've not even scratched the surface, very exciting times.
It really is. I can say from working with you for years, I truly appreciate the partnership. very much appreciate you being here today, and I look forward to building those next few you know, fun toys with you.
Right. Yeah.
Thank you again.
All right. Thanks.
Put your name on the list please.
Customer gets clapped. Not we don't get claps. Maybe y'all give claps.
This video is...
Arianna has a-.
We're now gonna begin our Q&A session for the first half, and I'd like to invite Mike Walrath, Christian Ward, and Marc Ferrentino back to the stage. While they're assembling, if you do have a question from the audience, please raise your hand. I'm gonna bring the microphone to you, and please ask your question into the microphone for the sake of the folks that are listening in on the webcast.
Oh my God.
All right, who has a question? Here we go.
Great. Tom White, Davidson. First off, thanks for doing this, guys. Really appreciate it. Maybe one for you, Mike, a higher level one. You know, when you rejoined or I guess took over the CEO role, I think one of the things you talked about was trying to kinda simplify the customer experience, simplify the go-to-market.
Yep.
You know, flash forward a couple quarters in this explosion in AI as a topic and kinda just a dynamic backdrop there. Can you talk a little bit about how, you know, that may or may not kinda complicate things for you in terms of kinda simplifying the business versus kind of weighing.
Yeah
... you know, the opportunities that that creates?
Yeah, no, thanks for the question. I think, look, if this had happened two years ago, I'm not sure I'd be sitting here. You know, I think what we've been working on and, you know, what we pivoted towards, and I've talked about this a lot, very aggressively in 2019 and 2020 was the expectation that AI was gonna explode, right? We were super way too early on that, and I think it caused a lot of the challenges that we've seen, which is the conversation that, you know, we've been trying to have with customers around creating digital experiences using AI, and the burgeoning power of AI to do this, has been very difficult to have because they had. You know, there was just not a lot of interest in it.
You know, you heard some of this. You heard complaints about, "I don't know why you're in here trying to sell me something that doesn't exist yet," when you have product that, you know, does in terms of Listings and Pages. As, you know, hopefully what you're beginning to see is as we get through this cycle, how these things work together and how they fit together, and that what we're doing today is, I think we're taking another step in the articulation of, you know, how Listings, Pages, Reviews, Search, and the idea of digital experience, the idea of a next generation content management system, they all do fit together.
I think a lot of that kind of wandering around in the woods of expecting this to happen faster was, you know, was being, you know, well ahead of where the market was ready to be and having a really hard time explaining how Listings could possibly be related to support search or site search or things like that. You know, I mean, I don't think I'm telling anyone anything they don't know to say that that was a struggle for us.
What we're finding today is that when we present the type of discussion that we've shared so far today, and obviously this has been very product-centric with customers, instead of blank stares, what we now get is a lot of head nodding, a lot of, "Yes, you're doing a good job of describing my problems and a good job of describing the challenges that I face," and, "Oh yeah, my CEO's very worried about what we're gonna do about this AI thing 'cause it feels like opportunity, but it also feels like risk." You know, to me, this is not a... What we're doing here today and what we're doing in the market isn't a big bang new release. It's not new messaging.
It's the logical progression of our message, and hopefully it makes things clearer in terms of how we're going into the market. You'll hear more obviously in the second half about how we're taking it to market, marketing, go to market, things like that.
Please say your name and your affiliation in the microphone. Thanks.
Hi. Neil Gagnon, Gagnon Securities.
Hey, Neil.
I'm struck with the Cox example and wondering how much of the opportunity is with new logos or doing business with the existing ones you have?
Did you look ahead in the presentation?
Sounds like he did.
We're gonna talk a lot about that in the second half. I would say, I would tell you both. I think there are huge opportunities with the new logos and also with existing customers.
Yeah, just to add, I mean, think every single customer that's using our Listings product, Listings sits on top of the Knowledge Graph. They're already using a portion of our headless CMS system already. That's a big part of where the opportunity lies, in expanding the number of use cases, the number of channels that they can manage while already having that content management system that they're using today.
I mean, in fairness, you know, I told you the first call, you know, there was a little bit of taking a step back here, right? We had to get back to the basics. We had to re-convince our customers that we were committed to Listings and that Listings mattered to us. I think we had to go back and start over and explain why Listings matters to us and why it's an incredibly important part of this. I mean, when you talk about the fragmentation of the digital experience, a lot of that's happening off your own sites. Having the capabilities to do that on third-party experiences is gonna be an incredibly. Which is what Listings represents amongst other things.
There's a big opportunity in both categories, and we'll talk about it a bit more.
There we go. Hi, Ryan MacDonald with Needham. Thanks for hosting us today. Just starting with, I thought it was interesting in the Cox conversation about the number of entities that was in their Knowledge Graph versus Yext. It seems like as ChatGPT and generative AI continue to be utilized, that you're gonna need to grow the number of structured facts with your customers. How do you sort of drive that conversation and maybe get customers comfortable with that so that they can take advantage of sort of the fuller platform? Thanks.
Yeah. I think if, you know, what you don't do is you don't walk into the organization like Cox and say, "Okay, we're gonna take every piece of data right now, and we're gonna put it all on the graph." That would scare the hell out of them. Actually, I think it's the wrong. Someone might have done that once or twice in the past. Ultimately, what you're doing is, the best way to approach it and the way that we're approaching it, is you take it use case by use case. Today it's site search, tomorrow it's support search, the next time it's, you know, it's knowledge base, the next solution may be a partner portal and so on and so forth. Eventually, over time, the knowledge begins to build up in an incremental fashion.
the, there is a virtuous cycle, of course, that happens here. There's the virtuous cycle of the customer or employee interfacing with the different experiences, just feeding intent and requests for knowledge into the system, which then, of course, the response to that is to generate the knowledge or find the knowledge inside of the organization. the other thing that happens over time is that you become the place where all the knowledge is, and that has obviously a virtuous cycle that's driven there. the next project that come along is the next use case they need to solve, it becomes sort of very easy to just solve it on the Yext platform.
Yeah, I mean, I'd just say Christian, and Christian may wanna say something about this, but the thing he showed you with the clusters and the analytics on the search, there is no more. There's no better way from my point of view for a customer to realize that they need to put more information into the Knowledge Graph than seeing that they have questions being asked that don't have answers.
Yeah, that. One of my favorite parts about working so closely with our clients is, it's usually a few weeks into the launch. They're starting to get the data in from the customer journey. We're sitting looking at that screen I was showing you. They kinda cock their head and go, "Huh, I didn't think people would ask us that." It's, it's enough traffic to then realize that there's a huge opportunity if you can answer that. That cycle really accelerates, but it's one of the best parts of actually sitting and watching customer engagement start to build the prioritization of the next entries into the Knowledge Graph.
Hey, guys, Rohit Kulkarni from Roth MKM. Yeah, thanks for the demo. I think the demo was very cool. Whoever made that was very cool. I think, philosophical question for you, Mike. I think you said companies cannot force revenue growth and balance efficiency at the same time. You have this new, very large potential opportunity in AI. How do you balance growth versus investments in AI as the year progresses, as the next two, three years progress? Maybe for Christian, I think you've been at the cusp of BERT and all LLMs over the last so many years. Next, maybe talk about next couple of years. Where do you see this acceleration in AI kind of adoption evolve into?
We saw some use cases here from CMS, and you have a CMS, and you can create a lot of new use cases. How do you see that evolve in terms of not just internal but external CMSs that all these companies can have?
I'll take the first question. Philosophically, I mean, you notice I chose my words there very carefully. I looked down, and I read them off the monitor because I wanted those words to, you know, this is the English major in me. I wanted those words to be specific, and force is the key word in that, in that sentence. You know, if you take anything away from today from a go-to-market standpoint, a selling and marketing standpoint, it's this, that, you know, the attempt to force growth when the conditions for growth aren't good, aren't there, is what creates inefficiency, right? You know, there's an acceptable amount of inefficiency when the conditions for growth are right.
You're gonna hear Tom and Raianne talk about, you know, what the attributes of this are. You know, clearly businesses that are growing faster, they can afford to be a little less efficient. What we can't afford to do.
Is, you know, out of a sense of desperation to drive the growth faster than it's accessible to us, is try to force it, because what winds up happening there is you're gonna see our expenses are gonna go up, you know, particularly from a selling and marketing standpoint. The growth doesn't materialize because the conditions aren't right for it. A lot of what you're gonna hear me talk about as we go forward is, you know, trying to help everyone see more about how those conditions are developing and how things like productivity and retention and pipeline and the macro environment all have to work together to get to a place where you can have those investments actually pay off.
When it comes to, and Darryl will get into this in his section, when it comes to the investment that we made in AI, and we've talked about this the last few quarters, we're increasing our R&D spend. We're making the core investments that are required, to stay ahead of the market, because we're way ahead of where I think any competitor is, when it comes to being able to deliver the types of experiences that you heard Jim and Christian talk about, and many of our other customers.
Philosophically speaking, I think if you take anything away from that statement, it's know that we're not going to overspend to force growth that's not here when it comes to selling and marketing, but that we will continue to invest in R&D to make sure that we stay way ahead of the rest of the market, if that makes sense?
Excellent. On your second question, I think Michael, our Director of Data Science, did a great job of showing this concept. He had a slide, it was from March, and he said it's already out of date. I think since you've been sitting here, it's out of date. The number of refinements to these models is just gonna keep accelerating. I actually had that slide in December, it was half as many entities on, in that large language model construct. What I think you will see, it's definitely already happening, is continued refinement around particular use cases, right? We're gonna see that there's the healthcare version, the financial version, all these different elements of making sure, because as Michael demonstrated, what you train these models on is so critical to what they become.
If you're looking for a model that understands a particular vertical, let's say, or, how about UConn? Like, if you went to UConn, you want a great knowledge interaction. You're gonna have something that's trained on all the knowledge that we know or, about UConn and their dynasty, right? When you go through this process, a lot of it is where do the models really add that differentiation? In terms of the next several years, it's everyone trying to identify the use cases that match to the right models. And I think that's actually quite an interesting area for us to examine, which is different models may touch at different points for different questions and different digital experiences.
Leveraging our platform as a center point of the knowledge that's available to use, but how that's used in each digital experience is going to be an area that continues to develop very quickly. I'm not sure, Marc, if you...
Yeah, I mean, the only thing I want to add is just, sort of, Michael talked about this, the way we look at this and the way that the very simple way is I think people have been using these models wrong. They've been using them wrong for a little while. They are using them as a database. They use them to answer questions. Mike's example in the beginning about his bio is a great example of that. That is not how they should be used. What these models are really good at is reasoning. They're really good at instruction. They're really good at orchestration and generation once you give them the right facts.
What you see, what this will evolve to over time is that everyone's gonna start to figure out they're using the models wrong, because, you know, they're fun. It's fun to have it write a rap song or something, but that's not really the point of it. You know, that's really where we see this going is we talked about that high-level architecture of the data layer, the AI layer, and the experience layer. That's where I think that will become the standard architecture of computing going forward, in almost every realm of computing. I like to think that we have a nice head start on it because we were able to see it earlier than most folks.
You're starting to see the benefits of that foresight showing up in the platform as we showed in some of the demos today. There is, you know, there's obviously more that these things can do, so...
Go ahead.
Hey, guys. Chet Kapoor from Tenzing. I'm curious to understand, if you can differentiate the Mike's bio example relative to the Telluride blog post example, because as I understand, that wasn't pulling from a database of information about Telluride. Why was that able to pull data?
Very specific database, right? In the case of Telluride, we were able to ask the model to write a blog about a certain topic. We gave it the topic. It wasn't completely arbitrary. That one could have easily written, you know, a bad article, which is why the human being had to be involved, why you showed us, you know, an example of me inspecting it and looking at it and sort of fake reading it in the demo. The reality is that, yes, someone would have needed to look at that.
What we weren't able to show because we didn't have the time, but we would love to show in more detail is when you combine the graph with the actual models, they write very factually accurate articles and descriptions and things of that nature. That's part of the feature that we just rolled out with Computed Fields was the ability to inject graph information directly into the creation and the generation process. That's something that is completely up to, you know, the person configuring the platform. You can even have to go a step further and have it write in a different tone of voice and capture a certain brand uniqueness in how they write. Those are all sort of aspects of combining the two together.
if you wanted to write something factual like that where, you know, maybe this needs less inspection or less approvals, you would wanna make sure that it's writing from the data, the dataset itself.
If there are no more... Oh.
Hi. Bob Johnston with Herald Investment Management. Could you follow up on your comments there about developing more of a brand? I mean, do you feel that Yext helps do that enough?
You know, will AI help take that forward and really differentiate, you know, how, you know, one customer uses, you know, Knowledge Graph or, you know, other parts of your platform versus others?
You're saying our brand or you're talking about their, them using?
Their brand to me.
Yeah.
currently giving your customers the tools to really differentiate the brand they want to rather than just be able to check boxes that they're providing, you know, enough-
Yeah.
detail, in, you know, the way that they wanna do it, but not necessarily in, you know, the brand image?
Yeah. I...
Go ahead.
I mean, at the core of what we're offering them is a development platform, right? A platform for them to build the branded experience of their choice, something that fits their products, fits their buyer, fits their, you know, fits their, the market they wanna be in. There's lots of play for expression, lots of places for expression for that company, that business who's using the platform. I also look at brand and I say, you know, on some level, you know, brand is a, is...
The sum facts of who you are sort of makes up your brand in many ways, and so, you know, as we're able to capture sort of all the information, all the facts, or as many as the company wants to put in the graph, that will allow for a unique experience in itself. There's also lots of fine-tuning that comes into this where you can start to capture brand voice as part of this through the AI.
You can show it, for example, you know, a bunch of reviews that you maybe you've written or a set of articles that have been written by your organization over the past, and you can say, "Okay, well this, you know, please emulate this, and please, this is what we want it to look like and how it to read." That gives another opportunity for each business to sort of uniquely express themselves on the platform.
Yeah. I think part of the brand piece to this is, you know, Marc makes a great point about training models on brand voice so that they actually use your brand voice, and you start to think about use cases like chat and as they get more robust and they develop. You know, the old problem with chat was that you had to basically pre-configure the answer to every question, and it was a pretty limited universe, right? The experience wasn't very good. The new problem with chat will be that if you, which is what most companies are doing, you put a skin over OpenAI, and you put it up as a chatbot, it will answer every question, right? It's not gonna not answer any question.
The old problem was the chat didn't answer any questions. The new problem will be it answers every question, right? It gives you the answer whether it's true or not. You, you can start to, you know. When you think about the brand implications of allowing AI into your business without constraining the way that it answers, the way that it talks, the answers that it gives, imagine some of the questions that you could ask a brand's chat experience, if the AI that's answering the question, the generative model, the placing of words that's answering the question is unconstrained. Like, would you like to, you know...
this is a, it's a silly example or maybe not silly, but, you know, if you put an unconstrained AI chatbot on a website, and I ask the chatbot to tell me why the management team is racist, it will, right? I mean,
It will.
I, you know, it's, it's. It will do it, right, if it's unconstrained. Your brand has now basically published on the public internet why your management team is racist, right? That's the downside of not using these technologies correctly, and I think people are just beginning to understand how big some of those downsides are. As much of what we're doing, as Marc describes, as a development platform, it's a development platform that allows you to utilize these things but creates a brand safety that is gonna be paramount to anyone who has a brand. Does that make sense? Does that answer your question?
Yeah. I mean, it's putting in guardrails.
Yeah.
Not necessarily. It's putting in guardrails, but not necessarily, yeah, enabling creativity or anything else. Yeah, as to how, you know, that, you know, data that you're, you know, passing on to your customers is being used.
Well, it's like Marc said. The way it should be used is for the creativity, for the semantic understanding, for the generative creativity, it should be constrained by the facts, right?
It shouldn't create facts.
Right. It shouldn't.
It should create experiences.
It shouldn't create make up facts. You know, another, you know, made-up example I like to use, you imagine the world of content generation where, you know, if I, if I own 100 hotels in Southern California, and I can create infinite content about what do you do in Southern California, and I can do it in my brand voice, but not necessarily with my brand attached to it. Every one of those experiences is tied back to something you can do at one of my hospitality properties. That's an opportunity to create an unbranded brand experience, if you will, at scale using the combination of generative AI technology and the facts that exist in the structured Knowledge Graph. You can brand it or unbrand it.
Okay. That concludes our Q&A session for now. We're gonna take a 10-minute break. Invite everybody who's attending in person to please go out the back doors. You'll find a coffee bar right around the corner. For everyone who is on the webcast, we understand that there have been a couple of problems with the slides. If you would please refresh your browser, you should hopefully be able to address the problem. We'll be back in 10 minutes. Thank you.
I'd like to welcome to the stage Tom Nielsen, Chief Revenue Officer.
Hey. Thank you, Nils. There's three main areas that we'd like to explore today with our revenue organization. First, how we're organized in FY 2024. Next, the big bets that we're going to be making around operational excellence and growth. Lastly, when it's time to accelerate our investments in growth again. How we're organized in FY 2024. Beginning in FY 2024, we now have a global unified go-to-market team. We're organized between our enterprise vertical and geo businesses along with our mid-market teams. This also includes our reseller channel, which is a change this year, that now rolls up to our geo businesses in both North America and EMEA. We have flattened the organization this year in North America, essentially bringing our AEs, our customers, and our executive teams closer together. Previously, we had four layers of VPs in sales, and we've collapsed that to two.
In Japan, we've implemented a partner-first model where our direct sales teams are transitioning the renewal and the net new business over to our expanding partner ecosystem. Overall, what we've seen so far is more communication and collaboration between the teams. Secondly, we're making six big bets in FY 2024 around operational excellence and growth. I understand when we're talking about operational rigor and inserting more discipline into a sales process, it's not always the most exciting topic. We needed to act now if we were going to modernize around new responsibilities, around our move in to DXP and to CMS, and selling a full platform that customers want to build on. Our alignment and roles. We've discussed the alignment, and with our roles, we've had a change with our AEs, where the AEs now own all of the commercials with customers.
This means our renewals, this means our incremental ACV, and our net new logos. So far it's been well-received by our customers, mainly from the CSM side, where customers wanna see CSMs as trusted advisors. They wanna see them focused on adoption, not necessarily dragged into the difficult quarterly renewals and negotiations that were happening. This has given our AEs the opportunity to focus on the renewals and use the renewals as a compelling event to grow our incremental ACV. Secondly, we're going to be 100% data-driven in FY 2024. This means instrumenting all of our critical sales processes with KPIs that are measured weekly. The two primary measurements will be retention and incremental ACV. We have also put in place compensation plans that incent our AEs along these two metrics. Our CSMs continue to get compensated on retention.
Other KPIs that we have aligned on are pipeline generation, new logo acquisition, early renewals, and the optimal staffing levels. We have a renewed focus on pipeline generation. Traditionally at Yext, this was solely the responsibility of the AEs. Now, PG is the responsibility of the entire company, with sales, marketing, and our partner organization all holding KPIs. Our sales team is aligned on targeted sales plays. Our marketing BDRs now have clearly defined goals and objectives, and the investments that we're making in our partner org will begin to pay off in the latter part of the year. This year, our teams have extended their use of a value-based selling framework, 'cause we know what our productivity numbers look like when we use our sales tools.
We also know the outcomes when we lead with a point of view on our value drivers of gaining operational efficiencies by building on our platform, generating engagement, and driving conversions. This has all come together into a common proposal framework that has increased the speed in which sellers provide our customers with compelling commercials, especially now that they have responsibility for the entire renewal book. A critical part of capturing our opportunity this year is our ability to sell the entire platform. We've enabled our sellers on more creative deal-making that's aligned around land and expand and tool consolidation motions, along with doing many more multi-year deals with our customers. Value realization in the post-sale customer journey will extend the momentum that the teams built in the last two quarters of FY 2023 and the positive impact that it had on retention.
From our initial post-sale handoffs to implementation, to adoption, to growth, we are truly creating a flywheel effect between our AEs and our CSM organization. All of this is wired together across our six big bets in creating a cross-functional winning team culture. Lastly, we'd like to discuss the criteria around when the time may come that we're going to accelerate our investments into growth again. Now that we have a lean and flexible organization, and we're implementing our plan for efficient and sustained growth, let's discuss how we're measuring when it's time that we'll accelerate growth further and begin investing in new quota-carrying resources. We are doing this through closely measuring the relationship between sales productivity, pipeline generation, and product innovation. Increasing AE productivity is the most efficient way that we can grow ARR without incurring much additional expense.
We are seeing productivity gains and conversion gains through the adoption of our sales tools. However, we know that this curve will eventually begin to flatten, as there's only so much and a natural limit that AEs can produce. Increasing productivity singularly is not enough for us to begin adding additional AEs. Next, we're also tracking pipeline generation into this equation. Our sellers have quarterly KPIs, and they're much more disciplined in how they're executing sales plays. We have grown our BDRs, and we've implemented a lead flow process that Raianne is gonna talk more about next. We continue to invest in our partner organization, as we said, and expand the relationships with SIs. This is a foundational year for our partner team.
While it's still early, too early to tell in our company-wide approach to demand generation, the results so far have shown very high-quality leads and faster progression into qualified pipeline. We don't expect to see the same limits or the flattening with pipeline generation as we do with AE productivity, and this should resemble more of a hockey stick type of curve. We've added product innovation into the decision equation due to the impact that it may have on both sales productivity and pipeline generation. We expect our spring release and move into the DXP and CMS technology category to be a leapfrogging event. It's simply a much higher spend and essential technology category in IT organizations where we have never had this level of capability in previous releases.
This, along with the early responses that we have received from customers with AI chat and content generation, makes us think it's going to have an exponential effect on the pipeline. Let's double-click into full platform selling for a moment and its relationship to product innovation. With the confidence that we now have in our platform against the competition, we'll now expand on our full platform selling efforts because it's the opportunity that we have to capture this year. If we were to tell you that 76% of our direct customers have two or less products, you'd be inclined to agree that a large opportunity likely exists within our install base. We're still very much focused on logo acquisition as however, sellers and CSMs this year are enabled and expected to pursue tool consolidation, broader platform adoption, motions like that, respectively.
Darryl is gonna discuss this opportunity in a little more detail later in the program. In summary, there's no single one event that's gonna cause us to increase and invest more into our sales engine. Rather a combination of us reaching a limit with AE productivity, sustained increases in pipeline generation, and the market reaction to our innovation. All of these together are gonna signal the execution and the excellence that is expected at Yext. One of the benefits for me of starting at the same time as a new CMO is we've had the opportunity to reset many of these things together. One of these is how we work together as one go-to-market team. It's my pleasure to introduce Raianne, who's gonna talk about how marketing is helping to drive this support.
Thank you. Hi, I'm Raianne Reiss. I lead marketing at Yext. I'm gonna focus on two main areas today. The first one is a recap of my first six months in role. What observations have I had? What changes have we already made to put us on the path to success? The second area I'm gonna cover is what are we focused on within marketing to drive growth for the business. It's hard to believe that it's already been six months for me at Yext. That old saying of time flying when you're having fun definitely felt true for me here. We've made a lot of progress in a short amount of time. It's been super rewarding to see small changes have a really big impact. Shortly before I joined Yext, the marketing organization was actually six different organizations reporting to six different leaders.
We've done a lot of work to bring the marketing team into one integrated team. We've broken down silos, we've reorganized the team, and we've made sure that every single role is focused on the most impactful work to drive results for the business. As a part of this work, I've upleveled the talent and brought in a new senior layer of talent. We have new leaders and functions like demand generation, content marketing, partner marketing, brand and communications, and the website. It was obvious to me pretty quickly in my tenure that the sales and marketing organization were not as connected as they should be. Sales had frankly all but given up that marketing would have a big impact on the organization, and marketing seemed to be driving activities in a vacuum.
There was a lot of stuff going on in marketing, but no one could articulate the impact of most of that work. I have a great partner in Tom. We share a common vision for what connected sales and marketing organizations should look like, operating as one team with one set of shared goals. We're a lot closer to realizing that vision today than we were just six months ago. Speaking of shared goals, the marketing organization that I walked into was determining success based off a set of highly subjective measures. I also found a team that was hungry for real goals, tangible goals, and metrics. Today, we track over 100 KPIs across the marketing organization. Every single person on the team understands what their measurable goals are, how they ladder up to the marketing organization's goals, and how they ladder up to the company goals.
Every person in marketing now feels accountable for the success of the business. Six months ago, we had a marketing organization that was heavily focused on branding activities, messaging work, brand campaigns, acquisition of new logos. There was far less activity occurring that was focused on our customer base around how we would drive consumption and cross-sell across the platform. Today, we still care a lot about driving brand awareness. However, we are spending a heck of a lot more time on things like driving pipeline and growth. More about this in a bit. It's safe to say that just six months ago, marketing was having a much smaller impact on the business than we should be. One of the areas that we've been heavily focused on, as you probably guessed, across the marketing organization, is pipeline and generating pipeline for the business.
In one of the first meetings I had with the team, I painted a vision where we would move from a model of operating really as a in-house marketing agency to one of a growth driver for the business. It's a vision that the team got really excited about. We've made a lot of progress, we're getting closer to executing on that vision. One of the areas of progress that I feel good about is rebooting the marketing demand gen engine. When I say rebooted the engine, I mean a literal reboot. We went to the ground floor of defining our lead flow processes, taking a look at the tools we were using, how those tools talk to each other, our lead scoring models, pretty much everything on the operational side.
In addition to the operational side, we also rebuilt our marketing demand engine from the top of the funnel all the way to the bottom of the funnel. The activities and things that we were doing to target and identify new contacts, attract them into the top of the funnel, engage them, and accelerate their buyer's journey. We've also aligned on a consistent messaging strategy and put mechanisms in place to make sure that we're delivering consistent messaging throughout all of our integrated marketing channels. We've implemented a new campaign framework. As a part of this campaign framework, it's really designed to drive alignment across the whole go-to-market organization on what our priorities are for the business for that year. Included in our new marketing campaign framework is a focus on reaching new audiences. One of those key, new key audiences is IT and developers.
Those audiences are becoming increasingly important to us in the platform space. We're gonna spend a lot more time focused on attracting, bringing these new audiences into the Yext family. On our path to accelerate growth, we took a hard look at the BDR organization, and we made some pretty substantial changes. The BDR org now has full accountability for pipeline. They're fully integrated into the marketing engine and really working as one comprehensive team. The changes that we made in the BDR org, combined with the reboot of the demand engine, are already starting to show really positive signs. As was mentioned earlier, it's only been about six weeks since we turned this on, and I'm hearing really great feedback from the sales team on the quality of the leads that we're producing, and that's always a really great sign.
Last but not least is turning on our partner ecosystem. From our alliances to our cloud hyperscaler partners, to our systems integrators, we see a huge opportunity to go bigger here and drive co-marketing campaigns to develop mutual pipeline. We're gonna be spending a lot more time focused on integrating our partners into our marketing demand engine. It's been a really great first six months for me. I'm super excited about the progress we've made, the changes we made, and the opportunity ahead of us. I'd love to turn it over to Lexi Bohonnon, the EVP of Global Client Success.
Thank you. Thank you, Raianne. It's great to see all of you. I lead our customer success organization, which starts in the presale with our solution engineers, our solution architects, demo engineering, and free trials team, and then our day-to-day customer solution management team, our CSMs, then of course, our services and our support organization. As you heard from Tom earlier, a key go-to-market theme over the last year has been on organization and unification of our revenue team. As a more unified and efficient revenue organization, we're certainly more equipped to better deliver on the expectations of our customers and deliver value at each step of their journey as trusted advisors. Our goal over that last year has really been to simplify the customer experience by eliminating those silos we mentioned and also upskilling the talent across all the various departments within CS.
We're really proud. We've worked hard over that year to really enable the CS team to have more confidence in articulating the value of our platform and work better hand in hand with our customers each and every day to drive adoption, optimization, and identify new use cases for them to use the Yext platform. As you also heard from Christian Ward and our customer, Jim Robinson from Cox Communications earlier, we are solving so many different types of challenges now from the Yext platform across different personas, use cases, et cetera. Now to share a little bit more about her experience over the last couple of years working with Yext, please join me in welcoming the Director of Digital and eCommerce at United Rentals, Courtney Versteeg. Welcome, Courtney.
Thanks.
All right.
All right.
Thank you so much for being here, Courtney.
Thank you for having me.
Yeah.
My pleasure.
Can you start by sharing a little bit more about your background, and tell us about your experiences and your professional career?
Sure. I'm Courtney Versteeg. I lead our digital marketing and e-commerce team at United Rentals. For those of you not familiar with United Rentals, we are the largest construction equipment rental company in the world. We have over 1,400 branches. I have been at United Rentals for three years, leading the team, and then prior to that, I spent nine years at GE in a variety of different businesses and different marketing roles.
Awesome. United Rentals has been a customer with Yext for about four years now. Help us understand how you all use the Yext platform and how it helps to support some of your big organizational goals.
Listings is really our bread and butter product that we use for Yext. As I mentioned, we have over 1,400 branches. Those branches operate across about 10 different businesses. We offer a variety of different equipment rentals and services at each of these branches, so it's really important for us to have local discoverability 'cause our customers range, right? From our small contractors to large construction equipment, sorry, construction customers. we didn't have great discoverability, so we were really looking to partner with Yext on getting those branches visible and also being able to operate centrally at scale.
Awesome. Help us understand the first few years of our relationship together. What were we able to achieve, and what were some of those early wins?
We were again very excited to onboard the Listings platform within United Rentals. We saw some great results in improving our metrics around map views, calls, website visits, and we were able to manage this centrally. We really didn't want to have our branches have to manage their own listings. Automation was also key, right? Whenever we opened a new branch, we wanted to be able to have a template, be able to have that branch launch quickly, so that way the branch can already start getting local discoverability.
Thank you. We had some success in the early years, but a few years ago, you noticed a little bit of a shift in our relationship. Help us understand what changed, and how did you share that feedback with us?
Sure. Something that was really integral when we partnered with Yext originally was we had a very strong customer success manager from Yext. He was extremely technical. He spent time getting to know our business, was extremely prepared, really helped us automate and really launch the platform. Unfortunately, there was some churn, and we lost that level of expertise within Yext, from Yext. That really did kind of put us a little bit on the back burner. We also felt that we were such a big investor in the Listings product. We weren't hearing about new features, what Yext was investing in with Listings because, again, that was so important for our branches to be able to be discoverable on all search engines. We escalated that to our account management team and SVP of Customer Success.
How did the team respond? How did the team take the feedback? Kind of help us understand what changed.
I have to say, I was quite impressed with how that happened. Within two to three weeks, we had a new support person who really spent time getting to know our business, understanding the setup that we already had, was extremely technical, re-implemented quarterly QBRs with us, support, weekly support meetings, biweekly strategy meetings, so we felt that that really was a change in our partnership. Additionally, we started hearing about new investments within the Listings platform. Again, for us, that is our bread and butter. That increases our discoverability. We want to be able to get as much content into Listings, so that way our branches are discoverable.
Awesome. Let's talk about the future.
Mm-hmm.
What are you excited for, with the platform, and how do you think we can further achieve some of those goals in the next year together?
Yeah. I'm excited particularly for Listings 'cause again, like, that's our primary product. New partnerships that we're seeing within the Listings platform, I think there is a opportunity to really add more content, to automate some of the content from some of our systems within Yext that will hopefully help us, you know, grow our branch metrics, not just phone calls, but website visits and a lot of traffic and conversion on our site.
Awesome. Well, thank you so much.
Of course.
for your honesty and transparency, and thanks for being here with us today.
Yes. Thank you for having me. Thanks so much, Lexi.
Thanks, Courtney.
Thanks, all.
All right. It is now my pleasure to introduce our Chief Financial Officer, Darryl Bond. Darryl.
Thank you, Lexi. Okay, I'm Darryl Bond, CFO here at Yext, and today I'll begin by briefly reviewing our fiscal 2023 performance and then talk a little bit about fiscal 2024 strategic objectives and finish it up with a fiscal 2025 and longer term outlook. As a reminder, we may reference certain non-GAAP financial measures which are reconciled to GAAP in the appendix in this presentation, which is also available at investors.yext.com. Fiscal 2023 was a pivotal year for us. We had significant org changes. As Mike mentioned, we took our head count from 1,400 down to 1,100. Revenue was up to $401 million, up 5% on a constant currency basis, and we ended the year with $400 million of ARR, which was up 4% on a constant currency basis.
Our direct business, which represents 82% of our total ARR, grew 6% on a constant currency basis, and we ended the year with net retention of 97%. We saw improvements in both gross and net retention during the year, and we'll talk more about this in a few moments. Our reseller business represents the remainder of our ARR and declined 6% on a constant currency basis and ended the year with net retention of 92%. Keeping in mind, reseller is the primary way we access the SMB market, which has been impacted by a challenging macroeconomic environment. We made significant improvements in profitability this year, which is illustrated by our adjusted EBITDA margin and our non-GAAP EPS.
Since we made these improvements throughout fiscal 2023, we think it makes sense to compare Q4 of fiscal 2023 to Q4 of fiscal 2022 to illustrate the progress we've made. We exited Q4 of fiscal 2023 with $0.05 of positive EPS and an 11% adjusted EBITDA margin. Operating expense as a percentage of revenue improved from 80% to 69%, primarily driven by improvements in sales and marketing, which improved by 10 percentage points. We had positive operating cash flow for the third year in a row. We repurchased 13.8 million shares, which is just over 10% of the outstanding share balance, and we ended the year with $190 million in cash as we continued to maintain a strong balance sheet. In fiscal 2024, we're focused on three key strategic objectives.
Efficient growth within the Rule of 40 framework, continued focus on gross and net retention, navigating the headwinds impacting revenue, ARR, and retention. Rule of 40 is our North Star metric. The past few quarters, we've seen an inflection on the Rule of 40, primarily driven by our adjusted EBITDA margins. Progress will continue, but in the near term, it'll be driven by adjusted EBITDA expansion. Revenue growth will take time to impact Rule of 40, given our SaaS model, and over the longer term, we see the potential for more significant contribution from revenue, which will be driven by our platform expansion and the go-to-market changes that Tom a nd Raianne just talked about. This chart here is showing our direct ARR, excluding our direct SMB customers.
Direct SMB is $8 million of ARR at the end of Q4 fiscal 2023. We've excluded direct SMB for the purposes of this analysis because SMB customers generally have one or two products. We recently de-emphasized our direct sales into the SMB channel. We've segmented the ARR by the number of products the customers purchased. For the purposes of this illustration, the products are Listings, Reviews, Pages, and Search. As you can see, the CAGR from fiscal 2020 to fiscal 2023 was 10%. This illustrates the progress we've made on product adoption within our direct channel. Our Listings and Reviews ARR has grown at a 3% CAGR over that same time period. While Listings and Reviews was the majority of our direct ARR in fiscal 2020, we have made progress with Pages and Search. This despite the headwinds we've experienced the past few years.
As Tom mentioned, 76% of our direct customers have one or two products. We have a large opportunity to drive further product adoption within our existing base of customers. This will drive revenue and ARR growth, as well as net retention. Looking at the average ARR per customer in these cohorts, enterprise customers with four products are 10 x the size of a customer with one product. This represents a significant opportunity for us to expand ARR within the existing base. Customers that purchase more products generally have more data in the Knowledge Graph and show higher rates of retention. For the past few quarters, while we refocused on customer centricity, adoption, and value, we've provided renewal rates for our direct business, which we previously referred to as gross retention. We've seen these renewal rates recover from the low 80s in Q1 to the high 80s in Q4.
Renewal rates are based on ARR up for renewal in that quarter, which is impacted by seasonality. For example, in Q4, we have bigger dollars up for renewal than we do in Q1. We will no longer mention the renewal rate in the quarter, as we believe it's not useful since it only looks at a portion of the business and is not consistent with our software peers. Rather than renewal rate, we calculated a dollar-based gross retention rate. When we look at the gross retention on the basis of ARR, we ended Q4 fiscal 2023 in the high 80s. Now, this methodology looks at ARR for a cohort of customers at the beginning of a period, then comparing the ARR balance from that same cohort of customers 12 months later, excluding upsells.
We believe this basis is more consistent with software peers and provides a view of retention dynamics on our direct ARR balance. In addition, approximately 57% of our direct ARR is attributable to multi-year deals. Multi-year deals are a key part of our strategy, as Tom mentioned, and these will help with retention. Improvements in gross retention will help net retention, which is a key metric we're focusing on. We've made a lot of progress on renewal rates and gross retention this year. We're seeing improvement in the gross retention on single-product customers, given our focus on customer value. We've seen higher gross retention from customers with multiple products, as they generally have more data in the Knowledge Graph, and we can deliver more value.
We've heard from Cox and United Rentals, two customers with multiple products that have seen a lot of success with the platform. Customers with more products typically see more value and stay with us longer. Improvements in gross retention will help net retention. Having a larger customer base provides us more opportunity for upsell. Our continued efforts to drive customer adoption provide an opportunity to bring more value to customers with more products. Marc highlighted the level of innovation and expansion within the platform that we've seen over the past few years. We believe the DXP opportunity is significant. With 76% of our customers on one or two products, we believe we have the opportunity to return to 110% net retention or more. As I mentioned earlier, reseller is about 18% of ARR and has been declining.
However, we believe over the long term, we have the opportunity to grow ARR in this channel. Nearly half the resellers have multi-year contracts, and resellers primarily only resell listings. We have the opportunity to introduce additional products into the reseller channel. As we mentioned in Q4 earnings, fiscal 2024 will have some growth headwinds from strategic decisions we made in Q4. In total, this will impact our year-over-year growth rate by low single digits. First, we expect a headwind from services revenue as we are moving low-margin services work to our delivery partner network. We'll continue enabling our SI and partner network, and over time, this network will help generate pipeline. This will provide an immediate gross margin benefit in Q1 in fiscal 2024, which we'll talk about in a few moments. Second, we shifted our strategy in Japan from direct selling to partner-led, as Tom mentioned.
We de-emphasized our direct sales to SMB and will use reseller as the primary means to access the SMB market going forward. Lastly, we expect the FX impact we saw in fiscal 2023 to continue to be a headwind for the first half of the year, along with the uncertain macroeconomic environment we're working in and the elongated sales cycles we previously mentioned. Now, these headwinds will also impact ARR and retention. In recent years, growth in our lower-margin services revenue has pressured our overall gross margins. In Q4, we reduced the size of our services organization as we're transitioning the lower-margin work to our delivery partner network.
This will provide an immediate impact to Q1 gross margins, moving us to the middle of our 75%-80% range, and with continued improvement to the high end of the range throughout the rest of the fiscal year. In addition to our gross margin expansion, we also expect to increase adjusted EBITDA. Growth in adjusted EBITDA in fiscal 2024 will come from incremental revenue, gross margin improvement that we just spoke about, and also operating efficiencies as we now get the benefit of a full year of a leaner cost structure. Improvements in adjusted EBITDA and revenue growth will result in operating cash flow growth in fiscal 2024. As growth has slowed and efficiency improved, adjusted EBITDA and operating cash flow have come more in line. We expect this trend to continue.
With acceleration in revenue growth, we would expect operating cash flow to outpace adjusted EBITDA given the upfront billing nature of our contracts. Our guidance for Q1 and fiscal 2024 remains unchanged from what we provided during our fourth quarter earnings call. We'll provide an update when we get to Q1. In recent quarters, we've increased our focus on reducing dilution. Stock-based compensation in terms of dollars and as a percentage of revenue has continued to decline. We expect that trend to continue in fiscal 2024. This will be driven by lower headcount compared to prior periods, as I mentioned, also higher-priced grants rolling off. We've calculated dilution using the ending shares outstanding, excluding the impact of the secondary offering in fiscal 2020 and the buyback activity in fiscal 2023. Here we can see steady improvements over the past few years.
We've also calculated net issuance dilution, which is our grants net of forfeitures, and we've seen a steady rate over the past few years. The delta between these two is primarily option exercises, and in fiscal 2022 and prior, we saw higher levels of option exercises. At the end of fiscal 2023, we had approximately 4.6 million options outstanding. Going forward, we'll manage to a modest level of dilution. Now that we've spent some time talking about our expectations for fiscal 2024, we'll spend a little time talking about the longer-term opportunity we see at Yext. Our TAM today is over $32 billion, growing to $60 billion in 2026, a 22% CAGR. Our platform expansion and investments in AI have enabled us to expand into additional markets.
Here we've got a slide that shows how the products are mapped into the different IDC categories. We're bullish on our opportunity and ability to capture TAM with our platform strategy. As you heard from Marc, we've significantly expanded the platform, adding new products and features. With our platform approach, we can target new logos, upsell existing customers, and help our customers grow. We have the opportunity to displace multiple vendors as we offer a composable platform enabling customers to build unique digital experiences. We have a robust product roadmap, and we'll continue our track record of strong innovation. With the breadth of our platform and our transition to DXP, we can now sell into more verticals. Since Q4 of fiscal 2021, we've been able to expand into other verticals like services, consumer products, and technology. In recent years, we've seen revenue growth deceleration.
We heard from Tom and Raianne how they're rebuilding go-to-market around efficiency and productivity. We anticipate the business will grow again, and here we've included versions of slower growth and faster growth, and we wanted to share with you how we think about it. While it's still early in our go-to-market efforts and we are navigating macro uncertainty, we believe over the long term we can get back to 10% growth, and with better execution, get to 20% or more. We've talked about gross margins, and the improvement we're expecting in fiscal 2024 will put us in the higher end of this range. However, if we see higher revenue growth, we may reinvest. We've made a lot of progress on operating efficiency over the last 12 months, and it will continue to be a focus.
In sales and marketing, we made great progress in fiscal 2023, and we exited Q4 at 41%. Progress in the near term will be from better productivity, as Tom mentioned, and we expect our go-to-market changes to drive improvement over the longer term. With R&D, we've historically underspent here. However, we get a high level of productivity and innovation from the team, which is evident in the platform expansion you saw today. In G&A, we've seen modest improvement over the past few years, and further leverage will come from revenue growth. We've laid out lower growth and higher growth scenarios. If we see lower growth, we'd expect higher margins. If we see higher growth, we may make additional investments. Both scenarios contemplate us operating as a Rule of 40 company. We've mentioned Rule of 40 a few times.
We believe well-positioned software companies can achieve 40 or more, our progress against our fiscal 2024 objectives, plus the opportunity in fiscal 2025 and beyond, should provide steady progress on an annual basis. We will have some seasonality from quarter to quarter, primarily on adjusted EBITDA margins. However, on an annual basis, we will continue to make progress. As I mentioned earlier, how we get there will be a mix of growth and profitability, and as that mix becomes more clear, we'll provide further updates. Thank you for joining us today. We're excited about the opportunity ahead. We'd like to invite Tom, Raianne, and Mike back to the stage for Q&A.
As a reminder, if you have a question, please raise your hand and wait until the microphone has been brought to you, and please announce yourself and your affiliation.
We all want one?
All right.
Come on.
Hi, Ryan MacDonald with Needham again. Thanks for taking the questions. Maybe the first one's for Tom and Raianne. You obviously have joined the company recently in a very detailed sort of restructuring of sort of the organization, unifying of the organization. As you think about this year, you've got new categories that you're moving into, but then obviously a lot of maybe market demand for everything generative AI. Can you talk about how you're maybe structuring that lead gen engine to really take advantage of, one, sort of the inbound demand, but then two, sort of moving into these new categories? Thanks.
That's mine. As I mentioned in my presentation, we rebooted the demand engine as a whole. We turned it on about six weeks ago. We rebuilt everything from all the operational processes to lead scoring and the tools that we use, our new campaign frameworks, as an example. We have a plan to go after both our existing customer base as well as new logo acquisition. Obviously we're focused on selling the whole platform. AI is gonna be a big theme throughout our campaigns as well, our AI-enabled technologies. We're definitely focused on selling the full platform in all of our lead gen efforts.
Great. Thanks. Tom White at Davidson. Just maybe on the 10%-20%+ plus kinda long-term growth targets, given the various different products you guys have and are probably gonna continue to launch, I'd imagine there are a bunch of different ways that you can kinda get there. Curious just, maybe Mike, if you had kinda your crystal ball, you know, what's your best guess around kinda what your product mix will look like, you know, once you kind of achieve or get back to those kinda long-term targets you put out?
Yeah. Obviously the fact that we put out two targets tells you that I don't have a crystal ball. You know, I think the point of that is several things. You know, maybe the news item here is that, you know, I think it's the first time we've told you that, you know, with any level of confidence that we do see this as a growth business again. If there's a headline, that's it. Obviously we're also still being pretty cautious about those numbers and, you know, 20%+ doesn't have a ceiling on it. You know, it's a balance, right?
To be as excited as we are about the product opportunity to be, to know, you know, the needs of the customers and the opportunity that we have there, and to be patient with understanding that building the demand takes time, executing against that demand takes time, and obviously the macro environment is uncertain. It's always uncertain, it feels particularly uncertain right now. You know, I think part of the beauty of what we shared with you today is that we can be really flexible and thoughtful about the product mix because, you know, what's fundamentally differentiated about all the products is the CMS Knowledge Graph underpinning and the native AI natural language components of the platform.
First of all, I, we're nowhere near done driving innovation through the platform. I think we've seen a lot of it just in the last quarter with Chat, with Studio, with content generation, and there are, you know, there's a lot more to come in on those fronts. What we're gonna care about is, you know, is the customer installing Knowledge Graph, the customer enriching the Knowledge Graph, and the customer finding value in what's delivered through the Knowledge Graph and all the supporting products. I wouldn't take a guess at what the product mix looks like.
What I would tell you is that I think the position of our CMS and the DXP platform over the course of the next couple of years is only gonna become more important to the enterprise, and exactly how we break the value chunks of that out is a lot of the work that Raianne and Tom are doing, Tom's doing on the proposal side of things. Was that enough words to totally duck the question?
Hi, it's Bob Johnston with Herald Investment Management again. Could you talk a little bit more about the sales process at the enterprise level? I mean, how does that evolve, and could you talk a little bit more about, you know, the competitive landscape? Do you run into anyone consistently or not at all, or is it, you know, DIY or what? Thank you.
Yeah. Tom, I think.
Yeah. You know, I think it's all of that, in terms of the competitive landscape. The first part in terms of the sales process is we wanted to get back to some foundational items, and some of the blocking and tackling, and how we execute on sales plays, how we define sales plays. We've started doing those things really, really well again. I think what the challenge is gonna be for the team when we talk about full platform selling and when we talk about entering new markets, you know, it's not just the product enablement. That will be important, but the folks are very smart. They've been doing this a long time. They can pick that up.
It's also going into a different persona, and you're starting into a little bit more of an IT persona and an IT buyer as well. Right now, there's a portion of the sales team certainly that is very comfortable and has been successful for many years selling to more of the marketing folks and the CMO and things like that. We're now enabling and transitioning over 'cause we know that this is coming. That's really a big change and one of the enablement processes that we're going through right now in terms of the sales process. In terms of the competition, it's everything you would think it is, you know. I mean, you can, you probably have seen, you know, the quadrants, the waves.
We're gonna be entering, you know, certainly, going toe-to-toe with some of the largest companies out there, that's for sure. I think everybody's excited about doing it.
Hi. You had an interesting slide in your presentation where you showed kind of multipliers of customers with different numbers of products. Can you talk a bit about how you plan to kind of get that mix from, you know, one or two products up to three or four and kind of expand that impact on the company from a financial perspective?
Tom can talk most authoritatively about this, but I wanna tie it back to the presentation that we gave today for a second. We brought two customers here today. We're incredibly grateful that they came. You know, they're two very different customers and with two, you know, very different sets of products. Cox has been, you know, very much ahead of the curve, I think, at adopting the full platform and, you know, and you hear Jim talk about, you know, they're way out ahead of the market in terms of understanding the power of the Knowledge Graph and how much you can actually do with it.
You know, we really wanted to bring Courtney here because I think, you know, we've told you for quarters that we have this problem in the business, and this problem was this perception that we had stopped investing in Listings and we didn't care about it anymore, and that we had also dropped the ball from a lot of the servicing things. You know, United Rentals and is I think a great example of how we've turned that around and I part of that was communications and part of that was reprioritizing our roadmap to make sure that our core Listings and customers were getting the value that we had promised them and that we had delivered for many years and we had dropped the ball.
You're asking, what does this have to do with your question? You know, I think the point is that, like, you know, the customers have different life cycles, and obviously we, you know, we'd love to, and I, and I told her this today, and if she's still listening, she's not gonna be surprised, we would love the opportunity to go to United Rentals and talk to them about CMS and other solutions that are not part of their suite today. There was no scenario where that was happening at a time when they felt like we had deprioritized their core product and we had dropped the ball on servicing them.
First you know, with a customer in that situation, first you earn the right to have that conversation before you can go in there and you can sell a broader platform. You know, the reality is that, you know, the customer personas and the customers that we sell to, you know, they're anywhere on the spectrum from they've never heard of Yext or talked to us before to a Cox who's always been way out ahead of the market and has been an early adopter of our products. We just have to get real about how we approach the discussion with those different customers, and it's a unique and different discussion every time.
There's no one-size-fits-all approach to this and there's no sort of one path to, you know, there's another customer who we talked about in our Q4 call without naming them. It was a retail brand who had fired us for Listings. We went back in there, basically begged for a meeting to go back and give a chance to compete for their Listings business. We wound up selling them the whole platform because they were ready for the full platform discussion. First we had to earn our right back into the room. In a very long-winded way, what I'm saying is there's no one size fits for this. It's you have to meet your customer where they are, and you have to earn the right to sell them the bigger thing.
I don't know if you wanna talk about the actual tactics or anything, but...
I mean, the actual tactics, full platform selling, we of course focus on our strengths, which in a lot of customers is maybe in the example you gave were two or less. Say they have Listings and Reviews, we go pretty aggressively towards another insertion point, right? We find a pain point, whether that's in Pages or whether that's in Search. Of course, always talking about the value of the Knowledge Graph and how easy the extensions are after that. You know, we're doing a lot of that now, specifically with Pages and when you see, when you see the spring release and when you see what I feel is the stickiness, I think what we feel is the stickiness of Pages. You know, it's a great insertion point and on-ramp onto that platform.
Once you have three of the four, or let's say four of the five, if you count the Knowledge Graph, it's not exceedingly difficult to create a compelling commercial proposal to just have a full platform adoption, especially in this environment where customers are increasingly looking to, you know, essentially save dollars and reduce technology complexity.
Yeah. I mean, While we get to the next question, you know, increasingly talking to customers, the question isn't do you use a CMS? It's how many do you have?
Yeah.
Because they're fragmented and none of them are capable of doing everything that we showed you that we can do today. That'll be a really interesting conversation as we go forward, because if you have multiple, you're spending too much money.
A couple questions on sales productivity and pipeline. I'm looking at both of you. I think, maybe just basic tactical question. How are you measuring sales productivity? Is it through upsells to existing ones, or is there some level of new versus existing customer sales that you think at some point when you think flattening or the plateauing of sales productivity will happen at not so far out in the distant future, which is when you then go back to Mike and ask for more salespeople.
Mm-hmm.
I would love to understand how do you see that unfold? On the pipeline, both of you talked about pipeline generation, working closely with each other. I guess the question is how do you determine the quality of pipeline, and over what period would you see that quality versus quantity balance shake out, in terms of the various different things that you're doing with the partners, resellers, SIs? It feels like a lot of things are happening, or how do you figure out that the pipeline quality and quantity balance is kinda coming together?
First, on the productivity piece, you know, we measure it through essentially usage of sales tools, right? We have a set of sales tools now, and whether that's, you know, what we call business value assessments and really leading with value, to looking at whitespace tools. We have a bunch of automated tools that are already in-house, and then we're creating another set of, you can think of them as common reusable assets that essentially, you know, the way we do account planning, the way we do proposals, right? They all look the same now. Same themes, right? Where we lead with value. We talk about bottoms-up builds of demand. You know, we have compelling commercials, right? If you look at value realization and post-sale customer journey, right, we have common assets there.
It's all about efficiency and it's all about speed. When you talk about the plateauing effect, it's more just, I think, a natural, a natural limit that we're gonna hit with how many, you know, with AEs and their customers that, you know, you can only do so much effectively, right? They can only produce so much output.
The quantity versus quality is an interesting conversation, something we look at all the time within the marketing lead funnel. I mentioned that sales had given really great feedback over the past six weeks following the reboot on the quality, so we're now having conversations around opening up the quantity. You know, we can be maybe less conservative on our lead scoring models, as an example, because the quality is so high. That's something that we look at all the time. Then as far as increasing investment and putting more fuel on that engine, the same as Tom. Tom will be asking for resources, I will be as well, as we see those indicators start to kick in.
Yeah. The only, the only thing I'd add to that is, like, you know, it's not, it's not that Glengarry Glen Ross movie where there's a secret drawer that has the good leads in it. It's a, you know, it's a funnel of stuff and, you know, Raianne has got a machine built in the process of being built to understand that. Technically, I think part of your question may have been just like, how do you measure it financially? I don't wanna answer for Darryl, but I think when we think about productivity, it's in the incremental ACV bucket, right? It doesn't mean we don't Because we've moved the commercials to the reps as well, so they're in charge of the renewals also.
The point of that is to make the upsell path more, more frictionless and more opportunity there. From a mathematical standpoint, the way I think about it is when we have average productivity near the top across the selling organization and a high degree of productivity, and this is the point we're trying to make, that doesn't mean you turn around and start hiring. That means you ask the next question, which is: Is there enough pipe for us to grow? When you start deconstructing this, like, it's not one answer because every channel and every group has their own dynamics, right? You know, we talk about all the verticals groups and the geos groups, and there are different customer segmentations and things like that.
It could be that you're ready to grow in some areas and not ready to grow in other areas. What you're hearing me, you know, unrelentingly hammer on here is that we're gonna, we're gonna do this by the numbers when the numbers tell us to do it, not because we feel like emotionally we need to find a way to charge the growth.
You've stayed really tight on script on not wanting to say when you're gonna get to your 10% number. It seems like you had demos out there that would be natural growth drivers and what, you know, what percentage of your customers have a Knowledge Graph that would get you to that growth if you rolled out that beta of just the chat product, for example? It seems like a no-brainer on top of your Knowledge Graph customers.
Yeah, I mean, I think the stat that, you know, the most telling stat is the one we showed you a couple times, around 76% of our customers being, you know, one or two product customers, right? You know, and when you look at the multiples on the ARR of customers with higher products, obviously those are average numbers and blended numbers, but it just gives you a sense of the magnitude of the upsell opportunity that's here. You know, I would happily do a deal with you. You tell me exactly what the macro environment's gonna be like for the next 24 months, and then I'll commit to a growth rate. I'm not trying to be flip about it.
I'm trying, you know, like, part of the reason why I won't do that and why I stay so close to the script is because I just can't predict, you know, whether, you know, the buying persona is gonna be really excited about this conversation around centralizing and using these AI things or whether they're gonna be really focused on cutting cost. If I knew that, then I would be less coy about presenting you with a, you know, a range within a 24-month period.
Is it safe to say that once those are out of beta, you'll have a pretty rapid sense of upsell? I mean, it seems to me like that's a pretty easy thing to lead with.
Yeah, I mean, I would say that, you know, for every product that we offer, Tom and Raianne, they wanna, you know. There's a lot of pieces that go into generating demand and then accessing that demand and how fast we come out of beta and, you know, some of that stuff, you know, with some of those products involves other people's technology, and we don't know how fast that's gonna move. You know, I think the data dashboard we're building that would tell us. For example, you know, if a massive amount of pipeline shows up for any opportunity and we know that that pipeline is high quality and qualified, that allows us to predict the ability to sell against that pipeline, which allows us to start looking forward.
We would, yes, expect to see that, and that would drive some of those decisions that I'm describing.
As a reminder, please say your name and your affiliation and wait for the microphone for the benefit of those on the webcast.
Hi. Excuse me. Daniel from Tenzing. Tom, I had a question on sales productivity. When you think your team is at the natural limit, do you think the growth of the business is at that 10% level? Do you think it's still below? Is it above 10%? If you can kinda sketch out where you think the growth of the natural business will be with the productivity at the levels that you want. A second question on the chat product. Do you think that can be a land product, or do you think that's it's mainly gonna be expansionary? Thank you.
Mm-hmm.
I think on the chat one first, I think it can be both because we've seen that reaction from customers so far, right? Customers that we are pursuing as, call it greenfield customers that we've had conversations with for quite a while, just haven't landed yet, have wanted to get into a beta program. Customers that, you know, we have and we have an install base with, there's been an overwhelming reaction to a beta program. That makes me think that we can do it through both of those elements.
I can take the growth one.
Okay.
That's a joke.
This guy blows...
Yeah, it's actually, it's tough to answer because we've got a mix of sales reps across a wide spectrum. We've got reseller, we've got mid-market, and we've got enterprise. All of them have different quotas. They sell into different sizes. There's different average deal sizes. It's not really a mathematical, you know, way that we can get to an answer to say, "Okay, when we have full productivity on all these reps, that's gonna equate to growth rates of this," because all of it is a very fluid motion with, you know, pipeline, right?
If we see really strong pipeline and pipeline quality and quantity coming from Raianne in mid-market, that's gonna change the dynamics than if we have the opposite with our enterprise business because the ticket sizes are different, deal sizes are different, and it's just there's a mix within that.
Can I just ask one?
Yeah. Yeah, go ahead.
On the reseller SMB, what can you do to get it better? I can see that directors clearly outperform reseller and SMB. Is it just macro? Is there something else in there?
Yeah, I think there are two primary things there. You know, one is these environments are gonna be harder for SMBs. You know, more of them go out of business. Our reseller partners, you know, predominantly sell to SMBs. You know, it's just gonna be harder to sell stuff to SMBs in an environment where they're, you know, fighting for their lives more than large corporations are. The second is that. You know, and, you know, that's just the environment, right? I think the second is that, you know, the product as we mentioned, going through that channel is primarily Listings. It's not entirely Listings, but it's primarily Listings. The opportunity there is to.
Even if there's some level of pressure, is to expand the product that we're putting into that channel through the reseller partners. As we launch more of this innovation, there are things that, you know, small businesses, for example, you know, they're, they're less inclined to be really excited about support search or things like that. I suspect small businesses will be really interested in a chatbot, or a chat solution. You know, I'm not making necessarily predictions today on where we're going there, but I think as our innovation and our R&D continues to produce product innovation, there will be opportunity to put more through that channel, which will benefit both us and our partners.
At this time, there are no further questions.
Okay.
Thank you.
Thank you.
That concludes our Investor Day. For the folks on the webcast, thank you very much for joining us, and thanks to those who are here in person for joining us as well.
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