Coveo Solutions Inc.

Good afternoon. My name is Jenny, and I will be your conference operator today. At this time, I would like to welcome everyone to the Coveo Second Quarter Fiscal 2024 Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star, then the number one on your telephone keypad. If you would like to withdraw your question, please press the star, then the number two. Thank you. Mr. Moon, you may begin your conference.

Good afternoon, and thank you for joining us today. With me on the call are Louis Taitou, Coveo's Chairman and Chief Executive Officer, and Brandon Nessie, Coveo's Chief Financial Officer. Before we get started, I would like to note that certain statements made during this conference call are forward-looking statements within the meaning of applicable securities laws, including those regarding our plans, objectives, expected performance, and our outlook for the third quarter and fiscal year 2024. These forward-looking statements are given as of the date of this call, and while we believe any statements we make are reasonable, they are based on current expectations, which are subject to risks and uncertainties, and actual results could differ materially. We do not undertake or expressly disclaim any obligation to update our forward-looking statements, whether because of new information, future events, or otherwise. Further information on factors that could affect the company's financial results is included in filings we make with Canadian securities regulators, including under the section titled Risk Factors in the company's most recently filed AIF, and the section Key Factors Affecting Our Performance in the company's most recently filed MD&A, which are available under our CDAR Plus profile. at www.cedarplus.ca. Additionally, some of the financial measures and ratios discussed on this call are either non-IFRS measures or operating metrics used in our industry. A discussion on why we use these results and where applicable, a reconciliation schedule showing IFRS versus non-IFRS results are available in our press release and our MD&A issue today, which may be found on our investor relations website at ir.coveo.com and our CEDAR Plus profile. Please note that unless otherwise stated, all references to financial figures are in U.S. dollars. Lastly, slides accompanying this conference call are available for viewing and accessible on our IR website under the News and Events section. I will now turn the call over to Louis to begin. Louis?

Thank you, Paul, and thank you all for joining us today. Starting off with the financial results, I am pleased to report that second quarter SAS subscription revenue, excluding the impact of qubit churn, grew 19% year over year. Additionally, our adjusted operating loss for the quarter was $1 million, a significant year over year improvement from $4.7 million for the same quarter last year, and was ahead of our guidance for the quarter. We are incredibly excited by the promising early results and customer interest from our generative AI rollout program. And we're thrilled to announce Xero as our first customer to go live with Coveo relevant generative answering on their Xero central customer service portals globally. We believe this combined with the building momentum of our partnership with SAP and overall interest in our AI platform creates a substantial opportunity for us in the mid to long term and expect a positive impact on bookings in the coming quarters. Generative AI has been for Coveo a natural extension of the capabilities we have built over the last decade in our platform. Having said that, following the release of Chad GPT, We have observed so far this year a landscape where enterprises have been grappling with the hype surrounding generative AI and have been carefully assessing and evaluating its practical applications along with its many risks, including security, factuality, accuracy, and cost. With that hype came an onslaught of marketing messages and pitches that enterprises have been forced to sift through to a certain what is reality, and what is actually worth buying that will drive real business outcomes. As a result, many customers have been in evaluation mode as they further refined their strategies around generative AI and have been cautious about entering into new deals, especially in light of the ongoing macroeconomic headwinds that have more broadly impacted customer spending on software. While this has, of course, been disruptive to sales cycles, I believe this ultimately plays to our advantage as Coveo is one of the few companies on the planet with a decade of experience in AI, helping large enterprises realize superior outcomes at scale in a secure, reliable, and trusted way. We believe we're starting to see the hype cycle around generative AI subsiding and believe the market has started to coalesce around four main areas where tangible ROI, even in the current macroeconomic environment, can be achieved. These areas are customer service, knowledge management, commerce, content creation, and co-piloting. In the first three, we believe we are extremely well positioned to be successful through our unique ability to solve the many challenges of leveraging generative AI in enterprise use cases. Our approach to creating an enterprise-grade generative AI solution has always been guided by our last-to-hype, first-to-results principle. And as I'll talk about shortly, we now have the results to justify the hype. So in summary, we are very bullish on the underlying long-term growth drivers of Coveo in spite of what we believe are near-term macroeconomic headwinds. As I mentioned, we're extremely excited about the early progress and rollout of our Generative AI program, and I'm pleased to announce that we're on track for general availability of Coveo Relevance Generative answering for service, website, and workplace use cases in December 2023, a major accomplishment by all measures. We also recently announced early access availability of Coveo Relevance Generative Answering for B2B and B2C commerce, offering customers an enterprise-scale generative answering solution for commerce. This innovation empowers customer experiences with AI-driven question answering and advisory capabilities, fostering customer engagement and product knowledge discovery in e-commerce. Cale's generative answering solution is designed to enhance the digital storefront experience by providing a conversational way to share product information with customers. We signed our first five generative AI transaction in our fiscal Q2, including one in commerce that I'll talk about later, with a substantial number of additional deals expected to close in our fiscal Q3 and Q4. This significant customer interest in generative AI and our platform more broadly resulted in another good quarter for pipeline generation, with commerce and our partnership with SAP in particular continuing to represent a significant portion of our growing pipeline. Most excitingly, while we have been articulating the value that we believe Relevance Generative Answering can bring to our customers, we finally were able to see the benefits via A-B testing. And I am excited to share that the early results were far better than our expectations, with customers seeing case deflection improvement in excess of 20%. This would equate to significant cost savings, especially when considering the scale of most of our enterprise customers. Importantly, These savings are in addition to the cost savings already realized by our customers through the use of our AI platform. Being able to show concrete and tangible improvements in business outcomes, like case deflection in customer service, at the end of the day is what our customers care about. It was the one question we still had as we began providing product access to our design partners and advisory group customers. Now with this data, we have the answer. This is the most significant and massive advancement in self-service we have seen that can significantly improve self-service, case deflection, resulting in lower costs and higher customer satisfaction and loyalty. We believe this will result in a tectonic shift of value from the contact center into intelligent self-service, an area where Coveo is also uniquely positioned. As I mentioned, we had our first customer Xero go live with Coveo Relevance Answering to enhance its customer service for its 3.7 million business subscribers. We believe this is one of the first examples in the market of a company deploying generative answering for self-service use cases at scale globally. This enhanced solution aligns with Xero's vision of delivering real-time conversational answers, leveraging Coveo's unified index for secure and controlled access to information, combined with our AI-relevant stack that understands a user's context, complete with citations for traceability to sources of truth. By offering personalized and generative self-service journeys, Xero helps their subscribers to independently manage complex tasks, freeing up their contact center's capacity while delighting more customers. In the coming weeks, Xero intends to implement generative AI for internal employee support and knowledge management. Xero's vision is to streamline and enhance the overall digital experience across their organizations. which encompasses approximately 4,000 employees worldwide. This comprehensive approach underscores Xero's ongoing commitment to improving interactions with both customers and employees, and we at Coveo are thrilled to play a central part in this journey. Another compelling example of the potential of generative AI for Coveo within our commerce line of business is our collaboration with a major U.S. home improvement retail chain. We successfully created a proof of concept that can connect their extensive internal product documentation to their product catalog, resulting in a rich guided discovery and advisory experience for their customers. This groundbreaking capability showcases how shoppers can engage with in-depth product exploration much earlier in their buying journey while maintaining their focus on the retailer's website, removing the need to navigate to external sites. This achievement not only exceeds our initial expectations within commerce, but also shows our ability to rapidly implement new capabilities and applications of generative AI, thanks to our single AI platform. We are thrilled to continue innovating with this customer to further optimize results. Additional generative AI wins with our customers included a leader in high-performance silicon chip design, verification, IP integration, and software security, a leader in spatial data solutions, and a global enterprise software company focused on project management for customer self-service and agent proficiency use cases, respectively. We believe these early wins are a great start as we continue to penetrate what we believe is a very significant opportunity, both within our installed base and with net new customers. In addition to our generative AI successes thus far, we also want a new commerce customer that is a leading global B2B distributor of connector and sensor products. This customer shortlisted Coveo and one other competitor to replace their existing homegrown search solution, ultimately decided to partner with Coveo because of stronger product capabilities and our ability to handle large and complex catalogs. Their goal is to make significant improvements in the digital experiences they provide to their customers to maintain their leading market position. We also secured a significant new customer win with a major Australian retailer for their next generation employee portal, marking another important milestone in the ANZ region. The customer was looking to address critical gaps in their usage of Salesforce to improve employee satisfaction and loyalty as part of a key C-level initiative. Our belief is this is the first of many other opportunities we will have to expand our relationship with this large customer. As shown on the slide, We made several announcements during the quarter that highlight the strength of our AI platform developed over more than a decade, demonstrating our commitment to ongoing and high-velocity innovation. Subsequent to the end of the quarter, we announced more than 15 additional innovations to our AI search and generative experience platform to further enhance digital experience across our four lines of business, websites, commerce, service, and workplace. These advancements solidify Coveo's leadership position, empowering billions of individualized, trusted, and connected digital experience interactions on a global scale, enabling enterprises to excel in an ever-evolving digital landscape. I'm especially pleased with the remarkable speed at which In collaboration with our design and advisory partners and the strength of our R&D team, we have successfully established a truly enterprise-ready generative AI solution. It has been approximately seven months since our announcement of relevance generative answering to now having our first customer live with the solution. And Coveo is one of very select few vendors to have any customer live with a generative AI solution. that we believe generates a substantial and tangible ROI. This achievement, built on our robust AI platform, enriched and refined over a dozen years, offers customers a powerful technology to drive the new business to person imperative through semantic search, AI recommendations, and now generative experiences that are fully secure and accurate. We know what it takes. for enterprises to gain a trusted AI experience advantage. And this places us at the forefront of this journey where search, recommendations, generative answering, chat, conversations, and personalization all converge. This completely redefines a new, much more powerful digital experience paradigm. And we are proud to see Coveo at the forefront of that innovation. already delivering results at scale with some of the leading brands in the world. With that, I will now hand the call over to Brandon to discuss our quarterly results in more detail. Brandon?

Thanks, Louis. It's obviously a very interesting time for the business with several moving parts to it at the moment. So to try and succinctly summarize the most salient points, First, we delivered 19% SAS subscription revenue growth for the quarter, excluding the impact of Qubit churn, which was slightly ahead of our guidance. Customer retention and expansion remained solid with NER for the quarter at 111% outside of Qubit-related churn, indicating customers continue to buy more from us. We delivered our second consecutive quarter of positive cash flow from operations and are well ahead of our plans on adjusted operating loss and for ongoing cash flow positivity. This, combined with our strong balance sheet, keeps us well positioned in this fast-evolving market. And importantly, we continue to believe that we have multiple opportunities on hand to re-accelerate our revenue growth rates. The first of these, of course, is our relevance generative answering solution. We continue to see customer interest building for this product and believe we are one of a small number of companies that are actually beginning to monetize generative AI within the enterprise segment. We remain positive in our outlook for this offering and in the quarter signed our first five customer order forms with further signings in the current quarter. As a reminder, we've always said this will be primarily a fiscal 2025 growth driver. as customers evaluate the offering and finalize their purchasing decisions. Second is our opportunity inside of commerce, and more specifically with SAP as an endorsed partner. We are similarly seeing a lot of promising signs of progress, but again, I've said this will be a fiscal 2025 growth driver, given the work that goes into enabling their sales team and overall sales cycle lengths. Hopefully many of you will attend our Capital Markets Day next week, where SAP CX's Chief Operating Officer will be present to highlight to you firsthand how they're thinking about this partnership. While these growth drivers give us optimism, in the immediate term, you've heard from Louis that customers are being forced to dissect the noise in the market around generative AI while making their purchasing decisions. This affected sales cycles in the near term, and we've seen our typical pipeline conversion metrics and deal cycle lengths elongate. As we look into the back half of the year, we are seeing signs of customers resuming and finalizing their purchasing decisions after this period of disruption, and our pipeline of deal activity is encouraging. However, we will remain cautious in our outlook, as I'll get into shortly. We've also discussed Qubit churn in the past, and as expected, this churn is materializing. In aggregate, it is unchanged in estimated amount, but a portion of it did occur earlier in the year than previously estimated. And in the meantime, you can count on us to be efficient capital allocators. We have retired almost 5 million shares via share buybacks, and as mentioned, are tracking well ahead of our targets on reaching cash flow positivity. We are improving our AOL guidance for the year, even with a cautious stance on revenue. The leverage we're seeing in the business model is encouraging and gives us confidence that when macro conditions recover, we'll have plenty of levers to pull to lean into our growth areas. Now let's get into some of the details. For the second quarter, SAS subscription revenue was $29.4 million, an increase of 15% year over year. And total revenue was $31.2 million, growing 12% year over year. Excluding the impact of the legacy Qubit churn I mentioned, SaaS subscription revenue grew 19% year-over-year. In Q2, we observed both land and expand bookings across all of our lines of business, and consistent with previous quarters, all four of our lines of business continued to grow double digits year-over-year on an ARR basis. Overall ARR growth was largely in line with our SaaS subscription revenue growth, both including and excluding the Qubit churn. Generative AI bookings are off to an encouraging start with our first five customer order forms signed in the quarter. While we will not start to recognize revenue from these bookings until the earlier of general availability, which is expected in December 2023, Or when customers go live with our solution, I view it as a positive endorsement that these customers are choosing to sign order forms with Coveo in this hot market segment. Our net expansion rate as of September 30th was 106% compared to 109% we reported in our first quarter results. When excluding the legacy qubit churn, our NER was 111% for the second quarter, positive sign that more of our existing customers are finding success with Goveo and are continuing to buy more from us. Our second quarter gross margin improved 78% compared to 76% for the same period last year. Adjusting for share based and other payments, adjusted gross margin was 79% for the second quarter, an increase of 1% compared to a year ago. Product gross margin was 82% in the quarter, consistent with the prior year, and our adjusted product gross margin was 83% in the quarter, also consistent with the year ago period. Continue to view this as a very important leverage point in our business model that sets us up well for strong profit margins in the mid to long term. Adjusted operating loss for the quarter was 1.0 million, a significant improvement compared to 4.7 million a year ago and well ahead of our previous guidance. The ongoing strong expense management We generated approximately $800,000 in cash from operations in the quarter and $1.8 million for the six-month period, which is also well ahead of our plans. On a non-adjusted basis, operating loss for the quarter includes a $3.2 million non-cash impairment charge for the customer relationships recognized at the time of the QBIT acquisition. With the customer attrition realized in the quarter as a result of our decision to deprioritize this non-core capability, we concluded our write-down was appropriate based on updated valuation models. We ended the quarter with $168 million in cash and no debt. With ongoing operating cash flow positivity clearly in sight, we are well positioned to navigate the macro uncertainty and capitalize on our market opportunity. So finishing with guidance. We continue to see positive indicators that enterprises are viewing our space as a priority, given the enthusiasm for AI and its potential impact on business outcomes. However, as you've heard, customers have been forced to dissect the immense hype around GenAI and have had to take the time to diligence their purchases and select their AI partners. Of course, while our market is a priority for many enterprises, We are also not immune to the macro environment and its overall effect on enterprise software spending. This has delayed deal cycles in the first half of our year. As we look into the back half of the year, we're encouraged by the deal activity we are seeing, and we're seeing signs of customers and prospects finalizing their decision-making and moving through their purchasing decisions. We're optimistic with our position in the market and take the early order form sign for our generative answering solution as a positive indicator of this. However, these are early signs, and we would like to see this materialize further. Our revenue guidance also reflects the updated timing of the qubit-related churn, which occurred earlier in the year than previously expected, and impacts the amount of recognized revenue for the year. Further, we are seeing a greater portion of our bookings coming via expansion in this market, which typically carry lower professional services revenue than new customer bookings. So with that all said, we're now expecting to land at the bottom end of our previously issued guidance for SAS revenue for the year, and with a conservative stance, are updating our SAS revenue guidance to $117 to $118 million. For the third quarter, we expect SAS subscription revenue to be between $29.1 and $29.6 million. Approximately 60% of the expected QBIT churn for our fiscal 2024 occurred in the second quarter, which will have a negative impact on our sequential quarterly revenue growth for our third quarter. Excluding the legacy QBIT churn, we expect our core growth rate on SAS subscription revenue to be approximately 15% for the third quarter. Reflecting the lower estimated services revenue, for the reasons mentioned earlier, total revenue guidance is being updated to $124.5 to $125.5 million. For the third quarter, we expect total revenue in the range of $30.9 to $31.4 million. Due to our ongoing efforts on efficiency, we are improving our guidance range for adjusted operating loss to $9.5 to $10.5 million compared to the previous range of $11.5 to $13.5 million. In the third quarter, we expect adjusted operating loss of $2.5 to $3.5 million. And we now expect cash used in operations of less than $5 million in fiscal 24 compared to the $10 million stated previously, and reiterate our expectation of achieving ongoing positive cash flow from operations for the full year of fiscal 2025. Finally, both our third quarter and fiscal 2024 guidance assumes FX rates roughly in line with where they are today for SAS subscription and total revenue growth, as well as adjusted operating loss. And with that, operator, you may now open the line for questions.

Thank you. Ladies and gentlemen, as a reminder, should you have a question, please press the star followed by the 1 on your touchtone phone. You will hear a three-tone prompt acknowledging your request. Questions will be taken in the order received. Should you wish to cancel your request, please press the star followed by the 2. If you are using a speakerphone, please lift the handset before pressing any keys. Once again, that is star 1 should you wish to ask a question. Your first question is from Doug Taylor from Canaccord Genuity. Please ask your question.

Thank you. Good evening. Congratulations on securing the first orders for your generative AI product. With that, I'd like to hopefully revisit the discussion on the pricing dynamics that you're establishing around that module, whether there's anything more you can share with us at this time. about how that's priced relative to your core enterprise search solution.

Right. This is Louis. Hi, Doug. Thanks for the comments. The pricing, we went out initially with pricing that was in line with the industry, the first movers charging a percentage. Um, and so that percentage and, and we published it was, uh, was 40%, uh, which, um, which was obviously well accepted given that, uh, we've been able so far to land already five orders. Uh, and that pricing is 40% currently with, uh, six, a six figure minimum. So it's, um, it's considered, you know, for early orders to be pretty substantial. Uh, that being said, uh, Doug, um, We are looking at further refining pricing as we move into production from our early beta adoption program. And especially given both the cost and the value, especially in particular that we generate, we expect that pricing to go up.

Okay, that's helpful. Perhaps similar or along that same vein, Given you've now got a customer live with your generative answering function, can you tell us a bit more about the onboarding process? Anything that you've learned about how customers, once they've signed, what the adoption curve looks like relative to your core search solution?

I would say, Doug, that both the adoption and the speed and the results were beyond our expectations. Obviously, we've marketed the solution initially. The early adopters were customers of Coveo, which already had the Coveo infrastructure, the secure index, and all the relevant AI stack in place. to feed generative AI. And we've been clear in prior calls that this obviously puts Coveo in a very unique position to take advantage and for customers to take advantage of that technology. But while we had initially anticipated a month in terms of adoption, the customers themselves have been the ones probably putting more pressure on us to accelerate the rollouts, and in some instances, the global rollout of the solution. And the results, as you know, in our space are done through A-B testing. So we direct part of the traffic to the legacy solution, and we direct part of the traffic to generative answering, in this case, generative AI. And the initial use cases were predominantly in customer service and self-service. And so we've been able to achieve that and close the loop within a matter of weeks. Closing the loop meaning being able to actually measure consistent results and gains. And as I said earlier, and as Brendan mentioned, these were beyond our expectations.

And maybe one last question for me then. Having the return on investment from your customers being higher than you'd previously thought would suggest that the usage and the utilization of the the generative large language model function is higher. Can you speak to any observations you'd have about the cost to Covail for providing that solution versus your earlier expectations now that you've got the customer alive?

Yeah, that's a very good question. And we think we'll put us at a competitive advantage as well from a cost perspective. So not only We are in a position to deploy generative AI that generates based on current data, thanks to the index that is secure and that is accurate and well-grounded and fully traceable. But also, the way we engineer this, obviously, goes a long way in terms of optimizing the cost. Generative AI and the cost of generating an answer can be as much as 100 to even 1,000 times higher than running a query engine, if not engineered properly. The ability of Coveo to provide the stack of software that isolates the secure current and relevant corpus of information in order to feed the large language models goes a long way in terms of optimizing the cost. And right now, you know, although we don't have any completely accurate model of the cost as we're learning a lot from the use cases, we already know that the cost part is... is well under control and within the range of the zip code of the type of gross margin that we normally produce.

All right. Thank you for confirming that. I'll pass the line.

Thank you. Your next question is from from BMO Capital Markets. Please ask your question.

Hi. Good afternoon. Louis, with respect to the two factors you referenced, prompting the slowdown, the macro, and then customers also pausing to evaluate all the various options for Gen AI, maybe a hard thing to answer, but would you say one of those factors is having more of an impact than the other? Are they kind of similar? What would be your thoughts on that?

I think for us, thanks for the question. I think for us, it's really been, you know, the macro is the macro. And to the extent, however, that we can provide solutions that generate a high ROI that we can demonstrate, there are ways around it. I mean, you know, the main effect of the macro is more scrutiny and in the deal conversions and so on. Sometimes, you know, we say we don't lose deals. They kind of get delayed. The other one, however, was more interesting is that, you know, there's no question that generative AI and, you know, when Chad GPT came out and I always joke by saying, you know, when people started writing poems on their iPhone in December and January and got all excited about generative AI and, You know, when that got into the enterprise, enterprises, it took everyone by storm. It took the world by storm and everyone by surprise. And enterprises had to distill essentially how that would be applied in which use cases and really understand the implications from an IT and data governance perspective, security perspective. and frankly, also the cost and really understand the true benefit. Coveo is one of the first companies that has closed the cycle. And while this has sort of stalled a lot of decisions since the beginning of the year, we're now seeing as we can demonstrate results, obviously, and we can demonstrate to CIOs that we can run that in a way that is enterprise-grade and fully compliant and secure. We're seeing that level of activity. These companies re-engage and convert, especially as they see the kind of benefits that they can get. And so basically, You know, we expect the bookings, we've seen that, we're seeing that in the pipeline activity and we expect the bookings to re-accelerate and with an impact on the back half of the year and obviously fiscal 25.

That's great color. Thanks. And then on the SAP channel, you alluded to the fact that it's kind of in the process of ramping. Obviously, a lot of training, I imagine, that has to be done with respect to the Salesforce. But maybe could you update us in terms of where that process is?

Yeah, so we announced the global endorsed deal, which is a big deal, obviously, with SAP globally, if you recall, at our earnings call for last fiscal year, basically in April, beginning of May. And this deal happened really in April. So we started rolling it out and so on. You know, the sales cycle, as you know, and that's mainly on the commerce side, right? The sales cycle really are no less than six to nine months. So as we went through that period in the summer period, and we're now rolling out more and more enablements, we've seen that pipeline continue to increase and we're obviously in deal activity that is increasing as well right now. So for us, it's sort of the normal cycle, as you pointed out, of deploying the enablement. And given that it's a global deal, it's a rather large operation that spans through you know, Europe and North America mainly for now and parts of Australia, New Zealand. And so we're into it right now, but we're clearly seeing the growth. And commerce as a solution continues to be our biggest growth opportunity, but also now with relevance generative answering as well. But obviously SAP is a big part of the commerce side.

Great. Thanks, Louis. I'll pass the line. Yeah.

Thank you. Your next question is from Suzanne Zucamar from CIFL. Please ask your question.

Good evening, Louis, Brendan. Good evening.

Congrats on some of the early initial progress here on the Gen AI trial. Just kind of looking ahead to the anticipated commercial rollout, just curious, Can you share some details on what that might look like in terms of if you'll be focused on existing customers beyond some early adopters in your base, or will you be looking at it, looking at driving more net new business? Just curious what your priorities are from a go-to-market perspective.

Right. So, so initially, uh, we, uh, we announced obviously the, um, that, uh, we would, we would come out with, uh, with the product, uh, if you recall sometime around the April timeframe, we actually demoed the product initially to our customers in June, uh, in California, and then announced our beta program that started, uh, later that later the summer. and are gradually rolling out. We previously announced that we have approximately 45 companies. There's actually more than that aligned right now. And initially, our initial rollout was with existing customers because those are customers who already have used the large Coveo indexes across their enterprise, already have the data and et cetera ready. And for us, rolling out generative answering as an extension of that infrastructure was a natural. And so this is why we went there. There's no question now that relevance generative answering is a part of our platform. Our platform is a platform that's made of numerous AI models and behavioral machine learning, deep learning, large language models, and now generative AI. And so that's an addition that obviously we also promote now to new customers as well. And we expect to see some of those orders also from new customers from this type of functionality in the quarters to come.

Okay, great.

And just to follow on to that, when you look at your pipeline today, how much of that can you attribute, how much of that growth can you attribute from Gen AI and from net new customers specifically? And we think about, you know, call it the LTV to CAC opportunity, you know, for new customers. How does that compare to, you know, what you've seen today with your existing base?

Right. So we don't see any pressure, you know, given, given the increase in value and functionality, we don't see, and we don't anticipate any downward pressure despite the economy on the, on the deal size, first of all. So the value of our solution keeps increasing and the breadth of the functionality keeps increasing and we can connect that functionality to the value. So that's number one. What we're seeing is obviously more competitiveness for the overall platform. You know, GenAI is not a separate thing. And if there's anything that we're trying to tell the market and that, frankly, customers are realizing now, as many of them rush to GenAI as a separate thing. They have realized, and we're here to show that, that in fact, GenAI is a natural extension. You cannot activate generative AI, at least not in the types of use cases that we do, which are digital experiences, commerce, websites, customer service, workplace type of applications, and mostly real-time digital experiences. You cannot activate generative AI unless you master the science of data connectivity, unless you master the science of relevance, in fact, and understanding users at the other end in real time and their intent and their context and their behavior, which has always been the forte of Coveo. So what I want to say here is that relevance-generative AI is a natural extension to that platform. And so the way to think about it is not so much generative AI only as an incremental platform, product, which today it is for our existing customers, but really as an integral part of our platform that becomes suddenly more valuable and more differentiated, more competitive in our markets. And so we expect more and higher bookings as bigger deals, more deals as a result of that, I guess.

Okay, good. Thank you for the color, Louis, and congrats on your progress. I'll pass the line. Thank you very much for your kind comments.

Thank you. Your next question is from David Weiss from Scotiabank. Please ask your question.

Hi. Thanks for taking my question. Maybe just in terms of the change to your failure guidance, I just want to clarify if there was any increase to the qubit churn that was previously indicated in the prior quarter, or if that has remained consistent.

The aggregate amount is consistent. Just the timing of it, we did see it start to come in a little earlier in the year than we otherwise had planned. So the impact on revenue recognized. We lost a couple of months in some cases, but an aggregate amount remains the same.

Okay, that's great. And so then you've mentioned, obviously, the five orders for generative relevance answering in your release. And then maybe in terms of the guidance for fiscal 2023 revenues, just what's implied for generative relevance answering in that guidance? Is it those five deals or is it looking ahead a little bit as well?

Yeah, I mentioned in my comments there that of these five initial orders, and there will be more, that the way we structured these agreements is that revenue recognition will begin once general availability is met. And as you heard from Louie on his prepared comments, that's expected in December. So good news is we're seeing order forms flow, you know, for December. enterprise to put a signature on an enterprise on a order form, you know, they've done their diligence and run it through their processes. So that's, that's the positive news, but it's not going to really contribute a whole lot into our current, our current year revenue. It's more of a fiscal 25 driver for us.

Okay, no, that's great. And then You'd mentioned last call some large software firms that were asking for about a 50% uplift in pricing for co-pilot type of gen AI solutions. And I think you'd also mentioned on the order of a thousand times the cost of a query to run an LLM answer over your search index or over an index search. So you'd provided some several strategies on how Coveo would work with customers to help on cost efficiencies for these firms. including using different LLMs and several other strategies. Could you maybe help us to get a sense of like the order of magnitude of cost savings that your solution might offer against these other competing solutions on a rough level?

Well, I don't know that we can comment on the competing solutions because we don't know of many that are live. So that's number one. You know, we're... we do not see in the use cases that we run the scale, anything that's at scale like Coveo that is in full production in enterprises globally. That's number one. So it's hard to compare. However, let me give you some color on this. What we said before and what we repeat here is that generative AI can be very costly if not engineered properly. So if you're trying to generate an answer from an extremely large corpus of data and an extremely complex prompt with extremely complex prompt engineering, there's a very high cost to that. And so what we're able to do, given the platform that we already have, that, again, basically is based on large-scale high-performance enterprise indexing combined with the ability to, which we call relevance, the ability to understand in near real time who's at the other end. we're able to narrow the problem in a way that is extremely cost effective. And so relative to the gains, so we've been able to measure in excess of 20%, as we said in the earlier call, in excess of 20% call deflection with some customers. And so what that means essentially is if you run a contact center by applying generative AI, and that 20% was on top of Coveo, by the way, if you run a contact center by using generative AI, you suddenly have 20% less cases. So think about the economic benefits of that. So we think the costs are going to be Not negligible, but certainly reasonably low relative to the value, the economic value that we can generate. And again, as I said earlier, very much in the vicinity zip code or the range of the COGS that we're used to. As you know, we currently run at about, I don't know, last quarter off the top of my head, but roughly around 82% product growth margins. And so we think we can pretty much stay in that neighborhood. Okay. That's great. I'll pass the line. Thanks.

All right. Thank you.

Thank you. Your next question is from David Kwon from TD Securities. Please ask your question.

Hey, guys. So I was just curious, you guys talked about, I guess, from the A-B testing, it took probably about a few weeks. So when you look at when the customers, I guess, started the beta testing to moving ahead to at least deciding to go ahead with live production, it sounds like, you know, probably about a month or so. Is that about right?

It's, yeah, it's in that range, a little more than that, but it's been a matter of a few weeks. Yeah. Single-digit weeks.

Single-digit weeks, perfect. And I guess going forward, though, as you maybe get more data on whether it's A-B testing or maybe get some actual tangible ROI, do you expect those timelines to actually improve?

Yeah, they'll probably be about the same. What happens is, well, it depends whether we're deploying a new customer or an existing customer, obviously. But it'll probably be about the same. What we need to understand here is that the Coveo customers are all large. We deal with large-scale enterprises. And the common characteristic of our customers is they have a massive amount of content and variety of content and or products that they need to deliver to a very, very large and very diversified audience sometimes across the world. And so we're seeing volumes here that basically converge very, very quickly. When you have hundreds of thousands of visitors a day in some instances, You know, you can very, very quickly understand through A-B testing, you know, as you run 20, 30, 50% of the traffic through a technology and you can compare. So you don't need weeks. Now, what's fun, what's interesting and fun for us right now is that as customers have kept using the solution over the weeks, we're seeing the same, the exact same pattern. But I guess the timeline is, You know, in scientific terms, David, you know, the timeline to gain a statistically representative sample and an A-B test is not long. Oh, that's helpful. Thanks, Louie.

And on commerce then, are you guys all supply and do, you know, a beta program like you did with service workplace and websites? I know you've obviously, you talked about one customer that's already made a purchase order here. So I wasn't sure if you were looking to doing a broader beta program for commerce as well for that B2B, B2C offering.

Well, we have not launched one yet for commerce. We might actually be able to go into early production. But I mean, to a degree, we always launch large new features in a form or another of a beta process. But what we're doing in commerce with generative AI has some commonalities with what we're doing elsewhere, obviously, except that it's applied to product discovery and constrained by the data of the catalog and things of that nature. But so the short answer is we haven't announced a beta program in commerce, but we've already signed, as we said, one order and we expect more because the solution is quite innovative and should prove to be extremely valuable very quickly.

Great. One last question here. I know it's a tough enterprise spending environment here, but are you finding that CRGA is maybe helping you progress conversations with new customers? Is it making a difference here in terms of what's going on in the current climate?

Definitely, definitely. And I think you got to look at it both ways. If you're in our space, you know, look, we're an enterprise search vendor. I don't think of many industries that have been more disrupted by generative AI than search when you think about it, right? So this is sort of what Brandon and I explained, you know, during the call that customers, you know, in the earlier part of this year had to sort of distill the hype from the reality and the use cases and how they could apply that without getting in trouble. But the reality nowadays is if you're in search, if you're in the business variant, which is essentially digital experience, personalization, semantic search, AI recommendations, personalization engines, and that, You just cannot have a conversation now in November of 2023 without talking about generative AI. It would be like if you didn't live in the same era. And so now the good news is that generative AI is at the same time a catalyst for, I would say, a new wave of interest for search engines. for topics like knowledge management within enterprises, because you're talking about self-service proficiency. Think about a bank, for instance, and how financial advisors will use generative AI to gain proficiency on their own so much faster. With generative AI, you can literally ask a question such as, how do I activate a mortgage program in Fort McMurray for commercial property that has environmental issues? And get an answer on that. Or what is the difference between a TFSA and an RRSP? And can I have one for my 18-year-old child? And what's the tax impact? So, you know, think about the impact of that, even in the workforce and so on. So, you know, it's very topical. And, of course, it's driving more and better conversations. And especially if, like Coveo, you can show it. and show that you can deploy it and generate results and quantify those results, we think that's promising.

That's great. Thanks, Louie. Thank you.

Thank you. Your next question is from Koji Ikeda from Bank of America Securities. Please ask your question.

Hey, Koji. Hey, how's it going? This is George on for Koji. Okay.

Hi, George.

Hey, how's it going? You know, with, you know, some of the early adopters of the, you know, generative answering feature, have you noticed any commonalities among them or is this kind of more broad-based interest among the customer base here?

I think the commonality is within the spectrum of a certain set of use cases, right? You know, what's really interesting, you know, if you think about generative AI, think about it in three buckets, right? And two of them we don't do, by the way. So the whole co-pilot, co-generation area, right? That's one area. We're not in there. Number two is content creation. So whether it's images or symphonies or, for the most part, texts, you know, summarization, things of that nature, you know, account summarization and, you know, generation of marketing content and things of that nature. That's content creation. That's not something we do. The third area is in the digital experience and, you know, in real time. You know, when you have someone online, on a website, in a commerce experience, on a customer self-service experience, experience, you know, or an agent in a contact center or, you know, plain and simple, you know, as I said earlier, a financial advisor or an employee, you know, in the workplace. Think about, so this is the area where we're in. So it's really the commonality is among the use case, but I would say the common attribute is how do you make, how do you increase the self-service proficiency? How do you make that user smarter, faster about what is it that they're doing? What is it that they want to buy or what is it that they want to fix? And that from that perspective, it is a revolution. And in my personal career, I've never seen anything like that in terms of the magnitude of the quantum leap that it creates for a digital experience. So I hope that answers your question.

Very helpful. Thank you.

Your next question is from Paul Treiber from RBC Capital Markets. Please ask your question.

Thanks very much. Good afternoon. Just a couple quick questions. Just on the pilots that you're conducting with the customers, and it's good to see the number of them have converted and the data on the A-B testing. Could you speak to any alternatives that they were evaluating your solution against? Is it other competitors? Is it homegrown? How did that sort of comparison go?

Paul, it's really initially build. Every company went out and was looking to build something. And doing early tests in the area of generative with technology or technology of the method that we call retrieval or augmented generation or RAG, which is essentially you isolate a corpus of data. You generate from that data, from that corpus, what's called embedding. which you store in a vector DB, and then you engineer a prompt to a large language model. What companies realized in doing that is that, you know, they needed to integrate that with data that is current, data that is secure, and in a way that is across their enterprise. So it was a little more complex than working from a single knowledge base. And that also search channels, conversations, chats, and navigation, disambiguation of results, et cetera, weren't going away. So would you imagine a user going to a page where you would have a search box, a question answering box, and then a chat box? So ultimately, you know, obviously that becomes very convoluted and clogged. And so users realized that it posed some very significant security, currency issues, issues with veracity, big brands can't hallucinate. So for all those reasons, a lot of these projects didn't turn out. They were experiments, but they didn't turn out to be very successful. And so against a build, obviously Coveo comes with a solution that integrates the data component, the security component, the veracity traceability component, and the data currency component. And so we think that becomes very, very attractive versus the build your own solution. Especially that we can do it large language model agnostic although today we use GPT. Open AI GPT service on Azure pretty much, but we have side by side other technologies as well in the lab that we can we think down the road we can interchange depending on the on the the verticals and the application and so on. So we're keeping it very flexible.

And then just one last one for me. It sounds like generative AI is expanding the addressable market just given the productivity, potential productivity savings. What do you think happens to traditional enterprise search? Does it become merged or intertwined with generative AI or do you see them as separate use cases?

No, totally. Search results, you know, You can think about commerce or content. People don't just want an answer. They want to be able to navigate a corpus of content. They want to be able to disambiguate. They want to be able to narrow down the set of results or the corpus of data within which they want to generate an answer. And frankly, they want an integrated experience. As I said earlier, You can't have a digital interface, whatever it is, whether it's an intranet or an employee or a dealer portal or a patient portal or a customer portal, you can't have a page where you would have a search box on the left-hand side and a question answering box on the right-hand side. And you can't just deliver an answer. People still want to control the experience and so on. So the reality is you just set it. And many companies are realizing that is the worlds of search, navigation, disambiguation of results and answering and frankly, ultimately conversations and chats and personalization and recommendations are all converging, all converging right now. And this is in fact the platform that we're creating is to bring that all together.

Thanks for taking the questions. All right. That was a very good question.

Thank you. Your next question is from Adir Togbe from H Capital. Please ask your question.

Hi. Good evening, guys. Thanks for taking my question. Maybe there's one on the commerce use case versus customer service. use case for relevance generative answering are you finding or do you think that you know a lot of the heavy lifting that you've already done on self-service will kind of transfer over given that louis you mentioned that commerce could potentially go straight to production as opposed to going through maybe a beta test right so so there's there there are differences obviously but you're in the world what what generative ai really does in our space you know again we're not in

content creation or the other things I described, but in our space, which is real-time digital experiences, what generative AI does is it moves the experience from a search-based experience and results-oriented and recommendation-oriented experience that, you know, if sophisticated enough using AI, deep learning and behavioral-based machine learning is prescriptive in nature and anticipates and all of that, but still is limited to that. It moves it to an experience that becomes advisory. Should I use this or should I use that? You know, these are answers. These are questions that can be answered by generative AI. You know, what is the difference between product A, product B, product C? And in what circumstances should I use product C given that, you know, I live in the North and I don't like this, right? You know, these are the types of questions that in the context of commerce, you know, or workplace or so on can be answered. Now, in commerce, there's a couple of subtle things, which is, you know, you want to, if you're a large retailer, let's say, you obviously want to provide advice, but you generally would prefer to provide advice across products that you can sell and deliver. So, you know, you want to be able to limit or manage or control or tame essentially the answers and the large language model to be constrained by the catalog and all the specificities around that and so on. So you want to facilitate the consumer discovery towards the product that you want to sell. Nobody wants to serve advice for you to go to Amazon and buy something, right? And so these are – but we already are very competent in that. We already understand that. We understand recommendation engines, and we understand relevance, and we understand, you know, how to create a great consumer experience that will yield to stuff that you will sell, you know, bigger cart sizes and et cetera. So from that perspective, I know it's a long answer, but I think it deserves it. You know, it's – from that perspective, you know, we're applying – generative AI to that specific problem. And we think that's probably one of the best, one of the most important problems in commerce to create a much richer experience.

Understood. Thanks. And then one quick one and then I'll pass the line. Just on the SAP partnership, you know, it's been live for call it six or seven months now. Just on the pipeline that you're generating from the SAP endorsed partnership, are you finding that the deal sizes are changing from when you first introduced it to now as you kind of educate the SAP Salesforce. But just any changes in the pipeline from SAP, any color on that would be super helpful. Thank you.

Well, I'll give you an answer because, as you said, it's been going on for six months and so on. And, you know, we're building the pipeline and seeing the deals. But until we see a larger number of deals, We'll see. But the general trend or pattern that we see is in line with SAP as a corporation. You know, SAP in commerce, if you think about Shopify, for instance, Shopify has, you know, a very large number of customers, but they tend to be, you know, and they have larger ones, but they tend to be on average fairly small. meaning that they have a large number of customers that, on average, probably sell about $100,000 of gross merchandise value every year. And at an aggregate, Shopify probably processes today, I haven't checked last month, but in the range of about $150 billion a year of gross merchandise value across its customers. SAP has only a few thousand customers, but they process close to, you know, between one and a half and two trillion dollars. And so by definition, by design, those customers are very large global corporations, often that operate in many countries, often that have hundreds of thousands of SKUs. And in some cases, you know, we've seen as much as a million consumers a day. And so I guess that answers your question. So normally, you know, as this kind of technology generates more value, it commands a bigger price. Also because the consumption is higher. Yeah. Sorry about that. Also because the volumes are higher, obviously.

Awesome. Thank you, guys. Yeah. Thank you.

Your next question is from open timer. Please ask your question.

Hi, this is a Herschel on free tie. Uh, can you guys hear me?

Yes. Herschel. Hi.

Hey Louie. So I just wanted to ask in the early conversations that you've had with customers around generative AI, how are you finding them able to balance finding budget for these projects given the tough macro environment? you know, are you having to see them maybe reallocate a budget from existing projects or is this mostly a new budget category that customers are creating? And then just as a quick follow up. Yeah, go ahead. Yeah. Just as a follow up, given the opportunity you're seeing, are you planning to make any changes to your go to market hiring for next year?

Right. So, uh, so we're, we're, we're, growing the hiring obviously in line with, uh, with the pipeline. And, uh, and, and so we're, we're, as, as you heard Brandon talk about, uh, our second quarter, uh, cashflow positive and all of that. So we're sort of looking to balance everything. Uh, but we're really focused on growth. So as the pipeline growth will continue to, um, hire and top grade the team accordingly. As it relates to your first question regarding budgets, I would say it's pretty much all over the map, but in three categories. There are companies that just won't spend money right now because of budgets. Even if you hand them a bag of dimes, they basically won't give you a nickel. Not always logic dictates that, but that's the reality. In our case, I think the more important story is we can demonstrate the ROI. And the ROI that can be sometimes very fast. So in generative AI for self-service, I mean, look, if I go to you and you have thousands of agents in a contact center and I can say I can reduce the volume of cases by 20%, you will be able to translate the math. And by the way, it doesn't mean that you're going to necessarily slash your agents by 20%. It means that, you know, because it might be the tougher questions actually that get to your contact center. So there's a bit of a math there, but it's nevertheless very high ROI. In commerce, if you can do A-B testing that shows, you know, higher cart sizes and higher conversions, you generally can justify these projects. So it's more in the justification. I would say think about it as a two-by-two matrix. On the one axis, it's the ability to spend, which in some companies is just none, whatever. You would show them the teleporter. They wouldn't buy it. But if they have the ability to spend, then the second axis is really the justification, the scrutiny of the justification. Only the high ROI projects typically make it through. And, you know, the good news is we're geared for that part.

Got it. Thank you. I'll jump back in the queue. Great. Thank you, Herschel.

Thank you. Your next question is from Claire Gerdes from UBS. Please ask your question.

Oh, hi. Thank you. Yeah, this is Claire. I'm for Taylor. So, just a quick one on the guide. I think if you look at it, it implies that the 4Q sub-stroke guide would be a step up in sequential growth, but the 3Q guide would be flat. So, Yeah, is there anything you can share on what that setup might be, those drivers? I know you called out a couple things that are impacting this quarter, but yeah, anything you can share there?

Yeah, so for the third quarter or the upcoming quarter, the flat sequential guide that you're referring to, it's mainly driven by that qubit churn. As I said in my prepared remarks, we digested the bulk of that churn, the majority of that churn in the, in the second quarter, which of course then flows through, um, to rev rec into the third quarter. Um, and then thereafter, you know, we're through the heaviest part of the churn and we can, um, get back to, uh, growing again. Our focus is of course on making sure that bookings, uh, new bookings are leading the way revenue will always lag those, but, uh, So our focus is on the new bookings growth. But that's the dynamic. That's a play on the reported revenue.

Okay, thank you. And then real quick on NRR going forward, I know that on the adjusted basis it was 111%. How has that been trending, and is there anything you can give for us to look at that for the rest of the year expectations? Thanks.

Yeah, it continues to trend well for us. It's really a really positive indicator in a market like this that our NERs have held up the way they have. As we look forward, of course, we'll be mindful of just the broader economy and things that are impacting enterprises everywhere. But we see opportunity to grow our customer base. We've talked the bulk of this call about the generative offering and how that's increasingly applicable across the customer base. And that's certainly a wonderful opportunity for us to grow our NERs. But to this point, yeah, they've held stable and strong through this economy, which is a great sign.

Awesome. Thank you. Thank you.

There are no more further questions at this time. You may proceed.

Okay, well, thank you very much everyone for joining our call today. As you can see, we remain very positive around the positioning of Coveo for future growth. And with that, we would like to invite those of you who are interested to our Capital Markets Day next Thursday at the Toronto Stock Exchange. We hope to see you there. we'll have the Chief Operating Officer of SAP CX with us talking about what we're doing together as well as we'll go into much more details and present the customer use cases and so on. So we hope to see you there. And with that, thank you very much for being with us. And with that, operator, you can proceed and close the call.

Thank you. Ladies and gentlemen, the conference has now ended. Thank you all for joining. You may all disconnect.