EGAIN Corp (EGAN) Q1 2027 Earnings Call Transcript

Thank you for standing by and welcome to eGames Fiscal 2027 First Quarter 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 the star followed by the number one on your telephone keypad. If you would like to withdraw your question, please press the star on again. Thank you. I would now like to turn the conference over to Jim Byers, panel Wilkinson Investor Relations. Please go ahead.

Thank you, Operator, and good afternoon, everyone. Welcome to eGain's Fiscal 2027 First Quarter Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashu Roy, and Chief Financial Officer, Eric Schmidt. Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements, which convey management's expectations, beliefs, plans, and objectives regarding future financial and operational performance. Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate, or similar expressions. Forward-looking statements are protected by safe harbor provisions contained in the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects. Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. eGain is making these statements as of today, November 5, 2026, and assumes no obligation to publicly update or revise any of the forward-looking information in this conference call. In addition to GAAP results, we will also discuss certain non-GAAP financial measures, such as non-GAAP operating income. The tables included with eGain's earnings press release include reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial measures. eGain's earnings press release can be found by clicking News and Press within the Investor Relations page of eGain's website at eGain.com, and along with the earnings release, we will post an updated investor presentation to the Investor Relations page. And lastly, a phone replay of this conference call will be available for one week. And now with that said, I'd like to turn the call over to eGain's CEO, Ashu Roy.

Thank you, Jim, and good afternoon, everyone. I'll keep my comments on the quarter brief. Eric will take you through the numbers, but I do want to spend some time on our market, because we believe the market and the category we've been building toward for a while is now taking shape. So, our first quarter came in as we planned, with revenue and profitability ahead of our guidance. Total revenue was $21.9 million, above the high end of our guidance. As expected, that was down 7% year over year, reflecting the planned runoff of our legacy business. AI customer revenue was $14.4 million, up 7% year over year, and represented 66% of total revenue. AI customer ARR was 54.5 million, up 11% year-over-year, and grew to 75% of total SaaS ARR, from 72% at the end of fiscal 2026. Now I want to turn over to the category that I was talking about. In July, Gartner published its first-ever Magic Quadrant for Customer Service Knowledge Management Systems, and named EGA in a leader. We were positioned highest in ability to execute and furthest in completeness of vision. In the companion critical capabilities report, we received the highest product score for the compliance-driven service center use case. I want to underline why this matters beyond our position in it. Datna does not initiate a magic quadrant on a whim. They do it when enough enterprises, their clients, are asking about a problem that It becomes its own buying category. This is the category EGAN has been building for a while. It now has a name, a boundary, and a recognized leader. But let's be candid. For many people, knowledge management, quote-unquote, still penguins, document libraries, intranets, and maybe SharePoint on steroids. That is not what this category is about anymore, and it is not what EGAN does. So let me explain what we do, because it is key to the opportunity. Let me start by summarizing a little bit of what we discussed at our Solve event in Chicago last month, which is our annual customer event in the U.S. I talked about what I call the expert paradox. Simply put, AI works well for experts and struggles otherwise. Let me explain. As you and we all know, AI is successfully used now in business for coding and personal productivity. In both cases, the user controls what goes into the AI system, and the same user can judge the AI output quality. A developer, for example, can tell whether the AI-generated code works using tools and their expertise. Customer service use cases are at the opposite end of that spectrum in terms of user expertise. So, the person being given the AI answer, whether that is a service associate, a sales rep, or the end customer, is in no position to tell whether the answer is correct or compliant. And in the off chance that they do, they cannot control the input to AI anyway. This paradox largely explains the yawning gap in AI adoption for customer service adoption, sorry, automation today. So how do we make AI work at scale for such use cases? We do three things together. We ensure that AI is fed trusted knowledge. We make sure it has effective guardrails, and we continuously check for output quality. Policies, procedures, and know-how a business feeds its AI systems are, in effect, the instruction that AI follows. Wrong knowledge means wrong AI results. That is what eGain provides. Trusted knowledge and guidance for enterprise AI. Whether a business runs our AI agents, Salesforce's Agent Force, Microsoft Co-Pilot, or whatever they built themselves. We make sure every answer and every action performed by AI is grounded in knowledge the business can stand behind, kept current as products, policies, and regulations change, and continuously checked for quality. At Solve, again, IDC presented new research sponsored by eGain. Based on a survey of 520 U.S. organizations with 1,000 or more employees, Pre-finding stood out. 95% of organizations surveyed are already evaluating, piloting, or running AI in their knowledge processes. Trying AI is universal. Getting a return is not. The leaders report roughly twice the improvement of laggards on every business impact IDC measured. This is leaders and laggards as measured by their knowledge maturity. I'm ready. Returns are measured in terms of AI, ROI. What separates them is the foundation. Half of the leaders run a fully operational unified knowledge platform versus 5% of laggards, and 94% of leaders have automated more than half their knowledge processes versus 36% of laggards. IDC's conclusion is the one we have been making for some time. AI success does not hinge on the model. It hinges on the knowledge foundation beneath it. The most powerful part of Solve for us was hearing our customers describe their AI ROI success in their own words. I'll mention three. Country financial, a Fortune 1000 insurer, I've given more than 2,500 field agents trusted answers using the eGain platform instead of a list of documents to consult when they're sitting across a client or on the phone window. Answer accuracy they have seen is above 96 percent. Calls into their home office for escalation from their field agents has fallen by 25 percent in the first month. And adoption has reached 70% with no mandates or incentives. Their two lessons for the audience was, first, content beats model. And second, AI is not a project. It's a product. You keep operating. Health First, the second one I want to bring up, a New York-based health insurer. It talked about publishing one governed update to 6,500 users in their business across 50 personalized portals. They feed that knowledge into call routing now, which cuts misrouted contacts by 76%, and that was a result they saw within 30 days. Knowledge is not only answering questions, it is driving decisions. And the last one I want to bring out is Philip Morris International. They are now running 64 markets across multiple languages on one governed knowledge base on e-game. Their presenter put it best. Knowledge has left the customer group, he said. It is now used across IT, marketing, and field operations. This is a pattern we see again and again. We land in customer service where the bar for accuracy and compliance is high. Once the foundation is working, it becomes the knowledge layer for the rest of the enterprise. A common thread in all these stories is that trusted AI is not a one-time project. Products launch, rates change, regulations update, and the knowledge behind the AI starts getting stale pretty much the day you lock it. So, at Solve, we introduced AI Knowledge Ops. A new framework, along with a set of capabilities for running knowledge as a continuous operation. It runs as a loop. You start by learning what customers are actually asking from your customer conversations. You find that answer gaps. You prepare and organize the knowledge so AI can use it precisely. You test the answers, and then you feedback what you learn from users into the loop. We announced five new capabilities as part of this AI Knowledge Ops to make that loop fast and low effort. The first is a tool called WICA, which stands for What Your Customers Ask. It analyzes customer conversations to show which questions matter most and runs inside the customer's own firewall. This is crucial for our compliance-oriented customer base. The second is KForge, which turns content scattered across silos in SharePoint, Confluence, ServiceNow, Salesforce, what have you, into AI-ready knowledge with experts approving anything that is high-risk. The third was knowledge graphs. We added that to our knowledge platform. It gives AI agents a map of how a business works so they can reach and navigate to the right answer at much lower token cost and much faster. The fourth was Gini, which lets our users on the eGain Knowledge platform ask the platform and the tools in conversational interface to write or update content, run reports, or build workflows. And finally, the last one, and probably the most crucial to close the loop, is Smart Evaluator, a product that allows AI engineers to continuously test AI pipelines for answer quality. It flags the wrong answer. It pinpoints where it likely went wrong in the pipeline and suggests a fix. Here's why this matters commercially for us. For a major financial services company, our team ran the AI knowledge ops process one time, the first time, in about 10 person weeks. That company had been expecting that that whole exercise would have taken one to two years. Knowledge management has always been a great goal, but the effort it took to get knowledge ready and maintain it was the biggest barrier in this market, and we believe we have removed most of it. A question which we ask ourselves and is fair is, what stops the large platform vendors from doing it themselves? Well, three things. First is the depth of capability. The hard part is not search. It is governance, workflow, permissions, versioning, auditability for regulated businesses. We have solved that for many years, and this is where Gartner scored us highest. The second is we have deeply applied AI to our own platform to remove manual effort. To make knowledge projects that typically took years now take weeks. We have automated knowledge management so businesses can operate this loop with ease and speed. And third, we are open. We are modular. Enterprises will use many AI tools from many vendors. We sit underneath all of them. So customers can change AI models and agentic systems to drive their objectives without lock-in. Turning to customers in the quarter, we find several new customers and expansions during the quarter. Let me walk you through some examples because they'll show you where the market is going. The first one is Technology Credit Union. This is a growing multi-billion dollar credit union in the valley here. They recognize that their AI plans for members and employees depend on the knowledge underneath. And they're building that foundation across contact center, digital banking, branches, and back office. So all employees, not just contact center agents. They chose e-game over vertical specific vendors. For our governance, agentic capabilities, and our connectivity into their core systems, in this case, Jack Henry and Alchemy. Credit unions are now becoming a vertical for us that we are pushing in a dedicated way into. Alongside this new logo, we have customers like Rogue Credit Union, who presented at Salt, Oregon Community Credit Union, and Selco, among others, who are using eGain well beyond member service. And we are seeing that interest build in a pipeline. The next new logo I want to talk about is a privately held multi-billion dollar consumer packaged goods company, and their use case is outside customer service. They're deploying our AI agent to up to 1,000 blank floor operators, and this agent is going to be a virtual expert to assist them. Interestingly, this opportunity started with a self-signup on our website. This is a motion we've been building for some time, and it is starting to add opportunities to our funnel. Another exciting new logo that we closed, this one in early October, so not in Q1, is one of the oldest and largest investment management companies in America. They are deploying eGain as their trusted knowledge foundation to power all AI projects and thousands of employees. Our existing customers also continue to expand with more use cases and more functions. A large U.S. mutual insurer expanded the use of eGain alongside its investment in Salesforce, using eGain Composer, our platform capability, to making their governed eGain knowledge available inside their Salesforce AI. That is the role I described earlier. If the customer chooses a different AI application, eGain still provides the same consistent trusted knowledge underneath. Another one I want to bring out is an international bank added a real-time knowledge access capability, so the trusted knowledge they are built to start with for customer service now flows automatically into many more of the bank's systems and user groups. The pattern is consistent. Customers first establish the knowledge foundation, and then AI application and ROI follows. Looking at demand, the Darkner recognition is showing up at the top of our funnel. While it's early, I just want to bring out a few things. The Darkner Magic Quatern campaign for marketing has quickly become one of our most productive digital programs, bringing in new conversations across North America and Europe. Our event source leads have nearly doubled year over year this quarter as we have scaled marketing. We're seeing more RFPs from large enterprises, many of them inbound, with signals growing awareness. In addition, we now have focused go-to-market teams, albeit small ones, targeting banks and credit unions, healthcare, and government. And all this is complemented by our self-sign-up push and a growing partner network. To conclude my section, a year ago, very few people were talking about knowledge as a foundational requirement for AI. Today, Nartner has defined the customer service knowledge management category and named he gained a leader in it. IDC has quantified the AI payoff of getting the Knowledge Foundation right, and our customers are showing what... AI-ROI can be in customer service in production at scale. Now, our numbers do not yet reflect the size of that opportunity, we understand, but we believe we are in the early innings of a large and durable market. We have the products, the proof points, and the balance sheet to lead it, and we are now focused on effectively scaling our go-to-market execution. With that, I'll turn it over to Eric.

Thanks, Ashu, and thanks, everyone, for joining us today. As a reminder, we are using slides to support today's call. You can find them along with our updated presentation in the Investor Relations section of our website. As Ashu mentioned, we started the year well, delivering first quarter revenue and earnings above the high end of our guidance. I'll review our first quarter results, take you through our AI customer metrics. And touch upon our capital allocation, and then discuss our outlook for the second quarter and full fiscal year. Turning to our first quarter results, starting with revenue, total revenue was $21.9 million, down 7% year-over-year, but above the high end of our guidance and street consensus. AI customer revenue grew 7% year-over-year to $14.4 million above our guidance and represented 66% of total revenue. The year-over-year decline in total revenue reflects lower revenue from our legacy business, which declined approximately 28%, consistent with the transition we outlined last quarter. Turning to gross margins. Non-GAAP total gross margin was 74% compared with 76% a year ago. Non-GAAP SaaS gross margin was 78% compared to 81% a year ago, and the client reflects the lower legacy SaaS revenue against a largely fixed hosting cost base. Turning to operating expenses. Non-GAAP operating expenses were $13.6 million, up 4% year-over-year and down 4% sequentially. Sales and marketing expense were $4.6 million, up 17% year-over-year and down 16% sequentially, primarily reflecting the timing of program spending. Our largest event of the year, Solve26 in Chicago, that took place in October, so that spending falls in our second quarter. We expect sales and marketing spending to step up over the rest of the fiscal year as we expand the go-to-market programs Ashi described, with a focus on converting growing market awareness into pipeline, new customers, and recurring revenue. Turning to the bottom line, GAP net income was $1.6 million, or $0.06 per share, compared with $2.6 million, or $0.10 per share, a year ago. Non-GAP net income was $2.4 million, or $0.09 per share, exceeding our guidance and street consensus, compared to $4.7 million, or $0.17 per share, a year ago. Adjusted EBITDA was $2.7 million. A 4% margin compared with $5 million or a 21% margin a year ago. Turning to cash flow and the balance sheet, cash provided by operating activities was $1.8 million compared with $10.4 million a year ago. The difference is primarily due to the timing of an annual payment from a large federal customer. Last year, we received that payment in our first quarter. This year, it arrived early in the fourth quarter of fiscal 2026, contributing to the strong cash flow in that quarter. We ended the quarter with $71.3 million in cash and cash equivalence and no debt. Moving to our capital allocation, our capital allocation priorities are unchanged. First, invest in AI growth. And third, return capital opportunistically. During the quarter, we repurchased approximately 644,000 shares for $3.6 million at an average price of $5.064 per share. Since the start of fiscal 2026, we have returned $15.2 million to shareholders through the repurchase of approximately $2.3 million shares, and we have approximately $6 million remaining under the authorization. Now, turning to our AI customer metrics, as we introduced last year, we report our business based on whether a customer is on our current AI-enabled platform rather than by product. AI capabilities, as I should describe, are embedded across our AI-enabled platform, including identifying knowledge gaps, structuring content, authoring, verifying answers, and powering AI. We therefore measure AI adoption at the customer level rather than by the individual product. Our focus is on growing the number of AI customers and expanding those relationships over time. AI Customers. AI Customer ARR includes all SaaS ARR from customers on our current AI-enabled platform, and AI Customer Revenue includes SaaS and professional service revenue generated from those customers. With that context, AI Customer ARR grew 11% year-over-year to $54.5 million. As of September 30th, and represented 74% of total SaaS ARR, up from 65% a year ago. Total SaaS ARR was 73.4 million, down 4% year-over-year, reflecting the continued declines among our legacy customers. Turning to retention, trailing 12-month dollar-based net retention for AI customers was 104%, consistent with the fourth quarter, compared with 117% a year ago. As we noted last quarter, the year-ago period included the significant expansion with the U.S. megabank that we signed in the fourth quarter of fiscal 2025. Net retention for all customers was 92% compared with... 102% a year ago, reflecting the expected runoff of our legacy customers. Total remaining performance obligations were 71 million, down 18% year-over-year, and short-term RPO was 52 million, down 11%. The decline reflects the timing of large multi-year renewals and the continued runoff of our legacy business. Because RPO can swing significantly up or down with the timing of large multi-year renewals, We focus on AI customer ARR and net retention as a primary measure for our customer base. As AI customers become a larger share of our business, we expect their growth to increasingly drive our consolidated results. Now, turning to our outlook, before the numbers, let me give you some context on how we are thinking about the rest of the year. First, investments. What is the first quarter profitability of performance? Reflected the timing of sales and marketing spending. We expect that spending to increase beginning in the second quarter, including the Solve26 event. As we expand the go-to-market programs, our focus is on converting increased market awareness into qualified pipeline, new customers, and recurring revenue. Second, the legacy transition. We expect the runoff of legacy revenue to become more pronounced beginning the second quarter, including the planned wind down of our legacy messaging business. Our full year outlook continues to assume an approximately 30% decline in our legacy business revenue. We remain comfortable with our full year outlook. Our balance sheet and operating cash generation gives us flexibility to fund our AI growth initiatives. Our full year guidance reflects a meaningful increase in sales and marketing investments of the remaining three quarters. As a result, first quarter profitability is not representative of the quarterly earnings levels we expect for the balance of the year. So for the second quarter of fiscal 2027, we expect total revenue of $21 million to $21.5 million, AI customer revenue of $14.5 million to $14.8 million, GAP net loss of $500,000 to net income of $200,000 for a loss of $0.02 to an income of $0.01 per share, including approximately $700,000 of stock-based compensation expense. Non-GAAP net income of $200,000 to $900,000, or $0.01 to $0.03 per share. Adjusted EBITDA of break-even to $500,000, or a margin of 0% to 2%. For the full year ending June 30, 2027, we are reiterating our guidance. We expect total revenue of $84.5 million to $86 million. AI customer revenue of $59.5 million to $60.5 million, representing growth of approximately 8% to 10%. We continue to target approximately 20% year-over-year growth in AI customer ARR as of June 30, 2027. A gap net loss of $2 million to $3 million, or $0.08 to $0.11 per share, including approximately $4 million of stock-based compensation expense. Non-GAAP net income of $1 million to $2 million, or $0.04 to $0.07 per share. Adjusted EBITDA of $650,000 to $1.4 million, or a margin of 1% to 2%. We expect weighted average shares outstanding of approximately $25.8 million for the second quarter and $26 million for the full fiscal year. Finally, a brief reminder of the long-term financial model we introduced last quarter. We see fiscal 2027 through fiscal 2029 as a transition period, with total revenue growth converging towards AI customer growth as the legacy business runs off. By fiscal 2030, we expect that transition to be largely complete. Our fiscal 2030 targets are AI customer ARR of 100 million to 120 million, up from approximately 54 million in fiscal 2026, a 17% to 20% compounded annual growth rate. Total SaaS ARR of 100 million to 120 million, with AI customers representing substantially all of it, up from 72% in fiscal Until 2026. AI customer revenue of $105 million to $115 million, up from approximately $55 million in fiscal 2026. Total revenue of $110 million to $120 million, with AI customer revenue approximately 95% of the total, up from 60% in fiscal 2026. SAS growth margins of approximately 80%, with positive adjusted EBITDA margins as we invest in growth. The result we are building toward is a larger, foster-growing AI knowledge infrastructure company with a greater share of recurring AI customer revenue and significant expansion opportunity within our install base. We have shown we can run this business profitably at our current scale. We are choosing to trade some near-term profitability for growth because we believe the size of this opportunity warrants it. So, to summarize, We delivered first quarter results ahead of our guidance, and we are reiterating our full year outlook. AI needs the right instructions, and eGain is the instruction layer for enterprise AI, recognized as a leader in the first ever Guard the Magic Quadrant for our market. AI customers now account for 74% of our SaaS ARR on a path to substantially all of it by fiscal 2030. And with $71.3 million in cash and no debt, we have the resources to fund the plan and turn category leadership into durable long-term growth. With that, I'd like to open the call for questions. Operator?

Thank you, and we will now begin the question and answer session. If you have dialed in and would like to ask a question, simply press the star 1 on your telephone keypad to raise your hand and join the queue. If you would like to read more your questions, simply press the star 1 again. One moment, please, for your first question. And your first question comes from the line of Jeff Van Rie with Craig Hallam. Please go ahead.

Hey, good evening, Ashu, Eric. This is Daniel on for Jeff. Maybe, Ashu, if you would want to touch a little bit on the AI product suite, obviously, it's been expanding a lot. There's a whole suite of things that have been rolled out, and every Solve event, there's a lot more that gets announced. Maybe whether it's AI Knowledge Ops or AI Agent 2, or there's a lot of products that have been announced, maybe you could talk to what you expect to be the biggest needle movers in the AI portfolio and maybe what products or product developments you would want investors to be focused on.

Good question. So... So the way we think of it is the foundation first, and that's the AI Knowledge Hub. That is where it all starts. And then all these tools and products that go around the platform then get added on top. But what we have done with the AI Knowledge Ops as a framework is now to guide more and more people. So what we have heard from customers, including at Solve, was you have so many things to offer. We would love for you to tell us exactly how it all works together and how to go about it from where we are today to where we want to be. So it all starts with AI Knowledge Hub and then gets, if you think of it as layers, it's sort of AI Knowledge Hub as the core platform. And then on top of it, you have tools and you have these pre-built AI agents, right? So in the target market we're going after, which is the enterprise, I think AI Knowledge Hub with a platform is where it all starts.

Okay, that's helpful. And then maybe just on the S&M investments you're planning to make, just walk through a little bit more of the plan there, what you're thinking to change in terms of, is that about certain leaders? Is that about more quota-carrying reps? We're targeting a particular geography, just how you're thinking about what those plans entail.

Right now, as I mentioned, so we've scaled up our front-end marketing investments to drive more awareness and more footfall, if you will, into our digital and physical presence, right? So that's where we are most focused on in terms of increasing our spend on sales and marketing. And then the second bit behind that is building out the capacity to... Engage with those early leads and turn them into opportunities. So building out the SDR team, if you will, to make sure that we have got the right level of expertise and front-end engagement for those who we are able to bring in as leads. So I think those are the top two things for us right now. The back end of the funnel, which is more around selling and closing, we think that we have enough capacity for that. So it's really about filling the top of the funnel.

Yeah. And then just one modeling question, Eric. In terms of the messaging business exit, I know the first half of that was achieved in Q2 26, sort of took half impact to Q2, and then another half of the impact came in Q3. And then the remaining half of the messaging business exit, I know that happened here in Q1. Just in terms of thinking whether we got a full quarter of impact or not, was that exited at the beginning of the quarter near the end? How much of a quarter of impact did we get from that messaging exit?

So that was the full quarter. It's finished up at the end of the quarter. So that's why we mentioned that we expect to see that decline in Q2.

Okay, that's helpful. All right. Thanks, Ashu. Thanks, Eric. Thank you.

And your next question comes from the line of Eric Sapiger with D. Riley Securities. Please go ahead.

Hi there. This is Ethan Wydell on for Eric. Thanks for checking my questions. To start off with, so AI customer revenue, you know, up 7% versus 11% for ARR. Can you maybe help bridge the gap there between those two numbers, and, you know, maybe what would you expect going forward?

Yeah, so I think, obviously, like you would typically see with... The ARR is always the leading indicator. As you grow a business, then as those contracts get signed, that would take time for that to convert into the revenue going forward. So I think for us, as you look through the guidance, I think we're targeting the continued improvement in the ARR growth rate, targeting that by the end of the year, we'd be up to 20 percent. Whereas, currently, our guidance for the revenue is sort of roughly half of that, and that's more just the lag between the ARR booking and then the revenue that gets recognized.

Okay, that makes sense.

And then...

So, when we look at the kind of legacy ARR decline, of those customers rolling off, maybe can you speak to what share of that is customers churning out versus migrating to, like, the AI platform, and has the Gartner recognition changed kind of what that looks like?

It's a good question. We're working on it, but right now, it's not like we have seen a significant shift there.

Understood. Well, you know, it seems like a very powerful validation of your technology, so congrats on that, and that's all for me. Thank you. Sure. Thanks.

And once again, if you would like to ask a question, simply press star 1 on your telephone keypad. And again, that would be star one to ask a question. And I'm showing no further questions at this time. I would like to hand it back to the management for closing remarks.

Great. Well, thanks, everyone, for taking the time today. I look forward to continue updating you after our future results. Thanks. And I guess one final item, we'll be at the Roth Investor event. Later this month, so it would be great for anybody in the New York area that's participating, great to meet up with you. Thank you.

Thank you, and ladies and gentlemen, this concludes today's conference call. Thank you all for joining. You may now disconnect.