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Operator: Good afternoon, ladies and gentlemen. Thank you for standing by. Welcome to LivePerson’s Fourth Quarter 2022 Earnings Webcast. As a reminder, this conference is being recorded. I would now like to turn the conference over to Mr. Chad Cooper, Senior Vice President, Investor Relations.

Chad Cooper: Thank you, operator. Joining me on the call today is Rob LoCascio, LivePerson’s Founder and CEO; John Collins, Chief Financial Officer; Joe Bradley, Chief Data Scientist; and Dr. Matt Dawson, Founder and CEO of WildHealth. Please note that during today’s call, we will make forward-looking statements which are predictions, projections and other statements about future results. These statements are based on our current expectations and assumptions as of today, March 15, 2023 and are subject to risks and uncertainties. Actual results may differ materially due to various factors, including those described in today’s earnings press release and in the comments made during this conference call as well as in 10-Ks, 10-Qs and other reports we file from time to time with the SEC.

We assume no obligation to update any forward-looking statements. Also during this call, we will discuss certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP financial measures is included in today’s press release. Both the press release and the supplemental slides, which include highlights for the quarter, are available on the Investor Relations section of LivePerson’s website. With that, I will turn the call over to Rob. Rob?

Robert LoCascio: Thanks, Chad, and thanks, everyone, for joining us for the Q4 2022 quarterly call. We’ll be here with John Collins, our CFO; and Joe Bradley, also, our Chief Data Scientist, will be joining us to talk about our AI platforms. And then we also have Dr. Matt Dawson, who is the Founder and CEO of WildHealth, and he’ll be talking about everything we’re doing on the AI health care initiatives. Now there’s been a lot of excitement around AI, especially with everything that’s going on with ChatGPT since November. And obviously, the consensus is that this is not an evolution of technology, but a revolution. And for LivePerson, this is a profound time in our history as we have waited over 20 years for this day to come and we have spent the last 5 years investing heavily in being a leader in AI in the enterprise AI space.

We’ve had a vision for a very long time that humans, that we could talk to a machine and that machine could help us, through natural language, solve our most important aspects of our life, things in our health and things around our finances and our relationships with our brands that sometimes get strained when we’re sitting on calls on hold. And so we made a big bet 5 years ago, and now we’re here as one of the few companies in the world that can go after this opportunity as an AI company, especially in the enterprise. Now we spent the last year getting the house in order and creating a foundation to go after this opportunity. And we’ve created now a model for the business that I think will allow us to go back to where we came from in generating EBITDA, generating cash flow.

And I’m confident that the trajectory of what’s about to happen to us is going to be built on a solid foundation. And obviously, over the COVID time, there was a lot of things that happened to our company and others where we invested into areas. We went after different opportunities with our customers, but Q4 and Q1 is a place where there was a lot of noise, but we’ve all addressed and resolved those noises. And now what we’re going to do is, starting in Q2, we have a new company. We have a company that will generate, I think, strong earnings, cash flow, EBITDA, and from there, we’ll go after this massive opportunity. Now as we mentioned in previous calls, we’re really focused on getting rid of non-core revenues. These are revenues that don’t recur.

These are revenues that are not supporting the core vision that we have around AI, and Q1 will be our trough in revenue, in EBITDA and cash flow. And then from there in Q2, there will be an inflection point up, EBITDA will be positive, cash flow will be positive, and we will continue growing top line and bottom line throughout the year. The company as well as our shareholders will benefit, I think, from simplifying the business and being more transparent about our revenues. And already and starting Q2, we’ll do a couple of things, and John will go into them in a minute, but we’re going to focus on the recurring revenue lines. We have about $340 million of recurring revenue. That’s 80% of our total revenue. Now the growth rates in that are single to mid-single digits.

But obviously, we’ve got the wind in our sales to bring that to another level. Obviously, we balanced out how do we get to a strong business model, generate cash with growth. But from there, we can grow now at a different level. We’re also focused on achieving annualized EBITDA margins of 16% to 19% by year-end. We’ll start generating positive margins, as I said, in Q2, and we’ll also start generating positive cash flow in Q2, targeting 7% to 10% cash flow by year-end. Those are facts. Those are things that will carry forward. Those are things that we had in this company for over 20 years, and that’s what got us here, and that’s what will get us to where we need to go now with this exciting opportunity in our hands. What I’d like to do now is hand it over to John Collins, who can do a deeper dive on the P&L and really show you what’s making up the base that’s allowing us to go after this big opportunity.

John?

John Collins: Thanks, Rob. During the past 12 months, we have rationalized our cost structure, yielding an expected 36% or $200 million in reduction of annualized costs. We also wound down non-core business lines in order to enable profitable growth and sharpen our focus on LivePerson’s B2B core software business, including the AI technology that powers it. As I’ll elaborate on shortly, we have a strong base of recurring software revenue from top brands across the globe that is expected to grow, as Rob said, in the low to mid-single digits and represent approximately 80% of total revenue in 2023. Our success in reducing costs last year, coupled with additional cost reductions in the current quarter, is expected to yield double-digit adjusted EBITDA margins and positive free cash flow for the B2B core beginning in the second quarter.

On an annualized basis, we expect the B2B core to exit the year with at least a 16% adjusted EBITDA margin and up to a 10% free cash flow margin despite the expected decline of more than $70 million in non-core revenue on a year-over-year basis, most of which was set in motion by the profitable growth strategy that we launched last year. In short, we’ve materially improved the P&L and reallocated resources to focus on the B2B core, establishing a solid foundation to execute on AI-led growth in the second quarter and beyond. With this overview of the B2B core P&L in mine, we think investors and analysts would benefit from additional transparency into our total company revenue stack, particularly the non-core components of the P&L that we began winding down last year.

Beginning with total company revenue in 2022, of the $514.8 million that we recognized, again, over $70 million was non-core revenue that we do not expect to reoccur in 2023. Breaking that down, of over $50 million of the non-core revenue year-over-year revenue decrease, falls into 2 related categories: COVID-19 testing; and to a larger extent, professional services with a strategic partner in the diagnostics space. We now expect professional services, including a large software build, to complete ahead of schedule in the first quarter. Setting aside approximately $3.8 million of professional services revenue tied to the conclusion of the diagnostics project in the first quarter, we expect professional services revenue in 2023 to be exclusive to the B2B core and consistent with its historical relationship to expansions and new logos.

Next, over $20 million of the non-core year-over-year revenue decrease relates to Gainshare labor and pandemic-driven variable revenue, which we discussed as not strategic to the B2B core on the second quarter call last year. The largest remaining source of revenue in this category is expected to exit the P&L in the second quarter of 2023. As discussed on the prior two quarterly calls, the nonlabor software component of our Gainshare business has almost fully converted from variable to recurring revenue and represents committed usage of the conversational cloud that is identical to software consumption by our other B2B core customers. For this reason, e classified this software revenue as B2B core revenue, representing approximately 5% of total recurring software revenue, and we will no longer refer to Gainshare customers in the context of the B2B core.

Finally, and consistent with the sharpened focus on the B2B core, we have classified the business underlying the consumer segment as held for sale in our financial statements, with a targeted transaction close date before the end of this month. To summarize the key points directly, we have materially improved the P&L and we are drawing a line at the first quarter, after which we expect the B2B core’s performance to be transparent and its business drivers consistent with growing revenue by increasing the Conversational Cloud’s share of enterprise to consumer engagement, especially AI-based engagement across both voice and master. Turning to the fourth quarter of 2022. There were 2 unanticipated changes that impacted our results. First, as disclosed last week, we received notice in November that Medicare reimbursement for a non-core WildHealth program was suspended pending further governmental review.

We currently believe that the services rendered under the program in the fourth quarter of 2022 were valid and should ultimately be reimbursable. However, in view of the inherent uncertainty as to the timing and amount of further reimbursement, we have elected to take the most conservative position by not recognizing revenue for which reimbursement has not yet been collected. I’ll also note that the services under this program were subsequently discontinued, consistent with the focus on exiting non-core activities. But for this event, revenue would have been within our guidance range for both the fourth quarter and the full year of 2022. Second, in light of the remaining capacity under our 2019 stock incentive plan and in order to mitigate dilution to our shareholders, especially in light of current valuations and, more generally, to improve the quality of earnings, we’ve decided to pay the 2022 employee bonus in cash instead of stock.

Note that in recent years, we have paid annual employee bonuses in stock, which was the original plan for 2022. But for the WildHealth revenue reserve and this decision to change the method of settlement for the 2022 employee bonus, adjusted EBITDA would have also been within our guidance range for both the fourth quarter and the full year of 2022. With the impact of these changes, revenue for the fourth quarter and the full year was $122.5 million and $514.8 million, respectively. Adjusted EBITDA, reflecting these impacts for the fourth quarter and full year, was a loss of $5.2 million and $16.2 million, respectively. With that, I’ll turn to our reporting segments. For the fourth quarter, within total revenue, B2B revenue declined approximately 1% year-over-year, while revenues from our hosted software declined 8% year-over-year.

Excluding the impact of COVID-19 testing and pandemic-driven Gainshare variable revenue, B2B grew 6% year-over-year and hosted declined 1% year-over-year. Professional services revenue grew 30%, driven primarily by the diagnostics project that I referenced earlier. The consumer segment declined 3% due primarily to decreased marketing spend in the quarter. From a geographic perspective, U.S. revenue grew 3% year-over-year, and headwinds in EMEA caused international revenue to decline 8%. We signed a total of 90 deals in the quarter, including 1 7-figure deal, 46 expansions and renewals, 44 new logo deals. While the total number of deals decreased year-over-year, the enterprise deal count increased 22%, and the new logo deal count increased 63%.

In general, however, we observed more deals but at lower-than-expected dollar values, indicating that some macroeconomic impact may have compressed budgets in the fourth quarter. To highlight a few of these deals, we re-signed a top 3 global airline to a 2-year contract, which is using us for a wide array of service use cases, including refunds, baggage, reservations and travel credits. We also re-signed one of the top online travel companies to a 2-year extension and re-signed a top 25 financial holding company in the U.S. to a 2-year extension. For new logos, our APAC team had a strong quarter with the signing of an Australian multinational banking and financial services company as well as one of the leading providers of pension and investment products in Australia.

As for net revenue retention, consistent with our focus on the B2B core and recurring revenue growth in particular, and in light of the more than $70 million in non-core revenue that we do not expect to reoccur in 2023, we think investors and analysts would benefit more from net retention for recurring revenue than from net retention for total revenue. For this reason, we are providing both net retention metrics for the fourth quarter and expect to provide only net retention for recurring revenue in the first quarter of 2023 and beyond. In the fourth quarter, net retention for total revenue was well below our target range of 105% to 115%, driven as expected by lower Gainshare variable and labor revenue and the elimination of COVID-19 testing revenue.

Net retention for recurring revenue was above 100%, but below our target range of 105% to 115%. Finally, average revenue per customer was $680,000 in the fourth quarter, up 11% year-over-year. As I discuss guidance, note that there was a clerical error in our press release that was just issued with respect to guidance. The guidance I’m about to provide is correct in the press release. An 8-K will be reissued as soon as possible to reflect these corrections. For total company metrics in the first quarter of 2023, we expect total revenue to range from $106 million to $109 million or a decrease of 19% to 16% year-over-year. For adjusted EBITDA, we expect a loss in the range of $8 million to $6 million. The year-over-year decrease in revenue and the sequential decrease in adjusted EBITDA are both driven primarily by the large amount of non-core revenue that is exiting the P&L beginning in the first quarter.

The largest drivers in the first quarter are decreasing Gainshare labor and variable revenue and decreasing professional services from the strategic partner in the diagnostics space that I referenced earlier. As for total company metrics for full year guidance, we expect total revenue to range from $422 million to $436 million or a decrease of 18% to 15% year-over-year; and adjusted EBITDA to range from $15 million to $32 million or a margin of 4% to 7%. Consistent with the themes of transparency and focus on the B2B core, we also think providing guidance for our B2B core recurring revenue and adjusted EBITDA would be instructive for investors and analysts. For the first quarter of 2023, we expect recurring revenue to range from $80 million to $83 million or a decrease of 7% to 3% year-over-year.

For adjusted EBITDA, we expect a loss in the range of $6 million to $4 million. As previously discussed, in view of the cost reductions that will be effective this month, we expect the B2B core to produce double-digit adjusted EBITDA margins and positive free cash flow beginning in the second quarter and to improve sequentially throughout the year. For the full year, we expect recurring revenue to range from $334 million to $347 million or 0% to 4% growth year-over-year; and adjusted EBITDA to range from $27 million to $40 million or a margin of 7% to 11%. On an annualized basis, we expect the B2B core to exit the year with a 16% to 19% adjusted EBITDA margin and a 7% to 10% free cash flow margin. Before passing the call back to Rob, I’ll emphasize a few key points to conclude.

Relative to the first quarter of 2022, when we launched our plan to achieve a balanced approach to profitability and growth, we’ve reduced our cost structure by an expected 36% or $200 million in annualized costs. By winding down nonstrategic and low-margin lines of business, we have also provided immediate clarity on LivePerson’s foundation, the B2B core business, driven by a growing base of software recurring revenue that represents approximately 80% of total company revenue in 2023. Beginning with the second quarter, we expect B2B core to be profitable, generating double-digit adjusted EBITDA margin and a positive free cash flow. We also expect AI-led growth in recurring software revenue to further improve profitability throughout the year to an annualized rate of at least 16% in terms of adjusted EBITDA and up to 10% in terms of free cash flow, further strengthening both our balance sheet and the P&L.

And with that, Rob, I’ll hand it back to you.

Robert LoCascio: Thanks, John. Obviously, we took the opportunity. There’s a lot of noise and a lot of puts and takes, but we put the past in a box, and now we have a foundation come Q2. And what I want to do is talk about what’s next and what do we build from that foundation based on our strategy around AI that we’ve been pursuing for over five years. What I want to do is first talk about what our strategy is and address it from really three perspectives. One is a strategic perspective, an engineering product perspective and an industry perspective around our view of AI and how we’re going about it. From a strategic perspective, AI has two foundational pieces. One is the data models. And that’s the stuff we’re seeing with OpenAI and large language models.

And the other, just as important, is actually the data set that the model uses to generate outcomes. There’s been a lot of focus on the model because of what’s gone on with ChatGPT. But we can see sometimes it has issues because the underlying data set is using public data. LivePerson has one of the largest and most precise conversational business data sets in the world. And I use the word precision because over 350,000 people on our platform each day, and they’re messaging their live agents and they are generating 1 billion conversations a year, and these are high-quality conversations around many different topics, but the conversations are precise. There are answers to questions in the way the brand would want it answered. These agents, they create a long tail of answers.

Today, as we said in the past, we automate fully about 20% to 30% of the conversations that are on our platform. About 75% of conversations have some form of AI, but end-to-end automation is about 20% to 30%. With what’s happened with large language models and what we’re doing with them is that we can go after a much larger pool of intents. We can go after the long tail of intents that’s sitting in our data today, and we believe we can automate 80% to 90% of conversations, not only on messaging, but in voice. We are bringing our voice AI product. It’s weeks away now, and we’re basically going to be able to go after both channels. So we sit in a very unique space to power the enterprise at scale and in a safe way with large language models, and we are going to accelerate what we’re doing.

Now from an engineering perspective, we began developing our own AI capabilities 5 years ago. And today, 50% of our engineering budget is on AI and automation. And this is backed by world-class team of product and engineering leads, data scientists and many who came from the acquisitions we did over the past several years, from BotCentral, from VoiceBase, Tenfold, , WildHealth. As a matter of fact, we recently took one of the founders of BotCentral, who we acquired 5 years ago, and now he’s leading a group that is working with our largest top 100 enterprise customers so that they will adopt this technology at a faster rate. Because what we’re hearing from our customer base is they want to talk. They want to talk to engineers, they want to talk to the product heads because they’re going to buy AI from those people.

And even our sales team, although they’re out there and they’re looking for more new opportunities, our base of customers want the implementation, want the knowledge from the people who built these platforms. So I think the impact of that on our net retention rates and also our ability to grow the company at a different level will be profoundly changed with a restructuring of that business. And then lastly is, from an industry perspective, many of you may know, about four years ago, I founded a nonprofit called EqualAI with Miriam Vogel. Miriam was the Associate Deputy Attorney General under President Obama, and she led the team that developed the implicit bias training programs for federal law enforcement. She recently also has been appointed as a chairperson to President Biden’s National AI Advisory Committee.

And her and I built this nonprofit in this organization. We have a great Board of Directors and it’s focused on one thing: How do you bring AI safely to the enterprise? And there’s a framework and training that Miriam has developed with her team, and LivePerson has been a great supporter of that and we’ve obviously adopted many of those frameworks in our own technology. So what this all boils down to is that we’re not trying to catch the future of AI. We’re actually leading that and we’re leading in our area, which is the enterprise AI space. What I want to do is bring Joe Bradley on, who’s been with us for five years. Joe is our Chief Data Scientist, and I want him to do a deep dive on our platform, our data set, what we’re doing and what we’ve been doing with large language models and then some of the things that are happening recently that will also drive more profitability to the company.

Just a little background before I bring Joe on, before he was here, he was Head of Data Science at Amazon Search and Nike, in addition to spending many years as a physicist and research associate at Lawrence Livermore National Laboratory, and he holds a doctorate in physics. So Joe leads our team of world-class scientists. And with that, Joe, let me turn it over to you. I think it’s the first time that a data scientist has been on a quarterly call, but this is an AI-focused thing. So enjoy.

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Joe Bradley: Thanks, Rob. Very happy to be here. I’ll put up some visuals here real quick for people. The opportunities that the latest generation of generative AI and large language model technology creates for LivePerson are exciting. Essentially, they open up, as Rob mentioned, a large new addressable market for us. I’m here today to explain a little bit about how that works. Essentially, LivePerson has three core strategic assets for machine-learned model building in general, but which are particularly relevant for this new generation of large language models. First, we have billions of conversations running through the platform. These are what we call goal-oriented dialogues, which just means people solving real problems and they’re full of the real complexities of doing that with human dialogue.

This is one of the world’s richest baseline data sets for an LLM-based conversational system. Second, we already have a rich processing layer for understanding that data, which creates billions of derived data points, including sentiment, conversation quality, user intent, user problem resolution, customer satisfaction, et cetera, much of this using prior and current generations of large language model technology already. It’s part of why we have hundreds of millions of conversations handled today by our own first-party AI products. Third, we have, as Rob mentioned, over 300,000 human experts that log into the platform every day, providing feedback and helping models learn. Each of these assets is uniquely meaningful in this new LLM context.

First, these large language models need baseline data to train their basic behavior. This must be — is typically done with and really must be done with, today, Internet-scale data sets. LP holds one of the few goal-oriented conversational data sets at this kind of scale. Secondly, because each conversation on LP’s data set is already adorned with quality measures, associated business outcomes, et cetera, we can teach these models how to drive towards these outcomes by highlighting particularly successful conversations or by rewarding models differently as the conversation changes. Third, in addition to the scale of the latest generation of large language models, the other key performance driver has become increasingly human feedback through techniques called reinforcement learning.

The largest models in the world are typically trained with tens of thousands or hundreds of thousands of examples of such feedback, and our platform generates this scale of data daily. This is a powerful mechanism for controlling model behavior. It teaches them,- for example, when they might be hallucinating. It teaches them what not to say and when not to say it. This is incredibly important in an industrial context. And this — for all these reasons, this is why we’re partnering with the world’s leading AI organizations in the large language model and generative AI space. In particular, we’re very excited about the global rollout possibilities that we’re seeing in working with Microsoft’s Azure platform. Our assets allow us to move quickly to produce high-precision conversational systems today and to further push the state of the art tomorrow.

More importantly, they allow us to keep these models grounded, factual and aligned with the humans who will use them. What this means for us and for the world, I think, is that LLMs are transitioning from fit-for-purpose models that can understand aspects of the conversation and make specific decisions, into systems that themselves can drive and control a dialogue. We’ve all started to experience that with some of the latest ChatGPT technology, for instance. That’s a big change for us. Up until now, dialogues had to be programmed explicitly in our system and in any thought building system in the world. And this doesn’t scale because dialogues are, by nature, a very long tail process. They’re very unique. But we now have technology that’s capable of handling dialogues in their full complexity and nuance.

This is a critical development because it opens up a new scale for conversational automation and essentially a much bigger TAM for our products. Today, still, approximately 80% of business-to-consumer conversations occur over the phone. And I think none of us are satisfied with the IVR-style automation presented in that context today. One of the main drivers of this problem and this dissatisfaction is this concept that long — that dialogues are a long-tailed set of events. They are extremely unique. And existing automations can’t handle all that different uniqueness without a massive code to effort — without massive effort to code and maintain. Not only do the latest LLMs unlock this set of conversations and over time, will unlock more conversations, over time, they’ll unlock more conversations in other enterprise use cases as well.

Also, as we’ll see in a moment with Matt’s demonstration, opportunities open up outside the enterprise altogether, like the health domain. But what do we mean by long tail, I think, is worth diving into that just for a minute. If that’s the real problem here, what does that mean? It means that most dialogues don’t — are unique and don’t fit into a tidy box. The easiest way to understand this is to look at some real brands and some real conversations. For example, we have a major retailer who has millions of conversations on LivePerson annually, has a 20% automation rate. Here’s a conversation that’s a real conversation on their platform about scheduling a delivery. We all imagine scheduling a delivery to be kind of a simple task and kind of repetitive.

You fill out a form and you give it your address. But here, you can see this person is trying to figure out if they can coordinate this delivery with a kitchen remodel, and they don’t even know when the kitchen remodel is going to be finished. They want to make sure their appliance is going to show up on time and be hooked up properly and all the things that you and I would care about. Up until now, this would have resulted in a human escalation or programming some bespoke flow into a chatbot with a host of parameters that had to be managed. Now this is automatable. One more example, a major bank and a customer simply trying to pay their mortgage. Again, we think of this as a simple, repetitive event. But as you can see here, it’s often not.

The customer in this case has had a payment increase that they were unaware of. They’ve been made a promise by a previous agent that they claim. With if you take large language models, you combine them with the LP data set, this claim becomes an auditable fact, for example, and the brand’s policy on matters like this can be inferred from the data and executed by a machine. Incidentally, we worked with this particular brand to use the latest generation of generative AI to recognize thousands of intents like this with greater than 90% accuracy, which up until now have been unheard of. One of the more exciting aspects of all of this is that to build systems — the effort to build systems like this is radically reduced as well. When we bring together brand knowledge bases, brand conversational data in our platform with large language models, we can have a working system up and running with minutes of effort rather than weeks or months.

In fact, over the last 6 to 8 weeks, we’ve built over 200 conversational AI systems in this way, and we’ve begun demonstrating this to some of our largest customers already. You’ll see examples of systems that can handle this level of depth and flexibility of conversation shortly in Matt’s WildHealth demonstration. And that’s what I’ve got for now. So thanks, Rob, and thanks, everyone, for listening.

Robert LoCascio: Thanks, Joe, for your insights into what we’re doing with our AI and using large language models and beyond that. I want to now bring Dr. Matt Dawson to speak about our AI health care initiatives. As many of you know, health care is a $4 trillion industry, or I’ll say, problem. And alone, I think this market is ripe for transformation by AI. As such, we acquired WildHealth last year, brought in our health care offerings. And Dr. Dawson is WildHealth’s Co-Founder and CEO. He’s — a little background on him. He’s a published scientist, accomplished entrepreneur, started 5 or 6 businesses. He’s won National Awards for innovation. Additionally, he developed a medical education app and podcast which touts millions of listeners and users.

And WildHealth, the reason we bought them is they’re based on an AI platform called Clarity and which analyzes millions of data points from a patient’s DNA, their blood markers, their gut, the biodata of wearables. And then they correlate that using machine learning, then give outcomes that are better than anything that’s been out in the market before, things like diabetes. I mean they’re solving diabetes on the scale that has not been seen before. And Matt will explain how the machine-based portion of bringing that data together, injected into a large language model, how it can change the trajectory of a broken business model of health care, and allow health care to be delivered to millions of people in a way that is radically different because precision health up to now was really for athletes.

So we have some of the biggest professional athletes on our platform and CEOs and people like that. But we can deliver that level of accuracy and precision in health care to everybody. So with that, I’ll turn it over to Matt to give us some perspective and show us a little bit of the platform.

Matthew Dawson: Thanks, Rob. So I’m really excited to show this technology and how it illustrates some of those points that Joe mentioned before about the long tail. But first, I want to quickly explain the health care vision and big picture. Our vision, WildHealth and LivePerson together, is to take the highest quality health outcomes in the world and scale that to millions of people. We’re already creating those outcomes because we have the most precise and personalized data in the world. As Rob mentioned, we built the world’s first and only true AI-driven precision medicine platform, called Clarity. And now Clarity in and of itself is really a game-changing technology, as it combines potentially millions of data points from 700,000 unique genes and blood biomarkers, microbiome data, phenotypic data, subjective feedback from patients and even data from wearables.

And it takes all of that precise data and it generates a comprehensive report on how to fully optimize someone’s health. So this is really a blueprint or a personalized playbook for an individual. It shows the specific perfect diet for them, the perfect exercise program for them, what supplements will work for them, what medications may or may not work for them, and even what diseases they may be most at risk for in the future and what are we going to do to decrease that risk. So as Rob mentioned, this has led to incredible results such as reversing diabetes and prediabetes in 48% of our patients who have those disorders. You can compare that to 3% of patients in traditional medicine who have their diabetes reversed because of the smaller amount of data that they have, 3% versus 48% because of the personalized precision data.

Now right now, this is delivered by humans. You have doctors and health coaches combined with the precision data, which is actually really similar to how customer engagement LivePerson uses data, but with humans in the loop, constantly improving it. The difficulty, as you can imagine, is the cost of the humans and the ability to scale them. So just like what LivePerson is doing in customer engagement, we can totally change the game on the scale of what a doctor and health coach can do. So as normally a doctor may have 1,000 patients, this data combined with LLMs can massively increase that ratio of patient to doctor, and at the same time, we radically change the margins on the health care business from maybe the 20% to 25% range to be more like platform margins while improving outcomes.

But instead of just telling you, I actually want to show you a demo of how we can do this right now, how we can take our rich precision data to train a large language model with all of our precision medicine knowledge and all the patient-specific data to get really incredible results. The model here that you’re looking at is trained with my data, so all of my DNA, blood work, microbiome data, the millions of data points about me, and I can ask it questions. So just like the long tail discussion Joe was talking about, I can essentially take this conversation wherever I want based on what’s important to me personally. So for example, let’s say someone in my family recently had a heart attack. Well, right now, I’m young and healthy. I don’t have any real medical problems, but I want to look into the future.

I would love to have a crystal ball where I can look and see what my risk factors are. So I can ask this model that has all of that genetic data and all the data about me, am I at risk of having a heart attack? And while I’m thinking about this, I’m going to go ahead and ask, and what about getting cancer and dementia? So those are things that run in my family as well that I’m worried about in the future. And I feel fine now, but these are things that kill most Americans. So as just looking into the crystal ball of all of my risk factors, the DNA, the blood work, it tells me, great news, I have a 0% risk of having a heart attack in the next 10 years based on the MACE score. However, not great news when I look and see, I’ve got an increased risk of dementia, have an increased risk of colon cancer here.

And then as I go on down, it says based on my genetics, I have an increased risk of late onset Alzheimer’s with sleep disturbance, if I have that. So that reminds me, I haven’t been sleeping well recently, so any recommendations based on my genetics. I’m not asking it, just give me sleep tips. I’m saying look at my DNA, which also, while I’m asking, I want to go and ask what labs can I improve to decrease my chance of getting those diseases as well? Again, this is about me, specifically me, reporting to my DNA, my labs, what can I do, not just the risk factors that I’m at risk for, but what am I going to do to decrease those risks? And I already have a specific example there for when it comes to sleep. So it tells me about my increased risk for Alzheimer’s disease or sleep disturbance, but it gave me very specific things to do.

It says avoid eating when it’s dark, you need to fast for 12 hours, and it also says I have a very interesting clock polymorphism here now in my DNA. It’s been associated with “night owl” . So maybe I should go to bed a little later and get up a little later, maybe adjust my schedule, my work schedule. And then for labs, the same thing. I should focus on LDL, ApoB levels, okay, great, tell me other things to do, how to do that. And then when it gets to dementia, it says to decrease your chance of getting dementia you should focus on improving your omega-3 levels. So I think I remember my omega-3 level was off in my last check, but I’m just going to ask the model, what is my omega-3 level? Because remember, it has all of my data, those potentially millions of data points.

And while I’m thinking about that, okay, it is low here. So then the next question obviously is, should I take a supplement for this? And what other supplements should I take? Why stop at omega-3, it has all of my data. What should I be doing specifically for me? It says, yes, I should be taking an omega-3 supplement, and I’m going to take it. I’m motivated right now. I don’t want to get dementia and have these diseases. And as far as other supplements, it looks like vitamin D, zinc to reduce DOMS, okay, well, what is DOMS. This has all of the Internet’s information, not just my specific information. And now I’m thinking, I don’t have an omega-3 supplement, but I’m going to have dinner, so what foods are high in omega-3. And I see here that DOMS stands for delayed onset muscle soreness, good to know.

I think fish are high on omega-3. There may be some other things, why guess, let’s just ask the model. Great example of the long tail we talked about. And it says, yes, salmon, mackerel. I love salmon. So give me a good salmon recipe, right? I’m not going to ask my doctor this, but also give me a shopping list. Again, I could go on and on with this AI. And in fact, I have. I’ve played with this an incredible amount because it’s a wealth of information. I can learn so much about myself, like what to eat, how to exercise, my risk factor for scary diseases, and the conversational rabbit holes of the long tail, like Joe mentioned, that I can go down that we expect to really drive increased platform volumes for LivePerson. This is basically like having a doctor in my pocket, except it’s one that knows all of my DNA, my blood work, my history, everything about me, and it’s not going to get irritated when I ask for a salmon recipe and shopping list to go along with it.

Just to be really clear, though, the way that this is going to work initially is that there’s going to be a human between the AI and the patient, so the patient can ask a question or a provider can ask a question of the AI as well. And the answer is going to come back, and then that provider decides whether to send it to the patient or to edit it and send it to the patient, but in either instance, it’s going to be providing feedback to the model and further training for the model as well as giving this recommendation to the patient. So you can see it’s going to be not just a massive upgrade on the quality of recommendations that truly personalize to the level of DNA, but also to efficiency. It’s going to be a massive time savings for a doctor who doesn’t need to look up someone’s omega-3 level, look up a supplement, calculate a 10-year heart attack risk and, of course, give a salmon recipe with a shopping list.

We’re going to let the AI do all of those tasks with the incredible data sets and algorithms that it already has. We know that these large language models have great potential. That’s clear. But if combined with extremely rich data, this precision data, then we have something truly transformative. It should be in the hands of every doctor and every patient in the world. And this combination of precision data and large language models is going to transform the health of millions of people. Thank you, Rob.

Robert LoCascio: Excellent, Matt. And when you think about why we acquired WildHealth is that like ourselves, we know we have this precision data set, and we looked at them and said, well, you’ve got the precision data set on humans and a way to collect that data and the way to correlate it. And so obviously, we’re excited about — what you just saw was the LivePerson platform with our large language models on it, combined with the Clarity platform. When you saw like page numbers, that’s the Clarity platform giving you the information that comes off the patients’ data. So it’s exciting. I think in itself, it’s a multibillion-dollar business one day because of the impact it’s going to have in the health care industry. I want to sort of put a period on the end of this hour and talk about something.

A year ago, I announced on our call that we were chosen as the #1 AI company in the world by Fast Company, and interesting enough, OpenAI was #3. And we were rated #1, if you go back and look at the article, is because we gave business outcomes. We are working with the — we work with the largest brands. And at that time, OpenAI was really a set of APIs that our engineers were using and stuff. Obviously, the impact of OpenAI is world-changing since November. And their success has really kicked off, I’ll call, the AI space race. And there’s going to be a handful of companies like LivePerson that are going to be the next set of technology leaders. And I’ve said this many times, I think the current technology companies, whether it’s Amazon, Google, and it was hard to understand that they could even be taken down and are impacted.

But the next set of companies will most likely be a set of AI companies, and LivePerson and our vision and all we’ve done with our data, our relationships with our customers, our ability to bring our platforms and this technology in a safe way, in a usable way to the enterprise is what puts us in the pole position right now. Obviously, that’s got to be sitting on top of a solid foundation, and we’ve been around 28 years. We didn’t get here from not having a solid foundation. And yes, we took some twists and turns, but starting in Q2, we will have that foundation, and generating cash flow has always been important for us. It creates a discipline in the company. And we’re really excited about our future. And I think as shareholders, and I’m still one of the largest shareholders, I just know that we’ve been waiting for this day.

And when you wait this long and it comes, you don’t squander the opportunity. And I know everyone in the company is excited. Even through all the tough times in the last year, we’re all excited. Everyone is working hard. We’re going to bring this stuff to the market in a very different way, in a LivePerson way, because we build and innovate. We don’t copy. And I just want to thank everyone in the company. And with that, I’ll turn it over to the operator. We can take a few questions before we end this Q4 call. Operator?

Operator:

Chad Cooper: Okay, first question comes from Ryan MacDonald at Needham. Can you help us quantify the sources of the revenue shortfall for fiscal ’23 relative to ’22? And how much is the lack of WildHealth revenue versus Gainshare versus weakness in the core business?

John Collins: Arjun, yes — or Ryan, sorry. I think we covered this in my prepared remarks, but I can reiterate, of course, the primary sources here. There’s $70 million in total that we consider non-core that will not reoccur in 2023. $50 million of that has 2 primary categories: COVID-19 testing and professional services with a strategic partner in the testing and diagnostic space. With regard to Gainshare labor and pandemic-driven variable revenue, that’s approximately $20 million of that figure. It is not WildHealth revenue that is a large contributor here, setting aside the Q4 disclosure that we’ve provided.

Robert LoCascio: And the other thing is, although WildHealth’s coming off a small base, its core business, like the stuff Matt showed you, will double this year in growth rates. So — but it’s a small base compared to our size, but the — he’s got good trajectory there right now.

Chad Cooper: Next question comes from Siti from Mizuho. How should we think about the sequential ramp in profitability throughout the year? How do you get from positive EBITDA and free cash flow in Q2 to a 16% to 19% annualized exit rate by Q4?

John Collins: Yes. Siti, so the primary driver here relates to the cost-out actions that are underway as we speak in the first quarter. That’s a large body of cost that will exit. So that’s what will flip us to profitability from the Q1 numbers directly in Q2 in a couple of weeks. That alone will drive substantially the profitability in the ramp throughout the year. In addition, of course, our recurring revenue base is growing as well. And that’s a function of the core software bookings that we expect throughout the year and the AI-led growth. It’s interesting to note where we’re seeing a lot of action. And at the moment, there’s extraordinary demand for AI demos along the lines of what you’ve seen here. So that’s flowing into our views, of course, on bookings going forward. But we’re being conservative, hence the low single-digit to mid-single-digit guide on recurring revenue.

Chad Cooper: Thanks, John. Next question is from Arjun Bhatia from William Blair. How do you plan to contain the distraction from several moving pieces in the business in order to ensure you can execute on the core?

Robert LoCascio: Yes, we — I think that’s what we try to get across, we’re done with the distractions. So starting in Q2, we have a clean P&L. Basically, we have our core business. We’ve got our health care business and that’s what we’re going to live and die on. So we’re focused on it. We had to do a lot of things over the last year to prepare for this. But starting Q2, we have focused only on those areas.

Chad Cooper: Next question, also from Arjun. How is WildHealth monetized? What is the revenue model and accounting for the business?

John Collins: Yes, I’ll start. But then I think since we have Matt on the call, I’ll kick it over to him to explain some of the core business drivers. In terms of accounting, it is currently in our hosted revenue line, and there’s both recurring and pay-as-you-go revenue. And Matt, I’ll turn over to you to talk through the quarter and what drives it.

Matthew Dawson: Sure. Just to be very brief, right now, we do have a recurring revenue model where we have patients coming in, we’re seeing patients, taking care of them. And as Rob mentioned, we’ve doubled our patients in the last 12 months. We expect the core business to double again over the next 12 months. We’re seeing good growth, but to be honest, this is more of R&D and we’re seeing patients. We are proving that this works, that this is the best health care in the world. We’ve now gotten the best health care outcomes in the world, and we think that millions of people deserve this and everyone should get it. So the real opportunity is taking this technology, adding the AI layer in and licensing it to every doctor so this can be everywhere. So that’s the future. We’re growing fast, but the future really is giving this out to everyone, not just the patients that we can see with our doctors and with care coaches.

Robert LoCascio: And Matt, you may want to also talk about that we know the stickiness of people who use the platform and is much different than someone who goes to a normal or some — or use an app or something like that. It’s highly, highly sticky once they get on the program.

Matthew Dawson: Yes, it’s a great point. It turns out, Rob, people like getting great health care. So our NPS is — when you look at normal health care, NPS is normally around 0. If you’ve got a positive NPS, you’re great. They’re never really over 30. And our NPSs are always in kind of the 60s and 70s. So people really enjoy it. We have really low churn because of that. People stay in the program, they get better. They tell their family members. They stick around with us. So we expect that to be similar for a doctor or a hospital who would license the technology as well. It’s going to be a really big upgrade, like I mentioned, for quality and efficiency, which are the 2 things that health care universities and centers really want, is quality and efficiency.

Chad Cooper: Thanks, Matt. Next question is from Zach Cummins from B. Riley. Does the current guidance include revenue contribution from the consumer business? Can you speak to the decision to sell that business?

Robert LoCascio: John, you’re….

John Collins: Yes. So currently, the consumer business is assumed to be approximately flat year-over-year. So whether it’s in or outs, you don’t have a material impact on revenue growth. There’s a small impact on EBITDA, but that is not in our guidance. So the guidance, to be clear, reflects the — for EBITDA reflects the business without a contribution from Kasamba for the consumer segment. And from a revenue standpoint, it’s neutral. And in terms of the decision to sell it, it’s a focus on the B2B core. This entails costs and management cycles. And we have a view for what the B2B core could be, especially the AI-led growth that we’re pursuing at this moment. And we think it’s the best approach just to kind of draw a line and move forward from there, focused on the B2B core and WildHealth.

Robert LoCascio: Yes. And our focus — we go back to putting the filter. The filter is, can we use AI in that business. It was a great chat business when we bought it for very small individual proprietaries who want to chat online with their customers. But I can’t — we can’t automate it the way we can on the stuff we’re doing with health care and the stuff we’re doing on the enterprise. So it just doesn’t fit where we think the biggest opportunities are right now. We’re getting a good return on it, though, so. It’s been 13 years, so.

Chad Cooper: Next question from Peter Levine from Evercore. Can you give a little bit more detail on the sale of the consumer business? How do you value it? And who would be the ideal buyer?

John Collins: Yes. Peter, I think we’ll have to wait for the PR on the eventual transaction, which, as I said in my prepared remarks, we expect later this month. I can say in terms of how it’s valued, clearly, a multiple of EBITDA is the way that, that works, and you’ll see more details soon.

Chad Cooper: Follow-up question from Peter. What gives you the confidence that you’ll see an inflection point in Q2? And what are you factoring in for macro headwinds for fiscal ’23?

Robert LoCascio: We took a pretty conservative approach to bookings and retention rates to create the model and then to get to a cost structure that we could guide to. So I think we feel like there’s upside from here. It’s going to be interesting. On the macro side, obviously, we deal with the enterprise. And the enterprise has slowed down, but they’re holding budgets. Like Q4 normally for us is a very big quarter because people, they want to spend the money that they had in their budgets. And this year, they didn’t, they kept it. However, what’s going on with the strategy and what’s going on with all the stuff on AI, it’s an open field right now. I said we’ve done — it’s like 200 demos we’ve created already. We just had a packed event.

Our largest customer is in the U.K. and it was a packed house. We had — we did an online seminar, 600 people showed up. So there’s a lot of action right now because the enterprise wants to use this to automate a bunch of, obviously, business processes that they have. Whether it’s customer care, HR, I feel like it’s kind of an open season for demand right now.

Chad Cooper: Follow-up question from Zach Cummins. Can you speak to the current sales capacity and plans to reorient the go-to-market motion in the coming year?

John Collins: I’ll start. And Rob, maybe you could — I’ll pass to you to say something about pods and how we’re thinking about bringing our technology team closer to customers. So just high level, in terms of quota capacity, we will end the first quarter with approximately 115 quota carriers. So a slight increase over the prior quarter.

Robert LoCascio: And then we’ve taken our — we have a base business of 100 of our largest customers, which is — I think it’s a very high percentage of our revenue. And now we created this thing called , which are pods led by product heads led by the guy who — co-founder of BotCentral. And now they — we’ve invited in those 100 customers to come into the pods, and these are engineering and product lines. And I think it’s going to change the game. The one thing I’ve not liked about B2B sales motions and service motions is you have a lot of layers connecting with the customers who are not always product or engineering people. And they’re very important as in I think getting new business, opening up new opportunities, but they’re also dealing with what we call account management.

And I just don’t believe in it as much as I do having our customers talk to product heads. And why is that? Because if you’re an AI company and someone shows up with you — I was just on a call with the CIO of one of the largest telcos the other day and we had a pod head, a pod leader come in, and they’re talking the language. They’re talking about the tech and that CIO is talking very deeply about what they want to do with this technology. It doesn’t work to have them bring in people and do another call and do — the people who are servicing should be engineers and technologists when it comes to AI, and that’s what an AI company would look like to a customer. So that’s a big change for us.

Chad Cooper: I have a question coming in actually by e-mail by Tom Blakey from KeyBanc. You mentioned 7% to 10% free cash flow margins and 16% to 19% adjusted EBITDA in the core by the end of ’23. What about the total business? When will non-core revenue go to zero?

John Collins: Broadly speaking, non-core revenue should be — will be worked on throughout the year, but the most — the largest components that still remain will exit the P&L in the second quarter.

Chad Cooper: Question from Ryan MacDonald at Needham. What incremental investments are needed to effectively sell to a new channel for LivePerson, physicians to start to license the WildHealth platform moving forward?

John Collins: the exact amounts and allocations of investments. I would note that we have set aside investment that is reflected in our guidance for both WildHealth and, of course, the generative AI integrations that we think will power our growth in the B2B core going forward.

Robert LoCascio: And the WildHealth business does burn a little bit of cash because of its growth rate. So we are funding for growth, not overfunding. Matt’s pretty prudent with the money, but we are — it’s a high-growth business and there’s an acquisition there that we’re doing. We do direct-to-consumer and then also they have a way to bring doctors in. They’re also working with other areas like hospitals. They’re starting to open that up. The other thing that’s exciting is we got Medicare and Medicaid, although Medicare is a tough group to work with sometimes, as we saw in Q4, but we’re also looking at private insurances and all that. So that will open up, cost of acquisition goes down. So there’s a lot of potential there right now. But there’s a lot of people in the world that want to pay out of pocket. There’s physicians who get — who want to be onboarded to the platform to change the game in how they’re providing health care to their patients.

Chad Cooper: I think that’s it for questions. Rob, do you want to wrap up?

Robert LoCascio: Sure. Just want to thank everyone for being here. I know this is a different type of quarterly call because we’re a different type of company. In 28 years of doing this, we’ve had 3 separate times in our history where an opportunity came, we were well positioned for it, but we had to make a lot of changes to free us to get there. And I want to bring also — obviously, Joe and Matt, thank you guys for being on a call. It’s like a high wire act on a quarterly call coming in because you mess up, something is going to happen to you, we’ll find you guys. But you did a great job, and I appreciate you showing the investments we’re making in the core, the investments we’re making in health care. And I think, once again, as shareholders, I think there’s very few AI companies that are public, purely AI.

You can bet Microsoft. I would bet it. I think it’s amazing what they’re doing over there. We have a great relationship with them. It’s progressing in many interesting ways. But I think there are very few companies, especially of our size, where you can make a bet. And you can bet on that this is a company that has AI technology, they’re an AI company, and they’re not just bolting it on as some feature to say that they’re on the bandwagon. So with that, I look forward to the next quarter and showing results and obviously growing in a different way than we have over the last couple of years. So thank you, and see you on the next quarter.