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.

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.

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, our Chief Data Scientist, will be joining us to talk about our AI platforms. And 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 five 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 could talk to a machine and that machine could help us, through natural language, solve our most important aspects of our lives, things in our health and around our finances and our relationships with our brands that sometimes get strained when we're sitting on calls on hold. So we made a big bet five 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. We spent the last year getting the house in order and creating a foundation to go after this opportunity. And we've created a model for our business that I think will allow us to return to generating EBITDA and 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. During COVID, there were many things that happened to our company and others where we invested into areas. We pursued different opportunities with our customers, but Q4 and Q1 presented plenty of noise. We’ve addressed those issues, and now we are ready. Starting in Q2, we will have a company that generates strong earnings, cash flow, and EBITDA. From there, we'll pursue this massive opportunity. We are focused on eliminating non-core revenues that don't recur and are not supporting our vision around AI. Q1 will be our trough in revenue, EBITDA, and cash flow, and from there in Q2, we will see an inflection point upward. EBITDA will be positive, cash flow will be positive, and we will continue growing both top line and bottom line throughout the year. Our shareholders will benefit from simplifying the business and being more transparent about our revenues. Starting Q2, we will focus on recurring revenue lines. We have about $340 million of recurring revenue, which is 80% of our total revenue. The growth rates in that are single to mid-single digits, but we have the wind in our sails to elevate that. We balanced our approach to achieve a strong business model with cash generation and growth, and we expect to achieve annualized EBITDA margins of 16% to 19% by year-end. We'll begin generating positive margins in Q2, and we’ll also start to produce positive cash flow targeting 7% to 10% cash flow by year-end. Those are facts. Those are things that will carry forward. Our journey has had ups and downs over these 20 years, but now we have an exciting opportunity at hand. With that, I’d like to hand it over to John Collins, who will delve into the P&L and highlight what's underpinning our goals.

Thanks, Rob. Over the past 12 months, we have rationalized our cost structure, yielding an expected 36% or $200 million in annualized cost reductions. We also wound down non-core business lines to enable profitable growth and sharpen our focus on LivePerson's B2B core software business, including the AI technology that powers it. As mentioned, we have a strong base of recurring software revenue from top brands across the globe, expected to grow 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 beginning in the second quarter. Annually, 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 anticipated decline of more than $70 million in non-core revenue on a year-over-year basis—a decline that was set in motion by the profitable growth strategy we launched last year. In short, we've made meaningful improvements to the P&L and reallocated resources to focus on the B2B core, establishing a solid foundation to execute AI-driven growth into Q2 and beyond. Regarding 2022 total company revenue, of the $514.8 million recognized, over $70 million was non-core revenue we do not expect to see recurred in 2023. Breaking this down, of over $50 million in non-core revenue decrease year-over-year falls into two main categories: COVID-19 testing and professional services with a strategic partner in diagnostics. We now expect professional services, including a large software build, to conclude ahead of schedule in the first quarter. After accounting for around $3.8 million anticipated for professional services revenue associated with wrapping up the diagnostics project, we expect future professional services revenue in 2023 to focus exclusively on the B2B core and align with historical patterns. Additionally, over $20 million of the decrease comes from Gainshare labor and pandemic-driven variable revenue, previously noted as non-strategic to the B2B core in prior discussions. The largest remaining revenue source within this category is expected to exit the P&L in Q2 2023. As discussed in previous calls, the non-labor software component of our Gainshare business has nearly fully transitioned from variable to recurring revenue and now represents 5% of total recurring software revenue, allowing us to categorize this as core revenue and ceasing to refer to Gainshare customers in terms of B2B core. Furthermore, focusing on the B2B core, we've classified the consumer segment's underlying business as held for sale with a targeted transaction close date before the end of the month. To summarize, we have substantially improved the P&L and set a clear trajectory for revenue growth stemming from increased engagement in the Conversational Cloud, especially regarding AI-based engagement in both voice and text across the enterprise. Moving to Q4 2022, we noted two unexpected changes affecting our results. Initially, as disclosed last week, we were notified in November that Medicare reimbursement for a non-core WildHealth program was suspended pending further governmental review. We believe the services provided in Q4 2022 were valid and should ultimately be reimbursable. However, considering the volatility concerning timing and amount of potential reimbursement, we decided to take the most conservative approach by not recognizing revenue for which reimbursement has not yet been secured. It is important to highlight that this program was subsequently discontinued, aligning with our focus on exiting non-core activities. Absent this event, revenue would have stayed within our guidance range for both Q4 and the full year of 2022. Secondly, to mitigate dilution to our shareholders amid the remaining capacity under our 2019 stock incentive plan, and to enhance the quality of earnings, we opted to compensate the 2022 employee bonus in cash rather than stock. Historically, we have disbursed annual employee bonuses in stock, which had been our plan for 2022. Excluding the impact of the WildHealth revenue reserve and the decision to change bonus settlement, the adjusted EBITDA would have been within our guidance range for both Q4 and the entire year of 2022. Consequently, revenue for Q4 and the full year was $122.5 million and $514.8 million, respectively. Adjusted EBITDA, considering these impacts, reflected a loss of $5.2 million for Q4 and $16.2 million for the full year. Turning to revenue segments, within total earnings, B2B revenue dipped approximately 1% year-over-year while hosted software revenues fell by 8%. Excluding COVID-19 testing and pandemic-driven Gainshare variable revenues, B2B grew by 6% year-over-year, whereas hosted revenues declined by 1%. Professional services revenue experienced a 30% increase, primarily driven by the diagnostics project referenced earlier. The consumer segment noted a 3% decline, primarily due to reduced marketing expenditures. Geographically, U.S. revenue saw a 3% growth year-over-year, while challenges in EMEA resulted in an 8% drop in international revenue. In Q4, we secured a total of 90 deals, including 1 seven-figure contract, 46 renewals and expansions, and 44 new logo agreements. While the total number of deals decreased year-over-year, enterprise deal count grew by 22% and new logo deals by 63%. However, we observed an increase in deal volumes at lower-than-expected values, suggesting some macroeconomic effects may have constricted budgets in Q4. A few noteworthy deals include a two-year contract renewal with a leading global airline covering a wide array of service use cases, alongside a two-year extension with one of the top online travel companies and another with a top 25 U.S. financial holding company. We also noted strong performance from our APAC team, securing new logo agreements with an Australian multinational bank and a leading provider of pensions and investment products in Australia. Reflecting our concentration on the B2B core and recurring revenue growth, we believe net retention for recurring revenue is more instructive than total revenue. Therefore, we are providing both net retention metrics for Q4 while intending to report net retention for recurring revenue only in Q1 2023 and onward. In Q4, net retention for total revenue remained below our target range of 105% to 115%, primarily driven by lowering Gainshare variable and labor revenue alongside the exit of COVID-19 testing revenue. Notably, net retention for recurring revenue exceeded 100% but fell below our target of 105% to 115%. Average revenue per customer stood at $680,000 in Q4, reflecting an 11% surge year-over-year. As we move to guidance, an error in our press release regarding guidance needs mentioning, with the information I am about to present being accurate. For Q1 2023, we anticipate total revenue in the range of $106 million to $109 million, indicating a 19% to 16% year-over-year decline. Expected adjusted EBITDA loss ranges from $8 million to $6 million. The year-over-year revenue decrease and sequential adjusted EBITDA decline principally arise from the large volume of non-core revenue exiting the P&L in Q1. Primary contributors are reductions in Gainshare labor and variable revenue alongside diminished professional services for our strategic partner in diagnostics previously referenced. For full-year guidance, we expect total revenue to be between $422 million and $436 million, corresponding to an 18% to 15% year-over-year decline; and adjusted EBITDA is forecasted between $15 million and $32 million, representing a margin of 4% to 7%. Consistent with our commitment to transparency and a focus on the B2B core, we intend to provide guidance for our B2B core recurring revenue and adjusted EBITDA as well. For Q1 2023, we forecast recurring revenue between $80 million and $83 million, reflecting a year-over-year decline of 7% to 3%. Expected adjusted EBITDA for this is also anticipated between a loss of $6 million to $4 million. Due to cost reductions taking effect this month, we project the B2B core will generate double-digit adjusted EBITDA margins and positive free cash flow starting in Q2, improving throughout the year. For the entire year, recurring revenue is expected to range between $334 million and $347 million, indicating 0% to 4% growth year-over-year; and adjusted EBITDA is projected between $27 million and $40 million, equating to a margin of 7% to 11%. On an annualized basis, the B2B core is expected to exit the year with 16% to 19% adjusted EBITDA margin and 7% to 10% free cash flow margin. Before handing the call back to Rob, I want to emphasize key points: relative to Q1 2022, when we launched our initiative to create a balance between profitability and growth, we have diminished our cost structure by an expected 36% or $200 million annually. By winding down non-strategic, low-margin business lines, we've also clarified LivePerson's foundation as a B2B core dedicated to the growth of software recurring revenue representing around 80% of total company revenue in 2023. Beginning in Q2, we expect the B2B core to be profitable while generating double-digit adjusted EBITDA margins and achieving positive free cash flow. We anticipate continued AI-driven growth in recurring software revenue, further enhancing profitability throughout the year, reaching an annualized rate of at least 16% for adjusted EBITDA and up to 10% for free cash flow, which will strengthen both our balance sheet and P&L outlook. With that, Rob, I’ll hand it back to you.

Thanks, John. We've addressed a lot of noise, and while there have been some challenges, we now have a solid foundation to build upon starting in Q2. I want to share what’s next and discuss our strategy around AI that we've been pursuing for over five years. I’ll present this from three perspectives: strategic, engineering, and industry viewpoints. From a strategic lens, AI has two foundational components: data models, as seen with OpenAI and large language models, plus the essential data set that the model uses to produce results. There's been significant focus on models because of the ChatGPT phenomenon, but we must also address the underlying dataset quality. LivePerson possesses one of the largest, most precise conversational business datasets globally. Currently, our platform supports over 350,000 users daily, generating 1 billion conversations annually. This is crucial, as we have a wealth of high-quality conversations addressing various topics. Today's fully automated conversations represent around 20% to 30% of our platform, and around 75% incorporate some AI functionality. However, our goal is to automate 80% to 90% of the conversations, including both messaging and voice. We are soon launching our voice AI product as well. This positioning allows us to serve the enterprise at scale and safely with large language models, and we plan to accelerate our progress. From an engineering standpoint, we’ve been developing our own AI capabilities for five years. Currently, 50% of our engineering budget is allocated to AI and automation, supported by a world-class team of product leads, data scientists, and personnel from our acquisitions. We’re speeding up implementation, and we've noticed customers are eager to engage directly with engineers and product leaders, seeking deeper discussions about AI in our offerings. This adjustment in our team structure is expected to significantly enhance retention rates and open growth opportunities. Lastly, I’d like to highlight our industry involvement. About four years ago, I co-founded a nonprofit called EqualAI focused on safely bringing AI into the enterprise. Our frameworks and strategies have been informed by experiences gained through these efforts, and we continue to harness them in our technology development. In conclusion, we are not just responding to the AI wave; we’re pioneering it, especially in enterprise AI. Now, let’s bring on Joe Bradley, our Chief Data Scientist, who will delve into our platform, data strategies, and recent developments with large language models driving profitability.

Thanks, Rob. Excited to be here today. I’ll quickly present some visuals to highlight how the latest generation of generative AI and large language model technology opens up exciting opportunities for LivePerson. Essentially, these advances expand our addressable market. LivePerson possesses three core strategic assets for machine-learned model building relevant for this new generation of large language models (LLMs). First, billions of conversations through the platform comprise goal-oriented dialogues, which means people are solving real problems involving complexities pertinent to human dialogue. This is one of the richest baseline datasets globally for an LLM-based conversational system. Second, we already have a robust processing layer that enables understanding of that data. This layer creates billions of derived data points including sentiment, conversation quality, user intent, user problem resolution, customer satisfaction, and more, much of which utilizes prior and current generations of large language models. This integration is one reason we handle hundreds of millions of conversations daily through our first-party AI solutions. Third, over 300,000 human experts log into our platform every day, providing critical feedback that helps refine models. Each of these assets holds significant importance in the context of LLMs. Large language models need baseline data to establish behavior, typically utilizing Internet-scale datasets. LivePerson collects one of the few goal-oriented conversational datasets at that scale. Every conversation within our platform is annotated with quality measures, assuring desired business outcomes can be driven by premium conversations or varying rewards based on conversation dynamics. Reinforcement learning has emerged as a key performance driver behind large models, needing a vast amount of human feedback to guide behavior. Our platform generates this type of feedback daily, an essential mechanism to control model functions. Thus, we are collaborating with leading AI organizations that focus on generative AI and LLMs. We are particularly enthusiastic about the global rollout possibilities via Microsoft’s Azure platform. Our assets enable rapid production of high-precision conversational systems today while pushing future innovations forward. More importantly, they help keep these models accurate and aligned with human users. The implications for us and the broader world entail a shift in LLMs from purpose-fit models that grasp specific aspects of dialogue towards systems that dynamically control and drive conversations. We've experienced this development with ChatGPT technology, marking a significant change in how we approach dialogues, which historically required explicit programming and corresponding management efforts. The uniqueness of dialogues poses challenges for existing automation, often leading to inconsistencies and escalations. The latest LLMs provide avenues to unlock unique conversations previously out of reach, expanding our enterprise use cases. As we move forward, I’ll provide insights from live examples, demonstrating how these capabilities directly translate to our platform and user interactions.

Thanks, Joe, for sharing insights on our AI strategies and leveraging large language models. Now, I’d like to invite Dr. Matt Dawson to share our transformative AI healthcare initiatives. As many of you know, healthcare represents a $4 trillion industry that is primed for AI-driven reform. We acquired WildHealth last year, integrating our healthcare offerings, and Dr. Dawson, as WildHealth's Co-Founder and CEO, will explain the AI platform called Clarity that analyzes extensive data points from genetics, blood markers, gut health, and wearable biodata. This platform correlates those data using machine learning to provide outcomes that outperform traditional healthcare solutions, addressing chronic issues like diabetes. Dr. Dawson will illustrate how this data-driven approach and integration with large language models can enhance healthcare delivery, making precision health more broadly accessible rather than limited to elite athletes. With that, I’ll turn it over to Matt.

Thank you, Rob. I’m thrilled to demonstrate our technology, emphasizing points Joe made about addressing complex health needs. As Rob stated, our vision is to scale the highest quality healthcare outcomes to millions. We are indeed achieving remarkable outcomes by leveraging precision data—built through the world's first true AI-driven precision medicine platform, Clarity. This platform amalgamates millions of data points from unique genes, blood biomarkers, microbiome data, and patient feedback combined with information from wearables into a comprehensive health optimization report. This report acts as a personalized playbook, detailing specifically tailored diets, exercise programs, medications, and individualized risk management strategies, demonstrating our capacity to address health issues like diabetes more effectively than conventional methods. We pride ourselves on reversing diabetes and prediabetes in 48% of our patients, in stark contrast to the mere 3% reversal seen in traditional medicine owing to their limited data. Currently, our approach relies on healthcare professionals supported by precision data, paralleling how LivePerson utilizes similar data in customer engagement—keeping humans involved and consistently learning. The challenge remains in scaling this model cost-effectively. We truly believe that by integrating our data with large language models (LLMs), we can revolutionize how doctors and health coaches interact with patients. For example, utilizing AI to manage higher patient loads and enhancing margins—transitioning from a 20% to 25% margin to potentially platform-level margins—is quite feasible. I’d like to take this a step further by showcasing a live demo of how we can harness our rich precision data to train a large language model that incorporates all our medical expertise and focuses on patient-specific information. You will see firsthand how the AI can dynamically engage and respond to personalized queries. For instance, consider a scenario where I have a personal concern after a family member experienced a heart attack. While I'm in good health, I seek insights about my risk factors. I can query this model that possesses my genetic data, asking about the likelihood of a heart attack or grappling with concerns over future illnesses like dementia or cancer. Upon inquiry, the model tells me I have a 0% chance of a heart attack in the forthcoming decade, but on a concerning note, I face an increased risk of dementia and colon cancer. Additionally, as I inquire more about how to mitigate risks regarding my Alzheimer's risk, it provides personalized suggestions based on my genetic profile. I can discuss lifestyle changes, such as dietary adjustments or fasting, along with the right supplements to manage health effectively. For example, the model might recommend increasing omega-3 levels based on earlier blood tests while also suggesting recipes and dietary guidance to follow. This interaction reflects not only the highly personalized yet comprehensive approach of our AI but also the efficiency and insight it affords to patients. Patients can receive tailored recommendations, medical insights, and lifestyle adjustments, all reflecting comprehensive understanding of their unique health profiles, transforming the medical consultation experience significantly. Importantly, our framework allows for human healthcare professionals to oversee and interact with the AI’s recommendations before presenting them to patients, ensuring accuracy and further refining the model continuously based on user interactions. Our aspirations for combining precision data with large language models will empower us to revolutionize healthcare delivery, allowing genuine health improvements and better outcomes for millions.

Excellent, Matt. What we’ve seen today exemplifies why we acquired WildHealth; they have a precision data set surrounding human health, and with our data integration and analytics, we will transform the healthcare industry. The opportunity in this sector is enormous, and we believe that what you witnessed today with the integration of our LivePerson platform and Clarity's capabilities represents a multibillion-dollar opportunity as we look to impact how healthcare is delivered. This experience reinforces our commitment to being recognized as the calendar’s leading AI company. A year ago, we were named the #1 AI company globally by Fast Company—highlighting our capacity to produce business outcomes—and at that time, OpenAI ranked #3. Initially, OpenAI focused heavily on APIs, but its influence has catalyzed a modern AI space race. Moving forward, we see an emerging class of AI firms stepping to the forefront, and LivePerson aims to lead among them. I firmly believe longstanding tech giants like Amazon and Google may find themselves challenged. The next wave of industrial leaders will likely be those embraced in AI, and our solid foundation and 28 years of experience primes us for that journey. We’ve faced our share of hurdles, yet as we stand on the cusp of this opportunity in Q2, I assure you we are aligned to generate vital cash flow, a foundational element for our company discipline. We’re incredibly enthusiastic about the future—especially since we’ve waited so long for this moment. Our team is invigorated and fully committed, ready to deliver our unique innovations to the market. I want to thank everyone at LivePerson for their dedication to this collective mission. With that, let’s transition back to the operator for a few questions.

At this pivotal moment in Q2, I can assure you that we are focused on generating essential cash flow, which is a key aspect of our company’s discipline. We are extremely excited about the future, especially since we have been anticipating this moment for a long time. Our team is energized and wholly dedicated, prepared to bring our unique innovations to the market. I would like to express my gratitude to everyone at LivePerson for their commitment to this collective mission. Now, let's go back to the operator for some questions.

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?

Ryan, yes. I think we covered this in my prepared remarks, but I can reiterate 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 two primary categories: COVID-19 testing and professional services with a strategic partner in the diagnostics space. With regards to Gainshare labor and pandemic-driven variable revenue, that's approximately $20 million of that figure. It is not WildHealth revenue contributing significantly to this shortfall.

And the other thing is, although WildHealth is coming off a small base, its core business is projected to double this year in growth rates. But again, it’s a small base relative to our overall size, yet there’s excellent momentum.

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?

Yes, Siti. The primary driver relates to the cost-out actions currently underway in Q1. That's a large body of costs that will be reduced, directly contributing to profitability transitioning from Q1 to Q2. Our core recurring revenue base is also expanding, fueling our growth along with AI-driven advancements throughout the year. Currently, there’s strong demand for AI demos and related advancements as reflected by good feedback.

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?

Yes, we are done with distractions. Beginning in Q2, we will have a streamlined P&L focusing strictly on our core business and our health care business. That focus is essential, and we have taken steps to prepare.

Next question from Arjun. How is WildHealth monetized? What is the revenue model and accounting for the business?

Yes, I’ll start. The current model is included in our hosted revenue line, featuring both recurring and pay-as-you-go revenue. Matt, would you like to elaborate on some core business drivers?

Sure. Currently, we have a recurring revenue model based on patient interactions. Over the past year, our patient count has increased significantly, with expectations for continued growth. This model keeps evolving, as we are optimizing it for efficiency, aiming to leverage technology to decentralize high-quality healthcare, making it widely accessible.

And Matt, I want you to mention the user stickiness of our platform when compared to traditional options.

Absolutely, Rob. Users appreciate receiving exceptional healthcare, resulting in our Net Promoter Score (NPS) being around 60s and 70s, in stark contrast to traditional healthcare with NPS around 0. High client retention indicates that our users stay on the program, experience improvements, and frequently refer family and friends, fostering growth. We anticipate that the same stickiness factor will translate for doctors or hospitals who license our technology.

Thanks, Matt. Next question comes from Zach Cummins from B. Riley. Does the current guidance include revenue contributions from the consumer business? Can you speak to the decision to sell that business?

Yes. Currently, the consumer business is assumed to remain approximately flat year-over-year. As such, its inclusion or absence will not materially impact our revenue growth. Minor EBITDA effects are present, but the guidance does not account for contributions from Kasamba in the consumer segment. The decision to sell was aimed at enhancing our B2B core focus, reducing management cycles and concentrating on AI-led growth.

Our filter for decision-making is based on whether we can effectively utilize AI in that business. The chat business was a great initiative when we acquired it; however, we found it challenging to automate similar to our AI operations in healthcare and enterprise sectors. We've seen a good return over the 13 years we've owned it.

Next question from Peter Levine from Evercore. Can you provide additional insight regarding the consumer business sale? How is it being valued, and who would be the ideal buyer?

Regarding details surrounding the sale, we'll need to wait for our public announcement concerning the transaction. Typically, it is valued according to EBITDA multiples, and further specifics will become available subsequently.

Follow-up question from Peter. What provides you the confidence that we will witness an inflection point in Q2? Additionally, what macroeconomic headwinds are you factoring into fiscal '23?

We adopted a conservative approach to bookings and retention rates while formulating our model. Consequently, we see room for upside potential. As for macro conditions, we’re observing enterprises that are being cautious, yet budget constraints have remained. Q4 traditionally serves as a significant quarter as clients want to exhaust their budgets, but this time around, clients opted to retain them. Nevertheless, our AI strategy has created an open field—numerous demos have shown strong demand.

Next question comes from Zach Cummins. Can you elaborate on current sales capacity and plans to adjust the go-to-market strategy moving forward?

High level, we will end Q1 with around 115 quota carriers, showing slight growth from the previous quarter.

We’ve established a dedicated base of about 100 major customers responsible for a substantial percentage of our revenue. We've created an initiative called LP One, comprising pods led by product heads—including a co-founder of BotCentral. We've invited those 100 clients to participate. The alignment between engineering, product, and clients will significantly enhance engagement. Historically, B2B sales have suffered from too many layers between the customer and product engineers, but we’ve recognized the importance of substance in these relationships. Such initiatives will reshape how our clients perceive our technology, leading to meaningful discussions and more significant opportunities.

I have a question coming in via email from Tom Blakey from KeyBanc. You’ve mentioned targeting 7% to 10% free cash flow margins and 16% to 19% adjusted EBITDA for the core by year-end 2023. What about the total business? When will non-core revenue likely cease?

Non-core revenue matters will be actively addressed throughout the year, but the bulk of it should exit the P&L in the second quarter.

Question from Ryan MacDonald at Needham. What incremental investments are necessary to effectively market LivePerson to new channels, specifically physicians aiming to license the WildHealth platform moving forward?

While I can’t divulge specific investment figures, we have allocated budget allocations for both WildHealth and generative AI integrations, which are expected to drive growth for B2B core in the future.

The WildHealth business does incur some cash burn due to its growth rate. We're funding for growth, but we’re careful not to overextend our resources. Health professionals are keen to join the platform and optimize patient services. Furthermore, we’re optimizing our strategies for Medicare and Medicaid along with private insurance providers, decreasing acquisition costs significantly. Overall, the potential remains substantial.

Thank you for the questions. Rob, would you like to bring us to a close?

Sure. I appreciate everyone’s participation. Today's quarterly call was somewhat atypical, reflecting the transformative stage we are entering as a company. In the past 28 years, we’ve experienced three significant opportunities in our history that came when we were prepared, but adjustments were necessary to position ourselves for success. I’d like to extend my gratitude to Joe and Matt for their input today, as it’s a rarity for data scientists to participate in quarterly calls, especially given the stakes involved. Your input today reinforces our commitment to innovation and growth. We’re one of the few public companies solely focused on AI; our principles stand in contrast to giants like Microsoft, which, while impressive, don’t offer as clear-cut opportunities for leveraging AI. Our unique approach enables us to deliver true AI capabilities while nurturing our innovation. I look forward to the next quarter where we can showcase our progress. Thank you, and we will connect with you in the next quarter.