Prepared remarks
Good day and welcome to the Fiverr Second Quarter 2026 Earnings Conference Call. All participants will be in listen-only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by 0. After today's presentation, there will be a question-and-answer session. To withdraw your question, please press star then two. Please note this event is being recorded. I would now like to turn the conference over to Emily Greenstein, Senior Investor Relations Manager. Please go ahead.
Thank you, operator, and good morning, everyone. Thank you for joining us on Fiverr's earnings conference call for the quarter ended June 30, 2026. Joining me on the call today are Micha Kaufman, Founder and CEO, and Esti Levy Dadon, CFO. Before we start, I would like to remind you that during this call, we may make forward-looking statements and that these statements are based on our current expectations and assumptions as of today and Fiverr assumes no obligation to update or revise them. A discussion of some of the important risk factors that could cause actual results to differ materially from any forward-looking statements can be found under the Risk Factors section in Fiverr's most recent Form 20-F and other filings with the SEC. During this call, we will be referring to some key performance metrics and non-GAAP financial measures, including adjusted EBITDA, adjusted EBITDA margin, and free cash flow. Further explanation and a reconciliation of each of the non-GAAP financial measures to the directly comparable GAAP measures is provided in the earnings release we issued today and our shareholder letter, each of which is available on our website at investors.fiverr.com. And now I will turn the call over to Micha.
Thank you, Emily. Good morning, everyone, and thank you for joining us. Q2 was a transitional quarter, reflecting an ongoing compression in high-volume, low-value transactional work driven by accelerating AI adoption. As large language models continue to evolve rapidly, we are seeing our customers accelerate their adoption of AI and workflow automation. Today, high-value work represents 15% of our completed projects gross order value, and is expected to continue growing while the transactional base that makes up the remainder continues to compress. That mix is why the strength of the high-value business is not yet visible in our headline numbers and why every investment we are making is aimed at shifting it. Based on this, there are two reasons we are excited about the upmarket shift: the ability to attract untapped demand and the opportunity associated with projects between our current spend per buyer and high-end projects. Over the last few weeks, we have seen clear deceleration in overall marketplace traffic and demand, a trend that has carried into Q3. We believe that recent model updates across various LLMs are a contributing factor behind this softness, and we are confronting these headwinds head-on. We are not competing for that work. Instead, we are aggressively executing our push upmarket toward higher-value work where demand is growing. Navigating this structural shift requires intense financial discipline and thoughtful capital allocation, which Esti will unpack shortly. We have both the team and balance sheet to execute through this transition, and our focus is to deploy our resources where they generate the highest long-term return. Fiverr's multiyear transformation is fundamentally about moving from a transaction-oriented marketplace to a trusted end-to-end work platform for high-end, high-value projects. Put simply, AI is automating simple tasks, and Fiverr is moving toward larger, longer-duration projects where AI deployment meets human judgment, strategic partnership, and accountability. This is a profound evolution in how the work on our platform is matched, delivered, and managed. Transformations of this magnitude require patience. We expect the financial impact to build over several quarters. But our North Star remains unchanged: positioning Fiverr as the ultimate destination for high-end, high-trust work. While the financials will take time to catch up, I believe that the underlying operational metrics are proving our strategy. Let me illustrate that across the four strategic pillars we outlined in our shareholder letter. First, our push upmarket continues to validate our strategy. Clients completing projects valued at over $1,000 continued to grow this quarter at 13% year-over-year on a trailing 12-month basis. While the macro environment is volatile for smaller buyers, we see areas of strength in our high-value buyers, particularly around programming and tech and graphics and design. Clients completing projects over $1,000 in programming and tech grew 34% year-over-year, while graphics and design grew 25% year-over-year on a gross order amount and TTM basis. From an AI services vertical perspective, we are seeing a clear evolution in how clients deploy AI with us. In commercial video and content generation, clients are using AI to scale their performance marketing. Demand for AI user-generated content video ads surged 265% while AI video ads rose 63%. Businesses are not just experimenting; they are actively using freelancers to produce commercial-grade media at a fraction of traditional agency costs. In technical integration and agentic workflows, buyers are moving past basic chatbots toward complex production setups. Search volume for AI voice agents jumped 49%. AI mobile app development rose 92%. And AI website development grew 39%. The biggest opportunity here is the shift from generic AI to what I call personal AI—AI built into the specific workflows of a specific business. Businesses, both tech and traditional, understand that automating their businesses is not a nice-to-have decision but a do-or-die decision to stay competitive. These are demand signals from our own marketplace. And as foundational AI models get more powerful, the execution gap widens. Businesses need skilled talent to orchestrate these tools into functional, revenue-generating outcomes. This quarter, we also continued to see businesses come to us for those multi-phase mission-critical projects. For example: one, a growing distributor that recently used a multidisciplinary freelance team on our platform to architect their entire B2B wholesale inventory and logistics ecosystem. This is not just custom code; it is deep operational software running their day-to-day supply chain, ultimately setting up for a Phase 2 rollout of a retail POS. Two, a manufacturer replacing a decade-old legacy system with a ground-up rebuild of their custom 3D design software. By tapping into top-tier engineering talent on Fiverr, they are integrating their new ERP system and driving a complete digital transformation. Lastly, an entrepreneur building a stadium access platform complete with security, ticketing, and automated post-event photo monetization. By coordinating multiple APIs and webhooks across payments, storage, and analytics, our freelance talent is delivering complex, integration-heavy consumer platforms at scale. This is where the market is headed and our target remains this segment of long-duration, high-value engagements. Our second pillar focuses on systematically upgrading our matching infrastructure to make trust and quality native to the user experience. We are leveraging our proprietary knowledge graph to capture incredibly nuanced client intent across four vectors: the client, the talent, the scope, and the order itself. We recently began development of live talent skill extraction capabilities, and based on early testing results of over 450 mismatch events, we were able to structurally resolve as high as 58% of conversation skill mismatches, directly tackling a major pain point for high-value order cancellations. Additionally, our newly developed graph neural network model, which helps improve matching capabilities, showed a 7% decrease in cancellation rates for dynamic matching compared to our current model in initial tests. Our third pillar is about moving Fiverr beyond passive matching into an active, comprehensive work platform. We are building a standardized fulfillment layer engineered to dynamically safeguard project success. Phase one of this framework is now live in production. It is currently evaluating transactional quality, reaching as high as a verified 91% precision rate across over 84% of completed projects in our initial testing. This architecture is designed to give us real-time telemetry and sentiment signals. It allows us to track project health, mitigate friction points before order completion, and create the exact infrastructure needed to seamlessly integrate our marketplace with advanced agentic business workflows. Our fourth pillar centers on expanding our growth engines to attract the right kind of demand. We have moved our advanced semantic onboarding models and corporate KYC initiatives into production. These systems capture deep corporate profile data the moment a business joins and route that demand directly to our highest-performing talent. Our development test results drove an 8% lift in new customer conversion. We are also opening up new, highly targeted acquisition channels. In June, we launched a dedicated discovery campaign targeting e-commerce merchants looking to scale their TikTok shops. Our goal is to capture higher-value merchants including Amazon FBA and Shopify merchants, and funnel them into repeatable, long-term service journeys on Fiverr. To close, we are repositioning Fiverr deliberately to where the value is—higher-end, high-trust work where human expertise, project orchestration, and accountability are irreplaceable. The operational signals across our strategic pillars tell us we are on the right track. Our job now is to translate those signals into durable financial performance. Backed by our balance sheet and focused execution, we are building the foundation for our next chapter while remaining disciplined in how we allocate capital. With that, I will turn it over to Esti for the financial details.
Thank you, Micha. Today, I will review our second quarter financial performance and provide additional visibility into the ongoing marketplace transition, an update on capital allocation, as well as third quarter and full-year 2026 financial guidance. Starting with the results: Q2 revenue was $97.8 million, down 10% year-over-year due to a decline in low-value transactional work. Adjusted EBITDA was $17.5 million, down 18.3% year-over-year, representing an adjusted EBITDA margin of 17.9% as margins declined 180 basis points from a year earlier. Margin pressure was limited by the proactive steps the team has taken to maintain a lean organization, including our continued effort in discretionary spend management, internal AI efficiencies, and balanced marketing. Turning to our revenue segments: Q2 marketplace revenue was $63.1 million, declining 15.5% year-over-year, driven by 2.7 million active buyers, $368 in spend per buyer, and a 28% marketplace take rate. The 22% year-over-year decline in active buyers was mainly due to the ongoing compression in low-value transactional work. Low-value transactional work currently represents the majority of our marketplace and is decelerating at a faster and stronger rate than the growth associated with our high-value work. Additionally, as Micha noted, in recent weeks we observed a noticeable deceleration in marketplace traffic and demand which has carried into Q3 and is expected to continue for the foreseeable future. These traffic and demand trends impact the entire marketplace. This pressure was broad-based across simple categories in all verticals. Transaction volume was down 10% or more year-over-year across the majority of projects under $1,000. On a TTM basis, writing and translation saw the steepest decline at more than 24%. Spend per buyer increased 15% year-over-year driven by the ongoing mix shift across the marketplace from low-value transactional projects to $1,000-plus projects. At the end of Q2, projects at or above $1,000 represented 15% of completed projects gross order amount on a TTM basis. Programming and Tech and Graphics and Design represented the fastest-growing verticals of completed $1,000-plus projects, with both increasing over 25% year-over-year on a TTM basis. On the other end, services revenue in Q2 was $34.6 million, up 2% year-over-year, and accounted for 35% of total revenue. Services revenue continued to grow but at a lower rate compared to last quarter. The deceleration is expected to continue into H2 and exit the year with a double-digit decline. The drivers for the deceleration include softening demand from influencer campaigns and dropshipping, as well as weakness in Fiverr Ads and Seller Plus. Services revenue is also negatively impacted by the lower expectations around marketplace growth given the traffic and demand issues we have discussed. Now on capital allocation: Our capital allocation priorities are guided by a disciplined and balanced framework. First, funding the organic transformation investments to reposition toward high-value work. Second, maintaining strategic and financial flexibility in the dynamic AI-automation environment. And third, evaluating opportunities to generate value for shareholders including further repurchases. In the current environment, maintaining a strong balance sheet represents the most responsible path forward. Our healthy financial position provides valuable flexibility as we continue to run a lean organization focused on cost discipline and maintaining profitability. At the same time, we recognize that capital allocation is a critical component of value creation. We finished the quarter with a total cash and investment balance of $308.5 million and generated $13.6 million in free cash flow. Given the uncertainty, full-year free cash flow is expected to be lower than the previous two years. We are operating in a dynamic environment and we will constantly evaluate where we deploy our capital for the highest return. That also applies to how we invest in our transformation and related growth opportunities. A primary focus will be on executing appropriate cost discipline so we can continue to generate profit and free cash flow. Now moving to financial guidance: Our revised guidance for the third quarter and full-year 2026 reflects the AI-related demand and traffic headwinds observed in recent weeks, which have continued into Q3, impacting our entire marketplace, along with ongoing weakness in categories most exposed to AI automation and declining services revenue. We now expect that meaningful financial impact from our transformation will require at least six quarters to materialize. For the third quarter of 2026, revenue is expected to be between $80 million and $88 million, representing year-over-year growth of negative 26% to negative 18%, and adjusted EBITDA between $8 million and $12 million representing an adjusted EBITDA margin of 11.9% at the midpoint. For the full-year 2026, we now expect revenue to be in the range of $356 million to $372 million, representing a year-over-year growth of negative 17% to negative 14%, and adjusted EBITDA in the range of $52 million to $62 million, representing an adjusted EBITDA margin of 15.7% at the midpoint. While our updated outlook reflects our view of the current operating reality and the extended timeline for our transformation, we are leaning into this moment with discipline: stabilizing the core marketplace, shifting towards high-value work at a faster pace, preserving flexibility, and allocating capital with a focus on long-term value creation. With that, we will now turn the call over to the operator for questions.
Questions and answers
We will now begin the question-and-answer session. To ask a question, you may press star then one on your touch-tone phone. If you are using a speakerphone, please pick up your handset before pressing the keys. If at any time your question has been addressed and you would like to withdraw your question, please press star then two. At this time, we will pause momentarily to assemble our roster. The first question today comes from Eric Sheridan with Goldman Sachs. Please go ahead.
Thanks so much for taking the questions. Maybe two, if I could. Just drilling down on the six-quarter transition period, can you give us a little bit more granularity about what it is about either business mix or the headwinds and tailwinds you see in the business today that underpin some of the framing of the duration of the transition across that type of time period? And then the second would be, you highlighted the recent updates of various LLMs as a contributing factor to the weakness. What exactly are you seeing out of those LLMs and how is that manifesting in these headwinds, if we get a little bit more detail there as well? Thank you so much.
Good morning, Eric. Thanks for the questions. As for the first one, the answer is yes: it is mostly headwinds which really impact our estimation of how long this transformation is going to take. That is mainly what is influencing this. As Esti mentioned in the opening comments, we run a very lean organization, at the minimal size required to move very fast. We are now a much smaller organization than we used to be as we have completed restructuring, but the pace at which we move is much, much faster. Given the fact that we assume the trends that we started seeing in the last couple of weeks of Q2 going into Q3 will continue, that is the assumption behind the time frame for the transformation. Now, I did mention the impact of a few of the LLM models. Between those LLMs, there are different types of usage, and there are some models that are more impactful or step functions versus those that are just adding incremental improvements. But the really interesting thing is that it is not just the LLMs themselves or the specific models, but how those LLMs are being integrated into other client experiences. I think most noticeable is how Gemini is being integrated into the experience of search on Google. It exposes more people to LLM experiences which, by turn, obviously impacts the traffic that we are getting. So traffic used to come only from search, then it became LLMs as well, and now it is becoming hybrid: LLMs and traditional search, which essentially lowers the volume of traffic, which we called out as one of the major headwinds.
Thank you. The next question comes from Ronald Josey with Citi. Please go ahead.
Micha, just a quick follow-up on Eric's question on the timeline in the six quarters and can you give us some idea or steps that perhaps we can look to watch in terms of this transition to see the progression as we go to higher-value projects upmarket? And then, as we do get to higher-value projects and given that foundational models get more powerful, talk to us about the talent on Fiverr's marketplace and the ability to deliver these higher-value projects. Thank you.
Thanks for the questions, Ronald. Good morning. I will start with the transformation itself. We have been calling out in the opening remarks what we are building and the early signals from tests. Those signals are being deployed more widely, which is why we expect to get more impact out of them. This transformation is really about the matching part of our proprietary knowledge graph. As we said, the initial deployment of sellers' skill extraction addresses about 58% of mismatches and the model decreases high-value cancellations by 7% in initial tests. These are impactful numbers. Since they have been running in tests, they are being deployed widely in production. The product now has an end-to-end fulfillment layer and phase one is live, evaluating the quality of transactions with 91% precision across 84% of completed projects. The go-to-market transformation: we have developed and tested new go-to-market initiatives—I mentioned TikTok as an example—which drove an 8% increase in new customer conversion. Deploying these at larger scale will matter. We do all of this while keeping operational excellence and making critical investments to strengthen the high-end talent flywheel, improving marketplace quality for high-value work, and executing with strong financial discipline. Regarding talent: going faster upmarket means cleaning our talent pool of those who do not have demand for low-skill work, because they occupy space on the marketplace. On the other end, we define the necessary skills and ensure that where we are missing talent to tackle client needs, we onboard them quickly. Our clients are not just looking for small AI deployments; they want AI integration to improve competitiveness and efficiency across business workflows. That requires highly skilled professionals who can assess business needs and create multiple agents tailored for a specific business so they can extract the most out of this AI transformation. This is not optional for businesses—it is essential—so there is significant demand for skilled talent to help implement these solutions.
Thank you. The next question comes from Jason Helfstein with Oppenheimer. Please go ahead.
Hey. Thanks for taking the question. Thinking about the financial outlook: you already run a very lean organization. What is the right way to think about EBITDA and cash for next year? If revenue is down next year, are there other cost actions you can still take? And how far along are you in deploying AI internally for your business and automation in the organization? If you want to put those two together as one answer, that would be great. Thanks.
Thank you, Jason. Good morning. Our revised guidance for EBITDA reflects first the issues we are seeing on traffic, and that affects the bottom line. However, as you know, we took cost reduction initiatives in previous quarters that help support EBITDA. In addition, as we see traffic issues, we also adjust our marketing spend as we did before, and we will continue to assess that if needed. We are protecting our R&D spend because that supports the transformation, and this is our number one priority to get through the transformation while continuing to be profitable. So you should think about it as a firm that will continue to generate cash during the transformation. We continue to run a lean organization and want to maintain cost discipline, especially around discretionary marketing spend. Again, the focus is going through the transformation.
To answer your second question on internal deployment: the first half of the year was about putting the infrastructure together and running multiple tests. I called out some of them to prove that doing so is significantly improving $1,000-plus transactions and attracting the right clients. In the second half of the year, we will deploy some of these solutions at scale and continue to work on new initiatives that we believe will improve quality, conversion, and optimize for higher spend from those customers.
I would just add on free cash flow: free cash flow follows EBITDA, so you should treat the guidance for EBITDA as the range for free cash flow guidance. As I said, we aim to continue to be profitable, which also means continuing to generate cash.
The next question comes from Nathaniel Schindler with Scotiabank. Please go ahead.
Micha, this is a big picture question. For years, you guys have said that the move to AI was not such a threat because freelancers are adaptable and the marketplace can just provide the connection point. That is a compelling argument. But in February, the release of Claude Code seemed like a watershed moment where people are doing more without freelancers and that's why revenue seems to be declining. I get the transformation and the need for people to help with transitions, but how can you be confident that in six quarters there won't be more watershed moments that make this worse? That freelancers' adaptability will not be enough?
Good morning. Thanks for the question. First of all, we stand behind what we have said: freelancers are adaptable and remain central to this ecosystem. What we are seeing—and why this transformation requires additional time—is that the rate at which some skills get compressed versus the rate of creation of new skills is not the same. The creation of new skills, clients learning how to navigate this new world, naming skills, and qualifying talent is a cycle; it does not happen instantly. We are seeing this in real time because we have deep expertise in this economy and constant discussions with clients. It takes time for businesses to understand exactly what they need. Thirty years ago it took time for businesses to realize a website was essential; the same applies for AI. Freelancers are at the forefront and can acquire skills quickly, but the compression rate is different. Also, because the high-end is still a small portion of our business, growth there is being masked by compression at the low end. But the growth we see in high-end projects gives us confidence that this transformation is the right move. Early signals from our testing support that, and that is why we are moving full force to accelerate the transformation.
And a follow-up: as you transition to the high end, is Fiverr moving more into being a staffing placement company where you are delivering ongoing work, as opposed to being a marketplace for completed business services? High-end work can be hard to define in a single listing and may look more like 'I need someone with this ability' than a completed-scope listing. Are you totally changing what you are in that respect?
I have addressed this in previous calls. We see Fiverr as very focused on project- and outcome-based work. Our role is to identify needs, qualify them, and, through the knowledge graph, match to the most qualified talent—or multiple talents—who can achieve the desired outcome. We are also taking a larger role in the fulfillment of outcome-based results: improving matching and taking responsibility for project success. That is what defines where we focus, and it allows us to remain agile.
Thank you. The next question comes from Bernard McTernan with Needham. Please go ahead.
Hi. This is Stefanos Crist for Bernie. Thanks for taking our questions. The projects above $1,000 are 15% of mix today. What mix do you expect that to be to offset the decline in the legacy low-value work? And on capital allocation, are there any assets out there that can help accelerate your plans, or are you only focused on internal investment? Thank you.
Thank you, Stefanos. While 15% is still small, you should remember that our current spend per buyer is $368, so there is a lot of room to grow into $1,000-plus projects. It will take time; we are changing the platform fundamentally—talent, matching, orders, and go-to-market all require work. We see opportunity both within our current buyers and with new go-to-market initiatives; TikTok is one example. We are planning additional partnerships and channels. On capital allocation, our primary focus is the transformation and we are investing in that. On M&A, nothing to call out; we are opportunistic, but top priority is to do the transformation internally and invest in R&D. We have sufficient staff and will run a lean organization while executing quickly.
The next question comes from Bradley Erickson with RBC Capital Markets. You may go ahead.
Hi. Thanks. I want to go back to the paper trail of the weakness. You mentioned Google integrating LLMs into search as a driver. Is this mostly a marketing/distribution issue where Google is making people aware of these tools and they're using them instead of clicking through, or is it that newer models are making it easier to do a wider range of tasks without freelancers? Or is it both? Which are you seeing more of lately? Thanks.
Thanks, Brad. The weakness in demand and traffic comes from Google integrating LLM or AI summaries into search. For some customers, they will go directly into the LLM instead of clicking on paid or organic links. The majority of links are paid, so organic is getting squeezed and SEO impact is declining. That is why we and others are investing in generative engine optimization to ensure we appear inside those LLM experiences. Click-through from LLMs is generally smaller than from traditional search, which creates headwinds in traffic. The same goes for other models: sometimes it's the introduction of newer tools like Claude Code or other model upgrades, and sometimes it's new regional competitors. Our assumption is that these models will continue to develop and compete, and depending on usage and integration, they will influence traffic. That said, the actual needs of the clients we target are not being addressed by just using a tool once: they require deep integration of agentic solutions that are beyond the reach of most businesses. Creating agentic organizations is complex, even for sophisticated tech companies. Therefore, many businesses will need human-in-the-loop expertise.
Got it. Bigger picture: you have a great lens into models and how to harness them. Can you share your thinking on open-source models versus proprietary models, and how companies build moats or lock customers in versus working with open models? How do you think about that for your customers and more broadly?
If you think about it, most companies in the world—excluding the foremost AI companies—are still behind. For sophisticated companies like ours, one key challenge is inference management because there are many different models, each slightly better for different tasks and with different latency and cost characteristics. Introducing latency can let you use cheaper models if you don't need instant answers. For us, it's about using multiple foundation models to maximize the task while managing cost. Some models are more expensive than others but not needed for every task. Throttling between models and handling inference is a major topic. For many businesses, this level of sophistication is too advanced; deploying and running agentic environments has ongoing costs. Our experts help customers avoid overspending on the wrong solutions. Open-source versus proprietary is not a binary moat in my view: open-source can allow foundational companies to iterate faster and reduce cost, but the key is how companies orchestrate models, manage inference and integrate solutions into workflows. If a model is good enough, you should probably be able to pay less for it, but the orchestration layer and productization of outcomes is where much of the value sits.
The next question comes from Matthew Condon with Citizens Bank. Please go ahead.
Thank you for taking the questions. First, on traffic: are you seeing a significant deterioration in your payback period in other channels, or is this isolated to SEO or Google traffic? Are other channels also deteriorating in performance? Second, would you ever consider dynamic pricing or other pricing changes to try and spur demand across the marketplace, or is that not a focus? Thank you.
Thanks for the questions. The situation with Google: fewer customers coming to Google impacts the amount of traffic Google has available to advertise for, so advertising density increases and cost of acquisition goes up. All of this influences marketing efficiency, which is why we take a multichannel approach and invest in GEO to increase organic traffic from LLM channels—it's showing great results but click-through is still small. We were also an early advertising partner on ChatGPT, but it is still very early days. It reminds us of the early days working with Google; it takes time to build up. They are figuring it out and so are we, and we are taking proactive measures. Our objective is to ensure that the investments in matching infrastructure and the fulfillment workflow enable Fiverr to integrate into agentic workflows down the road, so we can provide core experiences inside these LLM-driven customer experiences wherever they occur.
The next question comes from Marvin Fong with BTIG. Please go ahead.
Good morning. Thanks for taking my questions. Two, if I may. First, with the increasing sophistication in the projects you are targeting, do you feel your current suite of services—Fiverr Business, Fiverr Pro—is enough to address this landscape, or are you looking at producing additional service channels or something more comprehensive? Second, on services revenue you called out Seller Plus being down—are freelancers cancelling their subscriptions, or is the freelancer pool shrinking? Any insight on freelancer behavior related to services would be helpful. Thank you.
Thanks for the questions. As we transform, we are rebuilding a lot of core functionality from the ground up: the knowledge graph, the end-to-end fulfillment layer, the matching engine, and the experiences we expect to provide customers so they achieve outcomes better than alternatives. We are building new tools and refining the platform to ensure clients get the best experience. I don't want to go into product details that are being built and tested, but we are focused on delivering client outcomes and building the tools to do so.
On services revenue and seller monetization: these are affected by marketplace traffic. The traffic headwinds we saw in recent weeks affect Ads and Seller Plus, so we expect that to continue into H2. Additionally, services revenue saw headwinds related to AutoDS, which we included in our H2 guidance for services revenue.
The next question comes from Joshua Chan with UBS. Please go ahead.
Hi. Good afternoon, Micha and Esti. Two questions: first, as you do this transition, how much of your talent or customer base do you feel has to switch out or cycle through to complete the transition? How do you acquire the right talent and customers for the next phase? Second, regarding the six-quarter estimate, what is the starting point of that six quarters, and as you deploy the solutions you are testing, should we expect to see some improvement in trends even during that period? Thank you.
Thanks, Joshua. When I referenced cleaning up the lower-end talent, we are optimizing the display layer so clients find what they need more easily. For high-end talent, we identify skills in demand, define them, and qualify talent. We have one of our biggest moats in the extensive transactional data we have collected, and we use that data in the knowledge graph to deeply understand client needs, scope descriptions, and desired outcomes. Pairing that with qualified talent makes matching much more efficient, which leads to better outcomes, higher retention, and higher spend. Regarding acquiring talent and customers: because we have rich transaction data and strong matching capabilities, we can identify where talent is needed, onboard them quickly, and direct the right demand to them.
Joshua, as for the six quarters, it is starting now. Of course, although we are at the beginning of the period, we already have initial things that are encouraging and we will share more as we progress.
This concludes our question-and-answer session. I would like to turn the conference back over to Micha Kaufman for any closing remarks.
Thanks so much. Thank you for moderating this call, and thank you everyone for calling in. I wish you a great day and we will talk soon. Thank you.
The conference has now concluded. Thanks for attending today's presentation. You may now disconnect.