Thank you so much for joining us this morning. For those I haven't met, my name's Kendra Banks. I'm the CFO here at SEEK, and we're really pleased to have your time. I'm grateful that you're spending the morning with us. I'd like to start by respectfully acknowledging the traditional or local land on which we meet, the Wurundjeri Woi Wurrung people of the Kulin Nation, and the continuing custodianship of the land, waterways, and community on which we all live. I'd like to pay my respects to their Elders, past and present, and extend this respect to all Aboriginal and Torres Strait Islander peoples, as well as all the First Nations peoples across the Asia Pacific region. Now, today is a different kind of session than how we normally engage with you at our quarterly recorded sessions.
The goal today is to give you a deeper understanding of our product as our customers' experience, to show you how our product development is already helping to get those needs met, and why we're confident in the growth to come in the next few years. The speed at which our teams are developing and launching product post-unification has dramatically stepped up. We have way more that we can fit into kind of few minutes at the end of one of those conversations this morning, and so today is really about giving you that step on the product experience. For some of you, you're in our product every day. You'll have seen quite a bit of this. For others, it will give you a different lens on the business.
We think there's no substitute for seeing our product through our customers' eyes and for hearing from the people who spoke it, so for all of you, we hope to build you some confidence in the path we're on, demonstrate our speed of travel, and show you how we're already capturing the opportunity for growth both in this world and beyond, so in terms of who you'll see today, you probably recognize most of the top people that are there: our executive leadership team, led by Ian, our CEO, Simon Lusted, the executive director of the product, Grant Wright, our group executive for AI, and Peter Bithos, our group executive for commercials.
We're going to be joined today by three of our APAC product directors: Narelle Charity, who looks after the hiring side of our marketplace, Sophie Stafford, who looks after the candidate and talent side of the marketplace, and Xavier Russo, who runs the price and verification segment. We're also joined by Ricky Lam, who is one of our APAC commercial directors and the commercial leadership team. In terms of how today's lunch works and how the morning's going to work, Ian's going to give a brief intro to SEEK today, then we're going to see a video of some of our customers talking about how their experience is working with SEEK. Simon is going to go over to you about product strategy, and then we're going to move into product demos.
Then Narelle, Xavier, and Jesse will each do a demo for key aspects of their product, and Grant will be joining them to demonstrate how AI is woven through all of those product experiences. We'll take a short break. That'll probably be around 11:20 A.M. or 11:30 A.M. if we need to plan for a call, probably around that time. That'll be just a 10-minute break or so. We'll come back. Ricky will take us through an overview of how our B2 B business is traveling, also a few words on operating leverage, and then we'll move into the Q&A section. We should probably have about 30 to 45 minutes for Q&A. We hope to wrap up about between 12:30 P.M. and 12:35 P.M., more likely. And then we will have a light lunch for the other listeners who would like to stick around.
I'm happy to continue the conversation after that. Now, before I hand over to Ian, today is not about onboarding training update and volumes. In fact, I think this is the only SEEK presentation I've ever seen that doesn't have a volume chart in it, which means expectations. However, with only six weeks today to a year end, we wanted to share where we are heading in terms of our FY 2025 guidance ranges. So we're confirming the existing guidance ranges as per February and explain some detail of where we expect to end up within those ranges. If FY 2025 wasn't moved to the result in a statutory profit, we'd be unlikely to see much change in the guidance ranges. Ian said that's supported by the recent upgrade of our guidance series, which will support low double-digit growth in FY 2025 versus Q2.
You'll see Narelle's presentation and demo to help you get detail on how those growth scenarios are working. In terms of [audio distortion] volumes, the decline in global volume has continued to stabilize in recent months and is expected to continue. Overall revenue, as discussed in February, is to be in line with Q2. These does include the early impact of the Premium launch in Singapore, which has progressed well and as expected. On the adjusted profit line, interest costs will be lower than original guidance following the SEEK refinance from the Growth Fund's capital program in 2020, which we reported earlier this year and put those on the table, and finally, our total expenditure line that's set for 2025 will be in line with current expectations within the guidance range, so that's really the money slide for today.
I'm going to hand over to Ian to give us a brief update .
Thank you very much, Kendra and all. It's great to see you in person. I'm glad to be here to give first-timers a special welcome. I'm very encouraged by the fact that so many of you have stayed after we've gone through the guidance slides, so that's terrific to see. It does go a little bit to what we're hoping to do today, and it is a bit of a different story today. As we sort of speak to you straight to our strategy update , for all understandable reasons, if we meet for an hour, the first 50, 55 minutes are all about where volume is going.
And we've always been a thing of frustration from us and from you a little bit about, well, can we just put a bit of love out in the product and then go and do a little job to tie it back? So we won't be talking about volumes at all today. We don't want to talk about the near-term impact predictions at all. We won't do any Q&A on it. This is actually about the product and what we are building and hopefully what will convey you as a real high level of confidence and confidence in it. We're not an exclusive management tool. We try and stick to the facts. I think you'll find from the people you're going to hear from today a real sense of confidence in what they're seeing as it goes across the education world.
As opposed to sort of takeaways, it's really simple, which is we've got a fabulous foundation. There's a lot of growth, and we've got a clear plan. There's no big product launch. We're not here in Sydney to show you an next generation of our hardware. But underneath it is hopefully a little bit of an answer to the question that we often get about executives and executive representatives, and you seem to spend so much money, what are you getting for it? Which is sort of an underlying question that always wears off now since we've seen one. But if you start getting asked that question, I mean, what business is any media business in a stronger position as a bank? And that's sort of not management opinion. That's just what the data is telling us. So our starting point simply couldn't be a better starting point.
You all know this, but we tend to slip over it. We've got eight markets that we're the number one in all of them on the two major placements here. Now, that can go up and down a little bit depending on the country, but the Philippines and a bit with Thailand, but give or take. The brand awareness is ahead of Indeed as much as a piece of the pie we've taken in the transition to Asia. 92% in Australia. Asia is not at that level, but it seems to get about a third of the money we've asked you for, which will be our business. We'll be at well above 93% in Asia. Really strong customer relationship. And then these are the two things you're going to hear more and more about today, which is the product innovation and the platform and data.
And I'll come back to that in a minute. You notice through that, we were aiming for a very simple growth basis, growing yield in B2C with an operating leverage. And again, as you hear from the team today, we're going to keep coming back to how what we're doing is growing placement, how it's going to grow yield. And then Kendra will come back at the end, and we'll agree with him why we're confident we can do it within the target that's important to you and to us, which is delivering the best possible customer we are. We keep coming back to this. You're right to rolling over about it, and I was just speaking to Jesse about this before. Yesterday, in our ELT meeting, Jesse and Narelle came by for the quarterly update on what we're doing in product.
We went through 99 slides, and I've taken on this, and I wasn't bored, of just how much product we are building and shipping in this business, and it's that rate of velocity and impact that this business just has not seen before. It has not come close to before. When you speak to Jesse, it was shown earlier. I mean, this has been felt every day by the team. You're going to see him. Simon will talk a bit about what we've decided to say this month. What we've done here is a little slice of some of the bigger things. If we were to take you through all the stuff that's going on and why we believe in this, it will be a much longer presentation.
But these numbers, five times the increase in experimentation rate, three times the increase in last year's product releases, data protection, particularly in the commercial world through the APAC organization structure, 36 systems intermittent. Now, a lot of you said, "We took the costs out of it," and we've reminded you though we've depreciated to zero. But the upside of the speed of development and the downside, the greater resilience we've avoided with some 46 independent systems is really material. And then in terms of the greater ROI, you're going to hear about all these things, so I'm not going to elaborate about them, but we're pretty excited about them. And this is all due to the foundations we've built over the last few years in the implementation of the platform.
So, for all the stuff that we've done to date, what have you the investors got from the money that you've put against it? Well, we've got record placements here in Australia. Asia has been nice and up and down, economically positive trajectory, but at the same time has growth in yield. And I just want to emphasize the confidence of these two outcomes. We're growing share and yield. And if there's one thing I could see you take away from today, I hope you do when you hear the team. It's the underlying confidence and rate prediction, particularly on the yield side, that's going to come through every year. But despite those strong positions, we've got some good growth opportunities.
Now, we've had analysts here somewhere who were saying, "We haven't reached that sort of AUD 2 billion target," and we're desperate to present it, and I said, "It's not on the agenda for today." What I will say about the AUD 2 billion target is everything when we look at this as we do regularly, all the assumptions we made about the things we control in the core are taking at least as well as we thought they would for the AUD 2 billion target. We've now got the ability beyond the core of everyday unification to look at adjacencies again. You'll see Simon approved later the fact that we are in advanced discussions about reacquiring Sidekicker. Our exit today. I look forward because we understood that the funds are keeping new waves. We don't control the funds critically, so we can't control Sidekicker.
Given where we are with the platform, the obvious opportunity is adjacencies. We're in the process to reacquire so we can increase our work. That's another example of some of the assumptions underlying the AUD 2 billion in what we control in the core and what we control in adjacencies. The rest of the AUD 2 billion is a macroeconomic forecast. Most of my discussions with you all in the half hour prior were around what's going to happen in the next month. Clearly, forecasting 2027 volume is not easy, but we'll keep looking at that. We'll keep updating it. In our six-monthly revenues review, we'll let you know how we're tracking it. We're always going to be full-on thing to do within the day's time. Change it, expand it more, we'll do that then. We always usually are discounting it anyway.
But if you have a look at the opportunity here, convert more over and think of a particular placement, deepen and extend our higher relationships along our price to value as we travel across the higher. And probably the last thing I'd say there before I keep in the back of your mind as we're listening to the product take is in the world we're in today, every time you're going to hear from Narelle, you're going to hear from Jesse, you're going to hear from Xavier, and each time you're going to hear from Grant about what the AI is doing. There's a whole lot of fluff about AI, but as you're going to see, we've been doing it for over a decade. It's ingrained in absolutely everything we do.
The key thought I wanted to back in your mind as you watched through all those slides is that in the world that we are in, in particular when Simon was talking about, and you all know the importance of information in our business. AI, as it stands today, has got the potential to provide material response to stay to the market leader in data. The stuff that you're going to see that we can begin that we've invested in, you can do best if you're the market leader, so we've got to spend the right degree of confidence that only in that nearest feeling of growth, we're in an environment where if we execute well, we actually get increasing returns from being a market leader, and if we execute well.
It's core to why you're going to hear today, not from the people who get the credit at the very end, which is nearly as rich, but who will actually do the work. The folks we've got, the quality of the people you're going to hear on their teams is the best part of the big story in my mind. And we wanted you to hear directly from the people who are doing the work and who are invested with confidence in what they're building and what the outcomes are. And then we can go back to talk to anyone else in the Q&A. We also want you to hear the customer voice. So before I hand over to Simon, we'll just cut to a video which is just going to give you a little bit more of the customer talk.
Going back into finding a job and going back into job seeker mode, something that I hadn't done for a really long time, and apart from my first job, it hadn't been going and finding a job and applying. That is what millions of people are doing, and that's what I started doing. Having an up-to-date SEEK profile was really helpful. Initially, it was just to help me to apply for jobs that I found that were a good fit, but having my experience and my skill set in there actually enabled SEEK-recommended jobs that were coming through to me to fit my skill set, to fit my experience, kind of took the legwork out of that for me.
So when I would open up the SEEK app, the recommendations were right there, and I could really just sort through jobs that stood out to me that sounded good to me, whether it was saving them to look into further and apply for later or just applying while they're on the spot. I think what SEEK did really well was really led me down that path of, "Here's some resources," giving me those recommended roles so that once I had maybe depleted all of the roles that I thought were fit for me, offering up some fresh ones to start broadening my really mindset around maybe which roles I would apply for.
When you're running a new country, it's always going to be a challenge to position yourself as somebody who has a lot of work experience. Because when you are in a new market, the recruiters are always asking one question, like, "What's your local experience?" I remember applying for almost 100 jobs and getting 99 rejections and having that one call only for them to tell me, "Hey, your work rights are not verified, so you won't be able to take a profile forward." But the moment I got my work rights verified on SEEK, that's when I hit the right note. I started getting a few more calls. I had full work rights in Australia, and that gave them confidence to take my job application to the next step.
It's really important that the platforms that we choose as partners are ones that can get us the candidates that we're looking for. I've had a very long relationship with SEEK. I've been doing recruitment in Asia since 2011. I know that's dating me a little bit, but it's been a long time. When I first engaged SEEK, typically it was to hire very, very specific candidates, typically in sales and finance and fund managing positions. As I've evolved my relationship with SEEK, it's become one of the larger platforms for candidates, and so we continually use them in Singapore, Hong Kong, and Malaysia. Really, it's got some good return on investment. We're able to find those candidates pretty quickly and pretty abundantly on this platform.
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Some of their biggest recruitment challenges are workforce shortages. So there is a strong demand for roles like support worker, allied health professionals, employment consultants, but there is a limited supply of qualified candidates. Ability Options uses SEEK to recruit for a wide range of roles, but we've had the most success in recruiting for support worker roles, particularly in remote areas. 85% of our applications come from SEEK, so SEEK definitely plays a big part in our recruitment strategies. Another challenge would be NDIS providers are often competing with bigger organizations and sectors like health who can offer higher salary and better benefits. I think the company profile page really allows Ability Options to showcase our EVP and reach candidates that are outside of the industry. So I think that's really what sets SEEK apart from other providers.
Hi, yes. Thank you, so what you heard from there is voices of our customers talking about their needs, talking about why they choose SEEK. On the candidate side, it's very much about making sure you're fully resolved. There's a real focus on the candidate side that we really know, if it exists through finding a seat, it's a core driving force, and I really understand everything else we do for the candidates increasingly take away just the personalized and targeted experiences that fit with their journey. Sometimes they're exploring the labor market and trying to work out directions, and sometimes they're, we call it exploring or assuming there's a bigger target or it's bigger, and then when that time arrives, the people who want to be assured and confident that they're doing everything they can stand out in the competitive labor market, and to hire is a similar one.
They want to attract all the best talent as quickly as possible. You heard that. They want to explain to candidates why their type of employer and this job is right for them, and of course, they want to know that their sourcing and spend is delivering a competitive ROI, again, in what can be a very competitive market environment. So now you're going to take these pieces of demos that we talked about, and what we hope you'll see is you're going to link those demos to these needs and demonstrate how to bring the candidate to the client experience we deliver. But before we get to that, I want to give you a sense of what the workforce is like at SEEK, give you a sense of what our product set is and what we've just done.
At its core, labor markets are just a huge information problem, a really rough-edged information problem, largely driven by time and price. Companies spend billions and billions of dollars every year trying to help candidates and other hirers understand each other, work out their preferences, connect, negotiate as well. It's a big, inefficient, lonely, inconsistent process, and there remains an enormous headroom, an enormous gap for us to do better, and the way we think about doing better is we hold ourselves to a principle of delivering both, both nominal growth and real growth at the same time, so we're quite strategically selective. We're not satisfied doing one or the other. We're really focused on delivering more value and capturing that value, and that's when we know we've got the right value for what we're delivering to customers, and many of you will be familiar with our flywheel.
I think it might be coming up to 15th postcard, so if you were there, it was a good amount of coverage that you don't see right at that point, and our flywheel is still very much at the center of how we think about delivering placement for needs. The top half of the flywheel, of course, is all about data. It's all about having all the jobs and all the candidates in the marketplace, but especially over the last five or six years, we've become a lot more than that stuff. It's about being a safe choice. It's about the depth of engagement and how we stand up to hire, what we can learn from them, what they can tell us, what we can infer from their behavior.
Because that allows us to do a much better job of solving that information problem, getting better data into our system, delivering them better matches, and increasingly adding stuff to that experience. Because the same thing we're going to do in the role that I talked about, hiring spending, is actually not to discover who exists or what their claim is, but it's actually add stuff to that claim, and once we've done that and we've made a placement, our hirers are paid consistently, directly and indirectly, over many years, but have a very high readiness to pay for success, so from a product perspective, we're very focused on supporting and aligning our business models to our customers' success. This strategy is underpinned by three core enablers that we continue to invest in.
Ian mentioned Unification and how that's unlocked a much more scalable platform for us and allows us to release more quickly, and I'd only invite Ian to point out the volume evaluation, but saying it also matters about the quality of the evaluation, so I'd argue both the volume and the quality of the evaluation is a standout thanks to Unification. We talk a lot about our proprietary data and insights. It's one thing to know where we were five years ago, but we have our scale across our region, what's happening in the labor market right now. We have a lot of density to be able to tell you what's happening in the outback as well as our customers, what we think about in those customer voices, and we have a decentralized AI capability. We've been investing in that for 10 years.
In terms of the sense of how that shows up, that shows up in the way we do product. It shows up in the way we run experiments and data pipelines. We have a mature cold start capability in this space. It also shows up in the way we think about ethics and AI responsibility. We're very proud of how we build our products and how we think about how to get impact both candidates and hirers and society as well. I'll dig in now a little bit on our placement strategy. We're focusing on three core things. The first is we're very focused on building these personalized experiences for candidates, meeting them where they are in their journey. When you're exploring, you don't necessarily want to apply to everywhere. You want to just see what's out there. You kind of form your preferences.
So we can do a much better job of connecting you with information about the labor market, and then we can get you to act quickly when you're ready to hire. In terms of competitive matching, we've seen continued opportunities to leverage existing investments in enabling technology and the same kind of tools in the last few years to access even richer sets of data about the labor market, about people's preferences. And I'll talk a little bit more about that. And finally, we've been committed to market shaping for a long time. And I was working on that consortium just as they had it originally at the SEEK Pass. Because we think it's critical to have market shaping that's in general worth, but especially labor markets. As AI bots and agents emerge, we think the world's going to get very, very noisy.
Marketplaces that can't have trusted experiences, trusted environments where people are who they say they are, are going to struggle to deliver what we think will be a kind of span of scale. We feel ahead on this one. We've been working on this for many years. To be respectable, we've already validated connections on 3 million Australians. So that's about 20% of the labor force. So we've only just begun this, and we're only now just ready to open it. So how to think about the opportunities to guide placement. We're going to let people talk before they understand a little bit more about it, a little bit more said on this. What we've done is that we have uniquely verified in every market we operate. We only convert half of the placements that come to our marketplace to a successful placement.
And the reason we don't do as well as we'd like is typically on a very abstract level of time. We haven't had time to get to know the candidate. We haven't had time to get to know the hirer, to get to know the hirer and listen to each other in the marketplace. We've got to spend the marketplace a little bit longer. Eventually, we get there, but we're creating some competitive placements, and we're often not just competing with online competitors, but we're competing with offline sources. We're competing with internal mobility. We're competing with word of mouth, and sometimes we're competing with just deciding whether to hire or hold, so we've got an enormous headroom, we think, just to do that and do better.
As proud as we are of this and as well as we really know this is the next step for candidates, we see opportunities to do a lot better, and in the middle panel, I'm going to share with you some data about how we're doing this. So think about timing, so we want to get 10,000 hirers together much more quickly, and so first has been a big part of that. We've increased in success, and I'll talk a little bit more about this. Every year, we've made a few increments in success relevant for seven, eight years, but more recently, we've really invested hard in personalization features like notifications, improved recommendations, and just as we talked a little bit about this, our career feed, which is much more about building really responsive daily experiences for our candidates.
And what that means is that in terms of where we're generating applications, we're connecting with our candidates more and more quickly and more often by sending them by anticipating their needs, sending them recommendations. And you can see here what we think of as we're very proud of this growth in our ability to generate new applications from our core experiences. We think that plays through to placement. And Jess is going to talk a little bit further demo about how we've got a lot more to do. We're going to continue to invest in experiences to keep people connected to the labor market. The reason we want to do this is anyone finding 25% of people in the labor market say, "Yeah, I'm kind of actively looking. I'm not going to do a search on Zoom." But 60% say, "Oh, I'm more interested.
I'm keeping an eye on things. I'm reading my recommendations. And if I see something, I'll jump in," and so we're really going after that experience because it's those people, when we can get on our platform and connect with an unexpected opportunity, the highest chance of 60% converting to them. We're also going to make it easier for our customers and hirers to protect themselves, and again, we're seeing this in our demos. That'll be much more natural language, best experiences, much more conversational experiences in our career feed where we can go deep with the demos of explaining and understanding their preferences, and we're going to help explain the labor market and how it works today, not just in the past, for both sides of the marketplace. One of the reasons placements don't happen is because everybody goes to placements, and they can be very slow to learn.
You make 100 applications for something before you start to realize, "Well, I don't have the skills to do it." Right? That slows down our marketplace and reduces our conversion rate. So you're going to see in our demos opportunities to do a much better job of that. Because when we talk to candidates, we say, "What job do you want?" The most common answer is, "What have you got?" They're very pragmatic sometimes, but they need the information. So we're going to do a much better job in the next few years of leveraging the data we have and these new and relevant technologies and LLMs to really connect them with the reality of the marketplace. And finally, we're going to put this verified data very much at the core of our experience.
Xavier will take you through placing some interesting examples of what that has to mean in practice, why we need identity, how we use it in our platform. Because at the end of the day, candidates and hirers repeatedly are going to connect with real people, real jobs, real companies. And we think that's going to be a long-term differentiator for us. It's not easy for people who aren't in the market ready to position to do that and say, "We're just going to expand on the progress we're making there." So let's turn now to our unit strategy. There's three pillars of our unit strategy at the highest level. The first is we want to get as many hirers onto our platform with as many needs as possible. We want to aggregate as much demand into our platform as possible.
The reason for that is, by default, it helps us attract better talent. It helps us do better matching. For the purpose of the year, it's also critical in helping us build a cross-discovery environment where we're getting really good signals about what roles actually work, that data at a market level. Once we've got the best demand in our marketplace, we've got to discover the best price. This is a journey we've been on for many years now. Some of you have been there with us. That's really about building a product and pricing capability that helps hirers express their willingness to pay and complete the task in a fair and efficient way.
And increasingly, as particularly over the last few years, but we've been working on this for a while, new technologies like LLMs are raising our excitement levels about how we might help hirers convert placements. Right next to sourcing is a step-by-step communication process that are really basically about solving information problems. Reference checks. Do you really have the skill? What are you like to work with that person? But we've seen increasing opportunities to digitize a lot of that process and help our hirers do a better job of it by putting it onto our platform and thinking a little bit more about that. So what's the opportunity in getting more hirers onto our platform? We're very large, especially in Asia. 80% of hirers in Asia don't use digital. And 5% of hirers are post-pay type. Don't use digital but don't use it for all their roles.
We see this as a major opportunity and so, as you know, we've been rolling out Premium in markets to reduce the silos and get these 80% of hirers onto our platform. When we get them onto our platform, it gives us the opportunity to demonstrate value to them and to build new collection tracks if that's what the value is to them and at the other end of the market is our large corporates. We've been doing a lot of the unsexy work that I won't spend too much time on here, connecting with their HR systems and their process systems so that we can get their ads on them completely and high quality, and we can deliver our value back to them in a much more integrated way.
Over the next year, we're going to focus much more on using this new premium technology really to drive penetration of many hirers in Asia. We're going to look at the things we've learned from our Jora through the best experience over the last few years, which is really showing us we think we can build a great product that helps hirers with a lot of candidates, maybe 100 or 200 candidates, get from 200 to the best ones far more quickly and far more effectively than they are today, and couldn't find it in the end, and we're going from 1,200 candidates to one. We're going to make it easier for our larger corporates to manage their sourcing spend through building automated ad buying and budget management tools. That's getting as much demand into our marketplace as possible.
Then we need to discover the right price. We've been growing rates for quite a few years now. And I guess the question we get from people such as yourself is, "How do you know that it's effective? But how do you know that's sustainable?" And I think the answer, the way we answer that at a product level is really two ways to go. The first is we add an enormous amount of unit savings to our hirers. And by global standards, our rates are much higher. And when you compare what we earn to how much a hire tends to spend, it's just somewhat easier to model with this customer. So we think there's enormous room to grow, particularly better reflecting the value we create. And our journey to doing that has gone for many years.
But in the middle panel here, we're showing you a monetization roadmap. It's actually framed in two years before. I wonder if you recognize it in a slightly different form, and what it represents is really a kind of seven or eight-year journey of us methodically building our increasingly sophisticated monetization capabilities that give us the confidence to let hirers make choices on our platform, and that drives up around the performance we want, and that drives up yield better. The reason we've done it so methodically is in part it's complicated, it's sophisticated, we want to get it right, but it's also because our hirers need simple solutions, not complex solutions, and so we've been building customer market, embedding, driving the benefits of the performance data, hard data, driving the benefits of discount optimization, and then methodically moving to the next opportunity.
So when we last talked, I think we said we're up to variable pricing. We were about to launch variable pricing into Asia. We'd already launched one in Australia. And since then, it's fully rolled out in Asia, and we are optimizing the benefits of that. And what I wanted to share with you today is that last month we released our first release under Flag s hip, which is all about providing more transparency and trust to customers. And we are also going to run a key pilot. What does that mean? What it means is we want to have an Ad Ladder for our hirers that gives them far clearer inclusions about what type of performance they want to see. So that doesn't mean just how much exposure you're going to get or how many apps you're going to get.
Increasingly, we want that to mean highlighting our unit price, so we've launched a new concept called High-Fit applications, which we now put on each ad series, so we've looked at whether we're going to get 20 apps, but how many of those apps do you think are going to be High-Fit, then we've used a much more sophisticated AI targeting that we've had before to get access to a larger pool of candidates, get better exposure to those High-Fit candidates, so we're differentiating each of these pools in the lifecycle, and it's only been a few weeks since we've launched the first version of this in Australia, but we're really encouraged by the behaviors we're seeing from our hirers.
It's sort of aligning with what we expect, which is when we can make a credible claim to them that if you move up, you're much more likely to make a placement, they'll move up. This is good because it doesn't challenge my worldview that there's an enormous willingness to pay when we align our business model to the success of our hirers. We're going to do a lot more in this space. It's really just the first phase of providing more choices and transparency. We're going to do better jobs on how we set performance expectations. At the core of all this is our ability to predict the success rate. We're increasingly getting better data on that. We've launched AI targeting that we think we can get deeper, and we're moving deeper and better signals to our hirers.
And we're going to continue to work on moving to placement success. Because when we've known this before and when we don't want to placement, we think that really will help in several long years to bring the ladder up. So we've got all this AI in the marketplace. We've done an increase in the sophisticated rate for hirers to start working at the floor to pay. The next opportunity that's really come to the fore in the last two to four, five years is our ability as a platform player to help hirers increase the weight that we've placed in direct placement. Hirers are put there and they spend billions of dollars on the training of placements and getting candidates to making a placement. It's a process. It is also an information process largely. It's inconsistent. It's manual. And it's repetitive.
We're seeing opportunities to digitize more and more of that process. And when we digitize more and more of that process, whether that's a reference check or a credential, it gives us a really unique opportunity to automate the process, really take human effort out of it. It's not only makes it cheaper, it makes it better and more consistent in many respects. And more than that, it allows us to bring that information to the top of our marketplace. And so to bring that to life, there really isn't any reason to see why we should have to do the same reference check on the same candidate all the way throughout their career. Just do the reference check once, pass through it in an automated way, and then allow the candidate to start to get applications.
We think this will have the effect of really compressing the recruitment expense value pool, and more than that, it'll create an increased probability to convert if customers are on the floor, and more than that, it'll bring more data onto our platform, deeper and deeper data that maybe you'll have, which will feed back into better matches, so we're really excited about this opportunity, and that's why you can see us pushing really harder into things like reference checks and so on. That's our first step, we think, is more reasonable. Our goal for this is really two tracks. First, we're going to develop a core marketplace that we're going to look at individual high-level points where we can help our clients with reference checking, and we may understand something in interviewing and so on.
Increasingly, we're also interested in these end-to-end processes where we deliver an end-to-end managed service, an automated powered advice service that helps you find sourcing to source it to process to trust. We've been working on that. We're using ourselves as a hirer product, an outlier going through hirers. And we're really starting to see opportunities in legacy HR technology and coordination. We're excited about that. And we also think that the opportunity exists actually more so in on-demand labor. And that's why we're excited to be getting closer to Sidekicker because we really want to give our hirers the benefits of Sidekicker. And we really want to help Sidekicker automate a lot more of this process than we were able to do when we were here previously. So we think it's a really big opportunity for us.
And we feel like marketplaces are the meeting place because we're the ones who can scale the data and the technical capabilities to make this work. So what is it all means for hirers? Well, the focus of today is still very much on the job ads. We've updated our job ad hierarchy. We've got the Lite ads in Asia. We've got all the Advanced ads in between Basic and Premium. We're also upgrading our enhancements. We've launched some urgently hiring ad buys. We've got more. These are things you can attach to a job ad that couldn't be segment specific. The hirer specific needs for that role. And in FY 2026, we'll continue to invest in paid search. We won't talk about that a lot today, but we think there's an enormous opportunity to build a better and more effective paid search proposition in FY 2026.
You'll see us launching and optimizing hiring tools. This is really that first track, and you'll see us thinking about how we better integrate our placement support products and partners into our marketplace. So as we execute on all this or continue to execute on all this growth, we've seen enormous, significant growth potential. As proud as we are of what we've done, we tend to look at what's left. We still only can get 50% of placements on our platform, and what I didn't mention is that 40% of placements are offline, so we've got a long way to go there. We don't have all the hirers in our platform. We've still got a lot more ways we think we could be more sophisticated about the way we help hirers express their willingness to pay and the way we align that to their outcome.
We feel that the opportunity for us to help hirers make more and better placements more quickly is really opening up. That is a unique opportunity I think we think is probably only available to marketplaces like us. In the next session, we're going to try and bring all this to life. That's a lot of words. We're going to try and bring it to life with experiences. As Ian said, we've launched a lot in the last couple of years. It just won't be everything we've released. We've flipped the things that we think they're important to you in advance about. The other thing I'd say is we're finding that we are constraining ourselves to things that are in market or in live data in our marketplace. What does live data mean?
It means we're talking with live customers and we're ramping up to release in weeks and months, not quarters, so with that, I'll hand to Grant, and he can run us through the demo. We're interested in how our product innovation can grow placements in areas, and as we said, we really want to bring that to life, so we're going to hear three demos from our product leaders. We're also hoping to take us through the hiring experience, focusing on how we're improving our ad product and the way we communicate value around our candidate management experience. Jesse's really going to come and talk about the candidate experience, particularly focusing on how we're making more adaptive and personalized experiences that lead to better matching and more placements.
And then David's going to be driving trust and how we're using verification and convincing in the marketplace to provide confidence in connections and to trust that the segment of our marketplace is sticking in what we see as an increasingly noisy world with AI tools and live learning tools. I'm going to come back after each demo and just talk to you about the underlying AI capabilities that's powering the experience that you're about to see. But we'll get into it. So I'm going to turn around now to get into the hiring experience.
Good evening, everybody. It's great to be able to get up here and talk about what we're doing with our product team and excited about. Just really, really enjoying the demo together. So I'm going to walk you through the experience of Leigh, who's a City Plus entrepreneur based in Melbourne. And she is scaling her business across Australia and Auckland. And Leigh thinks that she's hiring for an operations manager in Melbourne. And she really leaves that role at Head Office so that she can focus here and here on scaling the business elsewhere.
I'm going to take you through how Leigh selected us and how Leigh will add that back, how we communicate the value and how she can understand what value she gets from her value set, as well as how we capture that really important information for her around what really matters around what a great fit for her as an operations manager role is. And lastly, I'm going to take you through how we see that match between what Leigh's looking for and the people who've applied for the role. To get started, who's next? As I said, this is Leigh. She's going to log in to search for things she said before because she knows what she's doing. And then she's going to start the job placement experience. We're going to get back through that pretty quickly.
She's going to enter the basics of what's needed for that job ad. So she'll run questions like title, user experience, and, it's, say, title, location, the work type. I'm going to talk to you a little bit here about our first experience around using AI in the job placement experience. So she's been doing business strategy as a play for this role. And we just took it. We took similar roles with similar job titles. And this year, that's what we'll chat. But right now, usually, she's absolutely in the ballpark for this role. So she moves on. But if she wasn't, we'd be telling her that it wasn't quite right and that we looked at placements for this placement as well. So then we move into the new Ad Ladder. And I know many of you will be experienced with that Ad Ladder.
We launched this about a month ago to really sort of deliver on the strategy Simon talked about. What you can see here is the multiple ad options we have that she can choose from for her role. In here, what we need is to move first, to move the right bit, and to make confidence that she's got going to get a good ROI and make that every important placement. As she moves up the Ad Ladder, she's more likely to make that placement, and she's more likely to make that placement quicker. This is really critical for Leigh in the process. What we're also saying to Leigh here is actually, as she goes up, she's going to get more and more applications higher and higher exposure of her ads.
But as Simon talked about, she's also going to be pushed to see that she's getting more High Fit candidates. And we'll talk a little bit more about what High Fit candidates look like as we go through the rest of the Ad Ladder. What our strategy is here, is that we want to provide more transparency on the value you will get from an ad. We want to give you more choice. She may not need to hire this role very quickly. She may not need really High Fit candidates because she knows she's going to get a lot of applications. But in this case, I think Leigh does need to have a higher level role and a higher performance role. We also want to really communicate that value.
So we've got the communication of the performance at the top of the Ad Ladder, but we've also got the communication of a number of other pieces that are included in the ad. And then at this point, we're also using all the information she provided at the start of the job placement slate to dynamically price her ad to reflect what the market is saying that her value will give her. And I'm going to talk a little bit more about that shortly. So the first ad we see is our Basic ad. This is an ad that used to be called Classic in Australia. And it's an ad that we tend to recommend for hirers to use for non-urgent and urgent roles. But Leigh needs more. She's hiring for a pretty critical role, and she needs to hire that role now.
So what you can see here is our Advanced ad. That gives Leigh a step up in performance, not only in the number of applications but the number of High Fit applications. And Leigh knows from what we've told her that she's three times more likely to make a placement on a High Fit application. So High Fit applications is really much higher. And then we've obviously got our premium role. But we've included a lot more value in the premium role which I'll talk about. But that is our best bid, fastest build, higher likelihood of placement ad. And we're really excited that we've been able to add a whole bunch of value to the first Ad Ladder. So overall, the higher people go up the Ad Ladder, the more likely they are to make a placement, and the more likely they are going to make that placement faster.
So Leigh also takes on hires in favour. This is an Australian role, so we're missing some people who don't do this. But sometimes when she goes in, well, when she goes into a higher role and owns a smaller piece of a live ad, the live ad doesn't promise any performance. It's largely free and it's a 30-day lease deal. So that wouldn't be applied here, but we use that innovation that Simon talked about to get ads paid in our market. So Leigh then starts to work through the features that we have in the Ad Ladder. And we'll start with competitive edge. We started our research, and we had Brendan talk about that as a customer earlier in the session. How important is it to stand out among your competition when you are placing an ad?
And so what we've done with both the Advanced and Premium ad is we've given them the opportunity for hirers to have their ads placed alongside other ads and in terms of other roles that look like theirs. And importantly, in the Premium ad, she can actually block somebody from having their ad placed and displayed alongside them. That really gives the opportunity in the Premium ad, this extra value of being able to block out competitive ads from advertising alongside of your ad. And it really matters. When we did our research for the Ad Ladder, it was the number one feature that hirers really cared about. They know it's highly recommended in the market, and they really want to stand out. But Leigh also wants to have a look at what does this High Fit AI targeting term mean?
We've talked a little bit here around how we're capturing information from Leigh, which I'll talk about a little bit more, but we've already captured some in the interest part of the flow. What does she need for this role? What are the skills and experiences looking for? What are the qualifications she's looking for? What we're doing here, which is very new for us, is that we are starting to ladder the inclusions around High Fit candidates. You can actually reach the best possible candidate using our highlight algorithms to target those candidates. Grant, once again, we'll talk a little bit more about this. We move on to the next layer of targeting, which is just Leigh bought a Premium ad with different exclusive targeting.
And this is an opportunity for Leigh's ad to be positioned in an email directly to candidates in an ad. So often our recommendations go in emails with a number of other job ads. And this is essentially enabled by the Premium ad to introduce exclusively when in an email. So Leigh's job ad will go one-to-one to the candidate. Once again, it really mattered to hirers when we were talking to them in the research around this new Ad Ladder. And these are especially useful for hard-to-fill roles. And then lastly, we've got the concept of candidate invitation. So we have a database of millions of candidates. A lot of them would be great candidates for Leigh's role. And so we did have the opportunity to reach out to candidates with personalized invitation. So it's even more valuable to candidates getting an exclusive email.
Actually, here, this is Leigh sending a personalized message saying, "We think you're a great fit for the role, and we'd like you to apply, and what this does is it actually starts to tap into candidates that may not see the emails that Leigh sends because it's an email from Leigh to the candidate, and I'll show you a little bit more about that shortly. We've also gone to some of the other inclusions in the ad, so you heard one of the hirers talk about the importance of EVPs and getting their value propositions to the press. In the Premium ad, we actually include branding and inclusion. This enables the hire to include their logo on the ad, free bullet points for them to sell what really matters about the role.
And they call that things like the perks and benefits and the reasons for existence of their company that really can resonate with candidates. So in the Premium ad, it's considered that you actually can buy it as an ad insurance for your value ad width. Leigh then scrolls down and she sees some of our add -ons. So we recently introduced an immediate hire ad. But hirers can actually signal to candidates in the job ad that they are looking to hire quickly. And it's very motivating for a lot of candidates to know that the hire is going to move quickly and make that placement. And so she can actually signal that in the ad. So Leigh looks at all of this and says, "I'm going to see the Premium ad." Of course, the Premium ad. Because it has the best targeting and targeting mix program.
It includes branding, gives you tools to proactively reach candidates who aren't looking for roles and a layer that has the urgent benefit to it, so this is a perfect ad for when hirers can see really that ad and see the value of the services, so then we move to the next step of starting to larger programs, and so a lot of our hirers take a few that they've created or they've used included ads into our ad writing experience, but we're not stuck in there. We're now starting to use AI to help our hirers write ads, and so what we do here is we suggest a series of suggested and increasingly speaking they can write ads, and what we're looking at here is we're doing this for a few reasons.
One is we want Leigh to create the best possible ad to explain to candidates what the role is, what the opportunity is for them, and give them the confidence to apply in that very great bit. The second thing we're doing here is we're collecting more information from Leigh, and we keep doing this because the more we know about what Leigh is specifically looking for for her hiring, the more we're able to provide better marketing delivery on that more important High Fit candidate and increase the probability of placement, so this is very valuable, and then the third is we're obviously pairing candidates with, so Leigh's written her job ad. She's really happy with it, and from there, we've gone to screening questions. This is another example where we're collecting more information from Leigh about what's important for the role.
The other important thing here is that Leigh just screened questions. Leigh just said Leigh is selecting from a series of questions that are not just advertisers, but screening questions that seem too much to ask the candidate on behalf of her. So that when she comes to selecting the best-fit candidate, she's been able to ask those questions. So they include often things like right to work in Australia, years of experience in the industry for the role, skills and things that they seek. And they've got to look at the work that we talked about around some of the verification that we asked as well. So we use these websites in the selection process to help people shortly select the best candidate for the role. So Leigh's got an ad draft and it's starting to have candidates apply.
But she doesn't have to wait for those candidates to apply. Because we've included candidate invitations to the role, we give Leigh the opportunity to connect with a series of recommended candidates that we could already use AI to recommend candidates that we think are a great fit for the role, with all of the information that we've captured from them about what matters for her. And we provide a series of candidate matches that seem to then invite to apply for the role. So you can see here the AI listing of candidates with key announcement key information. She can go deep for her candidates.
And these are things she can send a personalized message to those candidates saying, "Hey, I think you're a great fit for the role." And she's reaching candidates that may not apply to our email because this is something for her, and it's really motivating for candidates. So Leigh's done that. She's invited a series of candidates to apply for her role. And the candidate applications are now coming in. So she gets to a point where she gets about 50 applications for the role. She's actually sitting and talking with the staff and giving them a short listing and moving to the next stage. So she's basically put out a candidate management tool. And what she does here is she scrolls through the list of candidates. She can see the applications.
And once again, we've used AI to list them in an order who we think is the best fit for the role, based on all the information we've captured through the job placement process. So Leigh looks through this list. And what we're introducing here is when you're going through these 50 applications for a role, you want to be able to get to the best applicants' responses possible. So the more information we can provide and then sort job cards about the candidates quicker they are able to do that. So what we're introducing here is an AI summary that explains how these applicants fit the requirements of their role. And it uses them in a way that she's worked through a short list of applications as quickly as possible. There's a great summary of how we explain how all the requirements match the candidates.
Now she starts going through the roles, and she sees Emily here, who doesn't actually have the right to work here in Australia, and so she's not a great fit for the role. She remembers that she asked in those screening questions earlier in the process for people who only have the right to work in Australia, so she turns on that filter and Emily is out. So from there, she's going through all of the High Fit applications that meet her requirements. She sees Mark. His experience was good, and so she's engaging and looks at Mark's profile, and she can see here that he's got verified credentials, and so she knows that he does have the right to work for where he's trained. He's got the right to work, and he does have the right to work.
And she can go a little bit deeper into Mark's skills and experience with. If we can look at his profile, he's good there. He's a proud person. He's been through before. And she looks at Mark and says, "Oh, I feel great here." So then we go back and have a look at some other applicants. She then keeps scrolling through the list, and she finds Paul. And we'll say Paul a little bit later, but she sees that Paul said, "I'm hiring. You can screen that role." So this is the red tag on the top right-hand side. And she looks again at him, sees Leigh's experience. And she really likes it. She meets all the role requirements, but then she looks at screening questions. And she thinks he's great. So she adds him to her short list.
And then starts looking through the rest of the applications for the role. So at this point, Leigh's pretty happy. She's got a series of great applications for the role. And she's ready to move to the next stage of her hiring process. Okay. So I just want to recap very quickly for you. We've launched the new Ad Ladder, and we're really excited because we have actually quite experienced how customers engage with our Ad Ladder, how they look and think about the value they're getting from it. And what we've been able to do here is starting to get customers to really think about what they need to be able to successfully face the role. So we're pretty excited. You may be wondering if the new Ad Ladder is working.
And I can tell you from using the cable cord out at the start of this session that we've updated our forecast on new ad what we've delivered here. So the strategy's working. We're really pleased around how the customers are responding to the new customer value that we're providing in the Ad Ladder. We have our three-tier ad which we're working on in more markets. And it's working in terms of getting really important hirers on our platform and those job ads. In order for us to sell them these ads and demonstrate the value of being able to get to higher-tier quality candidates faster. We've also been able to materially improve the information that we get around making a placement.
And so we've done this through, I think, looking at our cost to hire to actually place the role on the platform, as well as saying that it is a great thing for the partners that we're with our live clients to provide that information about that going to be a placement to see. And it really matters in terms of our cost and our revenue to know whether that placement is made because it helps them to estimate as to where we're providing the value. The third thing is we've advanced our ad version at about 14% of the volume of ads. So we've really changed the mix of ads with this new advertiser. But what's really pleasing is at the same time, we've avoided cannibalization from our Premium ad to the Advanced ad and the lower tier because we've added our 400 more value into Premium.
So this is largely what's supporting our U.S. story here. And lastly, and really importantly, we're delivering on the promise that we made to our hirers in the Ad Ladder. We're delivering more High Fit applications to hirers in the higher tier ads. And we're really excited about what this means in terms of where we're going next. So that's it for me. Let me hand back to Grant to talk about how AI powers this experience.
Thanks, Narelle. I think we're running a little bit behind time, so I'll try and go quickly. But what you've seen there is Leigh having an easy feed to help her articulate what the right type of candidates that ideal candidates would be looking for and then choose the right job ad for the performance that she needs. And we saw a whole host of HR products and services through that experience.
But I just wanted to talk to you. Here are the big underlying capabilities that are about what we've been building over many years and continuing to advance in the High-Fit prediction and targeting and our price and performance management platform. So High-Fit is about how we understand what makes that ideal candidate. And we've got AI models that process millions of CVs and job ads and are able to read and extract and map their ontologies to skills and requirements that candidates have and what hirers are looking for. And our LLMs allow us to do that in the same steps as we've done everything before.
But combining that with our large scale of behavioral data and real-world success and placement is what really helps us understand what drives a placement, which of those attributes really matter, and particularly which are high-impact and not nice-to-have requirements when it really comes down to making the placement important and positive. We're continuing to advance that capability, combining our behavioral data with the LLM capability to understand and structure natural language. We're using new network capabilities that are increasingly personalized in how we do that. Understanding individual hire attributes. For example, if Pact Group is recruiting for product managers and they typically hire product managers from other telecom or large banks with three years of experience, we can start to understand that and target those candidates, as well as recommending them similar candidates that they may have overlooked.
On pricing and performance management, we saw Leigh giving short and much more clear explanations about the performance that we see alongside the cost that she's paying for the ad. And we think that's critical to give us the willingness to pay signals. But a core part of that is how we get the answer about performance, not just on reach, but also on quality. And so we have real-time data pipelines that are updating performance-used applications on the platform every second and a performance management system that can respond to those signals and manage performance in a much more adaptive way. So we've kind of put Leigh's ad at the top of the research and recommendations because we need to also just tailor to the experience. So we're becoming more sophisticated in how we manage that by channel and by user journey.
So when someone comes to the site and does an initial check, we show them all the relevant ads regardless of the ad series or single, and we re-engage them in the platform and get them coming back. But as the platform more and more is using those recommendation channels, that impacts about the target high-ticket candidates and hirers higher-yield. And then that ability to predict performance and display that to candidates and have effective pre-hirers and see them select those ad products is a really good way to scale it out so we can differentiate performance, create more value in a higher-tier ad, and then have confidence to be captured after three years. So those are key takeaways we've continued to advance and working on for a long time. And I'm now passing to Jesse, who's going to take us through the candidate screen.
Hi, everyone. I'm Jesse Stratford. I'm an adviser here focused on the candidate side of the business. And I'm here today to talk to you about Paul, one of the candidates that Leigh successfully recruited into. So Paul is a business manager with many years' experience in accounting and accounting industry. His availability has changed. He still wants to stay in business management, but he's hoping to move into the sports and fitness industry. And so I'm going to take you through Paul's journey and the good things to watch out for are the key, personalized recommendations with high-quality marketing efforts that are going to get you understanding Paul's journey. So we'll start at the beginning. This is Paul. He's looking for a new role in the fitness industry. He's already got the SEEK app, so he opens it up.
The first thing I want to talk about is Paul's expression of his interest. No longer is it too rare to know a person. Paul can express himself in natural language, confident that it will understand what he's looking for, and providing his already relevant recommendations. These nuanced contexts that Paul is providing us, he's already separating them out from other people. It's already helping us build a deep, personalized understanding of what he's looking for. We start this list and we're immediately retaining a number of what you've said. Paul can apply the traditional skills that you're used to. But we've also introduced AI-powered filters at the top of the UX journey, which allow us to get even more insight into what is the most capability that's clearly taken from.
As we go down through these roles, you'll see that each of them has an array of different badges. This one is the strong advertising badge, which we spoke about previously, where we compare Paul's fancy designs that we've seen for this role, based on how the ad advanced them and whether they have the right. Paul has selected this ad that has us primarily recommending to be more informed by the clients from the brand event and the ad management. He's interested in exploring the primary experience. There he goes. On the job ad, he's been clearly reading the information about the ad. Also here you'll see the competitive prediction and the role he's speaking about in the Ad Ladder. These advertisers have paid for an advance for three years.
But the ad that Paul is actually looking at is an ad in the nature of three years. And therefore, other advertisers give him opportunity to expose their opportunity to Paul, which gives them a greater chance of making a placement. Now, Paul could go ahead and apply, but actually what he wants first is a little bit more information. So he uses our short feature, which is live on the web app at the moment, and asks some personalized questions about the context of the event, how he responds, and he gives Paul the information that he needs, and that gives him more confidence that he's at the back end of the list and he wants to be included. So he'll go ahead and apply this with a few quick steps of simulation that we've done.
One of the most important things for candidates is to stay updated and know as soon as possible when they're going to be selected for an ad. You'll hear this all the time. Once they know that, they also want to understand how likely they are, what is the reason that they were not successful for the ad, if they were not successful for the ad. This uses a range of ensured and direct actions from the client's IS or our candidate management portal to keep the candidate advised as quickly as possible. Paul receives an email about his application. He goes in. Unfortunately, he logs into the manager's portal. We prompt and he expects an action to complete a section within his management portal. Maybe they've moved somebody else to offer. Maybe they've moved Paul to shortlist to kind of ensure the outcome.
In addition to this email that he's been unsuccessful, we also help him understand why that might have been the case. This is looking at how does Paul stack up against other applicants now. The role assignment question is similarly interesting. That's Paul having less preferred answers than other applicants and that managing the reason that he's been selected. We provide Paul with a way to continue his journey. Other highly personalized recommendations for the role selector is to his hands. Before opening the application, he ends up on a career page that gives him security products that Simon spoke about before. It's a kind of work rights type product. Every time you open the ad, your interactions will be updated on your recommendations to search. These are personalized features.
You can see here I think there's a number of recommendations that are highly relevant to what Paul's looking for. But he wants to provide us with a little more context. So he engages with our generative AI type experience, which is live at the 10% rate at the moment in order to provide us with more nuanced information about precisely what he's looking for. So you see the question being asked is, do you want to work in a large facility or are you looking to work in a smaller facility? Are you looking for a complement that's more high-speed? Do you want to be developing programs or delivering programs? What is the culture you're looking for? These are very personalized questions based on Paul's specific context. It's another candidate in town with a similar skilled basis that would get different questions.
These questions help us understand what's really important to the candidate in order to deliver those really personalized recommendations. They also ensure the AI ad writing type of scenario is speaking to the customer. The more information we get from candidates about what matters to them to see in the job ad and to determine which jobs are relevant. Our expectations here are to hire the fitting of that information in their job ad to enable that search. This is work with software happening. This is a recall conversation that's going to transcend all of them just to see what they can do. Paul can even provide free text and an answer field if we can deliver this as well. It's expected he's got the right to work already, which is obviously personalized.
We summarize what Paul has told us, and then we instantly respond to recommendations to respect them, and as you'll see, these are all different recommendations to the one before, and actually, Paul happens to be a candidate familiar with them, so that is not an accident. That's what I was talking about before, particularly in his recommendation channel. We tend to bank roles for which the candidate is applying to, closer to the selecting for that field that is relevant to what the candidate is looking for, because that ensures the higher likelihood the candidate's select value to roles for which they are interested, which increases our chance of that, without being inaccurate to the pricing, so Paul didn't see anything that he was interested in this question, but we've gone and we received. We've preferred both in our emails with our personalized recommendations.
Now, many of you will be familiar. We have lots of recommendations that I've sent you. You might get one a week that has 10 jobs in it but this is different. This is the example that we were looking at before, which was instant ads that were getting managed over the top so the top three positions are similar, and it was only available in four months and Paul is the highest advertising job ad. Paul has been contacting directly precisely to draw his attention to his ad. This email posts only that one link, which we got, was only going to Paul because Paul was one of the few high-fit candidates for this role so I think everything in that job clearly is everything in the right place.
So the four guys here weren't just for social skills and better self-directed support, but also why it'd be good support, and using LLM explainability and tracing LLM in the email to help understand why this role is a good support, so they're interested. They took on doing the ad. We ended up on the example of better selection. You can see here, this is an email I got from the night before. It's a new role. It doesn't fit his skills directly to him. And what Paul can do is he can go to every different job. But the first thing he wants to do is understand a little bit more about Paul's role. This is in a company we've heard about before.
First, he wants to talk to the people who do the company, take some of their pricing guides, and we say that we are verified with companies when we review a company. It's given the credentials of the company. He can go into the company review and see more information about the company. He sees that CoreSign has 10 reviews, 4.6 stars, 3.3 rated as a company, and these are reviews from people who've worked at CoreSign previously. We also provide an AI summary of the review so Paul doesn't have to engage with all 10 reviews in detail. He can if he wants to, but the summary provides him with the most information. Now, despite having the same old app, Paul wants more information about working at CoreSign. In order to get that, he can engage with our community team.
Many of you will have heard of SEEK Career Advice, Career Hub, the community productive part of the Career Hub offering. It enables people who are on the SEEK to ask a question of people in their street community. Paul jumps into the Career Hub to ask a question that goes on to our forum, and anybody else on the site who has been in SEEK has heard anything about anyone who has experienced with CoreSign. He could possibly be a better seller with a particular and is expected to make an information. You can see through that journey how we personalize the experience in a new curriculum based on his unique context. It's a fantastic result that he's got to be doing. We continue to keep him engaged through email, push notifications, even when he's left off to do some other stuff.
What really matters is pricing. We're next to the formerly privately owned business, and on the higher side of business, we prioritize pricing. Pricing is one of our key criteria, and hopefully, you can see how these range of products that we're offering, and that's just a sample that I showed you, are more likely to drive more pricing, and the other thing to quite compare is you can see on the left-hand side of the screen here, these products that we've been constantly improving and delivering over the course of the last several years have given increased visits, increased unique users, but importantly, we've given increased applications from those customers, so it's not just the more customers conducting the same number of applications. It's scaled from their applications based on the user base, more advanced to better personalization, and ultimately these are all leading indicators of their application. Thanks.
Is there an outcome very quickly? What you've seen in that experience is a continuous conversation with Paul, with Paul's questioning what he needs in the brand language, the understanding that, and showing relevant results. Then we're continuing to ask hard-context questions about what he's looking for. Underpinning that again is core AI capabilities. The first is a listening and understanding engine. You saw the example of just a reference about our ability now to understand these natural language queries, the client testing requirements. We can understand that background on quality and know that hirers talking about skilled sales or customer relationships might be a good fit for that kind of query. We're also leveraging our behavioral data and not just the LLMs to understand. Help Paul interact with us more about what he's looking for.
And this is an example of the call on the career feed where we're asking high-context questions from what we've learned about Paul, what we think the next most obvious thing we'd like to know to give Paul a better match. And potentially from the higher side of the roster, AIs are targeting a particular requirement from the roles we're showing to Paul. We can ask Paul if he has that requirement to make sure we're showing him relevant roles. And we're using that link from those 30-minute answers as efficiently as we can. In this world of more dynamic updating sequences, we also need personalized matching that. Insights and respond to that quickly and use that behavioral data to become a relevant case.
Simon mentioned we've been investing in this for over a decade, and we've seen really substantial gains year -on- year as the technology advances and we unlock new data, and with the technology trends we've seen every day, we see that opportunity continuing, so we've had talking outside this morning about the gains we've had from large language models and bringing that into our data sets to better understand this context, but then also combining that with the behavioral data, so we're building new algorithms to survey the crossover topics like LLMs which can fine-tune and train with the next bit of data with our traditional behavioral models and bringing together the best of those two worlds.
That's what really looks relevant from the results from bringing those two together, as well as using that real-time behavioral data to continue to adapt and think about the frequency that we send in chains. Where can we apply it? As you see what Paul and Jesse explained, being able to fix those management questions more regularly and telling us to engage more and allow people to work. The last bit is explainability. You've seen through the demo and how large language models can help us explain and speak in a really clear and understandable way in so much people's time. Using our real-time behavioral data to give a sense of velocity in the marketplace and where people need to respond.
The competition that's in the marketplace so that if you are applying for extra roles and waiting to see if you're going to be successful, if we know that you're not a perfect fit for that role, if we can give you that signal and encourage you to apply for more roles and get back into the marketplace, we increase the velocity of the marketplace and that results in more placements. So these three capabilities really come together to give that continuous first-rate experience that's continuously learning every time another candidate enters with our platform. And you see that through these things. I'm now going to pass you the floor to drill down on facts, and I'll come back and talk about what we're doing in the next few minutes.
Thank you, Jesse. And good morning, everyone. I'm Xavier Russo and I'm the General Manager of SEEK.
So I'll be talking to you today about customers and how we hope to make matches in the marketplace in ways that lead to engagement. When we talk about customers, we don't just mean they trust us on the brand of our platform. We mean they trust us on the other side of the marketplace, so from a candidate perspective, they trust us to make the right hire for the role that we describe, and from a hirer perspective, we do show real candidates that have been profiled for us, whether they have a genuine interest in the role, and also whether they have the skills, experience, qualifications that match what the job requires, and so trust, as someone in your role, you could bring over an architect, but we think it's particularly important in employment.
That's because it's a fundamental job of hiring someone with a fact-based decision. So there's very complex and nuanced information that can be hard to articulate. It also relates to ongoing relationships, not just a one-off transaction. That's why we can expect high-skilled candidates to use our strategy to be doing great. SEEK. As you've heard, we've been rolling these up in the space through SEEK, which is now the voice of SEEK Asia, the power of verification, and the KYC verification we've been moving at pace with quite a lot of our crossover tech. Let's resume the story as we had at the close. Your reaction. Here's Paul. As you saw before, you can see he has sufficiently long speech. In this case, he spoke to this particular ad with some people that don't know him.
And so, looking to choose, he realizes that this is a recruiter who does support this particular role. And from her first profile, you can see that the actual reputation is the most valuable in helping younger people find the right role. And we really like this idea of being able to connect with the real person and that it's a really good way to reach out and get in touch with young people on that role. And so this is the context. Firstly, we've got our recruiter reputation product that we acquired a while ago. We've got 4,000 users on the platform, and what they did is public profiles and raised to us customer reviews, which was the inspiration for us. And we just do host that reputation at the moment that makes us successful in the community that we're set up.
Paul returns to looking at more roles, and he came across, I think, that's what he sees here. Now, as Jesse has indicated, this is a brand that he is less familiar with, less comfortable with, especially with the sort of things he wants to do. Being able to get real experience that this phrase, such as our verification badge, looks like something new. This is a legitimate test of employer. Being viewed by the food processing safety team. It also seemed higher than it did for many years, but it's a pretty successful, very attractive role in the platform. As Jesse indicated, the way that raising the level can help provide some trust and confidence around the claims about what it's like to work there. Let's say, before he says the role, he'll reply later.
Before he replies, he wants to give himself the best chance of success. One of the ways that he can do that is by making a profile here and making sure it's up to date. He also sees that adding verification to his account later increases chances of being serviced. He sees he can verify his identity. In fact, the range of different credentials that he can verify including employment, education, and licenses and registrations for this particular unit strategy that we've got. We've launched there, and we're building that layer across APAC . That's two. The fourth is full powers to SEEK Pass. If he clicks across to SEEK Pass, generally to ask or use his own website, the SEEK Pass is our real career passport for us.
And so we have a secure way for him to verify identity experiences. And to figure out what the SEEK Pass like. Generally, to stand out for hirers. We've got over 2,500 users so far, and there's no one who's been here for over a year. So we're going to start with his identity as defined in the category. This is an important focus area for us. Identity is a key trend I've seen coming for a while, and all we are ever currently coming back to see that. And also, it's a great way of proving that we're really looking for a best-practice market. And particularly, as a strategy setting, the lowest thing that we're going to do is pay a high rate of mainstream and high-end community. Really important priority: identity. So Paul decides to verify his identity, and it's a nice easy process for him.
So he uses his passport to go to SEEK app. He checks out the background check. He then provides a LinkedIn profile so he's a real-life person, and we buy this agreement that says he is as functional as possible. And as he's been awarded, he's declared his credentials on LinkedIn and shares that with his SEEK profile to add credibility to that. And also in terms of his manner since he's quite articulate, which is a great thing. We really take pride in how he is. Let's just go over to verify his last work. So we heard about earlier how important it can be to verify early in the process to just understand. He's had a missing record right or he's a new apprenticeship. And one of the things we've seen to verify their work, right is to look through that initial screen.
So nicely, we test it, provide some information, provide a support document, we verify that it's brought to the government system, and so we draft it in the SEEK Pass verified and see if that's who's been profiled. I love all these variants because I've verified the methodologies and due diligence and the licenses, and that's a company, and people want to be a citizen that they want to follow. Now he returns to the bread-and-butter area with core form operation and reward, and we go through the process. He has a few screening questions before there. I just see how much work he's going to achieve in their possibilities. He's got his verified education already filling up immediately as he provides license, and he continues through. We've got those now. The identity verified to see if there's anything that's going to show up as well.
And so not only does his resume and career can match his user as part of that application, as his impact is profiled. And that includes the verified credentials that he's gotten. So we can then scan that and see that. And that profile is also visible across the entire search, which is our client historical information. And the more that we can get people out to us to update their profile, keep it fresh, and access to it through verification, the better the potential for us to test them. So Paul submits the application, and that's got his. So now he's able to stand up out there through verification. But he also has to figure his motivation. So he uses the Strong Interest signal here in the Graduate space to show that this is one of his work.
He gets three of these a month that he's been on SEEK since 2015, and he uses one of them in this year, 2020. That's a great way to not only stand out in his application to say he needs to refine his role, but that he's really interested in this particular opportunity. Typically, when he wants to stand out, great for hirers as well because they want relevant proactive candidates, but also want to generally gauge their interest in their role and their company. While a client's job is great, he's achieving his motivation. In a way, there is a bit more application to add, and also as I do think that makes it easier for people to apply in greater facility than it was in earlier.
And so we're looking for ways to have a sense of signals of motivation, and this is a great way to work that. That he's worked with this real credibility. This is also a feature that we wish to protect us. It's available only to candidates who have verified their identity. And so it's working out an extension for them to do that so they can see the real credibility of their identity proof. And probably even better, because we're obviously in the demo, if you have an existing value to verify. So that's an example of something you can generally unlock it with a reputation proof. How we shape the market role and the skills these guys intend to use is verification and delivery to the layer to enable the effective connection on our platform.
So Paul's done everything he can to stand out, and that's over the years to use the original application that he's built for in the lower section. And so you can see how, before Paul's application really stands out to me. Verified credibility, highly interested, meets the requirements of the role and things like that, stuff like that. So he came to us as a proactive applicant. And so we forward this to the team. And proceeds to interview. And we do have a great interview, and then he comes away just about convinced that he's the perfect candidate for this role, just about to make the work right. But before he goes there, he needs to make sure that he does a reference check. And this is now something that's completely on the SEEK platform. And so it's a good starting project.
Provided to him by a couple of employees. We go through an online written check process powered by SEEK. It's the result. Once it's available, then it's very natural to see if he can carry it down to it and so why does he suggest so like I said, we're trying to get a layer about automating a SEEK step in the reporting process to process the pricing. This is a great example of how it's much more modernized, also bringing useful data into the platform so it's been done in parts of employment, workplaces, business and also that's signaled that Paul is extraordinarily efficient with the ability to drive optimization of maximum efficiency. Also helps with their D&I so this is available right now as a free add-on and it's become essential for job ads.
And we think that we saw this failure coming soon in terms of how we make it straight up to us to find a way to move that D&I to help our application candidates. So we're inspired by our reference check, which is an open offer and first step. So we know how to successfully price things. So that's an example. Gives you a theory for how he could create a much more trusted and transparent journey for candidates and hirers. So we've got verification signals for recruiters and employers. Verified credibility to help candidates stand out. It's a new way to give you motivation and then see how we can help them process. So why is that important to him as an investor? Very much just about using a flexible way to SEEK instead of a tech platform.
So we've got the candidate relationship and the high motivation use cases to drive verification at really impressive scale. So they're not always in the client list on our client profile. That data scale then allows us to offer this safety net to hirers when they select them. So that can select with stronger confidence and efficiency than ever before. And that combination of candidates being able to stand out and hirers being able to select is what makes both sides of the market the first to use this. And so we've been doing this for quite a few years in Australia. We're doing it recently. That expanded a couple of years quickly. And so we've got that first turnover and looking at multiple opportunities. So it's not a distinct way. It's not going against that role of a candidate. It's deep locally relevant stuff.
So I think it's a very engaging to talk about how our organization plays in this.
Thanks, Xavier, so again, I'm going to talk particularly to some of those real AI things that we've seen here today that we're really investing into the future of where we think AI is going. Before I do that, I just want to reiterate what Xavier said about the importance of facts, and particularly knowing someone is a human, and that they're verified by our identity. Knowing that the data that they are providing is trustworthy, and not having information from a large library of symbols, and being able to put in motivation as AI tools to fit them out of the marketplace and automate processes. That is a really core capability for us to then adjust behind for automation and some of these new AI capabilities.
There are two that are really giving some of the excitement today, multimodal and agentic AI, that you're starting to hear a lot about. Multimodal, you've already seen us using OCR and image recognition to help people verify their credentials seamlessly with Paul and his passport selfies. We think there's opportunities here further, particularly around things like voice, which can help extend your needs really naturally and in quite part of the detail with less friction. That's a bit harder to do in written language. There's also really interesting technology and capability coming out around image recognition, where you can take a photo of a woman and get a sense of the company's workplace culture or occupational health and safety compliance through imagery, which when we come to agentic and automating processes is really interesting opportunities for us.
On agentic, this is still very, very early, and we've seen us with a lot of opportunity here in the future. We've seen us working to assist processes with things like AI add-on and finalizing information for candidates. The next step of that is, can we automate entire processes? And an example of that is the likes of examples that Simon talked about, but on our platform, can you also have to link out into quite comprehensive employee employer application forms? That AI filling can extract out of that process because it's quite complex. If we could automate that whole thing process as one simple tasking example, it increases conversion for hirers and improves the candidate experience and keeps them engaged. And that keeps them on the SEEK platform engaging with our opportunities. So that drives conversion and higher placement and keeps candidates engaged.
So we see a lot of opportunity in automating specific tasks and building out capability in agentic, which then allows us to start tying some of those processes together and looking at entire workflows and how we can pull friction and costs out of those workflows. We're also in very early experiments with agent-to-agent prototypes, which can help us connect with other agents and other systems in the value chain more seamlessly. That allows us to get more of the value of SEEK data that we're seeing today into other systems and have a bigger impact on the broader value chain. So we see a lot of opportunity in this space. As I said, it's still very early, but and it can feel a bit theoretical.
I wanted to leave you with a demo of some of the R&D that we're doing in one of the pilots we've got around using multimodal and agentic to automate reference checks with voice. This is a pilot. It's not in production like the vision we've seen here today, but it is a real working system and a live conversation with humans.
Hi Jane, thank you for taking the time to speak with me today. I'm here to collect a reference check for Mona Lisa. To start, can you please tell me about the key responsibilities Mona Lisa had in their role? Sure. She took customer inquiries and was involved in resolving complaints. Okay, great. And in your opinion, how would you rate Mona Lisa's overall performance in that role? Okay, great. I think I have all the information I need. Thank you so much for your time, Jane. You can modify the answers in the next screen if you want to. Have a great day.