Unleashing the Power of Data with Greenhouse

Speaker A: In this digital first world, the old ways of recruiting are becoming obsolete. Or are they? The talent demands on every business has put TA squarely in the hot seat. Welcome to Talent Acquisition in the Trenches, a real dialogue podcast with talent acquisition pros closest to the front line. We want to talk to our peers who are actually doing the heavy lifting day in and day out. You're going to learn what their biggest challenges are and how they're being solved. I'm your host, Matt Reimer, and I'm here to talk about TA. I've been in TA for over 20 years and what I know is that I don't know. I'm here to listen and learn, just like you. No scripts, just real dialogue. My friends call me Reimer. So, friends, let's create some new riffs with Rimer. Thanks for trenching in to another episode of TA in the Trenches Live. I'm your host, Matt Reimer and today we are going to talk about unleashing the power of data with Ariana Moon. Um, and so she is the, uh, recently, uh, new mom, uh, of a young boy, which we just learned a little bit about. But she, uh, professionally is the VP of Talent Planning and Acquisition at Greenhouse Software. Greenhouse, uh, if you haven't heard of them, they're a leading cloud based, uh, ats. And she's a key member, uh, of their senior leadership team. Uh, she is, uh, the mastermind, if you will, behind, uh, behind aligning all things talent strategy across the greenhouse, uh, ecosystem. And so, with a decade of experience, deep, uh, practitioner knowledge, a Harvard degree, uh, she's a pro bringing some serious operational skill, uh, to us, uh, here today. I'm also excited to have Ryan Efelter back here. Uh, he is rogue hire's go to expert for all things ta, Traction, recruitment, marketing, employer, brand, HR tech. Uh, so Ryan, uh, as co host today, what are we, uh, hoping to get into? What are we hoping to learn Today, uh, from Ms. Moon here?

Speaker B: Yeah, thanks, Matt. Uh, welcome, Arianna. So we're hoping to tackle some critical challenges and share a little bit about how a data driven approach, uh, focuses on candidate intent, uh, and how automation builds trust. So a few things, benchmarking and management by data to solve misaligned expectations, uh, how data cuts down process time from first interaction to hire, uh, building that high touch candidate, uh, experience with personalized interactions addressing bias in both ethical and uh, competitive situations, and strategies for transparency, and how AI and candidate intent data can kind of turn that doom loop into stronger connections. So thanks for having me here today.

Speaker A: Yeah, uh, well, we got a lot, and I think we've got, what, half hour, 40 minutes. And so we'll dig in, I guess. Ryan, as you're kind of going through that, uh, the one thing that popped, um, to me was expectations. And so I guess, Ariana, maybe we'll kind of lean in there. And so when you hear about, um, misaligned expectations in today's talent market, um, you know, as it relates to maybe candidate experience or even hiring manager experience, whatever that might be, what does that mean to you? So, like, when Ryan says misaligned expectations, where, what does that mean to you?

Speaker C: Yes, great question. Um, the first thing I'm reminded of is a quote I heard from Fortune magazine not too long ago. And, um, it was talking about this misalignment. So the quote goes. There's major misalignment in the hiring landscape right now because while applications are abundant, employers say that it's becoming harder to find and hire the right people. And I think, you know, a lot of us in the recruiting space are experiencing a version of that. So. Said another way, um, this was another headline that I saw recently. You know, young people are using AI to write their applications, HR is using AI to read them, and no one is getting hired. So that's a very pervasive problem right now. I think that's somewhat industry agnostic when it comes to what recruiters are facing, um, in the current market. And so I can break that down a little bit from our perspective, like what is happening on the candidate side and what we see is happening on the TA practitioner side. So on the candidate side, when it comes to job searching, um, the thing that we're hearing a lot is candidates feel that the job search right now is somewhat soul crushing because it's hard to see past, you know, standard employer branding, and it's hard to know which companies are worth applying to, um, especially based on, you know, whatever unique skill set you have. And there's a sense of being exhausted by the need to apply to so many jobs, um, and candidates feeling like they're being reduced to a number of and that their applications are falling into some sort of black hole. Um, so that's kind of the stuff that we hear on the candidate side. And then on the employer side, um, you have these recruiters and T leaders that feel absolutely inundated with applications, especially as more candidates are using AI to do things like mass apply. And in many instances there are false actors that are submitting fraudulent resumes and identities, which is like another thing that we can Dig into later if you want to. Um, what this is resulting in is, um, it makes it increasingly difficult to identify who's the right candidate for your role. And it's leading to recruiters feeling overwhelmed, especially as they continue to feel pressure from their organizations about like, do more with less, be more productive. I think, like, we're all used to hearing that at this point. Um, and so in a nutshell, like the thing that I see is, you know, there's all this excitement around AI. There's also this, this hype around AI is going to revolutionize the way that we work and make us all incredibly more effective and efficient. And at the same time, the thing that we see is AI is also complicating things. It's actually sometimes having the reverse effect. Um, even on, um, the employer side, the right AI tools can be really hard to identify and it could take a lot of rigor to adopt. Um, so while sure it's great to use ChatGPT or Gemini to help you write a job description here and there, a lot of the times recruiters aren't clear yet about how to use AI in a way that is like really moving the needle on their effectiveness. So that's kind of a dynamic that we see and that we hear from our customers and for job seekers. Um, that to me is the thing that, that's driving the sense of massive misalignment.

Speaker A: Mhm. So there's a lot of different directions I feel like we could go here. Um, I think the first direction that I'm interested in learning more about is, um, you know, and so since, you know, the role that you have, as I understand it, at Greenhouse, you have some maybe product, um, influence. Um, can you talk to us a little bit about um, and I think Ryan had said like AI doom loop as well, which I want to learn more about that one, that idea. But can you tell us a little bit more about uh, how Greenhouse as you know, a major ats, uh, among men, you know, among many options inside of the, uh, inside of the EcoSystem is viewing AI and specifically like where within this process that is, um, uniquely designed to historically reject candidates. Where am I supposed to be? From your point of view, using AI to get scale, create efficiency, so on and so forth, and then maybe the reverse of that from Greenhouse or from the product perspective there. Where are some spots where you're like not yet or m, we're not going to use AI here. Can you talk to us a little bit about that?

Speaker C: Yeah, there's a lot of different ways I can Also go with this answer, because that's a meaty question. So let me see, where should I start? Um, I'm kind of anchoring to one of the. You had one question in there, around, what is Greenhouse doing? Um,

Speaker A: in.

Speaker C: In the moment of the candidates being rejected. Um, because obviously that's a very disheartening part of an interview process.

Speaker A: Sure.

Speaker C: So there are some, There are some interesting numbers that kind of come to mind, uh, when I think about that topic. We did, um, a study not too long ago, a few quarters ago, and we're looking at, you know, how many. We're kind of discussing the topic of ghost jobs. And when you ask someone the definition of a ghost job, um, you might have very different definitions that come up. But the way that we were categorizing ghost jobs, um, in this study that we conducted is if, for example, let's say a job is posted on an external, um, job board during the quarter and it's received an application, but no hires have been made within that quarter or there hasn't been any hiring activity. So it's basically something that's posted, but there isn't a lot of activity happening.

Speaker A: So ghost job is like, it's posted, but it's not being recruited on.

Speaker C: Right. Like, not.

Speaker A: Like, not it's out there, but it's like nobody's really moving flow through that type of thing.

Speaker C: That's right. And so what we saw was, um, when we looked at our data, we saw that 31% of rejected applicants didn't even receive an email to notify them that they had been rejected.

Speaker A: So no automation upon rejection.

Speaker C: Yes. And like, and table stakes, which is communication. Right. Because, like, when you put in the effort of applying to a job, the least that you would want is some sort of communication around, you know, what your status is.

Speaker A: Um, one in three candidates in this study. One in three didn't even get a rejection. Like, hey, this job's closed. Sorry, we're moving on.

Speaker C: Correct. And that was close to about 14 million within that quarter.

Speaker A: Um, if I remember, that was an N of 14 million. A total N on that study of 14 million. So a third of them didn't get any communication.

Speaker B: Wow.

Speaker A: It's a big study.

Speaker C: And we saw during that time, um, there were close to 20% of jobs across our customer base qualify as ghost jobs in any given quarter. Um, and so, like, I share some

Speaker A: of those hundred jobs. I want to make sure everybody is hearing you correctly. So of a hundred jobs posted. Right. 20 that are out on the Internet, somebody can apply to them, so on and so forth, actually aren't moving. Like they're not moving in step status, they're not moving in applicant flow, they're basically idle.

Speaker C: Correct. Um, and again it goes back to very specific definitions of the amount of hiring activity, uh, that is happening within those jobs within our customer database. And so I share the those numbers not to necessarily paint the picture of like employers are, you know, intentionally creating bad experiences because I actually think that there are, uh, there can be multiple reasons why a company is posting a job, but there necessarily isn't that much hiring activity in it. So for example, I think one really clear thing that we started talking about at the top of this call is recruiters are facing a huge surge in applications right now. So what we saw back in the end of last year was that recruiter workload was up about 26%, um, year over year based on like how many applications that they were seeing. And so app review, which is like something that used to take X amount of time, is now significantly a lot more time because there is this influx of applications that is coming in and that is the experience of a lot of the recruiters on our platform. And so that, that can be a reason why some jobs are not being worked on. It's simply like you have these really well intentioned recruiters who want to do a great job and who care about candidate experience, but then they're not necessarily set up for success, um, or you can have outdated job descriptions that are listed again, there might be no nefarious intent, um, but it will contribute to that ghost job number. Um, and sometimes companies use agencies and agencies will post the same role in different places and that can lead to more job ads than an actual opening. So there are many reasons why this might be happening. It's not always some, it's not always that, you know, a TA person on the employer side is not doing their job, um, in the best way that they can. And so like, that's, that's one thing I just would just want to paint the picture of because I think there can be a lot of frustration on the candidate market around like, oh, like this employer like is never getting back to me. But the challenge is on both sides of the equation. So um, that's kind of one problem statement that we dug into somewhat. Um, and so I can go back to like your original.

Speaker A: Tell me though, tell me though. Yeah, I want to know like, all right, so if that's a problem statement, um, tell me, is there a solution to that problem in using AI to counterbalance some of that for you know, some, some way within the product. Like is there innovation out there to kind of deal with that or is that just going to be like the state of affairs for a ah, recruiter sitting, dealing with this um, particular challenge?

Speaker C: Well, I think that, well that should be the state of affairs. So we've been working hard on trying to create solutions to address both sides of the equation. Both on the candidate side and both on the employer side. So I can talk about both.

Speaker A: Tell me about that.

Speaker C: So let's start on the employer side.

Speaker B: Mhm.

Speaker C: So one of the things that we've rolled out earlier this year is an AI powered solution called Real Talent. I'm not sure if you're familiar with it, but.

Speaker A: No, I'm not.

Speaker C: We're pretty excited about it. And so what it's meant to solve is this problem that people are mass applying a lot of the times the generic or auto generated resumes. Um, and what we saw was, you know, we're seeing an uptake in um, false candidates or duplicate submissions, um, in some cases just full on impersonation. And um, you know, research firms like Gartner for example predicted that by 2028 a quarter of job applications, applicants will be fake. And so this is definitely, this is, this is very much a problem that a lot of recruiting teams are faced with today. It's not a hypothetical problem.

Speaker A: It's literally what if a fake applicant gets uh, uh, contacted or reach out to. It's just a dead, it's not a real applicant. Is, is that what you're saying? I'm trying to understand that.

Speaker C: So it could be people impersonating, you know, uh, an identity that nefarious.

Speaker A: Nefarious actors or whatever kind of like inside of the inside of. I'm just trying to understand like all right, so somebody creates a fake application, they put a fake application in. But why to get a fake job? Uh, you know, I'm just trying to

Speaker C: understand that that has actually happened. We've, I um, know customers who've had people show up for their first day who, who are not the people that we're interviewing. This is something that is actually happening quite commonly at this point.

Speaker A: And so where the fraud piece of this is kind of coming in here somehow.

Speaker C: And so you, you nailed it. So Real Talent is an AI powered solution that has three aspects to it. And uh, the first one is fraud and spam detection. So um, we're building a functionality so that all of our customers have the ability to understand who in our applicant Base is, um, being flagged for duplicate data, suspicious patterns. Maybe they say they're in one location, but they're actually dialing in from a different, uh, country. So that fraud and spam detection functionality is meant to protect our funnel, our recruiting funnel from, um, pulse applicants. And so that's kind of the first, the first piece of real talent. The second piece, also AI powers, is what we call talent matching. So this is now going deeper into the recruiting funnel, kind of like that first touch point with the recruiter. And what talent matching does is it surfaces, um, candidates that are, uh, the best fit based on skills and job match. So, you know, a recruiter can input, um, a combination of keywords and at the application review stage, work with the technology to basically have a subset of candidates that are best qualified or suited for their job based on how the job description is created or, um, described. Um, so that, you know, app review isn't necessarily a chronological thing where you have to just review the apps as they come in. You can prioritize reviewing the ones that are the best match for, um, your job. And then the third piece of real talent is, um, one piece that we think is incredibly exciting and a very innovative partnership. So it's identity verification with clear. Um, I'm not sure if you're familiar with clear, but as kind of the counterpart to taste tree in the airport world.

Speaker A: So I don't have clear. I do TSA PreCheck, but I always wonder, should I do clear? I travel a decent amount, so maybe, maybe I'm going to learn that here as we go through this. So.

Speaker C: Well, if you apply to a greenhouse job, you might be using clear in a different way that you never thought.

Speaker A: You never even thought it. So maybe double wham. So I should get it so that, um, I'm all clear to apply to through a greenhouse job. I love it.

Speaker C: So it's basically, um, using clear to confirm the applicant's authenticity and reduce impersonation risk in the interview process. And so if I were to, like, take the whole airport analogy to, like, another level, the, you know, fraud and the spam detection piece is like the bag scanners. Like, they're the bag scanners at the airport. They'll like, catch the things that you shouldn't have in your bag or, like, suspicious items, like, before you reach the gate, right? And then you have, um, talent matching, which I talked about. And you can think about it as like, oh, it's like the pre check line where the qualified travelers can go through the fast lane and, you know, things are more efficient in that way. And then the clear piece, you can think about it as like, okay, it's that final ID check before boarding so that we can be sure that the person um, walking onto the plane is the name on the ticket. And so that's one piece that we're really exciting about. I'm actually like in live betas with all of that with my team right now because at Greenhouse, obviously my internal recruiting team, we try to be the best users that we can. So that's on the employer side, like how we're thinking about helping address this problem that recruiters have. Uh, now go ahead.

Speaker A: I was going to ask Ryan, um, you know, as he's kind of sitting here listening to this, if this is uh, resonating or if uh, we're kind of like making it down the right track here of what we needed to make sure this ah, session became. Um, I see Lisa has a question, uh, is clear what LinkedIn verification uses. And I think the answer to that is actually yes, I think she's right about that. Um, that's the same technology, the same underlying technology that you all are using. And so it's interesting, like I, you know, just until this conversation I didn't really understand how many different uses that that underlying technology had.

Speaker C: Yeah, I think the distinction between the way we're thinking about clear and is, you know, on LinkedIn, if someone is verified through clear, there's like a check. I am a real person and it really serves like the real people. Um, the clear functionality on our end in the hiring process is not just a one time thing. It can happen, you know, before you get on the phone with someone or before someone shows up for an on site interview. Um, there could be multiple touch points. So it instills continuous confidence throughout the recruiting experience. Um, and it's really not necessarily meant for the real people. It's meant to catch, you know, the bad actors or the fraudsters or the people that are impersonating. So you know, for folks that are authentically applying and you know, which is the majority of people that are genuinely looking for jobs like it's, it should, it's not necessarily built for them, but it's built for the teams to filter out the folks that you don't necessarily want to spend time with.

Speaker B: Yeah, yeah, I had a quick comment so thanks Arianna. Uh, if I'm following correctly then, uh, also kind of the core issue is that many organizations are adopting AI to kind of filter out volume rather than to kind of prioritize and personalize the high intent candidates. Is that the kind of idea of the doom loop? And uh, what we're talking about is kind of the path out of this, right. Pivoting from the uh, volume processing mindset to that candidate intent first strategy. Right.

Speaker C: And that's really what our talent matching feature helps with. Like how do you, if you get a thousand app, uh, if as soon as you post a job you get thousands of applications and let's face it, that is the reality of a lot of recruiting teams. Like what do you start and how do you know, you know where you should focus your time. And so there is a lot of technology emerging right now around helping recruiters with that type of funnel. And we're focused on that space too with our talent matching feature to again like make the application review piece, which is the top of funnel piece, be a little more strategic so that um, you're spending your time with the subset that you actually want to. So um, I want to talk about the candidate side of this problem because I think that's equally as important. And I don't see a lot of as many companies focusing on the candidate experience side. And we really invested a lot into that at Greenhouse and internally. That's a huge part of our ethos too as a recruiting team. The candidate experience is something that we always try to be as white glove as we can around because we're recruiting software company, like we should get this right. And at the end of the day, no matter how great your technology is, we do want to preserve that human touch because people want to be inspired to take jobs and generally are excited to work with the people that they're going to be joining on a team. Um, and I think that remains true, uh, in the world of AI taking over seemingly all of our workflows. And so we do try to preserve that as much as possible. How do we use technology to do the things that are repetitive, that are super high volume, that maybe are very task oriented and how do we preserve the human for the experiential pieces of listening and strategizing and consulting and conversating and dreaming of things together? So, um, one way that we're tackling this problem is we recently rolled out what is called my greenhouse jobs. And so what this is, is it's basically a central hub for job seekers to explore and discover, uh, roles from thousands of leading companies that are our customers around the world. And there are specific functionalities within this portal, this, my greenhouse portal, uh, for the candidate. So a candidate can go. If a candidate has A my Greenhouse portal, they can go in and you know, they can subscribe to something like job Alerts. So uh, what this does is this allows job seekers to subscribe to receive automated alerts about a new, about new jobs at a company that they might be interested in. So, so if Matt, you were like, I'm interested in Greenhouse and I want to get a notification every time Greenhouse posts a role, then you can do that through my Greenhouse and you'll basically get a digest that's shipped straight to your inbox, um, whenever Ariana's team posts a role. So, um, that way relevant new job opportunities are kind of neatly packaged in either a weekly or a daily digest for the job seeker. Um, in a way where they're not necessarily searching. They can be proactive about. I'm interested in these companies and I want to know, I want to be alerted when they have job opportunities. Um, another really cool functionality of that is what we call dream job. And we think this is kind of like a first of its kind feature. And what the dream job feature allows job seekers to do is uh, they can mark one application per month as their top pick. Like, I would love to have this, this would be my dream job. Um, and that would actually notify the hiring team of their motivation for the role. And so internally what this helps them, helps the candidate do is um, stand out as like, hey, we are, we are people that are motivated to really, um, to join your team through this particular job and increase their likelihood of having their applications considered for the role. And so, you know, a day to day conversation that happens on my team is like, oh, hey, we have, you know, this many candidates in our funnel right now and actually X amount of them, you know, had selected Greenhouse. This particular job is their dream job. So we call them like dream job candidates. Um, and then that way there's a little bit more signal and communication happening through technology, despite all the noise, um, through the mass applications on the talent market, um, through these kinds of features. Um, and I'll give you another really basic one that again, I think should be table stakes, but not necessarily all hiring software companies do so. So another thing that we enable our candidates who can access the uh, Migraine House portal is they have a quick apply feature. So what that basically is, is I'm sure there are people on this call who have looked for jobs and every time you go to a career site you start filling out your application, you have to do so from scratch every single time. And I know we've heard frustration from candidates of Especially candidates that are applying over and over again, which is a very common phenomenon that we're seeing. Um, it's really tedious and to have to input the same information over and over again because you're applying to different companies. So what QlikApply allows you to do is basically um, autofills relevant application form fields, um, because it's already captured in the MIG Greenhouse portal through the user, through your user profile and you can, you know, simply click on the application field. Now I will say that that is, that is exclusive to Greenhouse customers. So our customers are a community of upwards of 7,000 companies globally, um, with some pretty awesome brands that we're really proud about. So I just want to clarify that this is not for every single job posting out there. It's unique to our customer community right now. But our mission is to make every company great at hiring. And so as more companies use our platform, our hiring software, then the dream and the hope is that uh, as that community of customer grows, then a portal like My Greenhouse will be far more far reaching and powerful. So that's kind of on the candidate side. Oh, Matt, I can't hear you. Ryan, can you hear him?

Speaker A: All right, uh, how about now? Good.

Speaker B: Yep.

Speaker A: How about now? We're good. All right. Uh, I was wondering um, kind of as you were talking a little bit about you know, the candidate um, behaviors and I was wondering if Greenhouse has any maybe data or leading indicators um, around this. But one thing that and I have two girls, we had talked a little bit about this. I have 17 year old and I have a 14 year old and um, I guess watching them use their um, devices, their social media and kind of how that um, for that generation is extremely intertwined into the social fabric of how they go about their day to day lives. Um, I have a couple of theories, one of which is I don't know that that group's going to be so concerned about engaging with AI or you know, bots at certain junctures like that. It's kind of like not. And I'm not saying it's normalized, I'm not saying that they don't want personal connection. But I feel like it's just part of the tech that they're growing up with and so they're just accustomed to it. And so I'm just, I'm like super curious about um, you know, as that group, um, and probably you know, kids that are coming out of college right now as they begin to engage in a job search, um, is it going to be traditional like we experience it today where I post a job, that job needs to be applied to, so on and so forth. Or is there going to be some different, um, expectation that that group has as it relates to, you know, to your, to your point around speed or to your point around convenience or so on and so forth that um, is going to maybe accelerate, um, tech companies to think a little bit different about how they engage humans, um, uh, as things progress, um, from an innovation perspective.

Speaker C: Yeah, I think that's a very important question. I read recently this um, comparison that was compelling, which was uh, you know, it took Netflix a million three, uh, to five years to reach a million users. It took Facebook meta 10 months, about a year, and it took ChatGPT five days. We're in a different world right now when it comes to technology adoption and the expectations for people to be tech savvy and what that means. So that like the whole tech savvy piece has, you know, its own implications in what the workforce now, you know, requires of people. But you know, in thinking about Gen Z, I think that's a very interesting population, um, because technology and humanity isn't necessarily like mutually exclusive things to them, right? Like there's, and it's not necessarily, um, contained to Gen Z either. Like how, how many of us, you know, know, organize our day to day through our phones, whether it comes to grocery delivery or you know, meal services or communication with the people in our ecosystem or what, what have you. Like, we can do so much mobile and we can do so much digitally and that's just um, what are, that's just the human, you know, experience these days. So it's not necessarily like the human is independent of that. Um, I think there's some interesting phenomenon around Gen Z, um, and I tend to hear conflicting things. So it depends on which company, employer, person you talk to. Um, and I think part of one of the things that I've heard is on one hand you have Gen Z that has very high expectations for companies to have smooth processes when it comes to using technology, with having a good tech stack, with doing really great asynchronous work, because we can do a lot of that digitally. And at the same time there's also an emerging narrative I hear around, hey, we want to be around people, especially for folks that are early in their careers. Um, I personally have a mindset that I learned the most early in my career when I was sitting right in between the CEO and my manager and I literally had a headset and was taking calls on the open floor Plan because I could hear everything around me. I had real time coaching, um, I had real time intervention. Um, you know, I, I was able to partake in conversations or be fly on the wall that I normally wouldn't have simply because we're physically all there in the space. And is that necessary for me to do my job today? Not necessarily. I absolutely adore remote work and Greenhouse is a remote first committed company, especially as a caregiver and a parent. Now that means volumes to me. But um, I still believe in the value of getting people together. And I do so together with my leadership team and my team, you know, on a quarterly biannual basis because there is nothing that can replace, I believe the uh, in person interaction and the expedited exchange of ideas that happens when people all sit around at a table. And the learning that happens, uh, unplanned learning too. And I think that's part, that's one of the most important pieces. When people like if me, you, Ryan, were sitting in a room for a day, we'd probably talk about way more than the topics of this podcast and seem to be very unplanned in and learn a lot more about the dimensions of each other. So going back to your question about Gen Z, like I do think that it depends, you know, how a company caters to who they're trying to recruit. Depends on what, who that pool is, what they care about, you know, maybe the ethos of the company, the product of the company. Um, but I do hear that, okay, while that generation is like super technically savvy and advanced, I tend to hear there is a growing appetite for. We do want, you know, really great mentorship. We want good management. We want, you know, opportunities to actually interact with real humans too.

Speaker A: Love it. Ryan. I know we got like uh, maybe under 10 minutes uh, here. What's, ah, one last question that maybe we didn't get to, that we should have got to here with uh. Arianna.

Speaker B: Yeah, uh, I'd like to kind of lean into uh, the data side a little bit and how data can cut down process time from first interactions. Uh, so in a candidate driven market, uh, is the goal of data driven ta to simply be faster or uh, is the value in using data to be more intentional at every touch point? So kind of leaning a little bit more into that uh, AI and hyper personalization.

Speaker C: Yeah, I think it's both honestly. Um, because finding great quality candidates doesn't matter as much if you deliver on hires too late to have meaningful business impact. And on the other side like moving Fast is valueless if you're not getting good signal, meaning like candidates who are actually potential great matches for your job. So, um, you know, some of the things that come to mind with your question about data is maybe there's two parts to my answer. There's like the stuff that Greenhouse is building or offering customers that help get the data in the first place. And then there's the stuff around like, okay, well you have access to the data. What are some of the outcomes that you can see? So I'll start with the former. Um, you know, one of the things we've been working really hard at is something we're rolling out called Greenhouse Analytics. And um, this is kind of like the next evolution of our reporting functionality. So traditionally our reporting functionality used to be designed around like predetermined questions, things that we assumed recruiters would be interested in, um, you know, when it comes to pipeline health and conversion rates, et cetera. And um, what we realized that is because our customer base is so broad and so varied and so diverse, different customers have very different starting questions. And so we built Greenhouse analytics, um, based on that feedback, um, so that we're not necessarily trying to predict the questions that are important for them, but our customers can design the reports based on what questions are most important, um, for their day to day. So we were evolving our reporting functionality to be dynamic enough for today's very fast, quickly evolving, um, and very data driven orgs. So the four pillars that we had around designing, uh, Greenhouse analytics were data flexibility, data availability, um, data exploration and historical data. And I can like kind of go into each of those. So when we say data flexibility, it means you have the power to slice and dice in the way that is best, that best works for you. Like no one, you know, no one on the Greenhouse side is assuming that they know how you want to do that. Um, data availability is you can do all that slicing and dicing because we are bringing more data from Greenhouse, more live interview data as it's coming in. As you're recruiting to misrecruiting data directly into reporting. So more accessible. Um, the data exploration piece is helping customers understand your data. So if you have that ability to slice and dice, you have access to the data, then you can explore more like, hey, like what trends are we seeing or what are the new opportunities that are being uncovered? Because we're like seeing, you know, a trend line going this way or trend line going that way. Um, and all of that, you know, is built on the foundation of you need access to really Good historical data, which sometimes is a big blocker when it comes to complex technologies. Like you can have the shiniest new features, but if you can't access um, historical data, that's critical then like, how do you even plot a trend or how do you even know that the trend is accurate? So we're working on unifying this into a singular experience where recruiters can basically recruiter or hiring leaders can drag and drop different ways they want to slice and dice, um, or report to be able to power some insights. So the other, the other thing, ah, this might be a little, getting a little bit technical is we really believe in transparency, um, in terms of uh, like what is how we build things and how we do things in our platform. And so um, within Greenhouse analytics, users will be able to access the raw SQL, um, uh, for the power user. So you can actually see what are the queries that are being run on the data for full transparency to know how it's being pulled into a report. That's something that we thought would be really interesting for the analytics nerds out there. Um, that's one way we're tackling solving it. When you have a lot of this data, then you can start reporting on things like um, you know, interviewer calibration. Is there an interviewer on the team that is always giving strong yeses or is always saying definitely not on people? Um, and what is that resulting into? You can, you know, report on things like interviewing, interviewer load balancing, which is always a pain point when it comes to scheduling. Like Matt is taking 3x more interviews than Ryan on a weekly basis and now we have the transparency to just see that on the drag and drop in a report, um, we can see how fast things are taking to be scheduled when it comes to interviews and stage transitions, et cetera. So when you have a lot of that data, you can uncover a lot of the trends around speed to your earlier point and quality. Um, so I'll stop there. I mean that was a good amount of stuff and I know we're kind of coming close on time but there's,

Speaker A: it was a great amount of stuff and I think it was a very, um, you know, maybe even a very important, you know, kind of place to land. You know, I like what you said about customers all have very different starting points. They also have very different, um, you know, past experiences as it relates to data and their relationship, um, you know, with data, their data literacy, whether or not they're comfortable with, ah, a spreadsheet or not. And so that flexibility, I Think, um, for um, you know, a modern day, uh, data platform to be able to um, simplify insights such that um, and I believe actually this is a place that um, AI and machine learning can play a significant role. Um, these data sets are not light data sets. These are data sets with a lot of different points on them. And um, I think a lot of different quote unquote rabbit holes or inferences that you could create or show up with. If you can't, um, feel that you've got to look at the entire um, you know, kind of slate of what is going on. And so I do believe that this is a spot where um, you know, organizations that do this right, can make data easy. They can give you insights that you otherwise never could have found. Even if you chose to spend 8, 10, 15 hours on it, you would have never been able to find them. Um, you know, and that are valuable and that are high leverage insights. Um, you know, and so I think sometimes our relationship at some level to your point, I think is very surface level. Like, hey, you know, are the, you know, how many counts of this, how many time to fills of this, how many of this, you know, so on and so forth. But we do live on a very rich, um, most of us live on a very rich data set that just hasn't been mined and cultivated and curated so that we, the user.

Speaker B: Right.

Speaker A: Has been designed well for the user. We, the user can sit in the cockpit and gain confidence, um, over kind of that domain that we're responsible for. And so I thought it was a very important rift. And so I know we're at time. Um, Ryan, um, uh, is there any kind of closing thoughts here? Ariana? It feels like we could talk to you for hours. Um, you know, you're, you're very, uh, a, uh, deep knowledge base here from a product and a practitioner standpoint. And we appreciate you coming in and having a conversation here with us live. And then, you know, obviously for the podcast listeners, uh, that will hear this when uh, we post it.

Speaker C: Yeah, I say in closing, I just want to like encapsulate the data piece because, um, I think data is great because data leads to education. But what you said about insights is really the important thing because insights lead to action and action leads to meaningful change and like actually meaningful outcomes. I think the data driven gets thrown out a lot and people love fancy charts and fancy reportings and visualizations. The thing I always go back to is like, if uh, someone asks me for a report, I always ask why? What problem are you trying to solve because I find that a common pitfall for leaders is they just want to see the pretty thing. They're just like really interested in seeing the trend. But what are you actually going to do once you are educated around what is happening? Do you even have the bandwidth to do it? Is it a priority to you? So before you go really deep into data and start poking around and slicing and dicing everything, like, start with the question of like, what is the problem you're trying to solve? And that's the thing that I would want to leave like all the listeners with.

Speaker A: It's good, good place. Ryan, close us out.

Speaker B: My final quick thoughts are really, you know, as, as I've been listening in, you know, how are we truly tracking the right metrics around that candidate intent and candidate experience and how we're prioritizing, you know, quality over speed in some, some instances. So as, as we continue on, I'm really interested to learn about how we elevate metrics like candidate drop off rate at key stages, candidate sentiment scores, hiring manager to candidate alignment, things like that, where I think AI can really, you know, cut in and um, you know, cuts down that cognitive distance between, you know, what we think we're doing right and what the candidate's actually experiencing when interacting with our, with our jobs.

Speaker C: I think the Canada drop offer is a really interesting topic. I won't go there because we could spend another hour there. But that's a really important thing about,

Speaker A: hey, maybe we'll grab another hour with you down the road. And so again, uh, just on behalf of uh, the TA in the Trenches podcast and rogue Hire, Ryan, uh, and I, uh, we thank everybody for joining and uh, listening to this conversation, uh, with Arianna Moon, if you're interested in learning more about her greenhouse, so on and so forth, uh, don't hesitate to reach out. I'm sure you can find her on LinkedIn. Uh, and uh, Arianna, thanks for stopping by.

Speaker C: Yeah, thank you.

Speaker A: All right, we'll see y'. All. Alright. We want to thank you for listening to TA in the Trenches. We are produced by Iron Mike and his team at Ironbound Media. Keep up the great work, team. Please subscribe to the show on your favorite podcast platform. You also can find me out on LinkedIn where you'll find quick show riffs. Feel free to ping me. I always respond. Bye for now.