Welcome to the Use Case Podcast, episode 234. Today we’ll be talking to Jay from Pulsifi about the use case or business case for why his customers choose Pulsifi.
Pulsifi’s People Data Platform delivers a true assessment of candidates so you can confidently make accurate people decisions in real-time.
Give the show a listen and please let me know what you think. Thanks, William
Show length: 27 minutes
Enjoy the podcast?
Music: 00:02 Welcome to RecruitingDaily’s Use Case podcast, a show dedicated to the storytelling that happens-
Music: 00:09 Or should happen.
Music: 00:10 When practitioners purchase technology. Each episode is designed to inspire new ways and ideas to make your business better as we speak with the brightest minds in recruitment in HR tech. That’s what we do. Here’s your host, William Tincup.
William Tincup: 00:26 Ladies and gentlemen, this is William Tincup and you are listening to the Use Case podcast. Today we have Jay Huang from Pulsifi and we’ll be learning about the business case, the use case, cost benefit analysis, et cetera, for why his customers and prospects pick Pulsifi. So let’s just jump right into introductions. Jay, would you do us a favor and introduce yourself and Pulsifi?
Jay Huang: 00:48 First of all, William, thank you for having me here. I love how your podcast conversations help us understand all these different use cases so much clearer. Okay. Hi, I’m Jay, co-founder and CEO of Pulsifi. Pulsifi provides a people data platform to help organizations find great hires and make them leaders. Our platform brings together different types of data into a single view to help you truly understand each candidate, or employee, across hard skills, soft traits, and other characteristics, compared against success profiles to predict role fit, culture fit, and then make recommendations from selection to learning. Our customers include Nestle, Heineken, financial institutions, professional service systems, telcos, airline schools all around the world.
William Tincup: 01:38 Wow. There’s so much to unpack there. I can’t wait. So a single view, which is really important because you’re combining, probably, a lot of disparate systems that people have had to use before. Tell us a little bit about that because you work in selection, but you also work internally with training and, I would assume, internal mobility, kind of, movement internally. So tell us a little bit about the single view and what does that look like for your customers?
Jay Huang: 02:06 Right. Maybe I would explain a typical scenario. Let’s say, in hiring, I think it’s common for recruiters, hiring managers, to receive a bunch of applicants. They might have some candidates from elsewhere. They would look at resumes or CVs, and sometimes they would administer certain assessments, whether it’s a skills base, cognitive psychometric, kind of, assessment. Maybe they run interviews as well and then they need to actually distill their takeaways from all these different sources into a single decision. For example, do I want to select this person for an interview? Or later on, do I want to make this person an offer? So while there are many tools that exist in the market, I think putting the tools together, usually, is always a manual decision, manual effort.
It’s sometimes painful when volumes are too high or time is too pressing. It could also be subjective, could also be biased. So that is the part that we seek to come in to be able to take different kinds of data, to tell you a story about this candidate. So we do storytelling, something also part of this podcast, tell the story of why this person is an X percent fit for the role you’re looking for across the, kind of, competencies experience you’re looking for, across interests, values as well as culture fit. So very quickly, the user can just use a percentage as something to sort through high volumes of candidates. And then they can dive deeper into the profile to really understand what the person is like. And our platform also recommends how you might want to interview that person to get the most out of it.
William Tincup: 03:49 So on the back end of that, what’s the technology, without me getting into the secret sauce. What’s the technology that’s kind of serving those things up as either recommendations or… When you just, at the very end, you’re like, “And how you should possibly interview this person.” There’s got to be some logic or machine learning or AI. There’s something behind that. Tell us a little bit about that.
Jay Huang: 04:12 Right. Right. So it’s a combination of a few things. Yes. So first of all, we can unpack those data sources, we’ve got resumes. So we use natural language processing to tease out competencies, experience out of that. Then we might also deploy psychometric assessments. So they could be our assessments. It could be assessments from other companies that the customer uses. So we use that to understand fundamental things, personality, work interests, what people value at their workplace, because all of these, we know, are predictors of job satisfaction and job performance.
In some instances, we also use other kinds of data, such as, maybe, competence in certain kinds of skills or certain desire to, let’s say, travel or not travel, maybe even video interviews. So basically trying to understand various hard skills and soft skills of people using all these different data. And then what we do is to compare that against, what we call, the success profiles. So our database has tens of thousands of the most typical jobs out there, which we have used a knowledge graph to collect. Essentially understanding what each job entails and what are the traits and qualities that are required for people to succeed in them.
So we compare the person against success profile and that’s how the fit score comes about. And we basically condense all of that into a single percentage. The learning comes in when companies can actually use their own data to refine those models. So for example, if we have data on their employees, who are the great performers, who are the average performers, who are the not so good ones, we can tease out the difference to be able to refine the model and say, “Okay. For this particular job, in this industry, for your company, there’s something that’s slightly different.” And that’s how it learns from there. So for customers that have been working with us for several years, I think they enjoy the benefit of having the model sort of live and grow with them.
William Tincup: 06:19 So two questions, and they’re probably very similar. One is a success profile. I like how you’ve thought about that. Because most people would say job description or job ad or whatever, competency framework for whatever that bit is. You’re looking at it as a success profile. And then I’m going to ask you about the employee profile as well. Same question about both the candidate employee profile, so that side. What I love about the candidate employee profile is you’re bringing… It’s got several spokes. I view it in my head, I’m a visual learner. It’s like a hub and it’s got all these different spokes of interest and passions and soft skills and hard skills.
And all of these things. The question is for both profiles, how frequently do, either, they update it or do you update it? So with the success profile, how frequently are you kind of recalibrating, I guess, is what I’m thinking about? And then with the employee or candidate profile, I know you have a different name for it, how often, if their interest change, their personality is probably not going to change, but maybe their interest change or maybe their experiences because they’ve gone through some training and otherwise maybe their training… Those hard skills have changed or maybe their soft skills have changed. How do we keep up to date with those things?
Jay Huang: 07:45 Right. Okay. So I think the quick answer is that, while we will love for these things to be real time, changing all the time, they unfortunately don’t. I think it’s just being practical in terms of how regularly the underlying data gets updated. So there are few sources over here. One of them would be from the organization and what can we get from the organization? We would have things like performance reviews and these could happen once a year, twice a year, once a quarter kind of thing. We might have some data related to engagement and satisfaction. So maybe these ones we can update it as soon as this data comes in. We also look at job research out there, like O*NET for example, like ESCO in Europe. So these databases, they also get refreshed on and what our knowledge graph also does is tap on these databases to refresh itself. So it’s a combination of these things, such that the data, our database and knowledge graph, would gradually evolve. But what it does is kind of at a pace of the organizations that we work with.
William Tincup: 08:55 That’s perfect. I love that. I love that a lot. Workflow wise is two questions. Where does this sit in the organization? Because we’re talking about selection, which is typically thought of as talent acquisition, and also learning and development and potentially internal mobility, which sits elsewhere. And so give us some insight with customers. Where do they play with it, workflow? And the second part of that is, who’s using it? Who’s in it, using it, managing it, et cetera?
Jay Huang: 09:26 Right. There are two usual scenarios. Number one scenario is that customers use our platform standalone. We’ve got ATS, applicant tracking system, features, as well as some employee dashboarding features so we can be used standalone. The other scenarios is integrated with existing HR systems, whether it’s Workday, SuccessFactors and things like that. So then the user can choose to have either platform be the interface to interact with, but we have integration going on to keep the data in sync and as well as Workflow. So that means, for example, when they create a job in the ATS, it shows up on Pulsifi and then when candidates come in to the applicant tracking system, it can also show up on Pulsifi.
Of course we push data back to the applicant tracking system, but not everything might be able to display there. So our platform is just a click away for them to get the kind of profiles that we provide. And same thing on the candidate side. It’s a mix of both platforms that is possible or directly just with Pulsifi. So who uses our platform on the hiring side? Generally they’re recruiters who are the day to day users, and then there would be hiring managers that would use it maybe after the initial screening is done. When they look deeper into candidates, when they use a platform as kind of a supporting screen for interviews.
On the employee side of things, our platform is used by talent managers, right now, to have a look at the employee group. So maybe I should elaborate a bit more on what they actually use our platform for in terms of employees. I think typically we help organizations, for example, pick high potential employees out of their entire base, for example. And then these people, they get more higher touch talent development in a sense that, how do we personalize the development plan? How do we recommend they should be learning certain things so that they get closer to that success profile?
William Tincup: 11:31 I love this. So neat. Let me back up real quick, because I love origin stories. Why’d you create Pulsifi? You could have built anything. I’ve looked at your LinkedIn. You literally could have built anything, like the next driverless car. Why did you build… Well, why did you point all your energy and passion towards Pulsifi?
Jay Huang: 11:57 Yeah. Interesting question. So I was trained a computer engineer, went to Cornell and UC Berkeley, and then actually I ended up being a VC and then I also ended up in the Boston consulting group.
William Tincup: 12:14 So did you have to do everything in two by twos when you went over to UCB? Did you have to do-
Jay Huang: 12:19 It’s a powerful matrix.
William Tincup: 12:20 It is a powerful matrix. The X axis and Y axis. Wait a minute, hold on. Everything can be fit. That’s what people don’t know about BCG. A, they invented it. B, everything can be fit in a two by two.
Jay Huang: 12:33 Yes. You’re right. You’re right. It’s very powerful. Yeah. So I had the opportunity to work with many organizations. It’s all about coming up with clever strategies, but I realized that the most difficult thing, actually, is executing those strategies without the right people. And then, obviously, dawned on me that, hey, it’s really people. That’s the most important thing in companies. And it’s always difficult to find the right people, whether it is a new hire or existing employee to do something. After BCG, I got the itch to go back to the startup role. I joined my current co-founder in his company, which is in online advertising, programmatic advertising, which is all about using bits of data about people, their web footprint and things like that.
To be able to assemble a picture of what these people could be and predict what they want to buy. So in that world, the holy grail is this thing called the customer data platform, which is a platform that can give you a single view of each customer across different kinds of data trails. And with that single view, then you can really understand the customer and take great action on it. So we did that for a couple of years and after a while, I think, I realized that, okay, maybe it doesn’t change our lives that much. But when I look back to my experience as a consultant, I realize, okay, there is a bigger problem that all companies will need help with. Maybe not necessarily advertising, but definitely that people.
So the idea is, can we just do what we did in advertising and apply it to the HR world? Because it’s a world with, also, fragmented data, which needs to be data driven. People want to do it, but it’s difficult a lot of the time. So why don’t we come up with the people data platform? Then the next consideration was, how do we take it to the world? And then we figure as much as we want to help each person love what they do at the workplace, so simply put, help them to love Mondays. It’s going to be difficult to build a sustainable business doing that. So we ended up providing a software to employers who achieve… Be more than willing to pay for the right people and to help them groom the people. And that’s really how we got into what we’re doing.
William Tincup: 14:57 I love it. So I know you’ve mentioned what category that you’re in, but I wanted to, first of all, I’ll go on record. I hate software categories. I despise them because it’s most… Yours defies a category singularly, but how do you want people to think of Pulsifi in terms of the solution itself?
Jay Huang: 15:21 Yeah, actually this is something that hasn’t been so straightforward for us.
William Tincup: 15:29 You and everybody else. Don’t worry. You’re not alone.
Jay Huang: 15:35 Yeah. Absolutely. Absolutely. Yeah. You are right there. You’re right there. So we like to think of it as a data platform that’s sort of the foundation to support various kinds of, sort of, HR functions or HR strategies. So if there could be a horizontal layer, that would be it. But I think today’s categories tend to be more vertical. So whether it is hiring, whether it’s onboarding, whether it’s learning, whether it is, and so forth. So not so much fitting into these verticals directly, but then when we talk to the market, the prospects, the customers, then we would say, “Okay. This is what Pulsifi does for hiring. This is what we do for talent management, for learning.”
William Tincup: 16:19 What would they say that you do? I’m always intrigued. We say, “Well, it’s talent analytics.” Or “It’s talent intelligence.” So we have these fancy things and they call it something completely different. So if unaided, what would you think that they would call Pulsifi?
Jay Huang: 16:43 Let’s see what we’ve heard. So yes. Analytics is a very well used term but what they call us is person analytics.
William Tincup: 16:57 Oh, interesting.
Jay Huang: 16:57 Because we focus on the individual rather than the group.
William Tincup: 17:00 Right. Right.
Jay Huang: 17:01 Which I think is quite fresh. I think most of the time it’s about looking at workforce, gender, things like that. But for us, we want to dive into each person. So they call it person analytics.
William Tincup: 17:11 I like it. Do you see a path forward in terms of team dynamics? You’re solving for that person, right?
Jay Huang: 17:18 Yes.
William Tincup: 17:19 But you’ve worked on a bunch of teams, back to the BCG. You’ve worked on a bunch of teams. Do you see it kind of informing how teams are formed, et cetera?
Jay Huang: 17:30 Yeah, absolutely. Absolutely. So actually, when we first started a couple of years ago, it was mainly role fit that we did and then the organization and culture fit was something we introduced last year. And then what we want to do, going forward, would actually be a leader fit and team fit.
William Tincup: 17:47 Oh cool.
Jay Huang: 17:48 Which is, I simply put, are you a good fit with your boss? And then the rest of the team, because I think that’s really the practical consideration.
William Tincup: 17:56 Oh, 100%. Are you a good fit for them? Are they a good fit for you? And if you’re looking laterally, is the team mix? Are we all a good fit? Because I mean, that’s the optimization that we’re all looking for. If we get that optimization, we have a chance for the project to be more successful, et cetera.
Jay Huang: 18:14 Okay. Yes. Yes.
William Tincup: 18:16 I like that. Okay. When do you… Well, we won’t say that. Let’s do demo. Let’s say, because I always like to kind of get into the demo. When you show Pulsifi to folks for the first time or you get to actually be on a call and get to show them things. What’s kind of your favorite part and what’s their favorite part of the demo?
Jay Huang: 18:41 Mm. I think what usually strikes our audience the most, whether it’s prospects or other people, is really how we provide profiles of people in interface that is clean, yet tells so much insights about that person. And that’s because many users are very used to seeing resumes as the first thing. But for us, the profile does not contain anything about where they worked at before or what school they went to.
William Tincup: 19:24 Oh, that’s cool.
Jay Huang: 19:25 It’s all about that percentage and okay, how strong is this person in these competencies and experience? So and so forth. Of course the resume is just a click away but we don’t believe that’s the thing to lead into. So I would say that’s what, usually, they enjoy. And what we like to show is definitely that as well, but I think a lot more of the other cool things that we sneak in from there. So for example, you can dive really, really deep into, not just the percentage, the competencies, but down to, what we call work styles, all the way down into work styles, which include things like level of leadership, adaptability, communication. And then we have got this interview guide, like I mentioned. So a lot of these additional features, which I think users would discover as they use the platform.
William Tincup: 20:25 I love that. I love both those. So buying questions that you’ll love, again we’re trying to kind of talk through and help folks kind of get a gauge on what they should be asking, what they maybe even should be avoiding asking. So one of my bits when I talk to practitioners about buying software is you’re going to talk about price. It’s inevitable. You can’t buy something without talking about price. It doesn’t need to be in the first four or five conversations. It needs to be… You’ll get there. Eventually you’ll get there, but there’re other things that are more important. So what are some of the questions that you love to receive from practitioners, your prospects, when you’re like… And you know they get it and what are some of them that you’re like, “They don’t understand what we do”?
Jay Huang: 21:17 I think we love it when conversations move towards quality of decisions. So I think every software out there that seeks to support, or replace, a manual process should definitely give you time savings, cost savings. So I think that’s a given and for us, it’s, I think, easily 70% savings we can give you. But I think that’s really just the hygiene factor. So what we want to talk about are quality of decisions, which is using our platform, can we help you to make better decisions? And that’s how we enjoy working with customers to say, “Okay. If I make predictions about a person’s performance level and you hire that person one year later, is the person still around? Is the person exhibiting the kind of behaviors that we predicted? Is the person really performing well?”
And that kind of validation we actually can get into the 90% range, i.e. hiring almost the right person all the time. And that kind of value is so much more important than their 70% savings. And I think many users are really a little jaded about how platforms came to do very stuff. But very few platforms actually stick around to validate that the decision was the right one. But because we have models that learn from decisions and whether or not people who hire were performing well and so on, that’s a big thing that we do. So whenever customers are interested in this, we really love it because that’s how we can really get the models to do their best for that organization.
William Tincup: 22:56 What’s being displaced here? Sometimes when I’m talking to software vendors, I’ll ask them, it’s a trick question, but I’ll ask them about competition and they’ll point to somebody else. They’ll point to another software firm. I’m like, “Yeah, that’s not your competition.” Competition’s the status quo. Them doing it the same way that they were doing it last week or et cetera. So I’ll ask you the same. Not necessarily competition, but what’s being displaced? When people bring in Pulsifi, are they getting out of Excel? Are they getting out of some broken processes or other technology? What’s being displaced?
Jay Huang: 23:33 Yeah. I think the first thing we displace is the manual effort to combine data, to make decisions. And sometimes it’s done on Excel if the organization is a bit more structured. Otherwise it’s something done in the head or the gut. And I think that is also the weakest link in the process oftentimes because we all have our own biases, rightly or wrongly, and things like that. So that’s what we seek to replace. So it’s not just about efficiency but also about quality. The other layer is then how a lot of the tools the organizations use, maybe some of them are not used enough, then maybe you don’t even need to use them. You can use something simpler that Pulsifi offers.
So example, let’s say, would be, let’s say psychometric assessments. It’s a huge role of providers out there. And some of them are pretty expensive, but the reality is that not every user is even trained to read that psychometric profile. And why do we need to have this kind of scenario when you can have a lighter version of an assessment that just gives you what you need? And in fact, we just automatically put it into the score. So you don’t even need to look at that thing. So just one example of how that could also be a replacement. But, like I said, we want to be a model that unifies. So we still partner with many different tools to be able to give customers that single score
William Tincup: 24:58 Love that. Last question and it’s a tough one in the sense of, I’m going to ask you, kind of, your most recent favorite customer story. And without brands, without names. You don’t have to do that. But just something where you just like that you’re inspired. You know how it is. Running a software company is a bit soul sucking at points. But then all of a sudden, a customer uses the technology in a way that you’re like, “Wow. That wasn’t on our roadmap.”
Jay Huang: 25:29 Yeah. Absolutely. So that’s actually what I was going to say, which is they used our platform for something that would sort of didn’t design it for. So this started when… Okay, when we first started our business, we focused on hiring selection because that’s usually the biggest problem that customers want to quickly solve. And then we found that companies would use our platform on their employees, which is these high potential programs or customer success programs, digital transformation programs. And then we realized that, hey, yeah, we should be supporting employees as well. So then that went into our roadmap. Then we released that. And then next thing you know, is they started to use our role fit score to look at culture.
So basically use a different model with culture and say, “Oh yeah, we should have a culture score too.” So outcomes like culture score. And then I guess most recently customers started to say, “Hey. Let’s put in some development content in there, whenever you help us to produce a profile for an employee. Because that helps the managers, the talent managers to be able to guide that person.” And then we thought, hey, yeah, we should be pushing our MD stuff. So that’s, I think, something that we really enjoy when customers tell us, actually, what we should be doing next. Of course we don’t, unfortunately, are not able to build everything that they want. But I think generally a lot of them guide us in the right direction.
William Tincup: 26:53 Jay, this has been fantastic. Thank you so much for carving out time in your day. I know you’re crazy busy, but to just thank you for carving out time for us and the audience. It’s been wonderful.
Jay Huang: 27:05 Thank you very much, William. Hope to see you again soon.
William Tincup: 27:07 Absolutely. And thanks everyone listening to the Use Case podcast. Until next time.
Music: 27:13 You’ve been listening to RecruitingDaily’s Use Case podcast. Be sure to subscribe on your favorite platform and hit us up at RecruitingDaily.com.
William is the President & Editor-at-Large of RecruitingDaily. At the intersection of HR and technology, he’s a writer, speaker, advisor, consultant, investor, storyteller & teacher. He's been writing about HR and Recruiting related issues for longer than he cares to disclose. William serves on the Board of Advisors / Board of Directors for 20+ HR technology startups. William is a graduate of the University of Alabama at Birmingham with a BA in Art History. He also earned an MA in American Indian Studies from the University of Arizona and an MBA from Case Western Reserve University.