Technology is evolving, teams are sprinting, excitement is building, features are launching, and nobody notices... (waa waa) which is why Chad & Cheese cooked-up FeatuRama, a brand new competition which pits 4 companies against one-another, but only one can win and emerge with the BadAss Belt of Technology.
Contestants will receive 2-minutes to pitch their new feature and the remaining 13-minutes will be spent with rapid fire Q&A.
This Chad and Cheese FeatuRama episode features HiringSolved's founder & CEO, Shon Burton. Chad & Cheese came equipped with questions, bourbon and snark, luckily Comuno's Cindy Songne, who was available to step in and inject brains into this judging panel...
Enjoy while Shon pitches HiringSolved's newest feature.
PODCAST TRANSCRIPTION sponsored by:
Intro (0s):
Technology is evolving. Teams are sprinting. Excitement is building features are launching and nobody notices. This is Joel Cheesman of the Chad and Cheese podcast. And that's why Chad and I cooked up FeaturRama, a brand new competition that pits 4 established companies against one another with only one emerging victorious awarded the bad-ass belt of technology.
FeartuRama 411 (27s):
Here's the four, one, one: Contestants will receive two minutes to pitch their new feature. And the remaining 13 minutes will be spent with rapid fire Q and A. This FeatuRama puts the eventual winner of our first ever FeatuRama Shon Burton CEO at Hiring Solved on the hot seat. Chad and I dropped a mad questions while downing drams of bourbon and Camino Cindy Songne helps inject brains and class to the judging panel. Enjoy.
Intro (58s):
Hide your kids! Lock the doors! You're listening to HRS most dangerous podcast. Chad Sowash and Joel Cheesman are here to punch the recruiting industry, right where it hurts! Complete with breaking news, brash opinion and loads of snark, buckle up boys and girls, it's time for the Chad and Cheese podcast.
Chad (1m 25s):
Alright guys, here we go. We've got you Shon from Hiring Solved.
Joel (1m 31s):
Burton!
Chad (1m 34s):
Go!
Shon (1m 35s):
Hey guys. Thanks for having me on I'm Shon Burton from Hiring Solved. I'm one of the founders and the CEO of Hiring Solved and let's see, I'm pitching you. So let me, let me tell you a little bit about Hiring Solved. We started about eight years ago now and what we are as a, we're a software company that specializes in talent intelligence. What is talent intelligence? I like to think of it as a, we've been hearing for a long time in this industry that your ATS, your CRM, your HRS, these are gold mines because you have all this great data and, and, and more so I think even today, then that's even more true than it has been because you've got tremendous applicant flow today.
Shon (2m 14s):
Of course, lots of people out of work, looking for new jobs. You've got folks in your HRS that worked for your company that you're trying to put to work maybe in different jobs as the business changes. So this idea of these systems as a gold mine is very, is very relevant. But what Hiring Solved does is we are the mining tools. So if you think about that analogy of the goldmine, that's a, that's a store of value and there's very valuable stuff in there, but it's actually really, really hard to get out. I think of it as you know, the ATS does a great job.
Shon (2m 45s):
It tracks applicants, it's an applicant tracking system. It doesn't say it's going to find you people to hire. So Hiring Solved, what we work to do every day is, is to like our name says solve hiring. And we do that by integrating with all kinds of different, anything that contains talent information, pulling it together, merging it, analyzing it, parsing it, and then creating structure from it so that we can then make it actionable and then make it easy for the user to make hires very quickly and accurately.
Shon (3m 15s):
So that's a lot of, that's a lot of what Hiring Solved does. We started as an aggregator, a social aggregator back in 2012. And for the last several years, since 16, we've been really digging into corporate data sets and that's where we are today.
Cindy (3m 33s):
Okay, so just fine. The fact that you mind first, the internal database and then go externally, you've scored huge points with me! Now, what if that external candidate is still the best candidate? Can I mine first, get my few best candidates and then look externally, then compare them.
Shon (3m 52s):
We started all external, as you may know, we were the, we were the company to knew the guys that got sued by LinkedIn back in 2014...
Cindy (4m 2s):
Let's mess it, I love it.
Shon (4m 4s):
in case anyone forgot. No. So, so what we do today, it's funny. We don't supply external data other than pure analytics. So we supply analytics on workforce. We supply analytics on supply and demand, that sort of stuff, but we don't actually supply the candidates. And our perspective on that by the way is just, it's, it's changed at time. We just think that that, that data is so readily available. There's so many folks that are supplying it. So we, we pivoted away from that. But yes, the short answer is we're 'bring your own data' sort of organizations.
Shon (4m 34s):
So if you have that data and you want to drop it in, that's no problem for us. And we can compare and do all that, all this stuff, that you mentioned.
Joel (4m 40s):
First of all, Shon, your quarantine hair is rocking. I want to give you a quick thumbs up on that. My legitimate one, is I love, I love that you're pivoting out of sort of the, you know, the public stuff to internal stuff. What did you learn being an aggregator that has helped you be a better sort of internal search engine if you will, or, or solution?
Shon (5m 1s):
Sure. So to mine this internal data, it turns out there's a lot of really complex problems in trying to understand who's going to fit where, and even just, just something as simple as understanding a job description, right? If you really look at that, that is an incredibly hard problem, partially because the, those descriptions are written in a hurry by job description by, by, you know, folks that don't really want to write write them or they're templated. So when you think about how to automate that or how to make something smarter, what you need to just a tremendous amount of data.
Shon (5m 34s):
And so I'll get, you know, as a practical example, we think about a job title is maybe like a mobile developer, right? And trying to understand having, having a system learn what is a mobile developer and what makes a good one. It turns out it's very rarely, very relative to different companies, right? So a mobile developer at a game company is going to do something very different than a mobile developer at say, Space X. So what that allowed us to do that social aggregation start, allowed us to is just analyze literally billions of bits, of pieces, of information, social profiles, all this stuff, to develop systems that learn from all of that information and they learn a lot of patterns about what companies need, what, where you know, how things are different, how a mechanical engineer is different in Wichita, Kansas than they are in Springfield.
Shon (6m 16s):
So it says and how that correlates to different companies. So I'm not gonna, I I'm, I don't like to say the buzzwords, so I won't bore you with that, but it let us learn a lot of stuff, about talent data.
Chad (6m 28s):
Okay. So recruiters don't need another platform or tab to work in, right? And most platforms are revising to be the recruiter desktop. Are you trying to compete with all those other systems to be that one workplace? Or are you just working closely with all the other core systems to build API integrations, to have them actually work in another system?
Shon (6m 51s):
Both, we have great API integrations. We have a full platform that you can work in. A lot of what we see, like you said, Chad is a, you know, "Hey, people are highly trained on the system of record, like the ATS" and they're, and they have to live in there no matter if they want to or not. So for that situation, what we do is we provide an extension. So if you're, you know, if we provide an overlay on that interface. So if I'm looking at somebody in Workday, you know, Workday is great for all the things that it does, but what I'm able to do at high, with a Hiring Solved they'll relay is say, Hey, if I'm looking at Joel, for example, here's all the jobs you'd apply to.
Shon (7m 28s):
Here's the jobs that hadn't applied to that we think he matches great for. Here's what the last person that contacted them. Here's all the notes, that sort of stuff. So we're able to extend those hiring solid capabilities of scoring, matching analytics, right on top of that ATS, CRM interface.
Chad (7m 42s):
How many, how many of those platforms are you integrated with and what are some of the big names you mentioned Workday? I would assume that you're integrated with them. What about, what about some of the other big names? Just a handful of big names.
Shon (7m 53s):
Yeah. So, so were the common ones that we are, are integrated with Workday, Taleo, ICMs, SAP, Success Factors connect the brass ring. Those are, those are we're, we're dealing with larger companies for the most part. So those, those are some Greenhouses, another one that's a more modern than we do a lot of work with. And then the CRMs that you can imagine, Avature some of the others. Yeah. We've done some work with Bullhorn. It looks like we'll be, we're working right now to get a deep ring with Bullhorn on something that I'm not allowed to talk about. But yeah, definitely Bullhorn
Chad (8m 24s):
Such a tease.
Cindy (8m 25s):
And then if I, as an employer have multiple ATS, is can I look at all of that data in one?
Shon (8m 32s):
Yeah. That's, that's a big part of what we do Cindy is ... if you, if you've got multiple ATS and all kinds of disparate systems, we're able to not only visualize who's in what ATS, but if you're looking at one record and Hiring Solved or on the extension, we're able to merge them all together. So we're able to say, we, we believe that even though nothing links them, we believe that these fire records in these three ATS, plus these records in the are all the same person. And that, that actually we learned from social. There's another thing we learned from social, because one of the complexities of understanding, like you're getting health, Facebook, Twitter, LinkedIn, email address are all components of you.
Shon (9m 8s):
So we use that technology extensively to merge those profiles.
3 (9m 12s):
Talk more about jobs scoring. Is this a text deal killer? Is this more like a Google for Jobs optimization, play, talk about job scoring?
Shon (9m 23s):
So a job scoring is probably more, a little more Google than <inaudible>, but what it is is the ability to quickly pair down, you know, no matter how many applicants you have, we have customers right now that are getting close to 10,000 applicants per job, which is a lot. So job scoring scores, every applicant from one to five stars. And then, and then you can do operations on top of that. So for example, you know, send an email, scheduling an interview to all the five star candidates, and you get analytics with that.
Shon (9m 54s):
So you can understand what makes a five star candidate, how many are there in the applicant pool? How's that trending? What if I add a PhD as a requirement or some other skill? How does that change my, my scoring And auditability and compliance and transparency in scoring is, is the really hard part here. So we've been doing, you know, as you guys know, matching, we've been doing since 2011 was when we wrote the first one, but those were always opaque systems that couldn't tell you why specifically someone was ranking higher than another, that's our new feature.
Shon (10m 25s):
We've been working a ton on making what we call transparent scoring it's trademarked, and we've worked so hard to make it both good and understandable. That's a really hard thing.
Chad (10m 36s):
But talk about bias. Talk about bias though, because we know that many algorithms, the whole I'm sure you've been hit left and right with the whole Amazon theory, right. They came up with our own algorithm. It was incredibly biased. How has Hiring Solved getting rid of the bias because you guys are doing it in an entirely different way. Number one, how are you doing it? And number two, why?
Shon (10m 59s):
The way we do it rather than remove it, we visualize it. So we don't believe in programmatically removing bias because we, we just think that that leads to humans rejecting the results set a lot. So what we do is we say, we, we visualize that through analytics, that every step of the way. We show you how the decisions you're making or are bias or are impacting your diversity, really. And then we're able to, again, transparently boost certain classes. So if you want, you know, out add a thousand employees, you want to consider females more relevant, for example, because you're, you're trying to hit your diversity goal.
Shon (11m 35s):
That's something we can do to boost relevance. And again, that's all done in a transparent way. So that's, you know, the Amazon problem. And more recently, Google did a very large project with a very large company. And one of the closets they had was they said they would walk away from it, if the system was biased and they couldn't figure out a good way, they had to walk away. We believe there is no good way to re programmatically remove that. We believe that what you should be doing is showing a what's happening, showing what the patterns are. So, you know, we think that that's much more powerful when we look at, you know, you're, you might be, you want to be crushing your top of funnel.
Shon (12m 10s):
You might have this great, you might, you use texting or something have this great, you know, diversity at the top of your funnel, but how is it changing? What are you, you know, when we see, when we see patterns in our, in the data that we analyze in our customers, what we find is, you know, it's, it's enlightening to them to look at it and say, wow, okay, we are crushing top of funnel. We've got, you know, 50% female versus you're 60% on engineering world, but no one's taking the offer. You know? And we're, we're able to analyze that at a step in, step in status, a per step and status and show them that, you know, how it breaks down in different regions, different titles.
Shon (12m 43s):
So visualization is what we think it's all about that educates the human, rather than trying to rip that decision away from the human.
Chad (12m 50s):
So total transparency, as opposed to black box, just trust us.
Shon (12m 54s):
Right. And just, just appreciate what we have exactly. Distrust, trust us, even if it doesn't make sense to you, rather than that, say, you know, what are your goals? And let's, here's how your, your actions are impacting those goals.
Chad (13m 7s):
Okay. Cindy?
Cindy (13m 8s):
I think I'm pretty good. I did read about you guys helping Lowe's hire 70,000 people in 90 days.
Shon (13m 16s):
Was that out of their own database, only? How'd you do that? Explain. Yeah, sure. That's a awesome case study. Lowe's is an amazing team to work with. So that, that was a seasonal hiring and it had had a combination of full time and part time, but yeah, 70,000 hires in 90 days back in 2019, and what we did was we, we mined their database. So they had 4.3 million candidates in their, in their database. I think it was it's it's an IBM database Connexa. And what we did was.
Shon (13m 47s):
Wow, it's get in dark. I I'm I'm in the window, The protection program
Joel (13m 52s):
Phoenix (AZ) is shutting down everybody, the aliens have invaded..
Shon (13m 55s):
Yeah, yeah. There we go.
Chad (13m 56s):
Hey, that's awesome.
Shon (13m 59s):
So what we did for Lowe's was, yeah, they have a tremendous wealth of candidates in their ATS and they have really, they really understand what's what relevant, what is relevant to them. For example, people that have worked at competitors. And so what we were able to do is get that 4.3 million down to about 700,000 that they thought were highly relevant and then help them quickly get those into marketing, marketing, and re-marketing programs.
Chad (14m 28s):
Did they, did they actually do a business case internally or did you help them at all to figure out how much money they saved instead of trying to go and pay for those candidates all over again? Because more than likely they had them four or five times over reacquiring them, did you do any type of business case to be able to demonstrate how much money you actually saved them so that they didn't have to do more recruitment advertising, marketing?
Shon (14m 52s):
Yeah, we do. We have a case study and I want to give the Lowe's team credit because they actually were, they were actually a team that already knew this. They had already done the study internally. We have a case study. You can go to our website HiringSolved.com and look at that Lowe's case study and breaks down some of those details. But Lowe's was incredibly educated and then that team is incredibly smart about using the resources they have and they already did the analysis on. It wasn't it was won't even feasible
Bell (-):
Ding, Ding, Ding...
Shon (15m 19s):
to do it without that data, preexisting data.
Chad (15m 22s):
Applause! There it is. Thanks so much, Shon. We appreciate it. Your time is up and keep watching because we have more to come!
Joel (15m 30s):
We out?
Chad (15m 30s):
We out.
FeatuRama Outro (15m 32s):
Look for more episodes of FeatuRama, a Chad and Cheese Podcast series devoted to breaking through the noise and highlighting new recruitment tech and platform features from established companies. Subscribe today on Apple, Google Podcasts, Spotify, Pandora, or wherever you get your podcasts. Don't miss a single episode, boys and girls for more visit chadcheese.com today.
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