FeatuRama: Jobvite's Zach Linder

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 Jobvite's Zach Linder, VP of Analytics and Machine Learning. 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 Zach pitches Jobvite's newest feature.


Disability Solutions provides full-scale inclusion initiatives for people with disabilities.

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.

Joel (1m 13s):

Let's do this. We got Zach.

Zach (1m 32s):

Hey guys. So I'm Zach Linder the VP of analytics machine learning at Jobvite and super excited to be on today. So real quick on Jobbite. So we are a end to end talent acquisition company. We're focused on hiring, onboarding engagement, attraction, and promoting a talent to make sure that all customers of ours can succeed in the TA marketplace. Focused on companies of all sizes and offer a full suite of solutions like application management, a talent CRM suite, intelligent messaging, including texting and chat box, interview assessments, mobile apps, onboarding applications, and what we're here to talk about today, a robust analytics platform.

Zach (2m 18s):

So we're really excited about the new analytics tooling we've started from scratch. So built this from the ground up. A lot of times analytics are, are locked inside of each of those individual individual features, but really where you get the power is when you, when you take it up a level and you can look across the entire spectrum of hiring and in your talent acquisition pipeline, and making sure that you understand where, where are things flowing? Where, where are the candidates coming from? How can you optimize those sources and how can you really find and tell that good data story that you want to?

Zach (2m 51s):

So our, our analytics platform is rolling out next month and it's going to be a GA to all of our JobBite customers on our ATS. And when that comes out again, there'll be lots of focus on data storytelling, optimization and source tracking. And how do we tell that end to end story from taking candidates from the beginning of the life cycle, through hiring and on-boarding all the way through back the beginning of the process.

Joel (3m 22s):

Thank you Zach

Chad (3m 23s):

All right, Cindy. Hit him!

Cindy (3m 25s):

Okay. Robust analytics. Can I with, with the data, can I see how I'm doing compared to other industries or other locations, other employers in, in my area?

Zach (3m 40s):

Yeah, sure. Thanks. So benchmarking is, is definitely one of the standard components of the tooling. So we want to make sure that you're not operating in a vacuum. That you not only understand how you're doing the, the members of your team are doing, but also all those companies in that industry, that size of company that, that location. So we want to be as responsible as we can with that data, right. And make sure that we're aggregating to the right levels. But we want to get down to the detail where you can really make some changes and make sure that you're performing or outperforming similar type companies.

Cindy (4m 11s):

Very good. Thank you.

Joel (4m 13s):

You guys have been around for over a decade. You just said that you built this thing from scratch. Number one, why? And was the old version really shit? And Dan Finnegan's watching so be careful.

Chad (4m 27s):

So I'm going to take that as a yes.

Joel (4m 30s):

Did you hear that pause right there?

Zach (4m 34s):

No, I'm a big fan of always upgrading, right? Let's always use new things, right?

Joel (-):

From scratch?

Zach (4m 41s):

No, the real reason is that it kind of goes back to a jump by Teresa acquisition of telemetry canvas and roll points. And, and so when you have multiple companies, everyone's got their own data set and data stuff to manage when you're one company, right. But four companies together and try to figure out how do you look at that data in a unified way that can, can help glean some information. And until I tell a good story, that's why we really started from scratch. So one of the first things we did once we consolidated all the companies was to say that that data was one of the first areas that we wanted to consolidate.

Zach (5m 13s):

And that's going to be what we're rolling out here next month, just to make sure that whether you're looking at internal mobility or texting and messaging or ATS versus CRM, that you've got that full view of all that data. So as you're using all those features, you can look at it across all the different applications.

Chad (5m 31s):

Okay. So if I'm a talent acquisition leader, what is the Holy shit moment that's going to get me to say yes, I need to have that?

Zach (5m 41s):

Yeah. So I think first of all, there's a lot of stuff that that's in there that, that we've got tendencies on that we'd like to see a lot of this stuff. Isn't very complicated, as much as getting the organization and structure to be able to, to glean those insights off of. So if you think source optimization, for instance, so how many candidates do I need at the top of my funnel down to a screen, to, to onsite interview, to offer, to hire what does that pipeline look like and where do I need to be at what point in time?

Zach (6m 15s):

And if you think about taking a couple of steps back, how do I get the right people in the top of that funnel at the right, right price points, right? So am I using my sources correctly and effectively, am I searching my own internal database as much as possible? Am I hitting up my own internal candidates as much as possible? So how do we have that full view of this life cycle and, and allow you to not start a new job every time, every time you've got a new job description in your system, how can you look at this in a very similar way and say, I'm a little ahead on I'm a little behind and here are the things that I need to do.

Zach (6m 46s):

So it's really action oriented, making sure that we're helping the recruiters understand where do they need to focus their work on for today.

Chad (6m 53s):

Well, and you can tie in, you're talking about being able to also guide that experience with aggregate data, right? So

Zach (6m 60s):


Chad (7m 1s):

sales positions in your new to the system. Then you have really no data to really drive off of what maybe if you imported a bunch of junk that you've had before, but you can really use your aggregate data. Is, is, is that correct? And you can slice it and dice it?

Zach (7m 16s):

Yeah. Yeah, for sure. So think about like, if, if I've got 10 people in my queue right now that, that I need to disposition in some way, but I know that for this particular sales job that I'm hiring historically within my company, I I'm going to need, I've got a 10% higher rate, so I'm going to need at least 10 candidates to get to a certain stage. And so if I don't have those, then I need to go focus part of my time on sourcing while I'm focusing the other part of my time on getting the candidates through the pipeline as quickly as possible to improve that time, to fill the spot.

Zach (7m 48s):

So, yeah, and a lot of that, that's, that's using your own company's historical information and then where that doesn't exist, right. For new customers, we definitely rely on benchmarking just standard across the board data that can help provide some insight as to where you need to be focusing your work on for the day.

Chad (8m 7s):


Cindy (8m 8s):

Are there reports updated in real time?

Zach (8m 10s):

Yeah. So real time ish is, is my best answer there. So it's the, it's going to be very close to real time. And in a sense of it's, it's going to be about an hour delayed. So now we're all always balancing this, right? So there we have, I, I checked recently and I, I think we've got about 24 terabytes worth of data in there. So we're, we're, we're crunching through a massive amount of data. And so we always have to balance the power of the data with the speed of the data and the refresh cycle.

Zach (8m 41s):

Now don't th that's, that's just in part of the analytics platform, right? Where there's the need to have an API to get real time information. That's definitely going to be a possibility, right. And so what information needs to be real time versus what information is okay to be an hour or so delayed. That's what we're, we're continually looking at. And if it were up to me, everything would be real time. I would just need probably an extra billion or $2 to make that happen. So if you guys can help us out with that?.

Joel (9m 6s):

That's what a mom's couch cushion kind of stuff. Cushions. We talk a lot about the platform and how important that is and how ATSs are going to grow by having, you know, third party vendors building onto their, onto their systems. Talk about how the analytics is bringing together sort of the third party information, whether that's what I'm paying on a per click basis on Indeed, or what messaging is coming through. I'm going to guess that because you guys own Canvas and stuff, those, those systems are ingrained in the analytics, but talk about how you're, you're playing nicely with your partners?

Zach (9m 38s):

Yeah, for sure. So integrations of course are key in a lot of different ways. And, and we, we would love to surface all the data that we can, right. So if we can go deeper with an integration and pull in more data that tells us more insights into how those other tools are working and how they're interacting with our systems, we definitely use those integration points to extract all that value that we can. So if, if we can get more data through an integration, we absolutely will.

Zach (10m 9s):

And then we'll just use that to improve the quality of the data that sits inside of the Jobvite systems.

Chad (10m 13s):

Excellent. So we talking about obviously analytics reports, are there diverse hire reports for hiring companies that your clients can model off of or actually benchmark off of?

Zach (10m 26s):

Yeah. So again, benchmarking is always something that we're working on. And specifically when you're thinking about DNI, we've got some standard reports that include that for sure. And the data is only as good as what's put into the system, right? So some of our customers choose to track this, others do not, but yeah, if the data's there, we can tell you all of the data that we talked about so far with the candidates in the pipeline, and who's making it through the various progression of the pipeline through getting to offer it and hired all that can be broken down by age, race, gender, those types of data points were available.

Zach (11m 3s):

So I think right now it's going to be very interesting. I think we've got some interesting things that are going to come out here in the very near future to, to just highlight this. This is something that we've gotten a system and it can be utilized in ways that people might not have thought about as much prior to the world's events.

Chad (11m 22s):

Now, with regard to that, let's say for instance, if a females in my talent pool fall below a certain benchmark, can I say, in a alert in the system to let me know, or maybe even trigger more jobs going out to some of the, some of those female friendly types of sources?

Zach (11m 38s):

Yeah. So when, when we think about analytics, we always think about analytics plus automation, right? And so the, the plus automation is something that, that is, is an evolving process, but that's when we're building things out, definitely directionally where we want to go, we want to stop having to push the button much and have the button push itself to make sure that, that it's not only using the data, but using it to act the way that we're telling it to do. But yeah, so alerts are definitely a possibility inside of the analytics tooling. And this is definitely an area that we're expanding on, but within the reports and data that's coming out, you can set a specific alerts on if I get above or below a certain number or a benchmark or threshold that you can be alerted as the individual user and the candidate.

Cindy (12m 20s):

Does a candidate. See also that there's been a change in their disposition within the company.

Zach (12m 27s):