Michelle Prebble (Director of Talent Acquisition at Visier) talks with us about attrition metrics, aggregating information, and what it actually means to make "data driven decisions." We're all down for making smart decisions based on numbers, but how do you actually take advantage of this knowledge?
One of the biggest hurdles in data driven decision making is lacking the imagination to visualize this information. Sometimes explaining what you have learned to someone not data-centric can be an incredibly daunting task. But there are tools in the industry that can guide you towards making the right call.
This HR Tech 2022 series is sponsored and made possible by our friends at Gem!
School is in session. This is Recruiting Daily Sourcing School Podcast. We're recording from HR Tech in Vegas. Thanks to our friends and partners at Gem. Sharpen your pencils and get your sourcing pants on, because we have the scoop on sourcing news, recruiting tech, and all the hot topics that you need to learn about. Here's your professor, Ryan Leary, with special guests Shally Steckerl and Mike "Batman" Cohen.
Mike Batman Cohen (00:29):
Oh yeah. We are back with another episode of Sourcing School, here live from HR Tech. If you hear that hustle bustle in the background, that's not made up in your mind. It is awesome. This is a congregation of amazing thought leaders, and I couldn't be happier to be sitting across the table, and getting to co-host this with Shally.
Shally Steckerl (00:49):
Yeah, man. It's kind of cool out here. There's so many booths, and so many booths that have names I don't recognize, but if you look them up, you realize it's a name you do recognize. They just crossed it off and changed it with another name. It's still kind of exciting though.
Mike Batman Cohen (01:04):
I love that. Hey, Shally, you actually were talking about this before we started. I think most people in the space know who you are. I think that'd be a generous 51% or more people, in the recruitment space globally, know who you are. Can you share one fun fact about you that they might not know?
Shally Steckerl (01:20):
My 12 year old son, who's about to turn 13, has just become the youngest black belt in his dojo, which is a 75 year old dojo, with more than a hundred thousand students.
Mike Batman Cohen (01:34):
What? Okay, first off, if you're listening to this, congratulations. That's awesome. You rock. Second, what's the martial arts style?
Shally Steckerl (01:40):
Taidō. It's a karate form from Okinawa.
Mike Batman Cohen (01:45):
Ooh. I love that.
Shally Steckerl (01:48):
So, here with us in this magical booth, we have Michelle Prebble from Visier, and Michelle came by to talk a little bit about data. Michelle, tell us about yourself.
Michelle Prebble (02:01):
Hey, how you doing guys? So again, Michelle Prebble, and I work for Visier, and I am the Director of Talent Acquisition. I think you're going to ask me about a fun fact. Pretty sure.
Shally Steckerl (02:11):
What's the fun fact?
Michelle Prebble (02:13):
I'm really kind of mulling over this one, and the best one I think I got is amateur furniture restorer. So I like to hang out in garages.
Mike Batman Cohen (02:24):
Shally Steckerl (02:24):
That's the smell of paint thinner.
Mike Batman Cohen (02:24):
I heard amateur wrestler, and WWE.
Shally Steckerl (02:26):
Amateur wrestler. Okay. Yeah, I was thinking paint thinner, lots of paint thinner and stains.
Michelle Prebble (02:29):
Mike Batman Cohen (02:30):
What's your favorite thing you've either worked on or completed so far?
Michelle Prebble (02:33):
Oh, I did a really beautiful, I took an old dining table, and then converted it into a coffee table. So that was fun.
Mike Batman Cohen (02:40):
Shally Steckerl (02:41):
Mike Batman Cohen (02:42):
All right. I love that. Can't wait to see those pictures attached to this recording.
Shally Steckerl (02:46):
So I have this beat up table, that is an antique, but it's really pretty. What do I do to make it come back to life? Other than just old English and elbow grease, which is what I've been trying to do.
Michelle Prebble (03:01):
Sand it down, and start again.
Shally Steckerl (03:03):
With a whole new paint or stain?
Michelle Prebble (03:06):
Stain. Yeah, yeah yeah.
Shally Steckerl (03:07):
Michelle Prebble (03:07):
And add some character to it. Get a little bit deeper in some spots, so it looks like you're just kind of bringing back the antiqueness of it.
Shally Steckerl (03:13):
Ooh, we're hearing the secrets here. Patina, all right. I'm going to have to do that. Excuse me. I'll be right back. Bye.
Mike Batman Cohen (03:19):
I'm sitting over here across. I have no idea what's going on. I'll cook something mean, but building? Nope, no thank you.
Awesome. Shally, you kind of touched on the fact Michelle's in a unique position to come talk to us a little bit about data. And we talked beforehand about the three things we really want to cover, which is looking at the industry change from this super people-centric network, who you know, shaking hands industry, over to a data-centric industry. looking at data's out there and how we can use it. Because I think we all hear all the time, "Make data driven decisions. Use data to inform yourself." And it's like, "Yeah. What data?" And then also, "How?" So I think you're in a really unique place to help answer that question. And so we'll start with you by asking, "Hey, what have you seen as a change in the industry from this people centric to elite data space?"
Michelle Prebble (04:19):
Yeah, honestly, I mean, recruitment has been a numbers game as long as I've been doing it, which is a fair while, especially in the agency world, where the main play is, "Hey, just talk to more people." Right? Get more people to know who you are. And this entire time, you're collecting all of this incredibly unique data about these people, and what they look for, and their salary requirements, and identifying trends. But then how do you track that information, and then put it somewhere that somebody can access it, as opposed to going into systems of record? And then just reading notes, right? Because it went from, "Hey, record your conversation and write down what's special for this person, and then go back in there and read it when you somehow come across this person in a CRM at some point in the future."
And so the way that we've now sophisticated the industry and search is that we know how to take data now, and push it to people, and make it accessible in real time that says like, "Hey, I'm not expecting you to recall a conversation with a person you talked to six months ago. I'm going to actually give you that data in a searchable form, so that you can bring up the people that are most relevant to the job, or the moment in time that you might be actually trying to choreograph."
Shally Steckerl (05:34):
Okay, but this is the biggest obstacle in analyzing anything related to data. Okay? I think, honestly, it's not having access to the data, because you could have a spreadsheet full of data, and I get we're more sophisticated than that and we should be, especially with all the technology that's available. But it's simply lacking the imagination to figure out what to do with it, to visualize it, so that somebody else understands it. To me, that's the biggest obstacle. Would you agree?
Michelle Prebble (06:09):
Yeah. Oh, for sure. Right? When you look at traditional business intelligence tools, there's always an analyst. There's always this very complicated row of people that stack up, to then end up with a deck that has to get presented to somebody.
Shally Steckerl (06:23):
That visualizes the data.
Michelle Prebble (06:23):
Shally Steckerl (06:23):
Michelle Prebble (06:25):
That just says, "Hey, this is how you make sense about it." And now where we've gotten to is that data is accessible to everybody, and it's just like, you don't need to be an analyst to actually understand this, because I'm going to, through the use of various software tools, I'm going to make sense of this for you, and just push that information in front of you so you can understand things like, "Hey, at risk employees, and attrition rates." And when in your talent acquisition team, you can use that data to say things like, "Hey, this person is more likely to move than that person, because of company tenure." Or, "Hey, they haven't been promoted for a long time, so that's an at risk person of exit in that organization." These are the reasons why you should be potentially reaching out to them.
Shally Steckerl (07:07):
So what do you do? You preload the questions, or you preload into the system, and then let the system figure it out?
Michelle Prebble (07:13):
A hundred percent.
Shally Steckerl (07:14):
You have to know what to ask though.
Michelle Prebble (07:15):
Yeah. But some of the tools that are out there, and the way the TA teams are leveraging this data, is because all of those answers are pre-built into the system.
Shally Steckerl (07:26):
Michelle Prebble (07:26):
So they are taking all of the biggest trends that are out there. And again, this is across the entire employee life cycle, not necessarily just recruiting. But if we stick on the recruiting side, taking out all of the things that are important for talent acquisition practitioners, baking that into the system so that insights are being pushed to you. Again, it's an intuitive tool with the answers baked into it, so that you're just getting the insights.
You're not having to go there and say, "Okay, I have an attrition problem. What do I do, when I just have this raw data looking at me?" It's saying, "Okay, well if you have an attrition problem, here's the reasons why you might have that." And it helps you map it against the various reasons why an employee might be leaving that organization.
Shally Steckerl (08:10):
Okay. And it helps you map out aberrations too?
Michelle Prebble (08:13):
Shally Steckerl (08:13):
Because one of the funniest stories I have about HR technology, and talent acquisition data, is a story about a client that had decided that they needed to enforce a 40 hour work week, because they weren't getting enough productivity from a specific team, and they were using their data to come to this conclusion.
Michelle Prebble (08:33):
Shally Steckerl (08:34):
But after enforcing a 40 hour work week, they found out that productivity went down. And then they started asking, "Why did that happen? Because we forced people to actually work more during their time here in the office." And they realized that the reason that productivity had gone down was because there was an individual who had left, and everybody else was carrying that person's workload, efficiently, but productivity is slightly lower for everyone, because everybody else was distributing that person's workload. And by forcing them to work more, they essentially stopped doing that other person's job, and now it's backfired.
Michelle Prebble (09:12):
Yeah. And so this is one of the most important things about understanding what your data is telling you. So what's the story, and what's the actionable insight to take away from this? Right? And I think that's where the challenge is, is that when you have that string that I was talking about before of all of these different people required to give you that insight versus, "Hey, industry best practice baked into something that is then going to give somebody the answer to go and then take the action." Right?
Human error is significant when you're working with anything that is high touch. So by having a software, and to be fair, you asked me this great question at the beginning around how is the industry changed so much. Well, it went from the fact that nobody gave HR or talent acquisition, or anybody, any tools to work with in the beginning, outside of a half bake CRM, to a point where now we are drowning in tools. And so probably the problem now is, "Hey, what's effective? What does my team need, based on the size we are, and where our milestones are?" Like, "Hey, what's our growth trajectory, and what do I need now versus what do I need when I hit a different employee number?"
Mike Batman Cohen (10:20):
Yeah. Yes. And what I'm hearing and listening to both of you guys riff on this, which is super cool to sit back and enjoy, is a big transition was going from acquiring data through conversation, to having now access to this data, to now a volume of data that we have access to, right? Particularly with all of these tools that we have.
Michelle Prebble (10:42):
Mike Batman Cohen (10:43):
So it's minimal data, tough access, to then easy access minimal data, to tons of data, easy access. So then the next question, logical transition here, would be like, what is the data that's important for making these types of decisions? Right? Because Shally's point that he brought up about the tenure piece, or the hour per week, what data should people be looking at and using, or what data have you found in your own experience is most important?
Michelle Prebble (11:18):
To answer that question, you have to really think about the stage that your company is at, and what you're trying to achieve, right? What's the mandate for the TA team? Are you expanding? Are you maintaining? What are you doing right now and what are you trying to get to?
Shally Steckerl (11:31):
Okay, so we're a 600 employee company, and we are trying to grow without breaking our culture.
Michelle Prebble (11:37):
Yeah. Okay. So some of the things that you need to look at are hiring efficacy. So what's your sub-one year turnover? So are you selecting the right people in the interview process? And if you are, then you should see a low instance of sub-one year turnover. Right?
Shally Steckerl (11:54):
Michelle Prebble (11:57):
And that's a really important question, because it's 600 people. Every hire is still crazy impactful into what is living and breathing in the organism in the company. Right? It's not until you get to the multiple thousands that individual effort starts to dissipate a little bit, and starts to get lost.
Shally Steckerl (12:17):
Doesn't have as much impact in the overall-
Michelle Prebble (12:21):
That's just not as visible.
Shally Steckerl (12:21):
But it's not my fault, because he looked good on paper, and he interviewed really well, but then he left before the year was up, and it was really just not a match for him. So you see what I'm saying?
Michelle Prebble (12:34):
Shally Steckerl (12:36):
Is this really something we can fix, versus is this just something that the candidate market is doing, because people are using switching jobs as a way to shop for a career?
Michelle Prebble (12:46):
Yeah, shop for a career, shop for a salary increase. There's so many reasons why people are leaving, but ultimately, depending on, again, your TA mandate. How are you connecting people to the mission and value within your business? And you can track that. You can look at your data and say, "Hey, this person actually had this type of hiring or onboarding experience," for example. They attended these sessions or what was their engagement within their onboarding?
Shally Steckerl (13:15):
Those who left before the year didn't do this. Those who didn't leave did this.
Michelle Prebble (13:19):
Shally Steckerl (13:19):
And what if you bookend the data, and on the one side you have how long before they leave, and on the other side you have how long before they get promoted?
Michelle Prebble (13:26):
Absolutely. So there's risk of exit data. Right? So again, this is probably not completely answering your question, because we've sort of gone into an active employee, as opposed to that TA process up front for search.
Shally Steckerl (13:42):
Michelle Prebble (13:43):
So I'll definitely get back to that, but to answer your question more aptly, risk of exit looks at, "Okay, when did they last get a pay increase? When's the last time that they were promoted? When's the last time that they had a performance review?" So you could map all of that on this beautiful graph, that's called risk of exit, and it gives you the likelihood, and then it benchmarks them against other employees. So you can actually compare them to your division, or divisions that are your division within the organization, and bearing in mind that companies that have thousands, 10 thousands, hundred thousand employees, they're all operating as these departments, are almost like their own individual companies in some ways.
They don't necessarily have the visibility about what a counterparts might be experiencing, so that ability to actually benchmark yourself against other departments is hugely impactful, because then it is just like, "Okay, well I can see how my colleagues are experiencing this," and then reach out to them, and ask them how they supported employee growth within their department.
But you can also look at industry. So, "Hey, am I above or below industry?" Because just because you have attrition, that doesn't mean it's a bad thing. Everybody looks at attrition, like it's a bad thing. It's not necessarily a bad thing.
Mike Batman Cohen (15:07):
Michelle Prebble (15:07):
People leave. That's part of keeping your organization fresh, and learning, and developing. So attrition is not necessarily a bad thing. So understand the story behind the data. Right? So you get a number and hey, it says I've got 25% attrition. And everyone goes, "Oh my gosh, that's scary." But then if you look up industry benchmarks and the industry benchmark is 35%, well then you're a rock star, because you're a 25.
Mike Batman Cohen (15:34):
Yeah. So I love this, but my brain immediately goes to like, "Yeah, but how are you getting this data?" Because I want you to know, and there's no way this is accurate, but in my brain I'm like, "Okay, as you're dealing with a 10,000 person company," so there's like 10 people locked in a broom closet, who have all these columns of information they need. How many days they've been at the company, where do they work from before that? What industry were they in? And all they do all day is update that information, with the 10,000 employees, and each of them wrote, "Well, asking for a friend." How do companies actually acquire the data to make these decisions, that don't involve just hundreds of hours of data input?
Michelle Prebble (16:18):
Not a hundred percent, right? There's not some secret room where there's all of these people just pounding away on keyboards.
Mike Batman Cohen (16:26):
Michelle Prebble (16:26):
No, all of this data is already there. The problem is that it exists in disparate systems that don't talk to each other.
Mike Batman Cohen (16:31):
Tell me about that.
Michelle Prebble (16:32):
This is not extra work. This is about aggregating all of your data, and putting into one place that becomes accessible to extrapolate insights. Right? So you don't have to do any more work. This data already exists within the company's ecosystem, because think of all of those hundreds of pieces of software I referenced earlier. You have got data from your ATS. You've got data from your learning management system. You've got data from your HRIS.
All of this data already exists within the company's ecosystem. All you're doing is aggregating it into a place where you can actually do something meaningful with it. And that is one of the challenges that HR practitioners have faced for so long, is that we have this rich data that exists within company ecosystems, and zero ability to leverage it, because they haven't had the investment to actually understand that people data is the most important data point, within an organization, to dictate it success.
Mike Batman Cohen (17:29):
Ooh. I like that a lot. I like that a lot. So we're talking about people data, which you were discussing beforehand, and you were bringing up people data that I wouldn't have even thought of. Right? About your sales projections, based on this year's sales goals over last year's, that you can predict with some level of accuracy, workforce planning for next year.
So, gosh, what are the data sources outside of what we normally think of as HR professionals? Of your point, ATS, CRM, et cetera. And then how are ways that you're seeing either yourself, and/or clients of your company using that data, in ways that maybe we don't think about every day?
Michelle Prebble (18:12):
Yeah. I mean think workforce planning, understanding when you need to be hiring and how you're filling your pipeline, based on where you're trying to get to from a company. So think of, I would say the easiest example would be how do we understand if our sales team, at current state, can get us our number next year?
Well, what if we had the opportunity to bring in information from Salesforce? And information from, say, that's your CRM. You know, and finance data. What's your quota attainment for those people? And then you take in data from your marketing team. "Hey, what's the lead gen? What's our conversion rates on leads? How much pipeline does our sales team need, in order to confer to hit their number?"
So take that, and then layer that on top of performance data on sales reps, and average tenure, and time to hire. Right? You take all of that information, and because that's a lot, right?
Mike Batman Cohen (19:15):
Michelle Prebble (19:15):
That's a chunky-
Mike Batman Cohen (19:16):
My brain exploded a minute ago.
Michelle Prebble (19:18):
That is a chunky bit of data to wrap your head around. But think of how powerful that is. If you can take the efficacy of your sales team, and then map that against, "How long does it take me to hire that person that's amazing?" And once they come into the business, how long does it take them to ramp? So then I can work with my CRO and say-
Shally Steckerl (19:41):
Aha, so we can predict how many people we need to hire-
Michelle Prebble (19:43):
Shally Steckerl (19:43):
So that we end up with the people that we need, and how long we need to start hiring so that we have them on time?
Michelle Prebble (19:49):
Shally Steckerl (19:49):
That makes sense. So that is how you can get data, to get TA a seat on the table?
Michelle Prebble (19:56):
Oh, a hundred percent. Absolutely. So you're upfront workforce planning with your CRO to say, "Hey, based on your sales team right now, you're going to miss your number by X amount." So we need to get in front of that-
Shally Steckerl (20:06):
Or based on your gold increase sales by 10%-
Michelle Prebble (20:07):
Shally Steckerl (20:07):
We're need to... Yeah. Okay.
Michelle Prebble (20:09):
And we can bank on X attrition. We can bank on X contribution rate from your current reps. And then the TA team then starts the hustle. Right? You start building that pipeline and that funnel, so that you can make sure that you've got enough people to bring in for the next quarter, the next year.
Shally Steckerl (20:28):
So I got one last question before you leave. US, Perth, Vancouver-
Michelle Prebble (20:38):
Shally Steckerl (20:39):
More than 15 years in the industry. What's not changed, that surprised you? In the last 15 years, in three continents, that has remained the same that you are surprised by?
Michelle Prebble (20:54):
Relationships and connecting people to mission and purpose of an organization.
Shally Steckerl (21:01):
It surprises you that that hasn't changed?
Michelle Prebble (21:03):
In some ways, because there's a lot of people that are talking about, "Oh, AI is going to replace recruiters."
Shally Steckerl (21:09):
Michelle Prebble (21:09):
And, "You're not going to do that anymore." And that's just not ever going to happen. Humans like humans. I want to talk to someone. The question that I get asked every single interview, without fail, "Tell me about your culture." Nobody wants to hear a big culture from a bot. I want an authentic experience.
Shally Steckerl (21:26):
Yes, we have a fun culture. Click here to find out more.
Michelle Prebble (21:30):
People buy authenticity. People align to organizations, based on who they connect with in that interview process, and so that's just never going to change. I wouldn't say I'm surprised, but I think given the amount of heat that comes in around, "Tech is going to replace all of these people, and there's going to be no need for recruiters anymore." Nah, not happening.
Mike Batman Cohen (21:52):
I love that. Yeah. It was so funny. I was just thinking, it was like kombucha, ping pong table, booze ball. Okay.
Shally Steckerl (22:00):
Clothing optional Fridays.
Mike Batman Cohen (22:01):
Yeah. Yes. So the way I've ended all of these so far is saying, we obviously have a bunch of people who are listening to you right now, and taking all this in. What would you leave them with? One thing. It doesn't have to be related to data or what we're talking about today, that's going to hit them in their mind, their soul, their heart's. Something that you want them to take away from this.
Michelle Prebble (22:24):
Oh my goodness.
Mike Batman Cohen (22:24):
Michelle Prebble (22:27):
What a question. I feel like you could have prepped me on that one.
Mike Batman Cohen (22:29):
No. No I can't.
Michelle Prebble (22:30):
So you could say something really impactful and interesting.
Mike Batman Cohen (22:31):
No, no. We want this straight from the cuff.
Michelle Prebble (22:35):
Do you know what? Honestly, I probably just did exactly what I'm about to say, and being super authentic. I am me every day, in every moment of my job, my life, my at work. And if you want to be good at your job, be yourself.
Mike Batman Cohen (22:49):
You heard it from Michelle. Be yourself. Everybody else is already taken.
Thank you so much for joining us. I know it's crazy here.
Michelle Prebble (22:55):
Thank you for having me.
Mike Batman Cohen (22:56):
You're being pulled in all these directions, so we appreciate having you here, and from all of us...
Speaker 2 (23:00):
Oh man, that means it's over.
Speaker 3 (23:08):
You've been listening to the Sourcing School Podcast, Live HR Tech in Vegas, sponsored by our friends Gem. For all of the HR, recruiting and sourcing news, check out RecruitingDaily.com.