Caitlin MacGregor
CEO & Co-founder Plum

Caitlin MacGregor is CEO & Co-founder of Plum. With vision, tenacity, and incomparable resilience, she has led Plum’s evolution and growth from a pre-employment assessment solution to a robust talent management platform that is revolutionizing how enterprise organizations manage their talent across the employee lifecycle.

Before founding Plum, Caitlin was the first employee at a social enterprise called Me to We Style, where she built the company from scratch as Director of Operations. She then joined goQ Software, an educational technology company, as President, where she opened and ran the new U.S. Head Office.

Caitlin is passionate about equipping business leaders with the talent data they need to match their talent to the right roles, as companies adapt in the age of automation. Caitlin is a regular speaker at women entrepreneur events, and she was selected by Springboard enterprises NYC as one of the top 10 businesses led by women.

Follow

On today’s episode of the RecruitingDaily Podcast, William Tincup speaks to Caitlin MacGregor from Plum about how the great resignation is a direct result of our failure of talent realized.

Some Conversation Highlights:

What do you mean when you talk about the concept of talent realized?

I’ve been in the trenches for the last two weeks normally as a CEO, I get to focus on strategy and stakeholder management. And I’ve been in the weeds doing customer success management with some smaller clients, just because it was what needed to be done. And I can do the job. I have the eligibility, I know my product. I know how to work with customers. I have the eligibility to be a customer success manager for the last three weeks, experience path, education. Everything lines up on paper. All of my historical data says that I am able to be a customer success manager and I just did it for the last three weeks.

It is the most miserable I have ever been in the last decade of work. I could do it, but I was miserable every single day, just everything that demotivates me, that drains me, that’s taxing was utilized in my day-to-day activities. Less than 20% of my day was using something that actually I was naturally gifted at. And in Excel that versus before that, before going in the trenches doing customer success management work, I, every day love getting up to work. I love my job every day. I love what I do at the end of the day. I have a sense of fulfillment. I feel like there is something that I am bringing to the table that my colleagues aren’t, that really is needed to complete the picture of what we’re doing. And most of the time, I get to use what drives me and gives me that sense of self-worth. And I’m aware of things that drain me, but I get to make sure that’s less than 20% of my work week.


 

Live Sourcing With your Hiring Manager Marvin Booker

 

Tune in for the full conversation.

Listening time: 26 minutes

Enjoy the podcast?

Thanks for tuning in to this episode of The RecruitingDaily Podcast with William Tincup. Of course, comments are always welcome. Be sure to subscribe through your favorite platform.

Music:   This is RecruitingDaily’s recruiting live podcast, where we look at the strategies behind the world’s best talent acquisition teams. We talk recruiting, sourcing, and talent acquisition. Each week we take one overcomplicated topic and break it down so that your three year old can understand it. Make sense? Are you ready to take your game to the next level? You’re at the right spot. You’re now entering the mind of a hustler. Here’s your host, William Tincup

William Tincup:   Ladies and gentlemen, this is William Tincup and you are listening to The Recruiting Daily podcast. Today, we have Caitlin on from Plum and our topic is the great resignation is a direct result of our failure of talent realized. So a lot of things to unpack in there, can’t wait to get into it with Caitlin. Caitlin, would you do us a favor and introduce both yourself and Plum?

Caitlin MacGreg:   So my name’s Caitlin MacGregor, I’m the CEO and co-founder of Plum. And we are able to assess and quantify people’s innate talents, like their ability to innovate, communicate, work well with others, and use that to ensure we match people to the right jobs, where they’re going to thrive, and realize their full potential at work.

William Tincup:   Done. I love that. All right. So we start with the backward, at the very end of our topic and it’s the concept of talent realized. For folks that maybe that’s new to hearing something like that, let’s bring people into what is that. What is that? When you say that, what does that mean?

Caitlin MacGreg:   I’m going to use an example that, I’ve been in the trenches for the last two weeks normally as a CEO, I get to focus on strategy and stakeholder management. And I’ve been in the weeds doing customer success management with some smaller clients, just because it was what needed to be done. And I can do the job. I have the eligibility, I know my product. I know how to work with customers. I have the eligibility to be a customer success manager for the last three weeks, experience path, education. Everything lines up on paper. All of my historical data says that I am able to be a customer success manager and I just did it for the last three weeks.

It is the most miserable I have ever been in the last decade of work. I could do it, but I was miserable every single day, just everything that demotivates me, that drains me, that’s taxing was utilized in my day-to-day activities. Less than 20% of my day was using something that actually I was naturally gifted at. And in Excel that versus before that, before going in the trenches doing customer success management work, I, every day love getting up to work. I love my job every day. I love what I do at the end of the day. I have a sense of fulfillment. I feel like there is something that I am bringing to the table that my colleagues aren’t, that really is needed to complete the picture of what we’re doing. And most of the time, I get to use what drives me and gives me that sense of self-worth. And I’m aware of things that drain me, but I get to make sure that’s less than 20% of my work week.

And I get to be conscious of developing coping strategies so that those things don’t overly drain me when I do have to utilize them. And that’s really about the core of what it is to realize talent is are we setting up people to thrive or are we looking at their eligibility and over rotating on that, and really not looking at the holistic whole human and how we can best really realize their talent as them being employees in an organization so that the company can be successful? But also for that individual, are they fully realizing what makes them exceptional?

William Tincup:   So you’re self aware, which is fantastic, right? Not everyone’s as self aware as you are. So, and again, everyone’s busy. So no harm, no foul, but how do we optimize people in the right spot, people getting the most fulfillment out of their jobs? How do we actually get to the place where talent is at a place where it’s realized, but also everything that makes that right, that people are satisfied, they love what they’re doing? They feel a sense of accomplishment like all the things that you feel in your normal job, all the things that we could all feel that way. But how does an organization go about unpacking and probably unpeeling some of the layers that have been built up, barnacles on the boat, that have been built up over the years and undo things to where we can realize talent?

Caitlin MacGreg:   I really see this first and foremost as a data problem. The only realize why I have self-awareness is because I have access to data that tells me what drives and drains me. I have access to data that tells me that the role like a customer success manager at this particular point in time at my particular company, with particular customers we’re dealing with that those are utilizing talents that aren’t my strengths. So I have the data. I’m feeling it obviously, but I have the data that makes it quantifiable and makes it make sense that this is happening so that I can diagnose exactly where the mismatch is, exactly where the misalignment is. And so I think fundamentally this is a data problem, and I think it’s a problem that we need to really look at in our industry in that most of the solutions on the market are really, first and foremost, looking at readily available data that you can scrape from a resume, that you can scrape from a cover letter.

It’s glorified keyword matching based on that data that focuses on historical data, where somebody went to school, where they previously worked, even how fast they progressed in their career. And no matter how much we want to talk about removing names and how by with the data scrubbing it, we can remove it of bias. At the end of the day, that historical data, it’s embedded with systemic barriers and biases that dictate access to education, access to internships, access to even how fast somebody progresses in their career. As far as I’m concerned, it’s dirty data that tells us if somebody is eligible, but it has nothing to do if somebody is going to be able to perform at a high degree over time, has nothing to do with if you were to just give them the opportunity, if you were to up skill, re skill, would they be successful and happy and fulfilled, which translates into higher performance and higher retention.

And so for me, it’s recognizing a lot of the systems, that data can still be helpful, but the data really has been designed by data scientists and computer scientists to show what that historical data, what patterns exist in that historical data, and how to replicate those patterns moving forward. And I’m challenging that actually there’s a complete set of data around people’s innate talents, what they’re born, you can’t teach this by the time they’re in their early 20s, people’s priorities in terms of do they get a sense of self worth from innovation or do they get a sense of self worth from working on a team or executing and checking off a to-do list?

We need to quantify that and that can be done in a 25-minute assessment about the individual. And we need to start incorporating that data to the equation so that we get a full understanding of if we are setting up people for success and that’s about the person, and you still need to know that about the job, but I’m going to stop there because I know that you love this stuff and I want to make sure I’m giving you time to talk too.

William Tincup:   No, no, no, no, no. I can listen to you for all day. And the things that you already, you already hit some on data, you already hit on some of the things I was going to ask you about in terms of biases and dirty data, unstructured data versus structured data, et cetera, but all also data that’s siloed. So you have data over here, you have data over there. You deal with a lot of practitioners, you know the pains that they’re going through.

Do you think it’s easier for them to just start afresh? Because I’ve gotten this sense actually from some of the folks that I deal with, the practitioners that I interact with, that are like, “I just want to start over. I just want to start afresh and just build a brand new data set,” because of everything else is just, they wouldn’t say it’s junk, they just wouldn’t be able to trust it. And I don’t know if you’ve gotten that sense or gotten that feeling from people either, but there’s a real feeling from practitioners like I just don’t trust the data I have.

Caitlin MacGreg:   I may get myself in trouble, but I would say absolutely. And the reason for it is that that data that we often have access to, it has that bias that embedded in the data in the first place. It’s not the practitioner that said, “Hey, you know, when we look at somebody completing university in five years instead of four, you know that I’m going to have a hiring manager that’s going to make an assumption that that meant because well, that person that took five years, they must be lazy or they must not be that smart.” Instead of no, they took five years because they were working three jobs and taking care of an elderly parent, and actually they have amazing hustle and amazing work ethic. And they’re incredibly smart because they were able to do it in five years with everything else going on their plate.

We’re not intentionally saying that by sharing five years versus four years that we want to be biased, it’s just happening. That data doesn’t tell the full story. And it’s not just that, it translates even into performance evaluations which is a really critical measurement that gets brought into equations. And the reality is that the dirty little secret is that 50% of performance evaluations are often wrong and they’re embedded with bias because three out of five from one manager does not equal a three out of five from another manager. And so that data is not fair and equal and consistent. So it brings in dirty data into the equation. If we even look at comp data, people are like, “Well, it’s financial, we can use the comp data at least.” Well, how come that person’s making more money than that person?

Is it because of better performance? Is it because they actually did their job better? Or is it that they were a better negotiator? Or is it that people coming out of their school, when they graduated, typically get paid higher than people coming out of that school? So it’s just there is a lot of reason to be suspect of that data and a lot of reasons to start fresh. And it comes down to a lot of that data we are using it as a proxy. When we look at historical data or eligibility data, we are using it as a proxy to tell us, can they do the job? Are they hardworking? Will they meet our needs? And it’s an incomplete proxy that has a lot of dirty data in it. So there is reasons to throw it out. I think that there is a reason to say, “Okay, well, what does predict on the job performance?”

And this is where I am bold and brash to say there is an entire field of PhDs, an industrial organizational psychology that have spent decades of research globally coming up with what bias free allows us to predict on the job performance. And it has nothing to do with that historical data. It has nothing to do with past performance. It has nothing to do with education. It has nothing to do with eligibility. It has to do with if somebody is naturally gifted in innovation and you put them in a role where they’re a cog in a machine, they’re not going to be successful. You take somebody who is driven by innovation and you put them in a role where they get to come up with out-of-the-box innovative ideas, they will thrive. It is that simple. And we can measure it in an amazing user experience with great scalability in 2022.

William Tincup:   What I love about this is, first of all, you’re telling the truth. And yes, that always gets me into a lot of trouble. But it’s always tenuous for people to hear the truth in the sense of, yeah, there’s a reason that you should mistrust or distrust the data that you’re sitting on. Okay. So first of all, if you’re feeling as a practitioner, when you’re listening this and you get that sense of I felt this way. Okay. I didn’t know why I felt this way, but I feel this way. Okay. You know what? It’s okay. Part of it is knowing that this is actually why you feel this way. But you know what? It’s fixable.

That’s the thing is there’s the intentionality of when we first started down this path 150 years ago in personnel and creating data, we’ve been sitting on data for the history of HR and recruiting. So it’s not like we, but we haven’t had the intentionality to then create it in the right way. And so maybe hitting the reset button and starting over sounds a bit intimidating or overwhelming, not, because it’ll get you to a far much better outcome.

Caitlin MacGreg:   I’ll push it one step further in that we have an amazing customer that’s really challenged the data that they’re using. And they’ve been the boldest that we work with today, which is Scotiabank. And they have a completely eliminated resumes for all their campus recruitment, early hires, internship roles. And what happens is that when you apply for a job, you’re not applying with a resume. Instead, you’re completing your 25-minute Plum assessment. What’s been amazing though, is that you’re not applying just to one job. You’re applying to the organization and they’re matching you to what jobs within that organization you would be the best fit. And why they’re able to do that is because of the second data problem, which is well, if they’re not using a resume, how are they matching people to jobs?

It’s not with job descriptions. They’re doing what the industrial organizational psychologists have been charging boatloads of money through consulting services to do, which is they’re doing job analysis, doing a job analysis on every role that they have open in the organization. Now, that wasn’t accessible to organizations a decade ago because you could never have a team of industrial organizational psychologists come in and interview basically the interview panel for every job and say what behaviors, what competencies do you need somebody to demonstrate to be successful in the role. It wouldn’t scale. It wouldn’t be affordable. But now in eight minutes, we’ve been able to automate that job analysis so that those job experts that are setting up what the behaviors for success look like moving forward, they can quantify those. And so now when somebody applies, they may be a 95 match for commercial banking and maybe a 65 match for a financial analyst.

And they’re being guided into roles where they’re the best fit. And then the bank is able to up skill and train them on the missing hard skills in order for them to be successful in the role. And so part of this bold move is not only rejecting resumes, but it’s also re imagining what are we getting from job descriptions. Job descriptions should be your marketing tool to get people excited to apply to your company, but they shouldn’t be how we are solely deciding who hiring managers should look at. We should be sending forward people that have enormous potential if just given the opportunity and just given some of that hard skills training. Those are the people we should be screening in to roles, especially in this tight labor market. Let’s screen in people that have the potential to be excellent.

William Tincup:   So some of the components there, what we heard, is behaviors and competencies. And I also heard a little bit of potentiality. So when we’re measuring someone, what, if I got that right A, and is there other things that we should be looking at?

Caitlin MacGreg:   I think that, again, the other data, could they hit the ground running that comes from that historical data. It’s helpful to know, but you want to make sure that, again, if it’s a role where you need somebody that’s innovative, if this person really innovation is something that drains them and they spend too much time on it, it’s going to lead to burnout. If I tell you that they have done an innovative role in the past, or I tell you that they even took a course in innovation or, do you want to spend your time interviewing and talking to that person? Or do you want to talk to the person that that’s their strength, that’s the thing that they love doing? And then say, “Okay, well, geographically, can they come into the office?” if that’s still a thing. Or comp wise, are they in probably the salary bands that we can afford for this role? Are they going to need some on-the-job training about our industry?

So there’s other data that you’re going to want to bring in. It’s still important. But the idea is that looking at somebody’s innate talents and aligning those to the behavioral needs of the job through a job analysis, that data and finding somebody that is a match in terms of those innate qualities, that data is four times more accurate than a resume at predicting on-the-job success. So we should be starting there.

William Tincup:   Right.

Caitlin MacGreg:   And this is like reference checks, eventually you can get to reference checks once you’re talking to the right people.

William Tincup:   And how do they keep that up to date, like over time? So let’s say we do that obviously initially in pre-hire, but once an employee is an employee, how do we keep that up to date in terms of just the behaviors and competencies if those things change? I don’t want to even assume that they do change, but if they do change, how do we keep those up to date over time?

Caitlin MacGreg:   So this is what’s great is that from the individual side after they’re about 22, 23, what drives them and gives them that sense of self worth, those are incredibly stable. So they can retake their assessment once a year if they want, but it’s really not necessary. So as you get more and more candidates into your database and more and more employees, you may have a brand new job pop up tomorrow, and you can look at everybody and see who are your 99 matches, 95 matches and those people’s data that’s going to be very stable. Now the role itself, let’s say you hire somebody who’s a 95 match. Well, we’re hearing from our customers, large enterprise customers, that their jobs are changing as frequently as every six months. So somebody that was a 95, if you’re curious about, “Hey, now we’ve changed the job. Is that person still a good fit?” It took eight minutes to create that job analysis. It can take two to go back and just edit it and say, “Are these still our priorities?”

It’s the same thing with any goal setting. We should be creating KPIs or something equivalent to key performance indicators for every role and reevaluating them and updating them as the role changes. This is automatically doing that for your KBIs, your Key Behavioral Indicators, KBIs. So our survey, you can go and update in a couple minutes and then be like, “Oh man, this person’s now a 99 match. Okay. Still good. Now I know if there’s any gaps I should be coaching them on or investing development dollars to because they’re such a great investment for this role,” or, “oh man, now they’re only a 70 match. Okay. Is there somewhere else in the organization where they would be an 85 match for that I should be talking about maybe moving them into that role longer term?” This is happening anyway. The difference is we have data that can quantify it so that we can ensure that we truly are setting people up so that they’re realizing their full talent for the organization and for themselves so that you’re getting that increased performance and that retention, which is so critical right now.

William Tincup:   I love it. Let’s link this back to the great resignation, now that we’ve laid a nice base for folks and what should be done. Why are we in the situation with the great resignation? What do you believe the parallels are? What’s causation versus correlation? We don’t need to give people a complete rundown on the differences between the two, but the idea is that, okay, we got here with a great resignation because we didn’t do talent realized.

Caitlin MacGreg:   I think it’s that the power balance has changed. I think that employers could be like, “Here’s a salary. Do what you’re told. And I don’t care if you’re not happy.” It doesn’t work that way anymore. Now, employees are actually saying, “You know what? The last two years have been really hard. I have taken on more work and I have been exhausted with no relief in site. And I now can work for organizations that don’t care where I live and I can work for countries that can pay me a higher amount. I can grow in new ways because there’s such a hot market right now. I can get an increase in title or a change in pay.” Some people just want a change. They’re sick and tired of doing the same thing day in, day out because COVID has made life monotonous and draining and difficult, and they’re hitting burnout because they’re burning the candle at both ends with families at home.

And they’re finally saying enough is enough. I want to be happy. I want to prioritize myself. I want to be able to wake up every day and be excited about the work that I do. I want to end the day feeling like I did something amazing. And they feel like now there is a collective voice saying that that is now the norm and employers are starting to wake up and go, “Okay. For new hires, we need to do this.” And so you’re starting to see big salaries and you’re starting to see them offering big things to the new people coming in the door, which is making everybody go, “Well, somebody else will offer me better.” And they’re a little slow, but starting to come around that they need to do that for their existing people.

For every open job in an organization, they really truly could be looking at their own employees first and saying, “By moving people around, how could we give them a new opportunity to grow, a new opportunity to be happy, a new opportunity to take all their institutional knowledge about our company and our customers and what we do, and breathe new light into that for the company and for themselves by taking a new opportunity internally?” instead of constantly offering better to new people coming in.

William Tincup:   I love it. Okay. So last question. And it is where do people start? So now we’ve got the linkage. It’s a really strong linkage between talent realized and the great resignation. So we understand that the audience, I think, gets that. Now they’re going to ask the next natural question which is fantastic, “Caitlin, William, where do I get started?” When you interact with your prospects to then become customers, they can’t fix it all at once. Rome wasn’t built in a day, we got all kinds of cliches we can add in there, but where do they start? Where would you like for them to start?

Caitlin MacGreg:   Again, if I think about this as data, let’s get as much of this data into their system as possible. So every single candidate that’s applying for every single job in your organization, have them complete a Plum profile. The listeners go to plum.io/ds for Discovery Survey DS. Take a Plum profile. Let every candidate see their own top talents, empower them with a positive candidate experience to see what makes them exceptional and give them that as they then start to populate your database with the most innovative or the best people at teamwork or the best at executing. Start collecting that data right away. Same thing with your employees, let every employee complete a Plum profile and get their talent guide, which is our professional development guide. That’s going to let them be the drivers of their own careers by becoming more self-aware as to what are those things that drive them and drain them.

That’s an easy thing right out of the gate. And it’s amazing to start going, oh my goodness, we believe in innovation, but only three percent of our employees are in the top 10% of innovation in the world. Maybe we need to be thinking through if we are truly rewarding innovation, truly attracting innovation. There’s amazing things that can be done just right out of the gate. Yes, there’s your typical use cases of campus recruitment hires. That’s an easy place to start. There’s examples of identify leadership potential so you can create a more diverse pipeline of candidates within your organization to start investing in leadership. There’s lots of use cases, but really it’s about the data. So getting every candidate and every employee and enabling them with this data is step one in my mind.

William Tincup:   Caitlin drops mic, walks off stage. Caitlin, thank you so much for being on The Recruiting Daily podcast.

Caitlin MacGreg:   Thank you so much for having me.

William Tincup:   Absolutely. And thanks for everyone listening to The Recruiting Daily podcast. Until next time.

Music:   You’ve been listening to the recruiting live podcast by RecruitingDaily, check out the latest industry podcast, webinars, articles, and news @recruitingdaily.com.

The RecruitingDaily Podcast

Authors
William Tincup

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.


Discussion

Please log in to post comments.

Login