The Use Case Podcast – Storytelling about Peoplelogic with Matt Schmidt

Music:Welcome to RecruitingDaily’s Use Case podcast, a show dedicated to the storytelling that happens or should happen 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 and HR tech, that’s what we do. Here’s your host, William Tincup.

William Tincup: Ladies and gentlemen, this is William Tincup and you are listening to the Use Case podcast. Today we have Matt on from Peoplelogic, and we’ll be talking about the business case or the use case for why people choose Peoplelogic. So let’s just jump right into it. Matt, thank you for being on the show. Please introduce yourself and also please introduce Peoplelogic.

Matt Schmidt: Fantastic. Thanks for having me on to your show today and really looking forward to the conversation as you said I’m Matt Schmidt. I’m the founder and CEO of Peoplelogic, little bit about me. I’m a lifetime entrepreneur. I think I started my first company when I was 12 or 13.

Matt Schmidt: That was a grass mowing company and prior to Peoplelogic, I’ve co-founded DZone, which was a developer marketing and developer evangelism site away for people to be able to connect with developers. So basically connecting marketers and developers with the tools that they needed to buy and also we built a software platform called AnswerHub, which powered developer communities for people Microsoft and IBM to engage their developers more. Peoplelogic, we’re really on a mission to help make organizational health be actionable and to be simple enough to be able to do that. So everybody’s probably been through all of the never ending number of employee engagement surveys and we’re here to be able to provide a data driven, actionable layer on top of that without having to do surveys. So really appreciate you having us on board and looking forward to the chat today.

William Tincup: Love it. So where are we pulling data from so that we can… Because I love the way that you put it and phrase it for folks. Listen, at the end of the day, you’ve got to be able to do something with the data. It’s great that you have the data or you’re aware of the data. You’ve got to do something with it. Where are you pulling from to then be able to render kind of here’s what to do next?

Matt Schmidt: Yeah. So we connect to the places where we work is actually happening. So think about your Salesforce, your Asana, your JIRA, your office, your Zoom. We connect to the APIs of those tools. The exposed endpoints that that give you back information programmatically and we take all that information. We aggregate it, we enrich it with different signals who’s involved in all the different interactions, the stress of those interactions, the impact that they might have on the organization or on your customers and then we use that to surface these actionable early warning signs around your organizational health or give you some indication of the likelihood that somebody is to stay with an organization or to be at risk for burnout.

Matt Schmidt: Companies have been using all of these types of SAS and cloud tools forever, right? Even before the pandemic, which only just accelerated this and all of this data was present but just being left on the floor and what we’ve done is pick it up and start to sift through it and start to make sense of it.

William Tincup: So I love that you’re focused on both sides of this. Well, a lot of sides. So let’s switch deal with stay versus flight. So give the audience some ways that data that you see data kind of both where you’re pointing the data, we got that but also the way it renders and in what they can do. So if it’s a flight risk, okay. So how does that render itself? I just updated my LinkedIn recommendations.

Matt Schmidt: We’re not quite that overbearing but it renders itself in a couple of different ways. So what we do and what we found that customers wanted to our conversation earlier about adoption of tools, customers are really tired of having to adopt all these good tools and so what we found is they want to be able to get in, get that quick glance of where they need to focus their attention, get the next steps that they should take to fix it and then get out right and they don’t want to spend more than a couple of minutes, particularly our customers who are the leadership teams, the managers of companies and so in our platform this data surfaces into graphs of how people are connected overlaid with colors of the health of the individuals. It surfaces as a score that we call stay factor, right?

Matt Schmidt: It’s not the leave factor, it’s a stay factor. Right?

William Tincup: Nice.

Matt Schmidt: And so that score gives you some measure of how likely somebody is to stay within the company. It surfaces as these recommendations, these next steps, these early warning sites that are, “Hey Jim is having 50% fewer interactions with the rest of the team than the team average.” That may be a sign that Jim is pulling away or becoming disengaged, or “Mary hasn’t been having regular one-on-ones with her team.” And as her manager, maybe you should begin to have a conversation about why that’s important and so some of it is based around behavior science and research that we’ve done, others is based on the best practices of our 80 plus years of running companies and applying the those best practices into, to every organization.

William Tincup: What I love about this is on one level it’s adopting or adapting to the customer. We’ll just call them employees just to make it simple for folks but it’s adapting to their ever evolving needs and changes.

Matt Schmidt: Mm-hmm (affirmative).

William Tincup: And on one so that I love, but it’s also… I’m sure you probably thought about this but it’s also making bad managers more aware, right? Of what’s going on.

Matt Schmidt: Mm-hmm (affirmative).

William Tincup: So there’s no excuse. You actually got the alert.

Matt Schmidt: Yeah. That’s a great point and we’re here to augment a manager. Right?

William Tincup: Right.

Matt Schmidt: Because, if you look at some of the recent studies certainly everyone is burnt out. The managers are actually burning out at an even higher rate than [inaudible 00:06:34] [crosstalk 00:06:34].

Matt Schmidt: Because they’re very often not just doing the management job, they also have their own things that they’re responsible for and some of this is management practices and that people are not used to needing to keep track of… Not so much keep track but be able to stay in touch with their teams in this remote and hybrid world and so we give them the ability to leverage the things that computers are good at, which is crunching data and surfacing trends and doing analysis so that they can get those signs of the things they need to pay attention to rather than being wandering around in the desert, not knowing where there might actually be a problem.

William Tincup: So without getting too wonky, I’m assuming there’s a bit of machine learning and AI on the back end that learns over time about these things.

Matt Schmidt: Mm-hmm (affirmative).

William Tincup: You mentioned interactions, the 50% less interactions with somebody over time, we’re just going to get smarter about what that means.

Matt Schmidt: Mm-hmm (affirmative). Exactly and so what we do is we run it on each individual organization, so we’re not getting smarter.

William Tincup: Cool.

Matt Schmidt: I mean, there’s some that is getting smarter because we have all these different things.

William Tincup: Right.

Matt Schmidt: And so you see a lot of different organizations, right? But at the same time, it’s important that each of these is specific to you.

William Tincup: Right.

Matt Schmidt: To your company because while each company obviously has similarities companies or companies. Right?

William Tincup: Right.

Matt Schmidt: And there are some best practices that apply, but the things that make your company distinct, the averages, the trends, the history, those are unique to each company and so what we have built is a platform that really lets you draw out those insights, leveraging the specific and relatively small amounts of data that are for each company.

William Tincup: I love this. So people listening to the show, we’re going to ask a number of questions of me later on in the row but first, before I get to that, one is your favorite part or your team’s favorite part of the demo when you show people software, when you show them Peoplelogic for the first time, what’s your favorite part? And then what do you think their favorite part is? What’s the difference between those two?

Matt Schmidt: I don’t know that there’s a difference. I would say that the first… The thing that is actually the favorite part is actually the part they show last, at least in the current demos that’ll be changing in a couple of weeks when we have a major new update to the platform but it’s the visualization of how your team is connected.

William Tincup: Cool.

Matt Schmidt: Because most people have no idea how that actually…

William Tincup: Right.

Matt Schmidt: One, it’s a pretty graph and it’s bubbles moving around and all that and most people have no idea how their team is actually interconnected. Right? And so that’s our favorite part. It tends to be the piece that we kind of end on. The other pieces that people get excited about are some of these early warning signs where, “Hey, this person is underutilized.” or “Hey, this person is over utilized.” “This person has been…” Their activity is spiked 345% in the last three days compared to historicals. Those things are just, they’ve not been accustomed to even being able to think that those are possible to understand and so it becomes this new experience for them to see this type of information surface to them.

William Tincup: I love that. Well, you’ll get the usual, big brother kind of stuff. This seems it’s too much. It seems it’s a bit invasive, which of course it’s not, but what are some of the buying clues or buying questions that you love? You can just tell that they’re ready. You’re interacting with somebody that’s ready, that’s primed that really wants technology to give them this type of information. So they make better decisions.

Matt Schmidt: Certainly, there’s always the concerns about big brother and while we are always conscious of that, we actually selected advisors that could help us understand how to use data ethically and we’re always up for the character. Staying on that side of providing more benefit to the employees, to the managers, to the companies than the access to the data that we take. Right? In terms of the buying signals that we can always tell, usually the best one comes from the… Were they’re talking about organ… When they actually understand what organizational health is and the impact that a positive organizational health has on their business. Right? When they’re talking about those things, then we know we’ve got them. Right? That’s a…

William Tincup: They’re speaking your language at that point.

Matt Schmidt: They are right. Usually that’s paired with they want to be data driven about their culture. They’re tired of surveys. They’re not getting the information they want. They want to get it right from the systems where people are spending. Those all kind of coalesce into, okay, this person gets it.

William Tincup: Right.

Matt Schmidt: If they come to us…

William Tincup: You’re not going to have to push the Boulder uphill with this person. They already…

Matt Schmidt: Exactly right. They understand data. They understand these different systems. They understand the benefit of using this type of promotion, if we’re pushing uphill and somebody, or we get somebody who is thinking that what they really care about is productivity, then that becomes an entirely different conversation and not one that really tends to go very far with us.

William Tincup: Yeah. So how can we get more or less? It’s a farmer’s way of looking at yield. How do we get more out the crop.

Matt Schmidt: Exactly.

William Tincup: Yeah. Those days are over. Okay. So let’s talk a little bit about and there’s almost a great knockout question. If you care about that, don’t talk to us. I mean, if you care about organizational health, again, wellbeing, mental health, et cetera, down to the individual, down to the team, down to the group, et cetera and you really want data that will make the better experience for all involved, great, fantastic. We have a way of doing that.

William Tincup: I always say, I like asking the kind of the unintended use and sometimes that’s really success stories. So let’s just think of it as success stories and no names, of course, no brands. I care about that but just people that have used… Your clients that have used Peoplelogic in a way you’re, “Wow, that’s really cool.”

Matt Schmidt: That’s a great question. We’ve had a variety of different use cases kind of come to bear some of the ones that were somewhat unexpected but which we were able to provide good value on is simply being able to help users of Slack who are using free versions or some of the lower end versions of Slack, being able to give them more analytics about who’s collaborating within Slack channels, what channels are most active, those types of things. That was sort of an unexpected side effect of some of the ways that our data comes to us and so we’ve seen some interesting value there and our pricing works out such that they’re able to get that at… It’s far cheaper than they would get from having to upgrade to the premium version of Slack.

Matt Schmidt: Other use cases have been really understanding where they have capacity problems. So we’ve got a couple of customers who have been… Were plugging in the system into their different tools, whether it’s engineering, whether it’s sales and suddenly being able to understand the underlying conditions for why their teams are burnt out. Right? And at the end of the day, the burnout tends to occur because they’re under capacity, right? And they don’t have enough people to be able to do all the work that needs to actually get done in the time that they’ve tried to do it. We tend to work mostly with growth companies that are anywhere from 50 to 1000 employees and so there’s always a never ending funnel of work that’s that needs to happen and there’s always a ton of pressure.

Matt Schmidt: Some level of push and burnout is normal there but very often when there’s a capacity problem, that’s the piece that they get surprised and say, “Oh, now I see, now I understand what everybody’s been telling me.” And that becomes the sort of… Surveys are here to stay. It’s the way people have decided that’s they want to get that reactive information but when they need more information about why that’s happening in these anonymous surveys, our system can then allow them to deep dive and get into that. Okay. I can say, “Oh, I see my customer success team is clearly doing too much at odd hours and they’re spending 85 hours a week in meetings.” Right? And so this clearly aligns with why they’re struggling with burnout and threatening to the loop.

William Tincup: A dumb question. So I’ll just say it the way it is when we talk about interactions, say Slack, do we have some type of way, I don’t know if it’s [inaudible 00:16:56] analysis or whatever to then say, “Okay, there’s 50,000 interactions.” But those could have just been emojis going back and forth, right? Or it could have been substantive and I’m thinking about myself. Of course, I’m thinking about myself why wouldn’t I think about myself? Because I don’t interact well in Slack with our team. When I need something, then I use Slack.

Matt Schmidt: Okay.

William Tincup: But I’m not in there. If someone were to look at the analytics for me there, it’d be more minimalistic but if they actually looked at the substance of what I was talking about and then they would say, “Okay, well he doesn’t use it as frequently as Janet or whomever but when he uses it’s for a good reason.” So again, dumb question.

Matt Schmidt: No, that’s a… We’ve been building up this type of information. We haven’t been exposing that yet and we’re going to be rolling things around sentiment and emotion and more conversational statistics, not just for Slack, but think about it in the sense of JIRA Commit or JIRA Comments or commit messages and reviews within your source code control to your email and those types of things.

Matt Schmidt: So we really are focused on the interactions, which can be of multiple people, not just one to one. Right? They can be multiple people that are participating in what we would call a conversation and we try to focus the sentiment and the stress interactions or the stress measurements and the impact measurements on those interactions rather than on a particular individual. So we’re never going to say, “Mary is really… She’s being a really negative today.” Right? But we can say, “Mary and Tom and Bob are having really increasingly negative conversations.”

William Tincup: Mm-hmm (affirmative).

Matt Schmidt: And so maybe we need… There could be conflict burn there or topic of culture is starting to emerge. Those types of things.

William Tincup: I love that.

Matt Schmidt: So that is the… We do keep those types of statistics around with Slack and we’re broadening that stuff out. Again, in things, Slack, it’s only in the channels, it’s not the direct messages.

William Tincup: Right.

Matt Schmidt: And then beyond that our team never sees any of the individual message data and all that kind of stuff. I want to just be clear about that.

William Tincup: No, yeah. It’s signal data is ultimately what you’re looking at is signal data and the signal data is then you can interpret the signal data and then do something with the signal data but you got to have the signal data. So it starts with… You got to understand what the signals are and how to get that data and then you can do something with it.

William Tincup: I could talk to you all day, Matt. Thank you so much for coming on the podcast and telling us a little bit about Peplelogic.

Matt Schmidt: Absolutely. I appreciate it and if anybody wants to reach out, please feel free.

William Tincup: Awesome and thanks for everyone listening to the use case podcast, until next time.