Full disclosure: This episode was recorded before our break. In the normal world, not much would have changed in 5 months. But in the new reality of Generative AI- changes have occurred since we recorded this episode. They do not make the research discussed any less valuable, because the trends were identified- but the execution of them is ever evolving.
More full disclosure: There are two sets of show notes presented below, one created by me and one created by Chat GPT. I have no shame in using GPT to help me with the show notes. It is super interesting to show what I came up with vs. what it came up with.
Even more full disclosure: This episode is also available as a video on my YouTube Channel– Don’t miss any of the fun- Subscribe Now!
My notes:
In this episode we welcome Stacia Sherman Garr co-founder of Red Thread Research.
In our discussion Stacia and I accomplish two things:
We share our perspectives on doing quality vendor/market research, evaluation and classification and doing it well
Stacia relays the results of her research into the current state of the people analytics software technology market and the vendors who define it.
Our discussion begins with level setting to define exactly what people analytics software technology is, and is not. This includes the reality that this technology is not really focused directly on talent acquisition. Stacia defines people analytics technology as follows:
“People analytics is the collection, understanding, and eventually application of people data. It’s designed to improve what’s happening with the people themselves and to improve business outcomes. So it’s really all data that has to do something with people and with an intention of making people’s lives better and making the business better”.
Our discussion into research delves into how to do good research into a specific market, looking both at the vendors themselves and the macro trends. We acknowledge how difficult it is, but also how important it is to those who provide and consume the technology.
Finally, Stacia reveals the major trends in the market and the impact of Covid and advances in generative have had and how they are shaping the future of the market. Covid changed things by introducing mass scale remote work and the increased need for monitoring analytics. AI has created so much change so fast, that Stacia has created an entirely new study to track it.
Trends identified include:
The size of the people analytics software market has grown rapidly- but is leveling off
Prices are dropping
There is a push to more of a SaaS model where implementation and services are handled by 3rd party consulting firms.
There is generally lower satisfaction amongst users of the products. Part of this is because the use cases have expanded but the products were not prepared for this.
AI demands entirely new levels of support
Multi-source people analytics software platforms are the biggest new trend. For instance surveillance and wearables are becoming more in focus- and these can create major trust issues amongst employees.
Here is what GPT came up with when I fed it the transcript of our interview:
Episode Title: “Navigating the Evolving Landscape of People Analytics Technology”
Host: Dr. Charles Handler
Guest: Stacia Garr, Co-founder of Red Thread Research
Episode Overview:
In this enlightening episode of “Science 4-Hire,” host Dr. Charles Handler engages in a thought-provoking discussion with Stacia Garr, co-founder of Red Thread Research. They delve deep into the realm of People Analytics Software, shedding light on its current trends, ethical considerations, and its impact on the HR industry.
Key Discussion Points:
Introduction to People Analytics: Stacia Garr clarifies the term “people analytics” and its significance over “talent analytics,” emphasizing its inclusive and comprehensive nature.
The Evolving Market of People Analytics Software: Exploration of how the field has expanded and adapted, especially in response to the COVID-19 pandemic and the shift towards remote work.
Vendor Landscape and Research: Discussion on the various categories of vendors in the people analytics space, the growth of the market, and insights from Red Thread Research’s latest study on people analytics technology.
Ethical Considerations and AI Integration: An examination of the ethical aspects of people analytics, the role of AI, and how these technologies are shaping organizational strategies.
Trends and Future Projections: Insight into current trends in the market, including the growing demand for people analytics solutions across different organizational roles and the importance of ethical considerations in technology deployment.
Practical Applications and Benefits: Conversations on how organizations can effectively leverage people analytics for improved business outcomes and employee experiences.
Call to Action:
Listeners are encouraged to explore more about the cutting-edge research and insights offered by Red Thread Research. For those interested in deepening their understanding of people analytics and its practical applications in HR, visiting Red Thread Research’s website is highly recommended.
This structure provides a comprehensive overview of the episode, highlighting key points and encouraging listeners to engage further with the topic.
.
You
can you focus on including more of the results of her research as relayed in our discussion?
ChatGPT
Based on the transcript of the “Science 4-Hire” podcast featuring Stacia Garr, here are the show notes focusing on the results of her research as discussed in the episode:
Show Notes for “Science 4-Hire: The Future of People Analytics – Insights from Stacia Garr’s Research”
Episode Title: “Exploring the Evolving World of People Analytics Software with Stacia Garr”
Host: Dr. Charles Handler
Guest: Stacia Garr, Co-founder of Red Thread Research
Episode Overview:
In this episode, Dr. Charles Handler engages with Stacia Garr to explore the latest trends and insights in people analytics Software. The discussion is centered around Garr’s comprehensive research in this field, particularly her study which has evolved since its inception in 2019.
Key Insights from Stacia Garr’s Research:
People Analytics Technology Study: Garr’s research, now in its fourth iteration, initially began in 2019 and offers a unique perspective on the changes and developments in people analytics technology since the onset of the COVID-19 pandemic.
Target Audience and Market Growth: The study targets both people analytics practitioners and HR professionals. A significant growth in the vendor landscape was noted, expanding from about 50 in 2019 to 150 vendors, reflecting a rapidly evolving market.
Market Trends and Customer Satisfaction: The research revealed that the fastest-growing market segment is the ’employee experience and voice’ category, driven largely by the shift to remote work during the pandemic. However, overall satisfaction with people analytics technology has declined, with multi source analysis platforms receiving the highest Net Promoter Scores among customers.
Shift in Use Cases for People Analytics Tech: A notable shift in the use cases for people analytics technology has been observed, extending beyond HR practitioners to include leaders, managers, and employees across organizations. This broader application is necessary for informed decision-making around talent decisions.
Impact of Vendor Focus on User Satisfaction: Vendors that focused on making their technology accessible to managers and employees tended to have higher customer satisfaction scores, as indicated by Net Promoter Scores.
The Central Message of Data Accessibility: Garr emphasizes that data about people should be made accessible to individuals across an organization, not just confined to the HR function. This approach can enhance both individual lives and overall business outcomes.
AI and Ethical Considerations: The future of people analytics technology is closely tied to the development and integration of AI. Garr’s ongoing research is examining AI sophistication among vendors and its ethical implications, highlighting the need for balance between technological advancements and ethical considerations.
Invitation to Engage with Red Thread Research: For those interested in cutting-edge developments in organizational behavior, people, talent, and data, Garr invites listeners to explore Red Thread Research. The platform offers a community for interaction and access to high-quality research.
Call to Action:
Listeners are encouraged to delve deeper into these insights and join the conversation about the future of people analytics by visiting Red Thread Research’s website and becoming part of their membership community.
Unedited full transcript of the episode:
Transcription for: “S4H_Garr.mp3”
Speaker 0: Welcome to Science for Hyre. With your host doctor Charles Handler. Science for Hire provides thirty minutes of enlightenment on best practices and news from the front lines of the improvement testing universe.
Speaker 1: Hello, and welcome to the latest edition of Science for Higher High am your host doctor Charles Handler, and I am here today with a brand new guest, Stacia Gaur, who is the cofounder of Red thread research. And we are going to talk today a little bit about talent analytics and some of the great research that Red Thread has been doing in area. I’m excited because I think there’s a lot I can learn from this and therefore I think our listeners can’t too. So I always love to have my guests introduce themselves because who knows them better than them. So stay safe.
Go ahead and give us some introduction. Who are you? We’ll be up to?
Speaker 2: Yeah. Well, thank you, Charles, for having me. I’m Stacia Gaur, as you mentioned, I cofounded Red Thread five, a little over five years ago with Danny Johnson. And we are a human capital research advisory membership, and we focus on a range of areas, including people analytics, obviously, as well as talent strategy and DEIB employee experience learning careers, skills. Basically, I like to think of it as all the cool stuff in HR with no disrespect to talent acquisition.
We just don’t happen to go there. But all the other cool stuff.
Speaker 1: Gotcha. Well, I’m still gonna push you a little bit into talent acquisition and see how far we can stretch you. But I like going the other way too because this stuff’s all related. It’s it’s it’s people work, data, you know, all that exciting stuff. And we had a chance to present together on one of my favorite SciOP panels I’ve actually been on before about producing research for kind of a non research savvy audience, if you will, and that was really great.
So I I got a real feeling for, you know, your capabilities and kinda how how you do stuff. I felt like that was a great session. I don’t think we can recreate that or summarize it. But just know if you have a chance to to go to Siap, there’s really good stuff there. It’s it’s varied.
So so tell us a little bit about talent analytics. What is it? I feel like that term gets thrown around a lot. Your analytics analytics, this analytics, that and I don’t know what it means specifically.
Yeah.
So it’s
Speaker 2: a it’s a great question. So maybe just as a point of clarification, we actually use the term people analytics. Oh, okay. The reason we do that is I think the word talent can have some baggage around it, if you will. Right?
If you think about where that term came from, it started, you know, really kind of with this idea of almost, like, Hollywood or a movie. Right? You’ve got the talent that’s there, and then you’ve got everybody else. And I think, you know, long time ago when I started my career when we were talking about talent. A lot of it was, oh, we’re talking about high potentials.
We’re talking about the leaders. And then talent kind of became this fuzzy term. So to get rid of that lack of clarity, we use the word people because that’s what we’re talking about is people. And
that also makes clear that it doesn’t have to be employees. It could be contingent workers. It could be
Speaker 1: ours. Right? Yeah.
Speaker 2: So and also, to your point, it could be candidates. Right? So is is there any talent when they are not yet in the organization? Unclear. Right?
So people analytics. People analytics is what we refer to it. And basically, it’s this collection and, you know, understanding and then eventually application of people data and it’s designed to improve what’s happening with the people themselves and to improve business outcomes. So it’s really all data that has to do something with people and with an intention of making people’s lives better and making the business better.
Speaker 1: Yeah. That’s what we’re all about. Right? I mean, I always look at it as two sides of a symbiotic relationship and that’s, you know, the organization can achieve their goals without the people and the people have a real, you know, honestly, psychological contract with the employer, real, hopefully, me finding meaning in their work, etcetera. So it’s great when it works properly, and maybe the data and analytics helps you understand if it’s not working properly or why.
Right? So
Speaker 2: Yeah.
Speaker 1: So what areas? Talk a little bit about areas, I wanna get to your research because I think that’s gonna really provide a good framework for our discussion and maybe now is the time. I mean, tell me about the research that you do. You have some really good intelligence that you all put together. Tell us a little bit about that.
What’s going on? In there, and then we can start talking about what you found, etcetera.
Speaker 2: Yeah, sure. So the most recent study that we released was actually on people analytics technology. And that’s a study that is in its fourth iteration. And we’ve been able to do and, you know, it’s so funny as scientists. Right?
There there was so much that was hard about the pandemic, but for a social scientist, it created all these natural experiments, right, of of, you know, things that have happened and changed. And so what’s exciting about this study from our perspective is we started it in twenty nineteen before the pandemic, and we’ve been able to really see what’s changed and shifted over the course of that. Yeah. It’s been interesting. But, yeah, the the most recent study we’ve done is specifically targeted at the tech market, but it obviously requires a deep understanding of what’s happening more broadly in the market to understand what the tech should be doing.
Speaker 1: That’s right. So if someone is buying that piece of research, and I encourage people for whom it’s relevant to to check it out. Why are they buying it? I mean, what are the what’s the output in general that they’re gonna get from it then, you know, because there’s so many ways that you can look at these things. Right?
So tell us a little bit about that and who your target consumer is for this thing.
Speaker 2: Yeah. So it’s really two sets of consumers, if you will. One is the people analytics practitioner or the HR practitioner who’s doing this work, talent management leader, you know,
potentially somebody with an IO psychology degree who is at Sayop. You know, that group of folks are looking to buy technology and understand what’s happening in the market. So to give you some perspective, when we first started this study, I think we had some fifty vendors in it. Fifty, sixty, something like that in twenty nineteen. This year, we identified a hundred and fifty vendors in this space. So even if you as a practitioner just looked at the market pretty recently, you, you know, two or three years ago if you were implementing some sort of solution, now you’re gonna be, you know, it’s changed. There’s different folks doing different things. They all integrate differently.
I mean, it’s just a different space.
Speaker 1: Yeah.
Speaker 2: So we’re trying to help the buyer understand what’s happening in the market, what’s changed and what they need to be paying attention to and aware of. When they’re starting their buying journey. On the other side of the vendor community, that’s another clear audience for us. And The reason for that is one, they obviously wanna know what’s happening in the market, but two, we want to help influence what they choose to build. So for instance, when we did the first iterations report, we’re talking a lot about people analytics and ethics.
What are what are y’all doing? You know? And and It was right around when GDPR was coming into force and but there wasn’t any similar laws on the books here in the United States. And so, but we are pushing. You know, this is coming.
This is coming. And now here we are. Right? And similarly, this year’s study has a very strong perspective on what the vendors should be building and where they should be going because of what we can see coming.
Speaker 1: That’s so cool. You know, there’s some parallels to the work I did on the, you know, map in the assessment space. Right? I mean Yeah. For me, I’ve done that for a long but during the pandemic, I was slow, and I can’t not work.
So I I dove into that. And, you know, the last one I did had probably, which is admittedly maybe it was twenty fifteen or something. I don’t know. Maybe we had fifty vendors in there. I had three hundred in some and there’s more coming And, you know, it was it’s the same thing kind of just how do we classify this?
What’s going on in the market? I think vendors can utilize that to understand the trends and see kind of what some other folks are doing classified everything. It’s actually pretty fun to do that. I love I love getting the data and then looking at how all the stuff kinda shakes out.
Speaker 2: Charles, I I wanna jump in there because maybe for your listeners, they may not I think this is an important point. You just mentioned the data. Right? A lot of folks, I think, will begin their search by a Google search. Right?
And there will be these kind of generically created lists that are out there. And I think the value of someone like a you or a me actually doing this work is, I don’t know what you did for yours, but I, you know, ran we ran actually three separate surveys for ours. And we talk to vendors between, you know, forty five and sixty minutes for everyone who’s in our survey or in our study. We’ve been at least within the last two years Some of them, they’re like, hey, nothing’s changed. But keep going and you ignore it.
But Yeah. You know, and and so that’s fifty four vendors for this year’s study that we actually
looked at. And I think, you know, that can give folks a better sense of the nuance because the marketing is everywhere everyone’s gonna see, they can support everything. And these, you know, generated lists are just pulling from that marketing. So I think, you know, important to make sure that you’re talking to people and getting insights from people who have actually seen the software and talk to customers as opposed to just, you know, starting with the generic Googles, or you can start with a Google search all the way.
Speaker 1: Of course.
Speaker 2: But don’t stop there.
Speaker 1: Well, now you can ask Chad, TPT. Although, it’s not as good with names
Speaker 2: But it’s not it’s only till twenty twenty one.
Speaker 1: Yeah. I know.
Speaker 2: Funny story for you. I was working on a blog because, you know, I go to all these vendor a lot of these vendor events and I was trying to write a summary of what was there. And my husband is just like a I’ve used chat GPT, but he is, like, over the deep end on chat GPT.
Speaker 1: Right.
Speaker 2: And he’s like, you need to use chat g p t to write this blog, like, what are you doing? So I write my outline. I pop it into chat g p t. It was not good. The result was not good.
I could not have stood behind what came out of that thing.
Speaker 1: Yeah. I mean, I’ve had some similar experiences and I feel like every one of these podcasts no matter what it’s about. I’m gonna not mention chat, GPT, and then it comes up everywhere. So there’s no there’s no stop in that thing, but Yeah. So mine was a little different in that, you know, in the past when I had less vendors, I did a survey.
Mhmm. What I ended up finding in that situation is I would get a lot of marketing copy. Just blast it back at me. Or Yeah. I also did ratings.
And so if I rated somebody in an unfavorable way or whatever, then I, you know, it was just because I let them review their their things. I would I would end up having to change stuff and just all these, you know, gyrations and stuff to to make sure everybody’s okay with what I’m publishing. And I with the volume of what I had and that experience. I basically relied on my instincts and knowledge and experience of looking at what people are doing and figuring it out. And also, you know, deep review of their website, other information.
A lot of these vendors I know, and so I I created self rated parameters. I had, like, twenty eight parameters, you know. So how much risk they have? What their methodology is? Worked out really well.
Then we made a dataset and we crunched the data to look for trends and stuff. And I feel like what we what we got was great. We also did a market market growth and, you know, market value analysis was was in there. So there’s there’s a lot of fun stuff but it wasn’t quite I I wish I had the resources to have done an interview with everybody, but the scale of it just didn’t support it. But anyway, Enough about me.
Speaker 2: The scale is challenging. I told we just finished this thing, and I’m, like, coming up for air.
Speaker 1: Well, have to do it again. Right? So that was the other thing for me. It’s like, oh my god. Now I just did it.
Now by the time it took me a year, now I gotta go do it again. So I need a staff, but my area is so esoteric compared to the broader area you have. I think that’s where it’s a little bit harder. You know, we built an online database with filters and you could subscribe and all that. That worked pretty well, but not enough to sustain, you know, a company like you all do.
So back to your stuff. So, I mean, you’ve got vendors in there. Are you do you have a market map, a classification system, trends. Like, tell me a little bit about the substance about the vendors that you have in there and how it fits into your holistic, you know, picture.
Speaker 2: Yeah. So, I mean, the answer is yes. We have all of the above. So, we actually divide our study into two. The first one is a trend study that just looks at what did we see growth rate of the market?
How it’s changing? Who – which parts of the market did especially well, etcetera, etcetera? And then the second part of the study is that vendor landscape and is very much so, like I said, that one with the strong perspective on what needs to happen on the market.
Speaker 1: Uh-huh.
Speaker 2: So yeah, so so two two parts of it because, you know, to your point, you know, the trends are interesting, but then people also wanna know what’s happening with individual vendors.
Speaker 1: Oh, yeah. They fit together really important because the vendors are are a big part of the trend. Right? And I think there’s there’s a two way street. You know, a lot of times vendors come up with stuff the market looks at that and says, oh, man, I could really use that.
That’s feeling a white space or a need. But at the other end, you know, the vendors need to be listening to what the market wants. It’s it really needs to happen in both directions. But I feel like, you know, in the assessment space, the vendors have driven the change more than anything because they’re providing the substance. Right?
And companies who are end users might come up with a lot of great ideas, but they can’t really build that. And if you have a SaaS product or, you know, a lot of clients, it’s difficult to start making all kind of different changes just because one client wants something. I’ve been there. That’s that’s brutal, you know. So Yeah.
Speaker 2: No. That’s really hard. Yeah.
Speaker 1: That model And
Speaker 2: I think, you know, what we’re we’re we have the opportunity to do we, you and I Yeah. Those of us who do this work, is to bubble up some of the things that we’re hearing and also that we can see across the different vendors that maybe, you know, for them, it’s one potential feature or one potential direction of a bunch they could go in. But when you see that, hey, you know, sixty percent of the market is thinking about going into that direction, either you
realize that’s table stakes and you gotta get there or you realize that’s not something that you wanna do at all and you wanna do a different direction. But it it can really inform your thinking as a vendor in terms of the strategy on product development.
Speaker 1: Do you let I’m just curious, do you let the vendors look? Like, do you do a profile where you have straints and weaknesses and all that. And do you let them look at that and have any input to tell you? Oh, you can’t publish that? That’s not us.
You missed it. You know, that kind of stuff.
Speaker 2: We do within our platform, our membership platform, we have a tool. Yes. And so it includes our write up and assessment of them. We don’t do weaknesses because I think weaknesses is a little bit honestly, it’s a little unfair because what could be a weakness what I could perceive as a weakness could actually be a strength for one of their customers
Speaker 1: Right. Right.
Speaker 2: Whoever it is. Right? So I don’t think that that’s that so we don’t take that approach. We also don’t rank. That’s and that’s a very deliberate decision because we have a a foundational belief that you know, there can be different vendors that fit different needs and
Speaker 1: I represent.
Speaker 2: I’m not in any place to say, know that. Right? But what I can write about and what we do write about is we we will say we give a high level of review. We say what excites us, like, what we did is really great. We do things that you should definitely note.
So, you know, for instance, if someone just got a big round of funding, or if they have, you know, it just made some interesting hires. You know, that may not excite us, but you should probably know it.
Speaker 1: Sure.
Speaker 2: We’ll do things that are coming up on their roadmap as well. And then also will include things that have within the context of that roadmap kind of things that have been done that were on their previous roadmap. So all of that goes into the tool, and then also we include in their case studies, we include some screenshots if we have we include basic demographic information. So how many employees they have, what year they were founded, where they’re based, you know, all that kind of stuff that might matter, but it can hard can be kinda hard to gather together for folks.
Speaker 1: Yeah.
Speaker 2: To your question though, you asked, you know, did we let them see it? We do let them see it. And mostly, that’s because to your point about volume and scale, like, at some point, honestly, around vendor, like, twenty three, your eyes plays over a little bit. Right. And and you’re like, like, I had someone who was like, station, like, this was a major thing.
I’m like, yep. You’re right. Go back and look at the notes. I There was me not in my head saying yes. And I just missed it.
So, you know, it’s good to have the give them an opportunity community. Sometimes they will say things or at or want me to say things, and it’s a pretty hard no. Like, I’ve had a lot of Yeah. Have probably more no conversations than yes conversations.
Speaker 1: Yeah.
Speaker 2: But usually, it gives me an opportunity to explain to them why. You know? And I think so much of it is the learning and the dialogue around, okay, you think this is really important, but here’s why I don’t think it’s actually that interesting. And and I think that is where the learning happens on both sides. And then we can then magnify that to our practitioner clients who want to know more about what’s happening in this space.
Speaker 1: Awesome. So we’ve spent a decent amount of time with the setup. Now we can move maybe into the reveal a little bit. So I think as we would start talking about your findings, it might make sense to talk a little bit about the vendor landscape first so people have some context around what you’re looking at. I don’t think you really need to to dwell on specific vendors, but more your classification you know, what who’s in your vendor landscape in terms of the various categories of vendors and and what they do because that we wanna know that before we start figuring out what your transit findings are.
Speaker 2: Yeah. Sure. So we have seven categories in there. One is workforce planning. Second is labor market intelligence.
So that is obviously a lot closer to your world. Third is HCM, HRS vendors who have a robust analytical capability within them. Mhmm. We have what we call multi source analysis platforms, which are those platforms that gather data from a bunch of different sources and bring it together for analysis. So think like a busier or a cruncher would be examples.
We have employee networks and communications, so those are the O and A vendors primarily.
Speaker 1: Like a glance or something like that, you know, like listening stuff?
Speaker 2: No. No. Like a no.
Speaker 1: Okay. Gotcha.
Speaker 2: This would be so, like, apology.
Speaker 1: I haven’t heard of that.
Speaker 2: An example, yeah, they’re a small vendor. But yeah, they’re they’re folks who are doing yeah, that that organizational network analysis, understanding how the network itself actually is created and who goes to whom for what type
Speaker 1: of Oh, wow. That’s so cool.
Speaker 2: But frequency Yeah. It’s it’s, like, some of the coolest stuff out there. And then we do learning analytics is another one. And then finally, the one that you mentioned is employee engagement experience and voice. So that is like, Glint and Receptics and pecan and medallia.
It etcetera.
Speaker 1: So why isn’t it? Tell an acquisition in there. It’s just I’m not I’m not beating you up or anything, but it just seems like it would be a natural piece to go in there, you know.
Speaker 2: You know, it would. The thing is and this is where I’d love your guidance. But we have not ended up coming across that many vendors who just do the analytics piece.
Speaker 1: Oh, right. Yes. Yes. Of course. It’s analytics focused.
Yes. You’re right about that. It’s it’s a piece. Everybody has an analytics dashboard. There would probably be some companies right now if they’re listening, jumping up and down going.
We do that. We do that. But that’s fair enough. Right? This this all these other areas have just maybe dedicated analytics pieces that a talent acquisition may not I’m trying to think myself. Right? Because, again, if you have an ATS system, right, or platform. I mean, there’s a lot more going on than just the analytics. The analytics are a byproduct of what’s happening there.
Speaker 2: The only kind of similar equivalent Well, so there are two similar equivalents to that with what we have, and one is HCM. And then the other one is the employee engagement experience. Because, obviously, those serve in employee facing they have an employee facing component as well as the analytical component on the back end. But all but both of those have it is not just a dashboard. Right?
So analytics is not a dashboard.
Speaker 1: Say that again. That sounds like a theme.
Speaker 2: Analytics. It’s not a dashboard.
Speaker 1: It’s more than that. It’s more than that.
Speaker 2: Yeah. So we think, you know, we’ve made that exception because of the robustness of what’s in those tools. And then and I think, you know, we we just haven’t seen the same thing with Towne acquisition. Again, if y’all feel differently, You can reach out to me on LinkedIn.
Speaker 1: There you go. There you go.
Speaker 2: Talk to me. But we just we haven’t seen it. Similarly though, actually, Charles, learning analytics, we only have one vendor in that category. And I know a category one does not equal a category. A couple of other ones who are in their bought up.
Speaker 1: Oh, okay.
Speaker 2: Gotcha. So it used to be more you know, I think there’s a similar opportunity to do more with learning data in the same way that with TA data.
Speaker 1: Interesting. So when and when you say HCM, are you talking about something like a workday or Oracle, like a big giant system of record that does all kinds of stuff?
Speaker 2: Yes.
Speaker 1: Which which is a trend in itself that we’re really moving toward a lot more of those. Cool. And I bet you’ve probably found trends within each layer each of the seven and then some that that transcend and, you know, in the interest of time kinda going through each and every layer may not be worth it. But if we wanna start with with those individual threads, I would call them whatever, is there anything across those seven that’s more unique to that strata or that classification of people. That’s worth the time talk about here before we go into what are the macro trends when you kind of looked at everything.
So any findings that you feel are relevant to any of those seven, specifically that you feel our audience would wanna know.
Speaker 2: Yeah. So I think a a couple of things. One is that when we think about just growth of the market, as you would expect, the employee experience and voice category has grown the fastest. And that has largely been driven by the pandemic. So, you know, as we went to remote work, people couldn’t look around and feel like they knew how people were thinking and feeling, etcetera.
And so they needed a mechanism to do that. And then they kept with it because they found that the data were powerful in helping them make decisions and particularly, you know, as we had you know, return to office and now, you know, whatever we Who knows?
Speaker 1: Who knows?
Speaker 2: Who knows? But but they’ve they’ve kept it and that’s kept up the the drive there. The second area that’s I think interesting is what I call multisource analysis platforms. Those actually have had the highest Net Promoter Score. So those have been amongst the areas that have been most satisfactory for for customers.
And I think some of that and that that stands in contrast, I’ll mention now, in that overall, we’ve seen that promoter score in People Analytics Tech decline. So they’re doing better, but everybody else is doing. A bit worse. So that was interesting.
Speaker 1: Gotcha. So multisource analysis is at the tell us a little bit about that category. What what defines multisource analysis?
Speaker 2: Yeah. So those are the folks who are bringing in data from all these different places. So
Speaker 1: Right. Right.
Speaker 2: Like you said, the the visitors or the hunters. Right. You know, all that kind
Speaker 1: of Snowflake?
Speaker 2: Not snowflakes. So this actually it gets to a good question, which is what is actually people analytics tech? Because I on LinkedIn, I was posting about the study and someone was like, well, why is Excel not on there? Are you pretending that people don’t use sell for people analytics? No.
I know they do. I do. We all do. Right?
Speaker 1: Yeah. We all have to Our
Speaker 2: definition of people analytics tech is that it’s tech built specifically to analyze people data. It is not analytics tech that has a people data use case. So Snowflake Mhmm. Tableau. Any of these others not pretending that those are not used.
They absolutely are. But as we’re thinking about this market, we had to draw some lines somewhere, and that’s where we chose to draw.
Speaker 1: Gotcha. And we will talk I I would bet pretty much anything I have that as we talk about the macro trends that AI will be ever present in there or a huge theme. But, like, if you look at these seven areas, is there any one area that is really more advanced in using AI or has really, you know, has you going wow. This is exciting versus the other ones I’ve seen. I’m curious about that.
Speaker 2: Howard Bauchner: Yeah. So the multi source analysis platforms, again, are the ones that I’m seeing the most advanced focus on on generative AI and advanced AI in general. And the reason for that is that they are the ones for which there is the most natural use case because it’s a big big data sets different places. Right? And you think about the power of what LLM can do.
Like, for instance, I was just reading yesterday about somebody in the people analytics community had posted about how chat GPT just with two prompts was able to basically take two different data sets, meld them together, and then produce the outcome that they wanted. And again, we did two prompts in about ten minutes, whereas a normal person, you know, even with technical help would take four hours, baby.
Speaker 1: Yeah.
Speaker 2: Right? And so you think about this with multi solar powered analysis platforms. They’re bringing in great big data sets. They’re trying to figure out how to merge them. And then synthesizing what’s actually there within the data.
And and again, it’s just reading it. It Yeah. I put air quotes around that. But it’s, you know, saying something like, it appears this or this is what’s been generated. It it’s just a literal interpretation what’s there.
But that has a natural use case in a way that some of the others, they they they do have use cases, but they think they’re not as economically powerful. And by that, I mean, that for the multi source analysis platforms, that is the core of their business is bringing data together and and showing interesting insights. Whereas some of the other ones, and not as big a data sets, being able to do this faster won’t necessarily improve their user base or the extent of services that you provide, etcetera. It might give higher quality service or experience, but it won’t necessarily change. So, anyway, so that’s why we’re seeing it there, I believe.
Speaker 1: Gotcha. Very cool. So here we go. We’ve just been thirty minutes setting it up. So tell us what are the trends in people analytics that everybody needs to know about.
What what are the things that rise to the surface from your study, not specific to each area, but just in general tell us what we need to know? You’re the expert.
Speaker 2: Yeah. Sure. So I think the first one is as you might expect that the markets continue to grow. So we actually put the market at an estimated five point six billion in twenty twenty two,
and that was a growth from three point one billion in twenty twenty one. Still.
Gotcha. That and we were seeing a fifty percent CAGR for the last three years. So really strong growth. Obviously, it’s the biggest market in the whole world, but it has grown. Like, I remember when we hit a a billion.
And I was like, whoa. Yeah. It’s a real thing.
Speaker 1: Yeah. Yeah. Yeah.
Speaker 2: So I think that’s one one thing. That said vendors like everyone else were expecting this year to slow down, and that’s what we’ve seen. So I think that that rate, I think it was a forty one percent growth rate between twenty one percent and twenty two. Just for a technological sake, we asked everybody to give us their end of year numbers for twenty two, so we had the most accurate actual numbers, I guess. So that’s where those numbers are from.
But, anyway, so it’s gonna grow a bit more slowly. Customers’ needs are being met. Like, there’s a lot of new capability being developed in in new products. What’s interesting is that one, those are being offered at a slightly lower price point than what we saw before. But the way that vendors are making up for that is they’re charging more for services.
So things that used to be bundled to support rollout are now getting pushed onto the client. And instead, the client is having to either engage a consultant or to use internal resources to actually get these products into market, which if I’m a vendor, I can see the financial reasons for doing so. But as you and I both know, Charles, so much of implementation success is due to how you actually get it out there in an organization. So I don’t know Yeah. This is a good long term strategy for these vendors, but is what we’re seeing.
Speaker 1: Yeah. I mean assessment has got some definite pieces there. I mean, again, it’s not a direct parallel. But, yeah, there’s there’s things you have to do both technically and change management wise and, you know, the more we move towards SaaS, SaaS, SaaS, it’s there’s always a tension there. But there’s there’s gonna be I think pieces of the market that’ll pick that that slack up.
It just may end up crossing. So anyway, sorry. Go ahead.
Speaker 2: The other thing is we and this is kind of the big takeaway this is the big takeaway from the second study, and that is that People analytics tech users are less satisfied with the solution with the solutions than in years past. Right. The reason for that is, we believe, anyway, is that the use cases for people analytics tech have actually shifted So they’re not just people analytics practitioners or HR practitioners, but increasingly it needs to be across the organization. So people leaders, HRBP’s, some of whom uses, some of whom don’t, senior leaders, managers, and then eventually employees will have need to have more access to these data to support their decision making around talent decisions.
Speaker 1: Gotcha.
Speaker 2: But but not that many vendors actually focus on that. But we did find a statistical significance between if vendors focused on making the technology available to people managers and employees, we found that they’re tended to have higher NPS scores.
Speaker 1: Interesting. So in other words, in a lot of cases, there’s there’s one trained expert or one cadre of trained experts who kind of sit over this oracle of knowledge and and everybody else doesn’t necessarily get to partake as much. They just see the outcomes you’re saying that you can you can spread the use case.
Speaker 2: Yeah. I think the the key though is it’s not just spreading it’s not spreading the use case. There are different use cases for those different audiences. And in the past, I think that the space was trying a bit of a peanut butter approach, you know, and so what you or I want as data analyst because we like data, and that’s where we live, is very different than potentially, you know, somebody else in the organization who’s you know, just just tell me what I need to know and and let me move on with my life. And that needs to be reflected in the tech.
And often it’s not, or it has to date not been for some of these solutions. Some of the ones who are doing better are more effective at that.
Speaker 1: Gotcha. Interesting. Interesting. Anything else? What else do you have to surface here as we wind down?
Speaker 2: You know what? There’s always more Yeah. I think that each of the papers are fifty
Speaker 1: Yeah. Exactly.
Speaker 2: But but I think, you know, the big message is is that data about people should be coming to the people.
Speaker 1: Yeah. You
Speaker 2: know, it is not it should not be data about people for the HR function. It should be data about people that enables people to do something powerful, meaningful, insightful. That will again make people’s lives better and make business outcomes better.
Speaker 1: There’s always interesting parallels I think in technology between business and consumer. Right? And the more that we are exposed to consumer technologies that help us, the more we expect on our job to have those. So, you know, I got the the aura ring and it tells me all my stats. So I’m starting to get data about myself or even my financial planning software, whatever it is, it’s tracking what I’m doing.
I’m putting inputs into it, and it’s helping me run my life, hopefully. And, you know, if you’re not getting that when you go to work, there’s plenty of data at work. Right? So I feel like that’s a real parallel. Our our expectations are set, and I always talk about, you know, a consumer grade experience.
I think in the assessment world, we don’t always get that consumer grade experience that we would want, and maybe that’s across the whole world of HR tech or whatever. But but it’s just not the same. So we want it we want anything we do to look like our banking or our shopping or whatever, you know. Cool. So tell us there’s obviously, you’re gonna have focus.
What’s next? What’s the next big thing that’s gonna be driving this space? I know that’s a broad question when you’ve got a lot of stuff, but what should we be on the lookout for as your pro prognostication?
Speaker 2: Yeah. I mean, the obvious answer is AI, but I think that what it’s going to look like is going to vary a lot in these organizations. And particularly when you think about the intricacies of the ethics, legal, and privacy aspects
Speaker 1: of
Speaker 2: this, that is going to dramatically change this space. So we’re working on a report right now. It’s a follow on from this one, which will have the vendor’s AI sophistication based on index that we had in there. And then we’re the we’re Now, I’m not gonna make any promises because I haven’t read this report yet, but we’re comparing that to their answers on the ethics questions in trying to
Speaker 1: get a sense
Speaker 2: of what that, you know, for instance, for those who are farther along on the AI journey, to what extent are they actually helping their customers educate their consumers of the technology. How are they, you know, helping some of these insights be used but within appropriate bounds? So I think this this very significant balancing between AI and the tech that’s possible, and the ethical considerations is gonna be a big deal. And and that and we’re starting to study it. I know lots of people are, but they think that is something that’s going to be powerful.
Speaker 1: Howard Bauchner: Yeah, you know, I think about it. Immediately just what came to my head is even things like surveillance, wearables, you know, if you’re talking about analytical data, and that may not trip into what you’re doing. But, you know, there’s already there’s some really good research about how people’s attitudes change toward an employer on surveillance, and there’s been a history of of different interesting, and there’s a book I already called The Future of Work, which is probably like twenty years old, but they strap little little things onto everybody in the workplace tracked all the data, you know, what path it took to the water cooler, all kind of crap. And then looked at what the findings were. So people have been thinking about that.
It’s just one of those examples of what may not what may be possible, but not, I wouldn’t even say ethical, but just not palatable or acceptable to people. And we’ve got to look at those guard us, you know?
Speaker 2: Yeah, for sure. And, you know, it’s during the pandemic, a lot of that came out, you know, monitoring technology and I can’t tell you how many major publications I spoke to who were like, well, tell me all about this monitoring stuff. You know, those people are sitting in their seats and if they’re moving their mouths and if they’re, you know, I mean, it’s just this nonsense that, you know
Speaker 1: Yeah.
Speaker 2: Breaks down the trust between the employee and the organization, yet many people deployed it. And if you think about that, more broadly, it certainly has the potential to do harm. Pretty much every post I I tag on the subject, you know, I use RL l Adams and people data for good because I think we gotta keep in mind is this for good. What decisions are we making? And if what you’re doing would end up with your name associated with it on the cover of the Wall
Street Journal, how are you gonna feel about that?
How is the average person gonna feel? And if they’re not gonna be okay with it, you should not be doing it.
Speaker 1: Yeah. That makes total sense. Good stuff. Well, I really appreciate the rundown. This is a realm.
I have it. Worked in, but I’ve been right up next to, and it does deal with people in work. So that’s obviously under the umbrella. But really good to know there’s quality research out there that looks at this stuff, you know. I think the background in where you’re coming from is is extremely helpful and the quality of what or relaying, you know, to the point of our Saipa conversation or Saipa panel.
So tell everyone, I mean, we’ve talked about your company. Obviously, in this day and age saying how can people find you? To me, it ends up being a little irrelevant because Google will do that for you pretty quickly. But anything you wanna do, any kind of plug, any kind of and this is your minute or two here to to have your voice on anything really that you want.
Speaker 2: Yeah. So I think, you know, for for those of you who are interested in the cutting edge of what’s happening with in organizations and and people and talent and data, we’re we’re your place. You know, we I have I worked at CED Gartner before. I was a person by Deloitte for eight years. You know, I’ve kind of done the the whole thing and And we are very firmly looking at near future problems and helping people solve them practically as opposed to just, you know, way out there in the future of work or looking back at practices that may or may not work today. So if if that interests you, if you’re excited about the super fun stuff in HR that will actually change how people behave and and do good things in their organizations, we’re a good place for you. We have a both research and a membership community, and a way for people to interact with each other and ask questions and leverage the research and all that good stuff. And our idea is really just to get together cutting edge folks who wanna do great work. So if you want to join us in our mission to create high quality unbiased research, check us out. We’re at thread threadresearch dot com.
And if you wanna join the membership, it’s just members, period, red threadresearch dot com.
Speaker 1: Awesome. So what what just came to mind, I’ll close out as earlier on, you were saying, hey, there’s lists available for your Google searches. Right? And I found the same thing with the assessment stuff. But there’s a huge difference between somebody cultivating a list with a few lines about each company versus actual real intelligence.
And You know, one of the other things I found, I spent a lot of time in my career helping enterprise companies acquire assessment technology, find the right vendor, etcetera. It’s always so interesting when these companies come to me with a list of here’s who we think we ought to look at. The usual suspects are usually on there, but there’s so many times companies that do nothing related to what they’re looking for that they just might have heard about or found in the search and you know, I would just encourage people who are looking to acquire this technology. Spend a little bit of extra time, money, whatever, to align yourself with an expert. Who can really tell you what you need to know before you dive in.
It’ll save you a lot of time and potential heartache of going in the wrong direction because once you get in front of those salespeople, which are sales it’s a noble thing we love, so we have to have salespeople and we love them, But, you know, their job is to land the deal, not to say that
they would lie or anything like that. That’s not the case. But at any rate, you you know what I’m saying. Objective unbiased information that’s real is good. So thank you so much for your time. Very enlightening conversation today.
Speaker 2: Well, thank you for the opportunity, and thanks to everybody for
Speaker 1: As we wind down today’s episode to your listeners, I want to remind you to check out our website rocket hire dot com. And learn more about our latest line of business, which is auditing and advising on AI based hiring tools and and assessment tools. Take a look at the site. There’s a really awesome FAQs document around New York City local law one forty four that should answer all your questions about that complex and untested piece of legislation. And guess what?
There’s gonna be more to come. So check us out. We’re here to help.