So, hi. My name is Caitlin Cam. I am an attorney by trade, a recovering attorney, some may call me, and I'm head of people at Envoy.And so over the next few minutes or so, I'm gonna talk to you about happiness at work, but probably not the way that you've actually heard about it previously. My background is actually in law, not in HR. And what I've learned is that the same analytical rigor that you use in legal analysis and as an attorney is the same analytical rigor that you should use when interrogating assumptions, also known as best practices, rely on evidence over instinct, solve problems with structured analysis, and recommend a solution as a defensible decision.

And these are exactly the things that people work has been missing. So I'm going to show you how to turn happiness into from an abstract concept that it is into something that's actually measurable and how that shapes real decisions at Envoy. Here's the reframe I want to start with. Happiness at work is too often treated as a perk or as an an entitlement. A nice to have, a good mood, something we throw at retention, retention bonuses when things get rocky. Right? That's the old story. What it actually is is a piece of data. It's a set of measurable signals that we can act on. And when I say happiness, it's a bit of a cheeky it's a cheeky term because I happiness is there's a lot more underneath that. There's purpose. There's meaning.

There's connection. There's progression. So all of these things kind of ladder up into the happiness term. And my legal work and training taught me to follow evidence over instinct. And quite honestly, happiness feels a bit more like a gut instinct than it does necessarily hardcore evidence. But I'm here to show you how we can turn happiness into an actual piece of data that is evidence to support a program. So when I say happiness, I mean something that we can observe, measure, and use to make better decisions. In other words, it's a data point. But here's the core idea for this segment, which is happiness is not the goal. It's a signal. It's an input. It points to deeper things, things like purpose, pride, progression, how people connect to their work, clarity, and it's something we can listen for at every stage of the employee life cycle.

So the natural question, how do you actually measure something like purpose, clarity, connection, pride, all of those things, satisfaction? You can't put morale on a dashboard. You can listen to it, but we're not gonna see any leaderboards with morale competitions. But you can listen for this. So we do that listening by three different directions and looking for patterns where they overlap. So I want to walk you through our particular framework. So here's how we approach it. There are three types of signals that we watched. We didn't watch any single one of these in isolation. Okay? We read across them. Because when we're talking about happiness, we're talking about so much more than what's, like, might might be written off as a a simp like an emotion or or something, transient, like a transient feeling. Right?

We want to watch both transient moments as well as, like, repeated moments where we where we are asking these questions, different moments throughout the cycle, and using different methodologies, whether it's bottoms up, like engagement surveys, or whether it's, say, more of a top down process, like giving an a manager and leadership upwards feedback.

Or it might just be surveys that come along with really important moments that matter, whether that's the thirty first thirty, sixty, ninety days of a new hires journey or whether that's for, like, a really big all hands and big learning investments. So, again, these are sort of the three areas where we wanted to look for patterns in the signals that we were getting from this data. And so just to dive in a little bit more in more detail, We've moved away from rinse and repeat questions. So what we changed the first big piece that we changed about this whole process was not doing the same thing that we have always done, which is having our semiannual engagement survey that's 30 plus questions long, that is getting that is asking about a long period of time, but is highly dependent on how the person feels in a moment in time.

And so we decided instead to take an approach of asking fewer questions, and this is across the board. We are asking fewer but better questions. It is more qualitative and more open ended, and that is actually to get deeper insight. So on Upward manager and leadership feedback, how people experience the leaders above them and what those leaders are seeing from the ground and on life cycle, targeted touch points across the employee experience, candidate feedback, benefit surveys, pre and postmortems on programs.

The anchor line for all of this is that we're not just collecting a data point or an isolated set of data signals. We're building a three-dimensional, three sixty view of data and what that may or may not mean for future action. So, again, we're being very deliberate about what we ask and why when it comes to collecting data. The bulk of the time I wanna spend here, though, is the part that actually matters. What do you do with the data? Okay? You have the data. What do you do with it? And oftentimes, we have probably more data than we need. We need the time just to analyze it and understand it. And so one of the big one of the big learnings I've always taken with me from law school is a method of legal analysis called IRAC.

That is not the country. IRAC is an acronym, I r a c, and it stands for issue rule analysis conclusion. And this is pretty much what every law school, you know, charges you a couple $100,000 to learn. But I digress. But this syllogism is truly one of the most impactful ways to evaluate evidence, and data is evidence. So we followed a very similar framework at Envoy in that we evaluated the scope of the data needed. In other words, what's the issue here? How much data do we actually need and from where? Two, design of the collection. Right? I e. The rule for understanding data. And three, leveraging insights for systemic solutions. So I e, the analysis and conclusion part of the IRAC formula. Okay?

So I'm gonna walk through each of the three prongs, described, and I'm gonna use examples to illustrate this approach to give you a better idea of how this landed. First, we surfaced a key theme, a signal, in our engagement and our manager upwards feedback data. So one question that was consistently low now our baseline was not low. So we scored a 72% on my manager helps me connect my work to my strengths, and I know how my work connects to my strengths. We received about a 72%, which is which is a good engagement score. But compared to the other data points we had, this was the biggest area of opportunity for us. And so we doubled down on that. And after targeted very targeted action that was specifically intended to just move that one question on the engagement survey and that one question on the upwards feedback, so we knew exactly what we were measuring for.

After the program, we took a new we took a refreshed measure, and we had a 17 improvement of almost 90%, which is incredible. I mean, now it's one of our top performing indicators. So what was that in between? Like, what did 72 just end up at 89? Absolutely not. We had to get crystal, crystal clear on which is opportunity for a solution to actually fill the gap? Like, will will this thing actually solve the problem that we have? And it was an experiment. It we didn't know which way it was gonna go. Our programming, though, was highly focused on, essentially, making sure that we utilize qualitative feedback to inform our targeted manager programming. So we beefed up our manager enablement around strengths. We equipped our managers with tools to align work to individual strengths.

We gave them, their team assessments, and we have an internally built tool that allows them to create, enablement plans based on each of their, direct report strengths. And then lastly, we reinforce the message in all hands, which is a big annual, moment that matters for Envoy and broader enablement moments, an example I will walk through next. But just to highlight again, the takeaway is when you act on feedback in a very focused way, you can move the needle in a measurable way. And the lesson here is to get really specific and focused on the data point to measure to help you isolate the root cause issue. The more clear you can get on each of these pieces in the analysis, the better off you will be when it comes to demonstrating the value creation from the program that you established based on said data. Second example. This is about the surveys themselves, so it's a little meta.

We used to run like, I mentioned this earlier, we used to run, rinse rinse and repeat engagement surveys, 30 plus rating questions year after year. The data was wide but shallow, and we interrogated best practice research on engagement drivers. And we got brutally honest about what we really need to know. And this allowed us to shift to a more intentional approach. We got fewer and better questions, mostly open ended. Things like what's getting in the way of you doing your best work? Where do you feel most connected to work and why? The takeaway here isn't so much like the questions that we asked. It's it's that being data driven isn't about just getting more data. It's about asking better questions and using tools at our disposal like AI to help remove friction.

I think AI has taught us that asking the right question is a big piece of the work involved because, you know, you definitely wanna prompt AI correctly. But I do wanna just emphasize that Envoy is a huge data company, and we love data. And we have to be mindful that we don't always need every single piece of data. You need data that you need for a particular reason, and to be very honest with yourself about what that is. Okay. Third example is our modern angle. So what new insights can be generated with the data you already have? Because you probably have lots and lots of data. And now with AI, we can leverage that so much more powerfully. But you have to be really intentional to to actually drive that value creation.

So at Envoy, we started running a biweekly AI show and tell because a lot of people were building kind of, like, on their own, and we didn't know what everyone was building. And some people felt like they could learn from others, and we thought, hey. This is a great way to build a little community, get a little innovation in there. We started having AI, S, and Ts. And, basically, anyone can demo their solution, and peers are teaching peers. And what they ultimately share in the a AI show and tell is, they're ultimately focused on reducing manual repetitive work and streamlining workflows, but they also will show fun projects that others may wanna learn from and things like that. So it is a fun, it's a fun event, but it is it has a business reason. And the business reason is that AI doesn't replace work. It removes friction. So we wanted to be clear that for us, AI actually is something that might create more work.

Right now, everybody come to your s and t, show how you made it. Like, that technically is more work. However, AI removes friction, and so you have more time to do higher leverage work. And there's the opportunity. So those were kind of the three examples that helped us get a framing around this. So data, right, being the behaviors or actions we can observe, measure, and use to make better decisions, expanding the definition of data, but narrowing the scope of it. Expand the definition of data and that all data is data now. Like, AI can analyze it all even if it's just a long text response. So now we have more data than we've ever had before, which means it's all the more important to really narrow your focus on the issue that you are trying to solve for.

And, of course, sorry. And, of course, sorry. This is my, oh, tech issue here. There we go. Okay. Everyone can see that slide. And so just to kind of pull all of this together, I wanna talk about the attorney's rigor applied to people work. So the reason why this approach works for us because it's a little unusual, I know. Maybe for some, it might seem unusual for someone to go from law to people operations and how does that really, like, overlap. But the four things that I've carried over that I think might be helpful for everyone here is structured thinking. Anything that feels ambiguous, fuzzy, like, a fuzzy concept, like morale or happiness, break it into, like, tangible parts that you can battle with and really decide, like, which are the ones that are important to you and which are the ones that are not.

Make sure that you are recognizing patterns. You have to look out for the patterns across disparate datasets and find the through line. That is what will help you make sure that it that you stick to a solution that's actual actually gonna solve the issue you have identified. And then evidence based decisions, making sure that you turn subjective feedback like commentary into something you can put in front of an executive team. Take a qualitative data analysis approach. Break the break the full form text into component parts, and measure those. Right? So those are some of the ways there. And credit lastly, credibility under pressure is very important. And I probably don't need to tell anyone who's an HR practitioner this piece. But trust is everything, and data builds trust at the end of the day.

So the better your data, the more correct it is, the more clear you are on it, the more credible you will seem or the more credible you will be when you, in fact, have to present that data in order to justify a programmatic change, pivot, or new solution. So, again, data builds credibility, and that's particularly when the decisions are complex or high stakes. So to bring it home, three things I want to want you to take with you. One is measure signals across the life cycle, not just one survey, because a survey is a moment in time. So you want multiple touch points. Be intentional about what you collect. Right? We want fewer, better questions, beats beats more volume, shallow depth kinds of questions. And third, act on feedback visibly and consistently. That is how you build trust with the entire organization. Do not ask questions that you are not prepared to answer and act on. Okay.

Thank you so