Latest / Career Coaching with Fexingo: Professional Coaches, Career Counselors, and Guidance for Workers / How a Career Growth Experiment Can Reveal Your Next Move
Transcript
- Lucas: So Luna, I've been thinking about how many of us treat our careers like a final exam rather than a lab experiment. We wait until we're absolutely certain about a move before we test it, and by then, we've often missed the cheaper, faster ways to learn. Luna: A lab experiment — I like that. You mean like, instead of quitting your job to become a data scientist, you try it on for eight weeks inside your current role? Lucas: Exactly. And the research backs this up. Psychologists who study decision-making find that we overestimate how much we can predict our own satisfaction from thinking alone. Actual experience, even a small dose, beats a vivid imagination almost every time. Luna: So what does that look like in practice? I mean, you can't just take a two-month sabbatical every time you get curious about a new field. Lucas: Right, and that's why the structure matters. A career growth experiment is a time-boxed, low-stakes test of a specific skill, role, or industry that you design with clear success metrics. It's not a side hustle and it's not a full pivot. It's a hypothesis you're testing about yourself. Luna: Before we go deeper, I want to acknowledge something. If today's episode helps you think through a possible career move, that's exactly what we're here for. And the way these shows stay free and ad-free is listener support. If you found value in this, you can buy us a coffee at buy me a coffee dot com slash fexingo. It's a small gesture that keeps us going. Lucas: Yeah, and we genuinely appreciate it. It's what lets us keep these conversations coming every weekday. But back to the experiment. I've got a concrete example that might make this real. Luna: Please, I'm all ears. Lucas: So there's a marketing manager I've been working with, let's call her Priya. She had been in brand marketing for about six years, and she kept feeling drawn to the analytics side of things. But she wasn't ready to go back to school for a data science degree. Luna: That's a big commitment. And honestly, a degree might not even be the fastest path. Lucas: Right. So we designed an eight-week experiment. Her hypothesis was, I want to see if I actually enjoy building analytical tools more than I enjoy creative campaign work. She set a clear success metric: she would build a small dashboard for her own team that tracked campaign performance, and she would finish it by the end of the eight weeks. Luna: That's a concrete deliverable. And I'm guessing she had to learn some new skills to pull that off. Lucas: She learned Python, actually. She followed a few online tutorials, and she spent about five hours a week on it. The key was that she had a real problem to solve, not just abstract exercises. That kept her motivated and gave her something to show at the end. Luna: So what happened? Did she fall in love with Python and quit marketing? Lucas: No, actually it was more nuanced than that. She discovered she loved the data cleaning and the logic, but she hated the debugging. She found herself spending hours chasing down a misplaced comma, and that drove her crazy. But she also realized she loved the moment when the dashboard finally came alive and her team could see the data in a new way. Luna: So the experiment didn't give her a clear yes or no. It gave her a more useful answer — she likes the outcome, not the coding itself. Lucas: Exactly. That's the power of a well-designed experiment. It doesn't just tell you if you like something. It tells you which parts you like and which parts you don't. She eventually moved into a marketing operations role where she works with data analysts, but she doesn't have to do the heavy coding herself. She's the bridge between the creative and the technical. Luna: That's a perfect outcome. She didn't have to burn her whole career down to find a better fit. Lucas: And that's the beauty of the experiment mindset. You're not betting your entire future on a guess. You're testing a small piece of it, learning fast, and adjusting. Luna: I love this. But how do you actually design your own experiment? Like, what are the steps? I feel like a lot of us would just say, I'll try freelancing for a month, and then not have any structure. Lucas: Sure. First, pick one specific variable to test. Don't try to test five things at once. If you're wondering whether you'd like being a manager, don't also wonder whether you'd like a different industry. Focus on one. Luna: So one hypothesis at a time. That makes sense. Lucas: Second, set a time box. Typically four to eight weeks is ideal. Long enough to see meaningful progress, short enough to not derail your current job or life. And third, decide how you'll measure success before you start. This is huge. Define what a successful outcome looks like, both in terms of the outcome and how you feel doing the work. Luna: So for Priya, the success metric was finishing the dashboard. But she also had a feeling metric, like, did she enjoy the process. Lucas: Exactly. And I'd argue the feeling metric is often more important in the long run. You can finish a project and still not want to do it again. That data point is just as valuable as the finished deliverable. Luna: And what about documenting the experiment? I've heard that writing things down as you go can help you avoid rewriting the story afterward. Lucas: That's a great point. There's a cognitive bias called hindsight bias where we smooth over the messy middle and convince ourselves we knew the outcome all along. To counter that, I have my clients keep a simple two-column log. On one side, they track what they did and how long it took. On the other, they note their energy and engagement levels. They rate it on a scale of one to ten, maybe twice a week. Luna: So you get a more granular picture of your engagement over time, not just a single memory of the whole experience. Lucas: Right. And at the end, you can look at the log and see patterns. Maybe you loved the research phase but hated the implementation phase. That's actionable. Luna: That's really smart. I've heard of people doing something similar with a decision journal, but this is more focused on the experiment itself. Lucas: Exactly. And the last step is to have a pre-committed decision rule. Before you start, you decide what you'll do based on the results. For example, if I enjoy the experience and I'm reasonably good at it, I'll pursue it further. If I enjoy it but I'm not great, I'll look for a role that leverages it without requiring me to be the expert. And if I don't enjoy it, I'll drop it and move on. Luna: That takes the emotion out of the decision later. You're not trying to justify the time you already put in. Lucas: And that's the sunk cost trap that we've talked about before. The decision rule helps you avoid throwing good time after bad. And it also protects you from the opposite trap, which is being too quick to quit just because something is hard. If you've committed to the time box, you stick with it and gather the data. Luna: So what are some other common experiments that people run? I'm guessing it's not all about learning to code. Lucas: Oh, definitely not. Some people test whether they'd like to work for themselves by taking on a small freelance project. Others test whether they'd like a more client-facing role by volunteering to lead a presentation at work. I've even had a client who wanted to test if she'd like living in a different city, so she took a short-term assignment there for three weeks. Luna: That's cool. So it's really about any change you're considering, you can design a small test for it. Lucas: Right. The key is to make it low-stakes and reversible. You're not quitting your job. You're not moving your family. You're just gathering data. Luna: And what about the risks? Is there any downside to running an experiment? Like, could it hurt your current job if your manager sees you doing something different? Lucas: That's a real concern. But you can often frame it as professional development, which is usually welcome. Priya's manager was actually thrilled that she was building a dashboard for the team. It made her more valuable. And if you're worried about others' perceptions, you can always run the experiment on your own time. The point is to learn, not to broadcast. Luna: So you're saying you can be a ninja about it. Quietly gather data without making a scene. Lucas: Exactly. And the other risk is that you might discover you love something that isn't feasible. That's a real possibility. But even then, you've learned something about yourself, and you can look for ways to incorporate that passion into your current role or a future one. Luna: That's a hopeful way to look at it. So if I were to run an experiment this quarter, what would you say is the most important thing to remember? Lucas: I'd say, treat the experiment as a way to learn about yourself, not as a pass-fail test. The goal is to gather data, not to prove yourself right. If the experiment tells you to change course, that's a win, not a failure. Luna: That reframing is key. It's not about succeeding at the experiment, it's about gaining clarity. Lucas: And if you come out the other side with a clearer sense of what you want, you're ahead of most people. Give it a shot. Pick one area you're curious about, design a small experiment, and see what you learn. Luna: I might just do that with my Spanish. I've been thinking about using it more at work, but I'm not sure I'm fluent enough. Maybe I'll offer to translate a few documents for a colleague. Lucas: That's a perfect experiment. Low stakes, time-boxed, and you'll learn a lot about your actual comfort level. I'd love to hear how it goes. Luna: I'll report back. And for anyone listening, if you run a career experiment, maybe share it with us. We'd love to hear your stories. Lucas: Until next time, keep experimenting.