What Real Users Reveal About Your Product

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When you’re building a product, you spend a lot of time making decisions based on what you think people will need — how the interface should work, which features matter, what the thing should eventually become. Then someone actually uses it, and every one of those assumptions has to face reality at once. Things you thought were obvious turn out not to be. A feature you spent days on barely gets touched, while some detail you almost didn’t bother with turns out to matter more than anything else you built. The first real surprise is usually just how differently people use a product from how you pictured them using it — ignoring the thing you expected them to lean on, working around a problem in a way you never anticipated, using the tool for something you never designed it to do.

Behavior tells you more than opinions do

People can tell you what they want, and it’s still often less reliable than watching what they actually do. Someone asks for an advanced feature, you build it, and they never touch it. Someone else keeps coming back to a small thing you almost cut for being too minor.

This is something I’ve been paying close attention to with TrailStudio. I didn’t set out to build a video editor because I decided that was a market worth entering — I wanted to become a content creator, and the editing tools I was using kept getting in my own way, so I started building the tool I actually wished existed. That origin makes it easy to build around my own workflow, since I already know exactly what I want the editor to do — I’m the person who needs it. What it doesn’t tell me is whether any of those decisions make sense to someone who isn’t me.

So as other people start using TrailStudio, I’m less interested in collecting a list of feature requests and more interested in understanding the actual workflow: what someone does from the moment they open the editor to the moment they finish what they came to do. Which parts do they reach for without thinking? Where do they stall? What takes longer than it should? Which features do they keep coming back to, and which ones — the ones I was genuinely proud of building — does nobody touch at all? None of this requires watching everything someone does or collecting information they shouldn’t have to hand over. It just means paying attention to where the workflow holds together and where it quietly breaks.

Start with the workflow, not the feature list

A workflow is just the sequence of things someone does to get to their goal. For a video editor, that’s importing footage, arranging clips, adding audio, making adjustments, exporting. What matters isn’t the number of steps — it’s whether the product moves someone through them without unnecessary friction. When people keep abandoning the same part of that sequence, that’s worth digging into, even if nobody’s filed a complaint about it. A user who quietly stops using a product rarely explains why on their way out. The workflow is often the only place left to look for the answer.

Friction is usually small, and it adds up

Sometimes friction is obvious — a button that doesn’t work, a page that’s slow, an option buried somewhere nobody would think to look. More often it’s small: repeating the same action twice, having to remember something from three steps ago, a half-second of hesitation because it’s not clear what happens next. Individually these barely register. Repeated across enough users, they become one of the biggest problems in the product, quietly, without ever showing up as a single support ticket. This is why watching someone attempt a task beats asking them whether the interface feels easy. People are often reluctant to criticize something directly to your face — but if they can’t find an important feature without help, the workflow just told you the truth anyway.

Liking it isn’t the same as needing it

An idea can sound good before it exists. A demo can look great in a five-minute walkthrough. Even a finished product can collect plenty of polite positive feedback. None of that proves anyone actually needs it. The better test is whether someone can use the product to solve the actual problem it claims to solve — whether they can discover it, understand what it’s for, get through the important workflow, and come back the next time they hit the same problem. Those are genuinely different signals, and it’s worth not collapsing them into one. Visiting isn’t using. Using once isn’t returning. Returning isn’t the same as valuing something enough to pay for it.

A feature request is a clue, not an instruction

If someone asks for a specific feature, that tells you what they currently believe would fix their problem — it doesn’t necessarily tell you what the problem actually is. When several people ask for the same thing, it’s worth investigating, but the next question should be why they need it, not just building exactly what was requested. Sometimes the request is one possible fix among several, and a simpler change solves the same underlying problem for everyone who asked, plus everyone who didn’t think to ask. This is where a lot of product development quietly goes wrong: a team works through a list of requests one by one, the feature count grows, and the actual workflow underneath never really gets better.

One person is a story. A pattern is information.

A single user struggling with something could mean a hundred things — unfamiliarity, an unusual use case, bad luck. Many users struggling with the same thing points somewhere much more specific: the product itself. The same logic runs in the other direction. When people keep returning to one particular feature, or find their own shortcut through a flow you didn’t design, that pattern is telling you where the real value actually lives, sometimes in a place you didn’t expect. This is why product decisions built on a single conversation tend to age badly — the useful signal is the thing that keeps showing up, not the thing that happened once.

What’s actually worth tracking

You don’t need to record everything. What’s useful depends on the product, but a few things tend to matter broadly: when someone starts an important workflow, whether they finish it, where they stop, how often they come back to a key feature, how long the workflow takes, and what tends to happen right before someone leaves. The point was never surveillance — it’s answering questions you already have. If you can’t name the question a piece of data is supposed to answer, collecting more of it usually doesn’t help.

The hardest part starts after you have the data

Once you’ve gathered enough, the real work begins: deciding what it means. If people keep abandoning a workflow, there are several possible explanations — a confusing interface, a process that’s too long, a feature that isn’t working the way it’s supposed to, or simply a user who already got what they needed and left satisfied. The data doesn’t hand you the answer. It hands you a place to look. That investigation might end in a new feature — or it might end in removing something, simplifying a flow, fixing performance, or deciding the right move is no move at all. What matters is that the decision comes from evidence, not just from whatever felt interesting to build that week.

Building for yourself gets you started. It doesn’t get you all the way there.

Building for yourself has a real advantage — you understand the problem firsthand, no interviews required. That’s exactly why I started TrailStudio: I wanted to make content, the existing tools didn’t work how I wanted, and building my own editor let me solve that directly. But that same closeness is also a limitation. I know my own workflow so well that I know exactly why every part of the editor works the way it does — which means I’m the last person who’ll notice when one of those decisions doesn’t make sense to anyone else. That’s the real value of watching someone else use something you built. They’re not just telling you whether they like it. They’re exposing the assumptions you didn’t know you were making, because you were too close to see them.

Building a product doesn’t have to be a straight line from idea to finished thing. It works better as a loop — build, release, observe, learn, change, repeat — where the first version runs mostly on assumptions and every version after that runs a little more on evidence. That doesn’t mean chasing every suggestion that comes in. It means letting real usage earn a say in what gets built next, and asking less often “what should I build next” and more often “what are people actually trying to do, and what’s getting in their way.” That second question tends to tell you more about what a product needs than any feature list ever will.