How Dashboards Kill Great Products

Analytics has become the operating system of modern product development. Every team has dashboards. KPIs are defined. Retention curves are annotated and debated. There’s a quiet belief underneath all of it: that if a metric moves, the product caused it. If a group of users converts better than another, the feature must have worked. These assumptions make their way into roadmaps. They steer engineering work. They justify headcount and define success.

But most analytics tools only offer a surface view. They describe what users did. They don’t explain why. That gap—between visibility and understanding—is where the real danger lives.

The Comfort of False Precision

Before we get into where things break down, it’s important to understand what traditional analytics is designed to do.

It’s built on three pillars:

• Instrumentation: tracking events like clicks, pageviews, conversions

• Aggregation: summarizing behavior into rates, totals, averages

• Segmentation: slicing the data by traits like geography, device, user cohort

This structure gives teams clarity. You can see what users are doing. You can monitor spikes and dips. You can compare one group to another. It feels scientific. Concrete. Actionable.

But it all rests on a hidden assumption: that these patterns reflect meaningful cause-and-effect. That users behave differently because of what the product did—not because of who they are, or what was happening around them.

Most of the time, that assumption is wrong.

The Illusion of Insight

Just because a number changed doesn’t mean the product made it happen.

• Usage shifts with seasonality

• A tweet goes viral and drives a temporary spike

• Power users dominate new features early

• A backend bug slows down the UI and inflates session duration

Traditional tools can’t separate noise from impact. They show a before-and-after and let you fill in the blanks.

That doesn’t stop the charts from getting pulled into strategy decks. Roadmaps are built. Resources allocated. And what started as a shaky assumption becomes a confident direction.

One of the most common mistakes is reading a pattern and assuming a product effect, when what you're actually seeing is a selection effect.

A selection effect happens when the people who experience something are fundamentally different from those who don’t—and it’s that difference that drives the outcome.

Take a common example: your data shows that subscribers retain longer than free users. It looks like a case closed for monetization.

But here’s what’s really happening:

• Power users are more likely to subscribe—and more likely to stick around

• Professionals have stronger product needs—and churn less by default

• Early adopters self-select into paid plans—because they were already committed

The subscription didn’t cause retention. The type of user did. But dashboards don’t show that. They present the pattern. And the team builds a story to fit it.

This is how product decisions get made based on data that only looks rigorous.

When the Wrong Thing Looks Right

Sometimes a feature seems to be working—until you realize you were measuring the wrong thing.

We once launched a new onboarding tutorial. Completion rates looked strong. Users who finished it activated at a higher rate. The data pointed to a win.

But something didn’t add up.

The users completing the tutorial weren’t reacting to the design. They were more motivated from the start. Curious. Goal-oriented. The tutorial didn’t change their behavior—it merely tagged them.

This is the effect of a confounder: a variable that influences both the feature and the outcome. In this case, motivation drove both tutorial completion and activation. The tutorial just sat in the middle, soaking up credit it didn’t earn.

Confounders are subtle. But deadly. They make mediocre features look good. They steer teams toward optimizing noise. And traditional tools don’t flag them—they package the illusion as insight.

Even when confounders aren’t in play, traditional metrics can still deceive. Especially when they rely on averages.

If your average session time goes up, that sounds like progress. But:

• A few heavy users might skew the data

• Losing disengaged users could raise the average

• A frontend bug might slow transitions and stretch time-on-site

Averages compress reality. They hide outliers. They erase nuance. But they look good on slides.

And that’s often enough for teams to ship with confidence—on assumptions they haven’t pressure-tested.

Tools Aren’t the Problem

It’s easy to blame the stack. But the tools aren’t the issue.

Amplitude. Mixpanel. GA4. Looker. Tableau. These platforms are built to show you what happened. Funnels. Drop-offs. Cohorts. Retention trends.

What they can’t do is show you what would have happened if you had done nothing.

They don’t estimate alternate realities. They don’t tell you whether your product actually caused the change—or if the change was coming anyway.

The tools aren’t broken. The mental models are.

That’s where a different kind of thinking is required—one that doesn’t stop at observation, but digs into causality itself. This is where causal inference enters the picture—not as a buzzword, but as a disciplined framework for proving what truly drives outcomes.

To make a real causal claim, you need:

• Temporal order: The change must happen before the result

• Association: There must be a consistent, observable link

• No confounding: Other variables must be accounted for

• Counterfactual clarity: You must estimate what would’ve happened without the change

That last one is the hardest—and the most important.

It’s where most teams fall short. Because it’s not just about what you shipped. It’s about the world you didn’t ship into.

I once worked on a team that wanted to test five new onboarding flows. Design tweaks. Animation timing. Layout changes. A multivariate test.

No plan for causality. No counterfactuals. Just raw comparisons.

Knowing how flawed the setup was, I proposed something provocative: include a broken version. One with slow transitions, vague copy, and deliberate friction.

We shipped the test.

The broken version didn’t tank. It performed like the rest. Not even the worst one.

The team was confused. I wasn’t.

The users were high-intent. They were going to complete onboarding no matter what. The metric—completion rate—didn’t measure design quality. It measured user determination.

The Pattern We Never Talk About

Now imagine this kind of mistake at scale.

A great indie app launches. It’s fast. Thoughtful. Focused. People love it. The developer raises money. Hires a team. Metrics become the heartbeat.

Data is everywhere. Dashboards light up. OKRs are tied to graphs.

Then something changes.

The product slows down. Becomes noisier. A beloved feature disappears—it wasn’t “driving engagement.” The magic fades. But the data looks fine.

Until it doesn’t.

• Evernote lost its core through feature bloat

• Google Reader got shut down despite passionate users

• Quibi overfit to the wrong model of mobile behavior

• Clubhouse mistook a spike for a signal—and built around it

Maybe data wasn’t the only cause. But mistaking movement for meaning? That’s part of the story no one wants to tell.

The fix isn’t throwing out your tools. It’s changing how you think.

Causal thinking means questioning every spike. It means asking what really drove a change. It means building experiments that simulate alternate timelines. It means knowing when your metrics are lying to you.

It’s not a dashboard feature. It’s a discipline.

Every time a team acts on a metric that moved, without asking why, they drift further from truth. Roadmaps fill with false signals. Products evolve around the wrong ideas.

Eventually, the user leaves. The charts drop. And no one knows what happened.

The team scrambles. More dashboards. More KPIs. Still no understanding.

It doesn’t have to go that way.

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I write weekly deep-dives on data, decision-making, and the hidden mechanics of product development. No fluff. No hype. Just signal.