Some PMs Are Like Duration-Obsessed Car Drivers (Tracking the Wrong Metrics)
Some Product Managers are like duration-obsessed car drivers.
I'm sure everyone has talked to someone who was very proud to have reached a destination faster than average. They'll be like: "You know what, I came back from the tourist destination in just 4 hours!" In my mind, I'm thinking: "What's the point? Why do you have to drive so fast to flaunt this? Who really cares?"
This is exactly like product managers tracking metrics that they feel are relevant but when I ask them "What's the point? How does it tie to the product or business goals?", they don't have a clear answer.
TL;DR: Being data-driven is important. But tracking the right data that impacts business goals is far more important. Always draw a line from your feature's metrics to business outcomes. Otherwise, improvements in those metrics are just good for your resume but useless for the product.
The driver analogy
When the driver should be focusing on safety, enjoying the ride, maybe even the scenery, they chose to track one metric: duration. And they optimized aggressively for it, ignoring the actual goal of reaching safely and comfortably.
They tracked what was easy to measure, not what mattered.
Same thing happens in product management daily. PMs track:
- Page views (but not conversions)
- Feature adoption (but not retention)
- Time on page (but not task completion)
- Number of users (but not revenue per user)
- Sprint velocity (but not customer outcomes)
Each of these feels data-driven. Each can be reported in a dashboard. Each can go in a slide deck. But none of them, in isolation, tell you whether the product is actually succeeding.
The "so what?" test
For every metric you're tracking, ask: "So what?"
- "Page views went up 20% this month." So what? Did more people sign up? Did revenue increase?
- "Feature X has 40% adoption." So what? Are those users getting value from it? Are they staying?
- "We shipped 15 stories this sprint." So what? Did any of them move a business needle?
If you can't answer "so what?" with a clear connection to a business outcome, you're tracking a vanity metric. It looks good on a dashboard but doesn't drive decisions.
How to choose the right metrics
Always take time to understand the goals and strategy of the business before you finalize the product metrics you want to track.
Step 1: Start with the business goal. What does the company need this quarter/year? Revenue growth? Retention improvement? Market expansion? Cost reduction?
Step 2: Identify the product lever. How does your product (or feature) contribute to that business goal? What user behavior, if increased, would drive the business outcome?
Step 3: Pick the metric that captures that behavior. This is your North Star for the feature or product area.
Step 4: Draw the line explicitly. You should be able to say: "If [metric] improves by X%, it will contribute to [business goal] because [clear logic]."
| Business goal | Product lever | Right metric | Wrong metric |
|---|---|---|---|
| Revenue growth | Trial to paid conversion | Conversion rate by cohort | Number of trials started |
| Customer retention | Feature stickiness | Weekly active usage of core workflow | Total logins |
| Market expansion | New segment adoption | Activation rate for new segment | Total signups |
| Cost reduction | Support ticket deflection | Self-serve resolution rate | Knowledge base page views |
Even feature-level metrics need this connection
Even if your scope of work is a particular feature, ensure to understand how you can draw a line from that feature's adoption to business goals.
"We improved onboarding completion from 60% to 80%."
Great. But why does that matter? Because users who complete onboarding are 3x more likely to become paying customers within 30 days. THAT's the connection. Without it, "80% completion" is just a number.
The line should look like: Feature metric → User behavior change → Business outcome
- Onboarding completion ↑ → More users reach "aha moment" → Higher trial-to-paid conversion
- Search response time ↓ → Users find answers faster → Fewer support tickets → Lower cost
- Dashboard engagement ↑ → Users see value daily → Lower churn → Higher retention revenue
Without this chain, you're the driver bragging about duration while ignoring whether you arrived safely.
The resume trap
Here's the uncomfortable truth: improvement in disconnected metrics is often just good for your resume. 😁
"Improved feature adoption by 30%" sounds impressive in a bullet point. But if that feature doesn't connect to revenue, retention, or any business outcome, what did you actually achieve?
Hiring managers who know what they're doing will ask: "That's great. What was the business impact?" If you can't answer, the metric is exposed as vanity.
The better resume bullet: "Improved onboarding completion from 60% to 80%, contributing to a 12% increase in trial-to-paid conversion for the quarter."
See the difference? The second one connects the feature metric to a business outcome. That's what real PM work looks like.
Common vanity metrics in PM
| Vanity metric | Why it's tempting | What to track instead |
|---|---|---|
| Daily Active Users (DAU) | Big number, always growing | Retention rate (are they coming back?) |
| Features shipped per quarter | Shows productivity | Outcomes achieved per quarter |
| NPS score | Nice benchmark | Churn rate (actions speak louder than surveys) |
| Sprint velocity | Shows team output | Time-to-value for customers |
| Page views | Easy to measure | Conversion or task completion rate |
When metrics mislead
The most dangerous metrics are the ones that go up while the product gets worse:
- DAU goes up because of notification spam. Users open the app but immediately close it.
- Time on page goes up because users are confused. They can't find what they need.
- Signups go up because of aggressive marketing. But churn spikes a month later.
In each case, the metric looks healthy while the underlying reality is deteriorating. That's why connecting metrics to outcomes matters so much. Outcomes are harder to fake.
The bottom line
Being data-driven is table stakes. Every PM tracks something. The question isn't whether you use data. It's whether you use the right data.
Before finalizing your product metrics:
- Understand the business goals and strategy
- Draw a clear line from your metric to those goals
- Ask "so what?" for every number you report
- Replace any metric you can't connect to an outcome
Otherwise, you're the driver bragging about how fast you arrived, while missing the point of the journey entirely.
How ProductResume helps
Your resume metrics should always connect to business outcomes, not just feature outputs. "Improved search speed by 40%" is less compelling than "Improved search speed by 40%, reducing support tickets by 25% and saving $200K annually." Score your PM resume to see whether your bullets connect metrics to impact.