The Shift in Metrics: How Product Engineers Measure Success

Why Metrics Matter More Than Ever

Imagine you’ve just wrapped up a major sprint. Your team crushed it: thousands of lines of code written, two major features shipped, and zero critical bugs in production. Everything looks fantastic.

But when the quarter ends, the mood shifts. Adoption of those new features is flat, and customer churn remains the same and the leadership is asking a simple yet frightful question:

“What’s the return on all this engineering effort?”

Many engineers must yet learn to accept that writing more code or completing more sprints will not improve revenue or save their product.

For years, engineering success was measured by outputs: the number of features shipped, tasks completed, or lines of code written. While these metrics quantify effort, they fail to capture what truly matters—the impact of that effort.

This disconnect between traditional engineering metrics and business outcomes is why many engineers struggle to demonstrate their value in their companies.

This article explores why traditional metrics fall short, what outcome-oriented metrics look like, and how they can transform how you communicate your impact. Ready to rethink how you measure success?

From Outputs to Outcomes

Story points completed, number of features released—these metrics are easy to track and provide a sense of progress. But they also create a dangerous illusion.

Just because you’ve delivered something doesn’t mean you’ve delivered something valuable.

The problem with output-based metrics is that they focus on activity (output) rather than impact (outcome). A feature might ship on time and within scope, but the effort has little value if users don’t adopt it.

Outputs: Deliverables that quantify effort.

Output metrics often fail to answer the critical questions:

• Did it solve the right problem?

• Did it make users happier?

• Did it help the business grow?

• Did this work make the product better?

• Did it help users achieve their goals?

This is why outcome-oriented metrics are so powerful. Instead of measuring what you did, they measure what you achieved.

Outcomes: Results that demonstrate impact.

Examples:

• increased customer retention

• increased feature adoption

• reduced churn

• increased revenue

• faster time-to-market

Outcomes reflect the value created by engineering work. These are the metrics that matter in a product-driven organization. It shifts the narrative from “What did we deliver?” and starts answering “What did we achieve?”.

Why the Shift is Critical

You might wonder why this change is more critical now than ever. Modern engineering operates in a context where business needs, user needs, tech advancements, such as LLMs, and the economy itself change rapidly.

To stay relevant, teams must demonstrate how their work directly contributes to these needs and goals. If it doesn’t, there might soon come a time when the company’s investment might be worth more somewhere else.

Outcome-oriented metrics provide that clarity. They ensure alignment between engineering efforts and the broader objectives of the organization.

Metrics That Count

Let’s say your team currently tracks several output-oriented metrics. What outcome-oriented metrics can they transform them into to better align with the business?

Mapping for your team might look different, but the list below should give you an idea of how to improve the visibility of your team’s contributions:

❌ Output: Features Released

✅ Outcome: Customer Retention Rate

Has the recently released feature improved the product? If not, does it still need to exist? A feature that does not contribute to a product's growth or stability is only a liability.

Retention metrics measure how well the product keeps customers engaged over time by providing solutions for their problems.

❌ Output: Lines of code

✅ Outcome: Feature Adoption Rate

High adoption rates indicate that features address user needs. They also show how effectively the written code impacts user engagement. The adoption rate is calculated as a percentage of active users using a given feature.

Delivering a feature with a high adoption rate is a great indicator, as it often goes hand in hand with retention rates. Building such a “sticky” customer base is one of the main goals of subscription-based services, as it provides a stable prediction for recurring revenue.

❌ Output: Sprint Velocity

✅ Outcome: Time-to-Market

The goal of calculating sprint velocity is to have predictability for team deliverables. Unfortunately, there is often a disconnect between “story points” and the work needed for a feature to be delivered to customers.

If this disconnect happens, it’s possible for the team to improve the velocity metric but degrade on time-to-market. People optimize what they measure, and if the measurement is story points “done”, then that’s what will be optimized.

Instead, track how quickly your team delivers new features to users. You’ll quickly realize that the main question to answer is what scope you will tackle in MVP and what will be part of further iteration.

Faster delivery enables faster testing of hypotheses and more effective adaption to customer needs.

❌ Output: Resolved Bugs Count

✅ Outcome: System Reliability

Has fixing bugs led to a more stable, reliable product for your users? Image a backlog of 10 bugs. You know that nine are relatively easy to solve in the code you are familiar with and are maybe even interesting, but their impact is low. The last one will be a pain in the keyboard and take a long time to solve, but it will improve the user experience and reliability the most. Which one will you choose?

Metrics like uptime, error rates, and response times reflect the stability of technical infrastructure and are directly related to customer satisfaction and retention. It might be more tempting to flex on stand-up with “I solved nine bugs this sprint.”, but if it hasn’t had any user impact, it’s just wasted effort.

❌ Output: Infrastructure Spending

✅ Outcome: Cost Per Active User

Suppose your company currently spends $2M/month on cloud services. Is it too much or not? Hard to say. What if the cost grew to $2.5M/month in the follow-up quarter?

Infrastructure spending is an important indicator to track, but it cannot be used alone to make decisions. In the example above, if spending grew by 25%, but the number of monthly active (and paying) users also grew from 1M to 1.5M at the same time, the cost per active user decreased from $2/month to $1.67/month. This might indicate that the platform scales well with the growing customer base.

Metrics that balance cost, performance, and scalability are highly valuable for engineering teams. Cost-efficient solutions save money and enable reinvestment in other areas, such as retaining engineering talent.

❌ Output: Code Coverage Percentage

✅ Outcome: Error Rate

A high code coverage requirement is a great example of a vanity metric. If developers must cover with tests, e.g., logic-less getters and setters in classes, just to hit the coverage bar, it’s just a waste of time.

On the other hand, measuring the overall error rate of a feature or a component is a highly valuable metric to have in place. If an error rate is high, the owning team might introduce or increase a required coverage percentage and observe if this effort impacts the error rate and, therefore, user experience, or they should try some other hypothesis.

❌ Output: Pull Requests Merged

✅ Outcome: Cycle Time

“I created a pull request for this feature yesterday. Please review.” is a statement that shouldn’t be overheard on any team’s stand-up. There are better methods to let your team know that a PR has been created (automatic chat, email, or push notifications) than waiting 12-24 hours for a meeting.

The cycle time metric tracks the time from opening a PR to merging it with the main branch or deploying it to a production environment. The goal is to ensure development efficiency. If a review takes multiple days or weeks, with daily ping-pong of “Here is a comment.” and “I pushed an update.” over and over again, it might mean the team is not prioritizing reviews, which can cause a lot of delays and context-switching.

Summary: Reflect on Your Current Metrics

Take a moment to evaluate the metrics you or your team currently use. Are they focused on outputs—like the number of features shipped—or do they reflect outcomes, such as user satisfaction or revenue growth?

Focus on whether these metrics measure the value your work brings to the organization.

Start by listing your team’s top three metrics and asking these questions:

1. Do these metrics align with the product’s goals?

2. Can they demonstrate impact on users or the business?

3. Are they actionable, helping guide future decisions and improvements?

For example, if one of your key metrics is sprint velocity, think about how it connects to company objectives. Does it only track effort? If yes, how could you adjust it to focus on outcomes, such as reduced time-to-market?

---

📖 Read Next

Discover more from the Product Engineering track:

---

If you’re looking for a space where you can learn more about software engineering, leadership, and the creator economy, with Dariusz Sadowski, Michał Poczwardowski, and Yordan Ivanov 📈, we’ve created the Engineering & Leadership discord community:

---

📣 Recommended Reading

• 5 skills to develop to grow from Senior to Staff Engineer by

  Gregor Ojstersek
  and
  Arpit Bhayani
  in
  Engineering Leadership

• The victim trap of engineering managers by

  Anton Zaides
  and
  Taha Hussain
  in
  Leading Developers

• Balancing Engineering Excellence with Business Value by

  Mirek Stanek
  and
  Tomasz Zaraś
  in
  Practical Engineering Management

---