The hidden trap in product measurement: The Iteration Death Cycle

The product team had been celebrating their "wins" for months.

Engagement metrics were up. Time-in-app had increased. The dashboard was green across the board. Yet somehow, customer satisfaction was dropping. A clear sign that churn was slowly saying hello.

Sound familiar? This team had fallen into what I call the "metrics-first iteration death cycle" – a trap that's ensnaring more product teams than ever.

Here's how to recognise if you're in this cycle and, more importantly, how to break free.

1 - The Metrics-First Trap

The scenario plays out like this:

• Team somehow comes up with a metric to improve

• They brainstorm ways to move that metric

• They implement small changes and measure impact

• They celebrate when the number moves

• They repeat the process, making incremental optimisations

The problem? They're starting with the metric, not the outcome. This approach often leads to:

• Gaming metrics instead of solving problems (like adding games to a business social network to boost engagement without adding value for meaningful interactions - hello LinkedIn 👋)

• Endless tiny improvements that never add up to meaningful change

• Teams that get stuck optimising numbers, not customer experiences

• Or even harming the business, e.g. through an image damage, or by only changing the way how to calculate the numbers without any actual product improvement.

2 - The Outcome-First Approach

Instead of starting with metrics, I coach teams to follow this sequence:

𝗦𝘁𝗲𝗽 𝟭: 𝗔𝘀𝗸 "𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲?"

Have the team describe the actual benefit of what they're building. Specifically:

• For the 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 of the feature (users, colleagues, systems, AI, agents, etc.)

• For the 𝗯𝘂𝘆𝗲𝗿 of the feature (who pays for it, or pays because of it)

• For their 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 (business goals, strategic fit, OKRs, etc.)

This forces teams to articulate the real value they're trying to create before jumping to how they'll measure it.

𝗦𝘁𝗲𝗽 𝟮: 𝗢𝗻𝗹𝘆 𝘁𝗵𝗲𝗻 𝗮𝘀𝗸 "𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗮𝘁?"

After they've articulated success in outcome terms, ask: "Which metrics will significantly move in which way if we achieve this outcome?"

This is critical! The metrics now emerge from the outcome, not the other way around.

3 - A Real-World Example

Let me share a real example from a team I'm currently coaching:

Feature: Automation of a recurring customer support issue created by a previous feature

Team's initial success description: "When our fix is successful, none of our team members will have to do anything anymore regarding this problem"

Their first proposed metric: "Number of support tickets because of this goes down to 0"

My challenge: "When there are no tickets, does that mean the issue is solved? Are there other ways this could surface? Could tickets disappear without the problem being fixed?"

Their answer: "Yes" to all questions). Meaning their metric wouldn't actually measure success.

Their revised metric: "Number of minutes spent on this topic by our team goes to 0"

My next challenge: "Is there a chance you'd stop working on this even if it's not solved?"

They answered "yes" again, revealing that even their revised metric could be misleading.

Final conclusion: They needed a combined metric: "The issue doesn't surface AND team time spent is zero" – only then would success be truly measured.

What really happened: Well… of course the result wasn't 0 🙂 They learned that even with the best fix can't remove all issues. New ones open up, old ones appear in new shapes etc.

4 - Applying This to Your Work

This approach works for everything from small features to massive initiatives:

• Small features: Define success outcomes before jumping to metrics

• Major releases: Start with the customer and business value, then determine how to measure them

• Strategic initiatives: Begin with the transformation you're seeking, then identify metrics

By reversing the typical order – putting outcome before measurement – you ensure that what you're measuring actually matters.

Remember the formula:

❌ Don't: Metrics-first success measurement

✅ Do: Outcome-first, metrics second success measurement