What is a north star metric?

A north star metric is the single metric that best captures the core value your product delivers to users. It should be actionable, meaningful to the business, and reflective of user value. Examples: Airbnb uses nights booked, Spotify uses time spent listening.

How do I answer metrics questions in PM interviews?

Structure your answer: First clarify the product and goals, then define the north star metric, add supporting metrics for different aspects (engagement, retention, quality), include guardrail metrics to prevent negative side effects, and explain how you would track and use these metrics.

What is the difference between leading and lagging metrics?

Lagging metrics measure outcomes after they happen (revenue, churn). Leading metrics predict future outcomes before they occur (activation rate, feature adoption). Good PM frameworks include both to understand current health and predict future performance.

Metrics Interview Questions for Product Managers

Published February 11, 2026

Metrics questions test your ability to define success, diagnose problems, and make data-driven decisions. These questions appear in virtually every PM interview and are critical for roles at data-driven companies. This guide covers the frameworks and practice you need.

Types of Metrics Questions

Define Success Metrics

"How would you measure success for Instagram Reels?"

Diagnose a Problem

"Daily active users dropped 10% last week. How would you investigate?"

Design an Experiment

"How would you test whether a new feature is successful?"

Evaluate Tradeoffs

"Feature A increases engagement but decreases revenue. What do you do?"

The Metrics Framework

North Star Metric

The single metric that best captures the value users get from your product. It should be:

Actionable: Teams can influence it
Meaningful: Connected to business success
User-centric: Reflects value delivered to users

Examples:

Netflix: Hours watched
Uber: Trips completed
Slack: Daily messages sent
Airbnb: Nights booked

Supporting Metrics

Additional metrics that provide a fuller picture of product health:

Engagement metrics:

Daily/Monthly Active Users (DAU/MAU)
Sessions per user
Time spent
Feature adoption rate

Retention metrics:

D1/D7/D30 retention
Churn rate
Cohort retention curves

Quality metrics:

Task completion rate
Error rate
Customer satisfaction (CSAT/NPS)

Guardrail Metrics

Metrics that ensure you are not causing harm while optimizing for your north star. They prevent unintended negative consequences.

Examples:

If optimizing for time spent, guardrail: user satisfaction
If optimizing for signups, guardrail: quality of signups (retention)
If optimizing for revenue, guardrail: user trust/satisfaction

Input vs Output Metrics

Input (leading) metrics: Activities that drive outcomes (feature usage, content created)

Output (lagging) metrics: The outcomes themselves (revenue, retention)

Problem Diagnosis Framework

When asked "X metric dropped. What happened?"

Step 1: Clarify the Problem

How much did it drop? (10% vs 50% require different responses)
When did it start?
Is it still declining or has it stabilized?

Step 2: Segment the Data

Break down by:

Platform (iOS, Android, web)
Geography
User cohort (new vs returning)
User segment (free vs paid)

Step 3: Generate Hypotheses

Categorize possible causes:

External factors: Seasonality, competition, market changes
Internal changes: Product updates, bugs, experiments
Data issues: Tracking bugs, definition changes
User behavior: Shift in how users engage

Step 4: Investigate and Validate

Prioritize hypotheses by likelihood and ease of validation. Check logs, run queries, talk to users.

Step 5: Recommend Action

Based on root cause, propose solutions and next steps.

A/B Testing Questions

Common A/B testing topics:

Experiment Design

What is your hypothesis?
What are you measuring?
How large is your sample?
How long will you run the test?
What is your success threshold?

Interpreting Results

Statistical significance vs practical significance
Confidence intervals
Multiple comparison problems
Novelty effects

Making Decisions

When to ship vs not ship
Tradeoffs between metrics
Segmented results

Example: Measure Success for YouTube Shorts

North Star Metric: Short-form video watch time. This captures user value (entertainment) and is actionable by teams.

Supporting Metrics:

Daily active Shorts viewers
Videos watched per session
Shorts creation rate
Creator retention
Viewer-to-creator conversion

Guardrail Metrics:

Long-form video watch time (ensure Shorts does not cannibalize)
User satisfaction scores
Ad revenue per session

Input Metrics:

Shorts uploaded per day
Average Shorts quality score
Discoverability improvements

Example: DAU Dropped 10%

Clarify: "When did it start? Is it 10% absolute or relative? Is it still declining?"

Segment: "Let me break down by platform, geography, and user type. Where is the drop concentrated?"

Hypotheses:

Bug in recent release
Tracking issue
Seasonal pattern
Competitive action
Failed experiment

Investigation Plan:

Check if drop correlates with recent releases
Verify tracking is working correctly
Compare to same period last year
Review active experiments
Check competitor launches

Likely Scenario: If the drop is isolated to Android after a recent update, likely a bug. Rollback and investigate.

Common Metrics Interview Questions

"How would you measure success for LinkedIn Learning?"
"Uber ride completions dropped. What happened?"
"How would you decide if a new feature is successful?"
"Design metrics for a productivity app"
"Time spent on our app increased but revenue decreased. What do you do?"
"How would you measure the health of a two-sided marketplace?"

Tips for Metrics Questions

Start with the user: What does success look like for users?

Connect to business: How does user success translate to company goals?

Be comprehensive: Cover engagement, retention, quality, and guardrails.

Think about gaming: How could people manipulate this metric?

Consider tradeoffs: What are you sacrificing by optimizing this?

For comprehensive PM interview preparation, see our main PM interview guide. Ensure your resume highlights data-driven achievements with specific metrics that demonstrate your analytical abilities.