Important Metrics

Metrics are not an afterthought. They are baked into the product from day one. The actual job of a pragmatic product leader is to continuously measure — not occasionally check dashboards or scramble for numbers after launch. You don’t build without a way to measure impact.

Every sprint, every release is an opportunity to move the needle. Think of a year as 52 weeks, roughly 26 two-week sprints. That’s 26 chances to create impact — and each impact point needs metrics to prove whether you succeeded or failed.

The pattern is consistent: metrics drive momentum

I have watched thousands of product managers struggle with metrics. The trap is confusing lots of numbers with meaningful insight. You have to pick the right metrics — the ones that tell you whether you are moving toward your goal.

Think about momentum in physics: momentum equals mass times velocity (M = mv). In business terms, metrics are the mass — the value you are moving. Velocity is how fast you’re moving toward your goal.

Say your goal is to improve retention by 10% in one month. Retention is the metric. Velocity is 10% divided by 4 weeks — about 2.5% per week. That’s the momentum you want. If you have no way to measure retention weekly, you don’t know if you’re on track or not.

Without metrics, your theories and efforts are just guesses.

What is a business metric?

A business metric is a quantifiable measure used to track and assess a specific business process. These metrics are not abstract. They serve clearly defined audiences:

• Executives want to see big-picture financial health.
• Marketing teams track campaign performance.
• Sales teams monitor leads and conversions.
• Product teams measure user engagement and retention.

Every function has its own metrics, but as a product manager, your focus is on the metrics that determine your product’s success or failure.

Good metrics versus bad metrics

There are many metrics you could track. The problem is not the number of metrics — it’s picking the right ones.

Good metrics:

• Are actionable: They guide decision-making.
• Are tied to business goals: They reflect value creation.
• Are understandable: Everyone on the team can grasp what they mean.
• Are measurable: You can collect reliable data frequently.

Bad metrics:

• Are vanity metrics: They look good but don’t correlate with success.
• Are lagging indicators with no leading signals.
• Are too noisy or fluctuate randomly.
• Are disconnected from your users or business goals.

For example, total page views is a classic vanity metric. It’s easy to measure but doesn’t tell you if users find value. Active users, retention rate, or task completion rate are better because they show engagement and satisfaction.

The honest truth about metrics questions in interviews

I have seen many aspiring PMs freeze when asked, “How would you measure the success of Instagram Stories?” or “What metrics would you track for a new feature?”

Here is what separates good answers from average ones:

• Don’t list every metric you know. Pick 2-3 that matter most.
• Explain why those metrics matter to the business and users.
• Show how you would measure them over time and act on the results.

For Instagram Stories, you might say:

• Daily active users (DAU) of Stories to measure adoption.
• Completion rate (how many stories do users watch fully) to measure engagement.
• Sharing rate (how often users share Stories) to measure virality.

Explain that you would baseline these metrics before launch and track weekly changes to assess growth or drop-off.

The metrics hierarchy: from feature to business impact

Metrics can be layered:

• Feature metrics: Usage of the specific feature (e.g., how many users created a Story).
• Product metrics: Overall product engagement (e.g., DAU, session length).
• Business metrics: Revenue, retention, churn, or customer lifetime value.

Good PMs know how to connect feature metrics to ultimate business outcomes.

The product momentum formula in practice

Let’s say your goal is to reduce churn by 5% in six months.

• Metric: churn rate.
• Velocity: 5% divided by 6 months ≈ 0.83% per month.

You set up dashboards to track churn weekly and monthly. If churn stays flat or goes up, you know you’re off track. If it decreases steadily, momentum is building.

This approach forces you to think quantitatively and set clear targets, rather than vague aspirations.

Measuring feature launch success: beyond “Did we ship?”

Shipping a feature is not the finish line. You need to measure:

• Adoption: How many users used the feature in the first week, first month?
• Retention: Did the feature improve user retention?
• Quality: Are there bugs or complaints? What is the NPS or customer satisfaction related to the feature?
• Business impact: Did the feature increase revenue, reduce costs, or improve conversion rates?

For example, after launching a search improvement, you might track:

• Number of searches per user.
• Search success rate (how often users find what they want).
• Time spent searching.
• Support tickets related to search.

If adoption is low, investigate discoverability. If retention doesn’t improve, revisit the feature’s value proposition.

Common metrics every PM should know

These are some of the core metrics you will encounter:

The trap is tracking too many metrics

I have seen teams track 20+ metrics obsessively. This dilutes focus and creates analysis paralysis.

Pick a small set of leading metrics that predict success, and lagging metrics that confirm impact. Use the leading metrics to course-correct early.

How to pick the perfect metrics

When you pick metrics, ask:

• Does this metric align with our business goal?
• Can we influence this metric through our product decisions?
• Is this metric measurable with the data we have?
• Does this metric provide early signals or is it too lagging?
• Will this metric motivate the team?

Avoid metrics that are vanity or unrelated to customer value.

The role of metrics in decision-making

Metrics are the backbone of experimentation and prioritization.

If you want to improve onboarding, measure the drop-off at each step. If you want to boost revenue, measure conversion funnels.

Metrics help you choose what to build, when to pivot, and when to double down.

How PMs differ from Business Analysts on metrics

A Business Analyst might focus on reporting and descriptive stats. A PM uses metrics to drive product decisions and outcomes.

PMs ask:

• Which metrics best reflect the user’s problem?
• How do these metrics change with different experiments?
• What is the impact of our feature on these metrics?

Metrics questions are common in interviews because they show your ability to think critically about product success.

Measuring metrics in the Indian context

Indian startups like Razorpay, Meesho, and Swiggy rely heavily on metrics to scale rapidly and compete.

For example:

• Razorpay tracks payment success rates and customer retention to optimize their platform.
• Meesho uses reseller engagement metrics to improve their social commerce network.
• Swiggy monitors order frequency and delivery time to enhance user satisfaction.

Understanding these metrics and how to act on them is essential for any PM working in India.

Test yourself: The metrics dilemma

Where to go next

• If you want to build a data-driven product culture: Building Data-Driven Teams
• If you want to learn how to translate metrics into decisions: Metrics and KPIs
• If you want to master user research to complement metrics: User Research Methods
• If you want to prepare for PM interviews on metrics: PM Interview Metrics Questions

PL alumni now work at Flipkart, Razorpay, Swiggy, PhonePe, Meesho, and 30+ other companies.