Introduction to Metrics

Metrics are the momentum behind every product decision. Without the right metrics, you are navigating blind — guessing whether your product is succeeding or failing. The trap is choosing metrics that look good but do not reflect real progress or customer value.

The actual job is to pick metrics that measure the outcomes you care about and that guide your team toward your strategic goals. If you cannot answer what your key metrics are and why they matter, you are not ready to lead product decisions.

Understanding KPIs and metrics is not optional. It is the foundation of data-driven product management.

KPIs are your navigational instruments; metrics are your gauges

KPIs — key performance indicators — are the handful of metrics that reflect your product’s success against its strategic objectives. Metrics are all the data points you can measure, but only some qualify as KPIs because they directly move the needle on your goals.

Not all metrics are KPIs, but all KPIs are metrics.

For example, an Indian e-commerce app might track hundreds of metrics: app downloads, page views, search queries, time spent per session. But the KPI might be conversion rate — the percentage of visitors who make a purchase. Conversion rate directly ties to revenue and growth, so it is a KPI.

Metrics like page views inform your understanding but are not KPIs unless they correlate strongly with business success.

KPIs are the few numbers you obsess over. They are actionable, measurable, and aligned with your strategic goals.

What makes a good KPI?

A good KPI must be:

• Actionable: You should be able to influence it through product changes.
• Aligned: It reflects your company’s or product’s strategic objectives.
• Measurable: You can reliably track it over time.
• Predictive: It should give early signals about future outcomes, not just past results.

Talvinder explains the concept of momentum in product management:

"You know the formula of momentum, right? M = mv. In business, I call m as metrics and v as velocity in the direction of your goal. Your goal is to improve retention by 10%. The metric is retention. Velocity is speed over time. So if you want to hit your target in one month, your velocity is 10% per month or 2.5% per week. That’s your momentum. That’s how important metrics are. They quantify your theories and efforts."

This framing shows that metrics are not just numbers — they are the measure of your progress toward a goal.

Leading vs lagging indicators

KPIs can be leading or lagging indicators:

• Leading indicators predict future performance. For example, an increase in software trial sign-ups can predict future revenue growth if your conversion rate remains steady.
• Lagging indicators measure past performance. Year-over-year revenue growth is lagging — it confirms success but does not forecast it.

You want to track both but focus your team’s energy on leading indicators that you can influence proactively.

Common pitfalls: vanity metrics and irrelevant KPIs

Choosing the wrong metrics is like using a faulty map. You might look busy but head in the wrong direction.

Vanity metrics — such as app downloads, page views, or raw user counts — often look impressive but do not indicate real user engagement or business success.

Talvinder warns:

"Avoid vanity metrics that look good on paper but don’t contribute to strategic goals. For instance, focusing solely on app downloads without considering user engagement is misleading."

If your KPI does not help you decide what to build next or whether your product is improving, it is not a good KPI.

The Indian market context: why local understanding matters

Metrics do not exist in a vacuum. Indian consumer behavior and digital infrastructure shape what metrics matter and how you interpret them.

For example, Flipkart’s analysis of conversion rates revealed that optimizing app performance for tier-2 and tier-3 cities drove significant improvements. Most of their new user base came from these regions, where network speed and device capabilities vary widely.

They correlated geographical data with conversion rates and invested in regional language support and app optimization for low-end smartphones. This nuanced approach improved their KPIs and customer satisfaction.

Swiggy focuses on delivery time as a KPI to boost customer happiness and operational efficiency. Delivery time is measurable and directly impacts retention and repeat orders.

How to pick the right metrics in complex scenarios

When tasked with improving a broad area, like search experience, it’s easy to get overwhelmed by many possible metrics.

Talvinder advises:

"Start with the end in mind. List all possible suspects affecting the metric — irrelevant pictures, high prices, poor filters, irrelevant search results. Then triangulate what is the main culprit by comparing your metric to industry benchmarks."

For example, if your conversion from search results to product pages is 10% while the industry average is 20%, you have a significant gap.

You then test hypotheses — maybe prices are too high, or filters are not working well. Run experiments like targeted discounts or improved filters, and measure the impact on conversion rate.

This systematic approach helps you focus on the metrics that matter and avoid noise.

Integrating AI and machine learning in metrics analysis

AI and ML are transforming how you analyze metrics. Predictive models can identify patterns invisible to humans — for instance, predicting customer churn based on feature usage or support ticket frequency.

However, the PM’s role is to translate these data-driven insights into actionable product decisions.

Talvinder explains:

"Machine learning models can predict that customers who don’t engage with certain features are more likely to churn. Your job is to design interventions — onboarding flows, notifications, product improvements — that address these risks."

Real-world Indian examples

• Swiggy tracks delivery time as a key KPI to improve customer satisfaction.
• Flipkart leverages conversion rate analysis segmented by geography and device type to optimize app performance.
• A SaaS startup in Bangalore might use Monthly Recurring Revenue (MRR) as a KPI and track trial sign-ups as a leading indicator.

The pattern is consistent: metrics must be tied to your product’s value proposition and business goals.

If you cannot say why a metric matters or what you will do if it moves, it is not worth tracking.

Test yourself: Picking the right metric

Where to go next

• If you want to learn how to conduct effective user research that informs metrics: User Research Methods
• If you want to understand how to translate metrics into product vision and strategy: Product Vision and Strategy
• If you want to build skills in data analysis and experiment design: Metrics and KPIs
• If you want to deepen your understanding of hypothesis testing and A/B testing: Data Science for Product Managers

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