Metrics and KPIs

KPIs and metrics are the backbone of data-driven product management. You cannot improve what you do not measure — but not every number is equally valuable. The actual job is to pick the right measures that reflect your strategic goals and provide actionable insights.

Most PMs confuse metrics with KPIs. Metrics are data points that track any aspect of your product or business. KPIs are the critical few metrics that indicate success or failure against your objectives. Your job is to separate noise from signal.

KPIs and metrics are not the same — but they are intimately connected

KPIs (Key Performance Indicators) are quantifiable measures used to evaluate how well an organization or product is achieving its key objectives. They focus on the critical areas that determine success.

Metrics are broader measurements that track the status of specific business processes or activities. They feed into KPIs but are not necessarily critical on their own.

For example:

• A SaaS company’s Monthly Recurring Revenue (MRR) is a KPI. It directly reflects the financial health and growth of the business.
• The same company’s website traffic is a metric. It shows engagement but does not by itself indicate success unless it translates into conversions or revenue.

Think of it this way: your KPIs are the vital signs of your product’s health. Metrics are the detailed lab tests that help you interpret those vital signs.

Leading and lagging indicators: predict vs confirm

KPIs and metrics can be further classified into leading and lagging indicators.

• Leading indicators are predictive. They give you early warning signals about future outcomes.

  For instance, an increase in trial sign-ups is a leading indicator of future revenue growth, assuming a steady conversion rate from trials to paid customers.

• Lagging indicators are output-oriented. They confirm results after the fact.

  For example, year-over-year revenue growth is a lagging indicator. It shows whether past strategies worked but does not predict what will happen next.

The cleanest way to think about this: leading indicators guide your proactive decisions; lagging indicators validate your past decisions.

Characteristics of effective KPIs

Not every metric is worth tracking. An effective KPI must be:

• Actionable: You can influence it through your work.
• Aligned: It links directly to your strategic objectives.
• Measurable: You can track it accurately and consistently.
• Relevant: It reflects value to your customer or business.
• Timely: It updates frequently enough to guide decisions.

Examples of KPIs and metrics in Indian product contexts

India’s diversity and market dynamics shape which KPIs matter. Here are some examples:

A real-world example: Flipkart’s analytics team found that optimizing mobile app performance for tier-2 and tier-3 users increased conversion rates significantly. They correlated app usage data with geographic location, revealing that many new users were on lower-end phones and slower networks, requiring targeted improvements.

The trap of vanity metrics

Vanity metrics look good on paper but don’t drive decision-making or strategic outcomes. Examples include:

• Total app downloads without engagement context
• Number of social media followers without conversion
• Page views without user action

The trap is focusing on these because they are easy to measure and impress stakeholders, while ignoring metrics that reveal true product health.

What I tell PMs is: if a metric doesn’t inform a decision or action, it’s not a KPI. It’s noise.

How to select KPIs for your product

Start with your product’s core value and business model. Ask:

• What outcome defines success for this product?
• Which metrics reflect that outcome directly?
• Which leading indicators predict that outcome?
• Are these metrics measurable with available data?
• Can the team influence these metrics?

Use frameworks like Objectives and Key Results (OKRs) to link KPIs to strategic goals.

The role of AI and machine learning in KPIs

AI and ML are changing how KPIs and metrics are tracked and interpreted. Predictive models can identify trends and anomalies faster than manual analysis.

For example, machine learning models can predict customer churn by analyzing usage patterns, support tickets, and engagement metrics. This allows PMs to take proactive steps rather than react after churn happens.

However, the trap is optimizing for model metrics (accuracy, precision) instead of user outcomes. The metric that matters is the business impact, not the model's F1 score.

Field Exercise: Define KPIs for your product (Time: 15 min)

Identify three KPIs that matter most for your product. For each:

1. Describe the strategic objective it aligns with.
2. Explain why it is actionable and measurable.
3. Identify one leading and one lagging indicator related to it.
4. Note any data sources or tools you will use to track it.

If you struggle, pick a well-known Indian product like Swiggy or Razorpay and research their KPIs as a reference.

Test yourself: Prioritizing KPIs at a mid-stage Indian SaaS startup

Where to go next

• Deepen your understanding of user behavior: User Research Methods
• Translate KPIs into product strategy: Product Vision and Strategy
• Learn to measure impact effectively: Metrics and KPIs
• Explore AI’s role in product analytics: AI for PMs

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