Metrics & KPIs: The Analyst’s Compass

One of the core responsibilities of data analysts is creating, updating, and monitoring KPIs (Key Performance Indicators).

To juniors, this might sound easy and straightforward — but it's actually one of the most complicated tasks you can be in charge of.
It’s often an iterative process that takes time, skill, and experience to get right.

In this chapter, we’ll explore the following:

• What Are Metrics and KPIs?
• Why Metrics Matter to Businesses and how they fuel decision making
• Types of commonly used metrics
• What Makes a Metric “Good”?
• Leading vs. Lagging Metrics

What Are Metrics? What Are KPIs?

Let’s start simple:

• Metric = A number that describes something happening in the business.
  Example: 1,000 users signed up this week.

• KPI (Key Performance Indicator) = A specific metric that tracks progress toward a specific business goal.
  Example: 15% of users who signed up last week made a purchase.
  (This might be a KPI if the company is focused on improving conversion.)

The two terms are often used interchangeably — and that’s fine.
But here’s the key difference:

• A metric is anything you measure.
• A KPI is a key metric — one that tracks something important to the organization.

Not every metric is a KPI. KPIs are the ones that matter most for decision-making.

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Why Metrics Matter to Businesses

Company leadership needs to evaluate progress toward specific goals.

• Product managers want to know if a new feature is driving real business impact.
• Analysts run A/B tests to see if a new version of a campaign performs better.

When crafted well, metrics help measure progress and push the business forward.
They highlight problems early and keep everyone aligned and focused on what matters.

Types of Metrics You’ll Work With

There are dozens of metrics for almost every use case and scenario.
You don’t need to memorize them — and honestly, it won’t help much.

Here’s what does matter:
Every metric you use needs your personal touch.
A metric is meaningless without an analyst making sure its definitions are accurate, meaningful, and actionable.

As you start working with data, it’s helpful to learn a few commonly used metrics — not to recite them, but to understand:

• What they measure
• Why they’re used
• And just as importantly — what they don’t capture

Every metric has gaps. Thinking critically about how it’s defined and where it falls short is part of the job.

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1. Product Metrics

• DAU / MAU (Daily/Monthly Active Users): How many people are using the product
• Retention Rate: What % of users come back after a period (e.g., 7 or 30 days)
• Feature Usage: Which parts of the product people actually use

2. Marketing Metrics

• CTR (Click-Through Rate): % of people who click on an ad or link
• Conversion Rate: % of users who took a desired action (e.g., signup, purchase)
• CAC (Customer Acquisition Cost):
  How much it costs to acquire one new customer
  Formula: Total marketing spend ÷ New customers acquired

3. Sales Metrics

• Revenue: Total money made
• ARPU (Average Revenue Per User):
  Formula: Revenue ÷ Total users
• Win Rate: % of deals closed successfully

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4. Customer Metrics

• Churn Rate: % of users who leave or cancel over time
• NPS (Net Promoter Score): How likely users are to recommend your product
• Support Volume: How many support tickets users open

5. Financial Metrics

• LTV (Lifetime Value):
  How much money a customer is expected to bring over their entire relationship with the business
  Formula: ARPU × Average customer lifespan

• Gross Margin: Revenue minus the cost of making/providing the product

What Makes a Metric “Good”?

Not all metrics are useful.
A common mistake is focusing on vanity metrics — numbers that look impressive but don’t actually matter to the business.

For example, the number of Instagram followers a company has isn’t tied to any real goal.
It doesn’t drive decisions, and it won’t suddenly drop if the product starts failing.

Here’s a simple comparison:

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Real-World Example: Evaluating Marketing Campaigns

Scenario:
You’re running multiple campaigns and want to know which one deserves more budget.

❌ Weak metric:
"Total impressions per campaign"
→ This tells you how many people saw the ad — but not whether it worked.
You might spend more money showing ads that don’t convert at all.

✅ Strong metric:
"Cost per signup for each campaign"
→ This tells you how efficiently each campaign brings in real users.
Lower cost = better performance = smarter budget allocation.

Why This Works

• Impressions are just visibility — they don’t tell you if the campaign worked.
• Cost per signup ties spend directly to results — making it clear which campaigns deserve more budget.

Want to go even deeper?
You could look at LTV-to-CAC ratio per campaign (i.e., how much value each campaign’s users generate vs. what they cost).
But “cost per signup” is simple, actionable, and great for junior analysts.

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Leading vs. Lagging Metrics

Another way to level up your metrics understanding is to know the difference between leading and lagging indicators.

This is all about timing:

• Some metrics show you what’s likely to happen
• Others show you what already happened

Knowing the difference helps you ask better questions, spot problems earlier, and influence outcomes — instead of just reporting them.

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Breakdown:

Type	What It Tells You	When It Appears	Examples	Why It Matters
Leading	Early signals that predict results	Before the outcome	- % of users who complete onboarding - Add-to-cart rate - Demo requests	Lets you act early and influence the final result
Lagging	Final results that confirm outcomes	After the outcome	- Churn rate - Monthly revenue - NPS score	Shows the end result — but only after it happened

Key Terms Recap

• Metric: Any number that quantifies something in the business (e.g., number of signups, revenue, click rate).
• KPI (Key Performance Indicator): A metric that directly tracks progress toward a specific business goal.
• Vanity Metric: A metric that looks impressive but doesn’t lead to meaningful decisions or reflect business health.
• Leading Metrics: Early indicators that predict future outcomes (e.g., onboarding rate, demo requests).
• Lagging Metrics: Metrics that confirm what already happened (e.g., churn rate, revenue, NPS).

Summary

• A metric is any number that describes the business. A KPI is a metric that tracks progress toward a business goal.
• Good metrics are tied to outcomes, easy to understand, and drive real decisions.
• Different teams rely on different metrics — from product usage to revenue to customer satisfaction.
• Your job as an analyst isn’t just to report numbers.
  It’s to define what matters, question what doesn’t, and help your team move forward.

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What’s Next?

In the next chapter — Communicating Insights Effectively — we’ll shift from what to measure to how to talk about it.

You’ll learn how to:

• Structure your findings (Problem → Insight → Recommendation)
• Tailor your message to different audiences
• Turn messy dashboards into clear, actionable summaries

Because even the best metric is useless if no one understands what to do with it.