As data team leaders, business intelligence (BI) professionals, and analytics specialists, your role isn’t just to collect data; it’s to extract meaning from it. The foundation of meaningful analytics? Choosing the right metrics.
Too often, teams fall into the trap of tracking everything - vanity metrics, redundant KPIs, and lagging indicators that don’t drive action. The result? Bloated dashboards, misaligned stakeholders, and insights that miss the mark.
So how do you cut through the noise and identify metrics that truly matter?
In this post, we’ll walk through a strategic framework for selecting impactful metrics, illustrated with real-world examples and practical guidance tailored for analytics professionals.
Chapter 1: Start with Business Objectives
Before you open your BI tool or write a single SQL query, ask: What problem are we trying to solve?
Metrics should not exist in a vacuum. They must ladder up to clear business goals. Whether it’s increasing customer retention, improving product engagement, or reducing operational costs, every metric should serve a strategic purpose.
-
Example: A SaaS company wants to reduce churn. Instead of broadly tracking MAU “monthly active users,” the analytics team drills down to “percentage of users who complete onboarding within 7 days.” Why? Research shows that users who complete onboarding are 3x more likely to remain active after 90 days (source: Appcues, 2022). This metric is actionable and predictive—making it far more valuable than a generic activity count.
-
Actionable Tip: Use the SMART framework (Specific, Measurable, Achievable, Relevant, Time-bound) to align metrics with business outcomes..
- For example: “Increase trial-to-paid conversion rate by 15% within Q3.”
-
Resource: SMART Goals Framework
Chapter 2: The North Star and Supporting Metrics
Adopt a hierarchy of metrics:
-
North Star Metric (NSM): Captures the core value delivered. For a music app: “Total Time Spent Listening.”
-
OMTMs (One Metric That Matters): Team-level drivers of the NSM.
-
Marketing: Customer Acquisition Cost (CAC)
-
Product: Feature Adoption Rate
-
Operations: Order Fulfillment Efficiency
-
This ensures alignment across teams.
-
Resource: North Star Playbook (Amplitude)
Chapter 3: Leading vs. Lagging Indicators
-
Lagging indicators (e.g., revenue) tell you what happened.
-
Leading indicators (e.g., % of users adding a second item to cart) signal what will happen.
Best Practice: Pair each lagging KPI with 1–2 leading indicators to enable proactive action.
-
Example: Average Order Value (lagging) paired with “add-to-cart after recommendation” (leading).
-
Resource: Leading vs. Lagging Indicators Explained
Chapter 4: Rules for Choosing the Right Metric
-
Relevance → Is it tied to a clear question?
-
Bad: “Total Page Views.”
-
Better: “Page Views by Product Category.”
-
-
Actionability → Can we act on movement?
-
Bad: “Daily Active Users (DAU).”
-
Better: “DAU by User Segment.”
-
-
Insightfulness → Does it explain why?
-
Bad: “Total Orders.”
-
Better: “Return Rate by Product.”
-
Chapter 5: Frameworks for Metric Selection
Frameworks bring structure:
-
AARRR (Pirate Metrics): Acquisition, Activation, Retention, Referral, Revenue (ideal for growth).
-
HEART (Google): Happiness, Engagement, Adoption, Retention, Task Success (great for UX).
-
Example: A fintech app using AARRR:
-
Activation: % users completing KYC on first login.
-
Retention: % logging in ≥3 times/week.
-
Revenue: Avg. transaction fee per active user.
-
Chapter 6: Segment to Unlock Insight
Aggregate metrics can hide the truth. Always segment by channel, device, or cohort.
-
Example: A declining conversion rate looks alarming—until segmented by device, revealing a mobile checkout bug.
Chapter 7: Avoid Vanity Metrics
If a metric can’t answer “So what?” or “What should we do differently?”, it’s vanity.
-
Example: “10M page views” means little if the average session duration is 10 seconds. A better metric: Returning Reader Rate.
Chapter 8: Ensure Ownership and Accountability
Metrics should have a clear owner. Without accountability, numbers drift into irrelevance.
-
Example: Instead of “Ticket Volume,” track “% tickets resolved within 2 hours” (owned by Support).
Chapter 9: Iterate and Audit Your Metrics
As businesses evolve, so must metrics. Conduct quarterly “metric audits” to ensure alignment.
-
Example: A startup pivots from “number of sign-ups” to “% of funded accounts within 5 days.”
Chapter 10: Case Study – Diagnosing a Conversion Drop
Imagine your dashboard shows a drop in Conversion Rate (CR).
-
Check Add-to-Cart Rate → normal.
-
Check Cart Abandonment → spiking.
-
Drill into checkout logs → bug with new payment provider.
Metrics aren’t numbers—they’re guides to action.
Conclusion: Quality Over Quantity
The best dashboards aren’t overloaded—they spotlight the few metrics that matter. By aligning with strategy, segmenting for insight, and ensuring ownership, you transform metrics into a competitive advantage.
Checklist for the Right Metric:
✅ Aligned with objectives
✅ Balanced leading & lagging
✅ Actionable and insightful
✅ Owned and accountable
✅ Not a vanity metric
✅ Reviewed regularly
Review the one-page reference guide to major Data Analysis Frameworks, which includes practical definitions, business use cases, pros and cons, and metric alignment tips for data-driven teams.
Or click on get my copy of the Google Sheet for the Data Analysis Frameworks cheat sheet.
👉 Which do you struggle with most: picking the right framework or identifying the right metrics? Share your thoughts in the comments - I’d love to hear your perspective.