Data analysts don’t just calculate numbers.

They explain what those numbers mean to people who make decisions.

If you can’t clearly explain business metrics, even perfect analysis can be ignored or misunderstood.

Here are the business metrics every data analyst must be able to explain clearly.

Why Business Metrics Matter More Than Charts

Executives don’t ask:

• “What function did you use?”

They ask:

• “What does this mean for the business?”

Metrics translate raw data into decisions, risks, and opportunities.

1. Revenue

Revenue answers one basic question:

How much money did we make?

Analysts must explain:

• What’s included (gross vs net)
• Time period
• Drivers of change

Misunderstanding revenue leads to false growth narratives.

2. Profit

Profit shows what’s left after costs.

You must clarify:

• Gross profit vs net profit
• Fixed vs variable costs

High revenue doesn’t always mean a healthy business.

3. Growth Rate

Growth rate explains how fast things are changing.

Examples:

• Month-over-month
• Year-over-year

Always explain:

• Growth compared to what
• Whether growth is sustainable

4. Customer Acquisition Cost (CAC)

CAC answers:

How much does it cost to get one customer?

Analysts must explain:

• What costs are included
• Why CAC is rising or falling

High CAC can quietly kill profitability.

5. Customer Lifetime Value (CLV / LTV)

LTV estimates how much a customer is worth over time.

Explain:

• Assumptions used
• Why LTV > CAC matters

Overestimated LTV leads to risky decisions.

6. Churn Rate

Churn measures how many customers leave.

You should explain:

• What counts as churn
• Time window
• Impact on revenue

Low churn often matters more than new growth.

7. Conversion Rate

Conversion rate tracks behavior change.

Examples:

• Visitor → signup
• Trial → paid

Always explain:

• Which step
• What affects it

Small changes here can have big revenue impact.

8. Retention Rate

Retention shows how many users stay.

Explain:

• Cohort definition
• Time-based retention

Retention tells a stronger story than raw signups.

9. Average Order Value (AOV)

AOV shows how much customers spend per purchase.

Explain:

• Drivers of increase or decrease
• Impact on revenue strategy

AOV often improves revenue without new customers.

10. Engagement Metrics

Includes:

• Session duration
• Active users
• Feature usage

Explain:

• Why engagement matters
• Which engagement drives value

High engagement without revenue can be misleading.

11. Funnel Drop-Off Rates

Funnel metrics explain where users leave.

Analysts must clearly show:

• Where drop-offs happen
• Why they matter

This helps teams prioritize fixes.

12. Forecast vs Actual

This compares expectations to reality.

Explain:

• Why forecasts missed
• What assumptions failed

This builds trust in future predictions.

13. Return on Investment (ROI)

ROI answers:

Was this worth it?

Explain:

• How ROI was calculated
• Time horizon

Bad ROI explanations kill stakeholder confidence.

14. Operational Metrics

Examples:

• Processing time
• Error rates
• Delivery time

Explain how operations affect:

• Customer experience
• Cost
• Scalability

Common Mistake Analysts Make

They explain:

• How metrics were calculated

Instead of:

• Why metrics changed
• What actions should follow

Clarity beats complexity.

How Great Analysts Explain Metrics

They:

• Use plain language
• Connect metrics to decisions
• Explain trade-offs and uncertainty

This is how analysts earn trust.

Metrics don’t speak for themselves.

Analysts speak for them.

If you can explain business metrics clearly, you don’t just analyze data —
you influence decisions.

FAQs