Custom Metrics: Best Practices Guide

See also: FAQs about Earned Media Value (EMV) and Measuring Business Impact: How to Calculate Earned Media Value (EMV) with Custom Metrics

Planning Your Custom Metrics

Start with Your Business Goals

Before creating custom metrics, identify the specific KPIs your organization needs to track. Ask yourself:

• What questions do we need our reporting to answer?
• What calculations are we currently doing manually in spreadsheets?
• Which metrics matter most to our stakeholders?
• How do we define success differently across networks?

Design with Scale in Mind

You have limits of 50 account-level and 50 group-level metrics per customer account. To maximize this:

• Prioritize broadly applicable metrics at the account level
• Reserve group-level metrics for team-specific or campaign-specific calculations
• Avoid creating redundant metrics that measure similar things
• Document your metrics so team members understand when to use each one

Creating Effective Custom Metrics

Naming Conventions

Use clear, descriptive names that make the metric's purpose immediately obvious:

• ✅ Good: "Engagement Rate per 1K Impressions", "Weighted Video Performance Score", "Share-to-View Ratio"
• ❌ Avoid: "Metric 1", "New Calculation", "Test Formula"

Write Helpful Descriptions

The description field appears in tooltips throughout reporting. Make it count:

• Explain what the metric measures and why it's valuable
• Include the formula in plain language (e.g., "Calculates engagement rate by dividing total engagements by impressions, then multiplying by 100")
• Note any network-specific considerations
• Keep it concise but informative

Build Network-Specific Formulas Strategically

Not every metric needs a formula for every network:

• Create formulas only for networks where the metric is meaningful
• Account for network-specific metric availability (e.g., Poll Votes only exist on LinkedIn)
• Consider platform differences when building cross-network metrics

Formula Design Tips

Keep Formulas Simple and Maintainable

• Use parentheses to make order of operations explicit: (Likes + Comments) / Impressions is clearer than Likes + Comments / Impressions
• Break complex calculations into multiple metrics rather than creating one unwieldy formula
• Test your formulas with known data before rolling out widely

Common Use Cases and Formula Examples

Engagement Rate (percentage):

((Reactions + Comments + Shares) / Impressions) × 100

Weighted Engagement Score:

(Comments * 3) + (Shares * 2) + Likes

Weights comments and shares more heavily than likes

Efficiency Metrics:

Engagements / Post Clicks

Measures how engaging your content is relative to click-throughs

Video Performance Index:

(Video Views / Impressions) × 100

Implementation Best Practices

Roll Out Incrementally

• Start with pilot metrics for your most critical KPIs
• Test with a small team before sharing account-wide
• Gather feedback and iterate on formulas based on actual usage
• Document learnings to inform future metric creation

Communicate Changes Carefully

When editing existing metrics:

• Remember: edits apply retroactively to all historical data
• Notify stakeholders before making changes to established metrics
• Consider creating a new metric instead of editing if the change is substantial
• Update any documentation or presentations that reference the metric

Organize for Discoverability

Since metrics display alphabetically:

• Use consistent prefixes for related metrics (e.g., "Video - Views Rate", "Video - Completion Rate")
• Group by category when possible (e.g., "Engagement Rate - Facebook", "Engagement Rate - Instagram")
• Avoid special characters at the start of names unless you want them to sort first

Using Custom Metrics in Reports

Select Metrics Purposefully

• Don't add custom metrics to reports just because they exist
• Choose metrics that directly answer the questions your report is designed to address
• Balance custom and standard metrics to provide context

Provide Context for Stakeholders

When sharing reports with custom metrics:

• Define your custom metrics in report narratives or annotations
• Explain the business rationale behind the calculation
• Note any limitations (e.g., "N/A for posts published before [date]")

Monitor for N/A Values

If you see unexpected N/A values:

• Check that formulas exist for all relevant networks
• Verify the metric was created before the posts you're analyzing
• Confirm none of the component metrics have been deprecated
• Review whether any posts are missing required data

Maintenance and Governance

Regular Audits

Schedule periodic reviews of your custom metrics:

• Quarterly: Review which metrics are actually being used in reports
• Remove unused metrics to stay within limits and reduce clutter
• Update descriptions as team understanding evolves
• Check for deprecated component metrics that may cause issues

Establish Ownership

Assign clear responsibility for custom metrics:

• Account-level metrics: Typically managed by Analytics or Insights team leads
• Group-level metrics: Owned by respective group managers
• Document who to contact for questions about specific metrics

Version Control for Major Changes

If you need to significantly change a metric formula:

1. Create a new metric with the updated formula (e.g., "Engagement Rate v2")
2. Run both metrics in parallel for a reporting period
3. Validate that the new metric performs as expected
4. Communicate the transition to stakeholders
5. Archive or delete the old metric after transition period

Common Pitfalls to Avoid

❌ Creating metrics you can't maintain: Don't build complex formulas you won't remember in 6 months

❌ Over-indexing on custom metrics: Balance custom KPIs with standard metrics for industry benchmarking

❌ Inconsistent network coverage: If a metric is critical, ensure it has formulas for all your active networks

❌ Forgetting to document: Always use the description field - your future self will thank you

❌ Editing without testing: Use the preview/validation features before saving changes to live metrics

❌ Ignoring stakeholder input: The best metrics come from collaboration between analysts and business users