Metrics

Welcome​

​In Optimizely Analytics, metrics are powerful computed values that help quantify and analyze user behavior, business performance, and other key outcomes. Unlike standard columns tied to a dataset, metrics offer flexibility through custom computations—allowing you to apply filters, aggregations, and logic across your data.​

​You’ll often use metrics when measuring KPIs like total revenue, average session time, or conversion counts. Once created, a metric functions just like any column in the Catalog and can be used across reports, dashboards, cohorts, or explorations.​

​By the end of this module, you’ll be able to:​

• Understand what a metric is and how it differs from derived columns.​

• Define and configure metrics using the block editor.​

• Use default aggregations like count and sum effectively.​

• Create and apply different types of metrics based on use cases.​

• Reuse metrics anywhere the catalog is accessible in Analytics.

What is a Metric?​

A metric is a calculated property that you define in Analytics. It can include operations like joins, filters, and aggregations across datasets—enabling you to track meaningful values tailored to your needs.​

​For example, in a transactions dataset, you might define a metric called Revenue, which sums up the sale_price column. That metric can then be used anywhere in the platform, adjusting dynamically based on your exploration.​

​Key characteristics:​

• Name: Must be unique across all catalog columns, including cohorts and other metrics.​

• Default aggregation: Define how the value should be rolled up—such as count, sum, or average.​

• Not tied to a dataset: Unlike derived columns, metrics are independent and reusable across contexts.​

​You can easily create a metric using the block editor. Watch the step-by-step walkthrough on how to create a metric using the Navattic demo: https://optimizely.navattic.com/lp2x087g

Types of Metrics​

Optimizely Analytics offers several templates to help you create metrics tailored to your analysis. Whether you’re working in the Flix demo app or your own data, these templates guide your setup while offering full customization.​

Simple Aggregation​

Use this template to create a metric that performs a basic aggregation like sum or count.
For example, measure Total Subscription Revenue by aggregating monthly subscription payments.​

​You can follow a guided demo that shows how to create a metric using simple aggregation template: https://optimizely.navattic.com/wla0ydo ​

Filter​

This template applies a filter to limit your metric to a specific group or condition.
Example: a metric for Users with over 100,000 minutes viewed.​

​See how to create a metric using Filter template in this interactive walkthrough: https://optimizely.navattic.com/hyi0ar8 ​​

And/Or​

Use logical operators (AND/OR) to define composite conditions for your metric.
For example: a metric that counts Purchases AND Time Viewed within the last 30 days.​

​Watch the demo to understand how to build a metric using And/Or logic: https://optimizely.navattic.com/9ba0wlp

Custom​

This flexible template lets you compose multiple blocks to define a metric tailored to your data model.
Example: Average Time Spent per Viewer across all content sessions.​

​Check out the metric creation process using Custom template in the provided Navattic demo: https://optimizely.navattic.com/0uh019f ​​

Formula​

Use algebraic operations and functions to define metrics with precision. Example: calculate Total Revenue by combining PPV, subscription, and ad revenue streams.​

​A dedicated walkthrough shows how to create a metric using the formula template: https://optimizely.navattic.com/x9a0487 ​

Further Learning

To explore more on metrics and how they function within Optimizely Analytics, refer to this documentation on Metrics.

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