Documentation Index
Fetch the complete documentation index at: https://launchdarkly-preview.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Overview
The topics in this category help you understand the different types of LaunchDarkly metric so you can choose the correct metric for an experiment or guarded rollout. You can use any of the metric event types to create conversion metrics, which aggregate and analyze events when an end user takes an action based on a feature flag they encounter. You must use custom events to create numeric metrics, which measure and analyze numerical values against a baseline that you set. You can use any metric types to create an A/B experiment or A/A test, or to use as a release guardrail in a guarded rollout or release policy. You can add certain conversion metrics to a funnel metric group for use in experiments, guarded rollouts, or release policies. This table includes examples of different different metric types and their common analysis units, and shows which metrics can be used experiments, funnel metric groups, and guarded rollouts: | Metric type | Example measurement| Example uses |
|---|
Example analysis unit:
user
| A/B experimentFunnel metric group
Guarded rollout | | Clicked or tapped conversion | How often do customers click a “Save” button?
How many times do customers click on a link?
When is the best point during a process to display a sign-up invitation?
Example analysis unit:
user
| A/B experimentFunnel metric group
Guarded rollout | | Custom conversion count | How many purchases did a customer make?
How many times per quarter do customers contact Support?
Example analysis unit:
user or organization
| A/B experimentGuarded rollout | | Custom conversion binary | Do customer searches call a particular service?
Do customer payments succeed?
Do customers contact customer service within a set period of time?
Do customers renew their contract within 30 days?
Does this process generate an error?
Example analysis unit:
user or organization
| A/B experimentFunnel metric group
Guarded rollout | | Custom numeric | How much do customers spend per transaction in my store?
How much do customers spend in total?
How many items do customers purchase per transaction?
How many items do customers purchase total?
How much time do customers spend on a page?
How long does it take for a server to respond to a request?
How long until the time to first byte (TTFB)?
Example analysis unit:
user, guest, or request
| A/B experimentGuarded rollout | You should choose a metric type that correctly measures the effect of a change on your customers or codebase. If you are unsure of what metric type to use, begin by determining what kind event you are trying to measure. For additional examples of common metrics and how to configure them, read Example metrics. When you create a metric, you must also decide how you want to handle its metric and unit analysis. To learn more, read Metric aggregation and analysis.