> ## Documentation Index
> Fetch the complete documentation index at: https://launchdarkly.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Choosing a metric type

<View title="Developer" />

<View title="Federal docs" />

<View title="EU docs" />

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](/docs/home/metrics/components#metric-events) 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](/docs/home/getting-started/vocabulary#a/b-experiment) or [A/A test](/docs/home/getting-started/vocabulary#a/a-test), or to use as a release guardrail in a guarded rollout or [release policy](/docs/home/releases/release-policies). You can add certain conversion metrics to a [funnel metric group](/docs/home/metrics/metric-groups#funnel-metric-groups) 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:

<table>
  <thead>
    <tr>
      <th>Metric type</th>
      <th>Example measurement</th>
      <th>Example uses</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>[Page viewed conversion](/docs/home/metrics/pageview)</td>

      <td>
        How many times do end users view a blog post?<br /><br />
        Example analysis unit: <code>user</code>
      </td>

      <td>
        A/B experiment<br />
        Funnel metric group<br />
        Guarded rollout
      </td>
    </tr>

    <tr>
      <td>[Clicked or tapped conversion](/docs/home/metrics/click)</td>

      <td>
        How often do customers click a "Save" button?<br />
        How many times do customers select a link?<br />
        When is the best point during a process to display a sign-up invitation?<br /><br />
        Example analysis unit: <code>user</code>
      </td>

      <td>
        A/B experiment<br />
        Funnel metric group<br />
        Guarded rollout
      </td>
    </tr>

    <tr>
      <td>[Custom conversion count](/docs/home/metrics/custom-count)</td>

      <td>
        How many purchases did a customer make?<br />
        How many times per quarter do customers contact Support?<br /><br />
        Example analysis unit: <code>user</code> or <code>organization</code>
      </td>

      <td>
        A/B experiment<br />
        Guarded rollout
      </td>
    </tr>

    <tr>
      <td>[Custom conversion binary](/docs/home/metrics/custom)</td>

      <td>
        Do customer searches call a particular service?<br />
        Do customer payments succeed?<br />
        Do customers contact customer service within a set period of time?<br />
        Do customers renew their contract within 30 days?<br />
        Does this process generate an error?<br /><br />
        Example analysis unit: <code>user</code> or <code>organization</code>
      </td>

      <td>
        A/B experiment<br />
        Funnel metric group<br />
        Guarded rollout
      </td>
    </tr>

    <tr>
      <td>[Custom numeric](/docs/home/metrics/custom-numeric)</td>

      <td>
        How much do customers spend per transaction in my store?<br />
        How much do customers spend in total?<br />
        How many items do customers purchase per transaction?<br />
        How many items do customers purchase total?<br />
        How much time do customers spend on a page?<br />
        How long does it take for a server to respond to a request?<br />
        How long until the time to first byte (TTFB)?<br /><br />
        Example analysis unit: <code>user</code>, <code>guest</code>, or <code>request</code>
      </td>

      <td>
        A/B experiment<br />
        Guarded rollout
      </td>
    </tr>

    <tr>
      <td>[Count distinct](/docs/home/metrics/count-distinct)</td>

      <td>
        How many unique products does a customer view per session?<br />
        How many distinct orders does a customer place?<br />
        How many distinct sessions does a user start?<br /><br />
        Example analysis unit: <code>user</code> or <code>session</code>
      </td>

      <td>Warehouse native experiment</td>
    </tr>

    <tr>
      <td>[Ratio metric](/docs/home/metrics/ratio-metrics)</td>

      <td>
        What percentage of users who initiate a search also click a search result?<br />
        The number of errors that occur compared to the number of prompts a user creates.<br />
        The percentage of opened files by session count.<br /><br />
        Example analysis unit: <code>user</code> or <code>session</code><br /><br />
        To learn more, read [Ratio metrics](/docs/home/metrics/ratio-metrics).
      </td>

      <td>Warehouse native experiment</td>
    </tr>
  </tbody>
</table>

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](/docs/guides/metrics/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](/docs/home/metrics/components#analysis-method).