Page viewed conversion metrics

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About page viewed metrics

• How many times a blog post was viewed
• Whether or not customers view a help article

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Example metric in SQL

WITH user_aggregates AS (
   SELECT user_id, COUNT(1) AS value
   FROM events
   GROUP BY user_id
)
SELECT aggregation_func(value)
FROM user_aggregates;

WITH user_aggregates AS (
   SELECT
      exposures.user_id,
      MAX(CASE WHEN events.user_id IS NOT NULL THEN 1 ELSE 0 END) AS converted
   FROM exposures
   LEFT JOIN events
      ON exposures.user_id = events.user_id
   GROUP BY exposures.user_id
)
SELECT AVG(converted) AS proportion_converted
FROM user_aggregates;

• events is a table that stores all of the metric events on which the metric is defined.
• exposures is a table that contains flag evaluation data collected during an experiment or guarded rollout that uses the metric.
• user_id is a table column that represents the context both for the metric analysis unit and for the randomization unit used in the experiment or guarded rollout. This example uses the “user” context kind, but a metric can use other context kinds, such as “account,” “session,” or “device.”
• aggregation_func represents the aggregation function that corresponds to the analysis method you select, such as AVG for average analysis or PERCENTILE_CONT for percentile analysis.

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Prerequisites

• Electron
• JavaScript
• Node.js (client-side)
• React Web
• Vue

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Metric definition

• Count uses sum aggregation to measure the total number of times a context views a matching page.
• Occurrence (Percent) uses average aggregation to measure whether or not a context visits a matching page. This option automatically sets the metric analysis type to average, to measure the percent of units that visit a matching page for a given flag variation.

• Average: The default Average analysis method calculates the average number of page views per context for a given flag variation. For metrics you plan to use in funnel metric groups, you must select “Average.”
• Percentile: You can choose between P50-P99, which represent the 50th through the 99th percentile. This method counts the page views per context, then finds the number of page views that fall into the chosen percentile.

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Create page viewed metrics

1. Open the Data section and navigate to the Metrics list.
2. Click Create metric. The “Create metric” dialog appears.
3. If you use warehouse native metrics, you can select either LaunchDarkly hosted or Warehouse native. Select LaunchDarkly hosted.
4. Select an event kind of Page viewed.
5. Specify the Target type you want to track behavior on. Expand the section below to view the four types of URL matching.

6. Enter the full URL or search pattern in Target URL.
7. (Optional) Click Add target URL to track behavior on more than one URL target.
8. Choose either Count or Occurrence (Percent) from the menu in the Metric definition section to configure the aggregation type. The window populates a full metric definition using default values.
9. Use the [analysis unit] menu to change or add analysis units. For page viewed metrics, the analysis unit defaults to “user,” “customer,” or similar, representing an individual person. Only context kinds marked as available for experiments and guarded rollouts appear as options. To learn more, read Mark context kinds available for experiments.
10. For the Count aggregation type, choose either Average or a percentile between P50-P99 click rate to specify the metric analysis method.

11. Enter a metric Name.
12. (Optional) Add a Description.
13. (Optional) Add any Tags.
14. (Optional) Update the Maintainer.
15. Click Create.

You can also use the REST API: Create metric