Example metrics

Clicked or tapped conversion metric options

When you create a clicked or tapped conversion metric, you first provide one or more CSS selectors to define the interface elements that should generate events, as well as the target URLs that should generate events. Compatible LaunchDarkly client-side SDKs use this information to automatically generate events when users click the associated targets.

Next, you must decide:

• What do you want to measure:
  • Count: the number of times a target was clicked.
  • Occurrence: whether or not a target was clicked.

Metrics that you plan to use in funnel metric groups must measure Occurrence.

Then, clicked or tapped conversion metric definition options include:

• Analysis method:
  • for metrics measuring Count, you can analyze by:
    • Average: “average” is the default analysis method. This method calculates the average or arithmetic mean number of clicks or taps per context collected for the duration of an attached experiment or guarded rollout.
    • Percentile: you can choose between P50-P99, which represent the 50th through the 99th percentile. This method counts the clicks or taps per context, then finds the number of clicks or taps that fall into the chosen percentile.
  • metrics that measure Occurrence automatically use the “average” analysis method.
• Randomization unit: one or more context kinds, such as “user,” “device,” or “request,” for which the metric measures events.

Custom conversion binary metric options

To create a custom conversion binary metric, you must first select Occurrence for what you want to measure. Metrics that measure Occurrence can be used in funnel metric groups.

Then, custom conversion binary metric definition options include:

• Randomization unit: one or more context kinds, such as “user,” “device,” or “request,” for which the metric measures events.
• Success criteria:
  • Higher is better: choose this option for metrics that measure positive things like cart checkouts or sign-ups.
  • Lower is better: choose this option for metrics that measure negative things like errors.

Custom conversion count metric options

When you create a custom conversion count metric, you must first select Count for what you want to measure. Metrics that measure by Count cannot be used in funnel metric groups.

Then, custom conversion count metric definition options include:

• Analysis method:
  • Average: “average” is the default analysis method. This method calculates the average or arithmetic mean number of conversions per context.
  • Percentile: you can choose between P50-P99, which represent the 50th through the 99th percentile. This method counts the conversions per context, then finds the number of conversions that fall into the chosen percentile.
• Randomization unit: one or more context kinds, such as “user,” “device,” or “request,” for which the metric measures events.
• Success criteria:
  • Higher is better: choose this option for metrics that measure positive things like cart checkouts or sign-ups.
  • Lower is better: choose this option for metrics that measure negative things like errors.

Custom numeric metric options

When you create a custom numeric metric, you must first select Value / Size as the measurement. Numeric metrics cannot be included in funnel metric groups.

Then, custom numeric metric definition options include:

• Aggregation method:
  • Sum: the total value per unit. For example, this option adds together the total purchase amount of all of a customer’s purchases.
  • Average: the average value per unit. For example, this option takes the average purchase price of all of a customer’s purchases.

Then, custom numeric metric definition options include:

• Analysis method:
  • Average: “average” is the default analysis method. This method calculates the average or arithmetic mean of values collected per context.
  • Percentile: you can choose between P50-P99, which represent the 50th through the 99th percentile.
• Randomization unit: one or more context kinds, such as “user,” “device,” or “request,” for which the metric measures event values.
• Success criteria:
  • Higher is better: choose this option for metric values that measure positive things like cart totals or sign-ups.
  • Lower is better: choose this option for metric values that measure negative things like errors or latency.
• Units without events:
  • Exclude units that generate no events: this option is best for latency metrics. If LaunchDarkly never receives an event for a context instance, you do not want to default to 0 because LaunchDarkly would interpret this as an extremely fast latency time, which would skew or invalidate the results.
  • Include units and set their value to 0: this option is best for metrics where an incomplete process can be treated the same as 0, such as tracking cart totals for an online store. In this example, customers who put items in their cart but never completed the checkout process are treated as if they purchased $0.
• Unit of measure: the label for what the metric is measuring, such as dollars or milliseconds.

Page viewed conversion metric options

When you create a page viewed conversion metric, you first define the target URLs that should generate events. Compatible LaunchDarkly client-side SDKs use this information to automatically generate events when users load the associated URL.

Next, you must decide:

• What do you want to measure:
  • Count: the number of times a page was viewed.
  • Occurrence: whether or not a page was viewed.

Metrics that you plan to use in funnel metric groups must measure Occurrence.

Then, page viewed metric definition options include:

• Analysis method:
  • for metrics measuring count, you can analyze by:
    • Average: “average” is the default analysis method. This method calculates the average or arithmetic mean number of page views per context.
    • 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.
  • metrics that measure Occurrence automatically use the “average” analysis method.
• Randomization unit: one or more context kinds, such as “user,” “device,” or “request,” for which the metric measures event values.

You can use this custom numeric metric to learn the average amount of money spent by users who made a purchase.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Value/size
• How multiple event values are aggregated per unit: Average
• Analysis method: Average
• Randomization unit: User
• Success criterion: Higher is better
• Units without events: Exclude units that generate no events
• Unit of measure: USD

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

This means that buyers spend an average of $9.

You can use this custom numeric metric to learn the average amount of money spent by all users, whether or not they made a purchase.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Value/size
• How multiple event values are aggregated per unit: Average
• Analysis method: Average
• Randomization unit: User
• Success criterion: Higher is better
• Units without events: Include units and set their value to 0
• Unit of measure: USD

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

This means that all users, including buyers and non-buyers, spend an average of $6.75.

In this example, you are only interested in learning about your typical spenders and want to exclude the few customers who spend significantly more than most and skew your data upward. This metric calculates the amount of money that 90% of your customer base spends less than.

This custom numeric metric only includes users who made a purchase. When using a percentile analysis method, LaunchDarkly automatically excludes units without events.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Value/size
• How multiple event values are aggregated per unit: Average
• Analysis method: P90
• Randomization unit: User
• Success criterion: Higher is better
• Unit of measure: USD

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

This means that 90% of all users that make a purchase spend less than $85.

This custom numeric metric sums purchase amounts per user who bought something, instead of calculating average amounts per purchase. This lets you find out about the purchasing behavior of users as a whole, ignoring whether the money they spent was all at once or spread between multiple purchases.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Value/size
• How multiple event values are aggregated per unit: Sum
• Analysis method: Average
• Randomization unit: User
• Success criterion: Higher is better
• Units without events: Exclude units that generate no events
• Unit of measure: USD

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

This custom numeric metric sums purchase amounts per user, instead of calculating average amounts per purchase. This lets you find out about the purchasing behavior of users as a whole, ignoring whether the money they spent was all at once or spread between multiple purchases. This includes users who didn’t purchase anything.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Value/size
• How multiple event values are aggregated per unit: Sum
• Analysis method: Average
• Randomization unit: User
• Success criterion: Higher is better
• Units without events: Include units and set their value to 0
• Unit of measure: USD

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

You can use this custom conversion binary metric to learn the average number of purchases for users who made a purchase.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Count
• Analysis method: Average
• Randomization unit: User
• Success criterion: Higher is better

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

This custom numeric metric calculates latency, excluding the slowest 1% of requests that may skew the data. When using a percentile analysis method, LaunchDarkly automatically excludes units without events.

The metric configuration options include:

• Event kind: Custom
• What do you want to measure: Value/size
• How multiple event values are aggregated per unit: Average
• Analysis method: P99
• Randomization unit: Request
• Success criterion: Lower is better
• Unit of measure: ms

Here is what the metric setup looks like:

Here is an example of how the metric calculates its results:

This means that 99% of requests, on average, have a latency of less than 70ms.