Metrics are statistics like "Revenue per User" or "Signup Rate" that Eppo will calculate for you. In order to create a metric, you first need to have set up a Fact SQL Definition to point to the underlying data for your metric in your data warehouse.
- Navigate to
+Metric, then select
Useras the subject of the metric
User is the default entity in Eppo, but you can also create a custom entity and select it here.
- Select a fact
This should be one of the facts that you created in the step above, and should correspond to a metric that you want to track in an experiment.
- Select an aggregation
The aggregation will aggregate over whatever the fact is measuring on a per-entity basis. So for example, if you select a
Revenue fact and the
SUM aggregation, the metric will be "average revenue per user;" if you select a
Name of Article Viewed fact and the
COUNT DISTINCT aggregation, the metric will be "Unique articles viewed per user."
Eppo supports the following aggregations:
- Set outlier handling
Eppo handles outliers through a technique called winsorization. Lower and upper bounds by percentiles for winsorization can be defined by the user for every metric. For example, in the screenshot below, we are setting the upper bound for winsorization at the 99.9th percentile. This means that any user with a value above the 99.9th percentile will be rounded down to the 99.9th percentile value.
Note that winsorization is only utilized for
COUNT aggregations. This is because conversion and retention metrics are binomial variables that are not prone to influence from outliers. As a result, winsorization is redundant for these metric types.
- (Optional) Create a denominator for your metric
You may actually want to create a metric that is a ratio of two facts. To do this, click on the Ratio tab, and select the appropriate fact and aggregation for the denominator as well.
In the example above, we're creating a metric that corresponds to the revenue per user per purchase.
- Select a minimum detectable effect
The minimum detectable effect refers to the smallest effect you want to reliably detect in experiments. The lower the minimum detectable effect you set, the more samples you'll need for the experiment to reach conclusive results.
Metric aggregation types
Sum computes metrics that are typically interpreted as averages per entity. If the fact value is NULL, it is discarded.
Examples: average revenue per user, sign-up rate, minutes streamed per user, average order value.
Unique Entities computes the number of unique entities with a non-null event. If the fact value is NULL, it is discarded.
Examples: % of users with a video watch, % of visitors who viewed an article, % of users who entered checkout.
Count leverages SQL's
COUNT to compute a total count of events per entity. If the fact value is NULL, it is discarded.
Examples: videos watched per user, articles viewed per visitor, orders per user.
Retention metrics measure the proportion of entities who have at least one fact value appear after a fixed number of days (X) from experiment assignment. For example, a 7-day retention metric on the website visits fact might measure the proportion of users who visit a website at least 7 days after being assigned to the experiment.
For example, if , Eppo records a retention event for an entity when
Conversion metrics measure the proportion of entities who have at least one fact value appear within a fixed number of days (X) from experiment assignment. For example, a 7-day conversion metric might measure the proportion of users who sign up for a free trial within 7 days of being assigned to the experiment.
For example, if , Eppo records a conversion event for an entity when
Threshold metrics measure the proportion of entities who meet a user-specified
COUNT of a fact within an optional time-period. For example, you might want to understand what share of users in your experiment spent more than $100 within 7 days of assignment into an experiment.