Configuring Experiment Analytics

Configuring experiment analytics

This skill answers: Who is included in the analysis? and How to measure impact?

Exposure criteria

Exposure criteria determine which users are counted in the experiment analysis.

Exposure event

Two options:

1. Default exposure event — users are included when the experiment's default exposure event fires for the experiment's flag: $feature_flag_called, or $experiment_exposure for newer experiments. Which one applies is resolved server-side — read resolved_exposure_event from experiment-get rather than assuming either name (both events carry the same properties). This is the standard approach — it means a user is included only when they actually encounter the feature flag in your code.
2. Custom exposure event — users are included when a specific custom event fires. Use this when you want tighter control over who enters the analysis (e.g., only users who actually visit the page where the experiment runs).

Multiple variant handling

When a user is exposed to multiple variants (e.g., due to flag changes or race conditions):

• Exclude from analysis — removes these users from the analysis entirely. Cleaner data, smaller sample.
• Use first seen variant — assigns users to the first variant they were exposed to. Keeps all users in the analysis. Note that "first seen" can introduce other biases as behavior cannot be clearly attributed to a single variant and is not recommended unless necessary.

Bias risk on uneven splits. "Exclude from analysis" combined with an uneven variant split can introduce bias — multi-variant users are dropped asymmetrically and the smaller variant loses a larger fraction of its assignments. If those users behave differently from the rest, the smaller variant's metrics will be skewed.

The right mitigation depends on experiment state:

• Not yet launched, or only exposed to a few users so far — switch to an even variant split and use the overall rollout percentage to limit test-variant exposure. This removes the bias and preserves statistical power. See configuring-experiment-rollout.
• Live experiment with significant exposures — changing the split mid-run reassigns users across variants, which is bad for user experience and data quality. Switch this setting to "Use first seen variant" instead — it keeps already-assigned users in their original variant (no reassignment) and removes the asymmetric exclusion.

Filter test accounts

exposure_criteria.filterTestAccounts (default: true) — excludes internal/test users from the analysis.

Resolving experiments

Metric changes require an experiment ID. If the user refers to an experiment by name or description (e.g. "add metrics to the checkout test"), load the finding-experiments skill to resolve it to a concrete ID before proceeding.

Metrics

A metric reaches an experiment one of two ways, both via experiment-update:

• Inline metric — defined directly on the experiment. Sent in the metrics array, which replaces the entire inline list, so always get the current experiment first via experiment-get to preserve existing metrics.
• Shared (saved) metric — a reusable metric object that can be attached to many experiments. Attached by ID via saved_metrics_ids (this list also replaces the experiment's existing saved-metric links, so resend the full set — see Step 1).

Prefer reusing a shared metric over duplicating it inline. Build a new inline metric only when no suitable shared metric already exists.

Step 1: Check for an existing shared metric (REQUIRED — match by definition, not name)

Before building any new inline metric, you MUST check whether the project already has a shared (saved) metric that measures the same thing, and reuse it. Duplicating a metric that already exists as a shared metric fragments measurement and is exactly what we want to avoid.

Reuse is decided by the metric definition — the event or action plus the metric type — not the name. Saved metrics are named by each team's own conventions, which you cannot guess, so you must compare on what each metric measures (its query), never on its title.

Workflow:

1. Know what you're about to build first. Settle the target event(s)/action(s) and metric type (mean / funnel / ratio / retention) before searching — see Step 2 to confirm the event exists via read-data-schema. You can only recognize a duplicate once you know the concrete event/action, so this check runs after you've pinned down the event, not before.
2. Search by the event, then compare each candidate's query. Call experiment-saved-metrics-list with ?event=<the event you're measuring> to find metrics that reference it — matched directly (an EventsNode) or via the step events of any action a metric references, so action-based metrics are found by the event their action fires on. Then for each returned row, inspect its query (not the name/description): a saved metric is a reuse match when its query measures the same event or action with the same metric_type (and compatible math) as the metric you'd otherwise build, even if its name is different.
   • Match on the event, not the action's name. An action-based metric is discoverable by the event the action fires on — pass that event, not the action's label.
   • Do not use search for this. search matches only the metric's own name / description / tags — never the underlying event or action — so it cannot find a definition match. Use search only when the user names a specific saved metric to attach (name resolution, not a definition match).
3. If a saved metric matches the definition — confirm the match with the user by name/description, then attach it instead of building a new one:
   • Call experiment-get to read the experiment's current saved_metrics.
   • Call experiment-update with saved_metrics_ids set to the full desired set — it replaces existing links, so include the already-attached ones plus the new entry. Each entry has shape { "id": <saved-metric id>, "metadata": { "type": "primary" } } — set type to "primary" or "secondary". metadata is optional and defaults to primary.
   • Watch the id when rebuilding the set: each item in the saved_metrics you just read has a top-level id (the link id) AND a saved_metric field (the metric id). saved_metrics_ids wants the saved_metric value, not the link id — sending the link id attaches the wrong metric or fails validation.
   • You do not need to build the inline metric — the shared metric already encodes its events.
4. If nothing in the library measures the same event/action + type — build an inline metric (Step 2+). When that inline metric is likely to be reused across experiments, offer to create it as a shared metric instead, via experiment-saved-metrics-create, then attach it as above, so the next experiment can reuse it.