Comparisons

When the user asks to compare A vs B, the right mechanism depends on what the comparison axis is. Use the decision table below to classify it — the user doesn't need to know this distinction exists.

Event/session properties → dimensionality

If the comparison axis describes the context of an individual event at the moment it fired — not the user who triggered it — use dimensionality. Common examples: device type, browser, OS, page URL, element. But the rule is the principle, not the list: if the property travels with the event, not the user, it belongs here. Express it as a single top_n metric with the comparison axis as the grouping dimension.

Example: "rage clicks on mobile vs desktop" → fullstory:build_metric(query="rage clicks by device type", output_type="top_n"). The result table shows mobile and desktop as separate rows.

To refine an established comparison metric (e.g., "add a Chrome-only filter"), pass its metric_id to fullstory:update_metric with a refinement instruction rather than rebuilding.

Do not use segments for event properties. Building a "mobile users" segment and a "desktop users" segment would assign all of a user's rage clicks to whichever device they ever used — even clicks that happened on the other device.

User-level properties → separate segments

Properties that describe a user rather than an event should use segments. The key mechanism: Fullstory resolves user properties to the user's last known value for that key. This canonical value is what segment queries match against — so you're asking "what bucket is this user in now?", not "what was their value at the moment of each event?".

Built-in user properties that work this way: signed_up (signed-up status), first_seen / last_seen (dates), total_sessions (engagement depth), and any custom user properties (user_var_string, user_var_int, etc.) set via setUserProperties — e.g. plan type or account ID. Build one metric and one segment per cohort, then compute each cohort in sequence: attach the segment via fullstory:update_metric(metric_id, segment_id), call fullstory:compute_metric(metric_id), store the result, then repeat with the next segment. Present the results side by side. Do not pass segment_id directly to fullstory:compute_metric.

Example: "do enterprise users experience more errors than free users?" → build two segments (enterprise, free), build one metric (errors), compute twice.

To refine a cohort after it's been built (e.g., "also exclude trial users from the free segment"), use fullstory:update_segment with the existing segment_definition rather than rebuilding with fullstory:build_segment.

Using top_n dimensionality for user properties is valid if you specifically want point-in-time values — each event is attributed to the user property value at the moment it fired. If a user changed plan tier mid-period, their events will be split across both values. For most comparisons you want the canonical (current) value, which is why segments are the default choice.

Decision table

If you can't tell whether a property is event-level or user-level, default to dimensionality — it's more precise and uses fewer API calls.

Why the wrong choice produces wrong results

Segments for event properties (the temporal scope problem): A segment matches users by their canonical properties, then includes all of that user's events. Alice uses both mobile and desktop during a 30-day window. She rage-clicks 5 times — all on desktop. With a "mobile users" segment, Alice qualifies (she used mobile once), so all 5 desktop rage clicks inflate the mobile count. With a device-type dimension, each rage click is tagged with the device it actually fired on — all 5 go to desktop, zero to mobile.

Dimensionality for user properties (the split-value problem): Bob was on the free plan for two weeks, then upgraded to enterprise. Using top_n grouped by plan tier, his events split — two weeks of errors under "free", two weeks under "enterprise". If the question was "do enterprise users see more errors?", Bob's pre-upgrade errors are excluded from the enterprise count. With segments, Bob's canonical value is "enterprise" (last known), so all his events count toward the enterprise cohort.