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Rich covariates for CUPED++

Standard CUPED that only uses pre-experiment activity on the outcome metric helps less for brand-new users, because there is little or no pre-period behavior to condition on. Eppo’s CUPED++ uses a regression on pre-period metrics available in the experiment and on properties attached to assignments. Passing informative fields through assignment logs can materially shrink confidence intervals.

You often have more information than you think:

Acquisition and intent​

  • Origination: channel, campaign, UTM parameters, app install source. These encode what was promised to the user and which media they trust.
  • First-session context: landing page, referral type, or signup flow variant.

Geography and environment​

  • GeoIP gives at least country; finer location can support features such as urban density, commute patterns, or proximity to relevant physical infrastructure (for example education or retail anchors), when those map to your product.

Weather and season​

Weather can look quaint, but it shifts time indoors, commute friction, and mood. Encoding rain, cold snaps, or seasonal buckets can improve predictions for engagement-heavy products when those patterns line up with usage.

Time structure​

  • Time of day, day of week, proximity to payday, school holidays, major retail events. Many products see spikes on “fresh start” dates or long weekends. Give each feature a small number of meaningful levels so the model can use them without overfitting.

Demographics and firmographics​

When available and appropriate for your use case and policies, demographic or account-level descriptors can explain baseline differences—used responsibly and in line with privacy commitments.

What to expect in the product​

Eppo’s analysis UI summarizes lift and uncertainty, not the full regression table. For stakeholders who want to go deeper into which factors drove adjustment, teams sometimes export aggregates or replicate a slice of the logic in a notebook; that workflow is outside the default UI path.

If you need a convincing narrative before launch, a historical rehearsal (a fake or retrospective split on pre-period data) can illustrate how much variance reduction you get from a given covariate set—paired with the intuition that a large CUPED adjustment usually means strong pre-existing differences between arms, not a frivolous correction.