Before-and-after update analysis: baselines and confounders

Design the comparison before release

Write the audience, change, primary metric, guardrails, expected direction, observation window, and decision rule. Save the exact deployment time and version. Choose a baseline with the same weekdays, hours, regions, platforms, acquisition sources, and cohort maturity where possible. If a seasonal event or campaign is unavoidable, include it in the plan rather than discovering it after the result.

Confounders are changes that can move the outcome alongside the update: ads, Home exposure, influencer activity, price changes, platform outages, holidays, another release, tracking changes, or a different player mix. Check them before interpreting the primary metric. Report “metric changed after release” separately from “release caused the change.”

Worked example: D1 rises after onboarding

A team shortens onboarding on Monday. D1 for Monday’s new users is 18%, compared with 14% for the previous Monday, while new-user volume rises from 5,000 to 12,000. At first this looks positive. Acquisition, however, shows a large Home cohort replacing paid traffic, and country mix shifts. The comparison combines a product change with a different audience.

Segmenting by source and country shows the largest stable cohort moved from 15% to 17%, while the new Home cohort is 19%. The safe conclusion is that overall D1 rose and the onboarding change may have contributed, but audience mix also changed. The team can run an eligible controlled experiment or repeat the release logic on a matched cohort before committing a wider redesign.

Run a defensible observational review

Keep the original plan and the final interpretation together so post-hoc metric switching is visible.

1. Freeze the primary metric, guardrails, baseline, and decision threshold.
2. Mark release, campaign, outage, and other event timestamps.
3. Wait until required cohorts mature.
4. Segment only on predeclared or clearly material mix changes.
5. Choose ship, rollback, or follow-up test and record uncertainty.

False certainty in update reviews

Do not compare a weekend release with a weekday baseline, mature D7 with incomplete D7, or a small country slice with the whole audience. Avoid selecting the best metric after looking at many. A large percentage from a small denominator can be noise; include counts and intervals where available.

Regression to the mean can make a change after an unusually bad week look successful. Novelty can raise short-term engagement and then fade. Tracking changes can imitate product changes. If the decision is expensive or irreversible, the limits of before-and-after evidence justify a controlled test or staged rollout.

Decision record

Keep the preregistered plan, release timestamp, result, confounder check, and final action together. If the team changes the primary metric or window, date and explain the change, then treat it as exploratory evidence that needs confirmation.

Primary sources

• Roblox Analytics
• Roblox Experiments
• Roblox Retention
• Roblox Acquisition