Management decision brief: Core event instrumentation and Login Onboarding A/B test
Important note
- No underlying metrics or experiment outputs were provided. The summary below defines the decision framework, prioritization, and validation plan required for management action. Replace placeholders with observed values (e.g., login completion +X pp, 95% CI [a, b], p=…).
Decision summary
- Proceed to rollout if:
- Variant improves login completion rate by at least the pre-registered MDE (e.g., ≥+2–3 percentage points absolute) and the 95% CI excludes 0, with no degradation in guardrail metrics (crash/error rate, latency, support tickets).
- Activation within 24h (first key action after login) is neutral or positive, and Day-1 retention is neutral or positive.
- Iterate and re-test if:
- Lift is positive but the CI overlaps 0, or guardrails show material degradation.
- Lift is concentrated in a narrow segment with neutral/negative effect elsewhere; proceed with targeted rollouts and segment-specific variants.
- Revert if:
- Variant is neutral/negative on login completion or reduces activation/retention, or materially harms guardrails.
Core insights to extract and report (fill with results)
- Event instrumentation quality
- Coverage: % sessions with required core events present; duplication rate; late or missing timestamps; client vs server timestamp skew.
- Identity: user_id/device_id stability; cross-device merge rate; anonymous-to-logged-in mapping completeness.
- Schema consistency: event/property null rates; cardinality explosions; version drift.
- Funnel performance (Control vs Variant)
- Views of login → Start → Submit → Error → Success conversion by step, device, OS, browser, locale, acquisition channel.
- Primary: Login completion rate (absolute pp and relative lift, CI).
- Secondary: Time-to-login, error rate by reason code, OAuth vs email/password completion rate.
- Downstream: Activation within 24h (first key action), Day-1/Day-7 retention, First-week engagement (events/user), monetization proxy if applicable.
- Guardrails: App/web crashes, backend 5xx, p95 latency, support contacts per 1k logins, privacy/consent violations.
Iteration priorities (ordered, impact/effort balanced)
1) Fix instrumentation reliability (P0)
- Enforce a minimal core taxonomy across client and server:
- view_login, start_login, submit_login, login_error (error_code), login_success
- onboarding_start, onboarding_step_{n}, onboarding_complete
- first_key_action (name), activation_complete (definition-aligned), session_start
- Ensure idempotency keys, deduplication, event versioning, client/server timestamps, and schema validation in the pipeline.
- Implement unified identity: stable user_id; link anonymous_id → user_id on login; backfill historical join keys.
2) Resolve top funnel frictions found in data (P0)
- Prioritize the step with highest drop-off or error spikes (e.g., password complexity, SMS OTP failure, email delivery).
- Default to higher-converting auth providers (e.g., OAuth) where available; degrade gracefully to email.
- Introduce real-time field validation, clearer error copy with actionable guidance, and progressive disclosure for optional fields.
3) Improve experiment design and analysis rigor (P0)
- Use CUPED or pre-experiment covariates to reduce variance.
- Stratify randomization by device, locale, and channel; pre-register primary/secondary metrics and guardrails.
- Prevent contamination: one variant per user across devices; exclude employees, test traffic, and previously assigned users.
4) Onboarding content iteration (P1)
- Test single-step vs multi-step, contextual hints, and progress indicators targeted to the first key action.
- Personalize by acquisition channel or intended use-case if heterogeneity is strong.
5) Performance and reliability (P1)
- Set SLOs: p95 page load <2s, auth API p95 <300ms, error rate <0.5%. Add canary alarms per variant.
6) Analytics & governance (P2)
- Build a durable funnel dashboard with drill-down by step, segment, and error codes.
- Establish event catalog, data contracts, and automated tests for schema drift.
Validation plan
- Primary and secondary metrics
- Primary: Login completion rate (per-user, first session after exposure).
- Secondary: Activation within 24h (first key action), Day-1 retention, time-to-first-value, error rate per step.
- Guardrails: p95 latency, crash/5xx rates, support tickets per 1k users, privacy/compliance flags.
- Experiment design
- Unit: User-level randomization; cookie → device_id → user_id reconciliation.
- Sample size/power: Pre-register alpha=0.05, power=0.8; compute N per arm for absolute MDE (e.g., +2 pp). Apply CUPED to reduce N if baseline covariates available.
- Duration: Run through full weekly cycle (≥14 days) to capture seasonality; avoid mid-experiment UX changes.
- Sequential monitoring: Use alpha-spending or group-sequential boundaries to control type I error if peeking.
- Analysis: Intention-to-treat; difference-in-proportions with robust SEs; logistic regression with covariates for sensitivity; CUPED-adjusted estimates; Benjamini–Hochberg for multiple variants/metrics.
- Heterogeneity: Report effects by device, locale, channel; interaction tests; control family-wise error where applicable.
- Pre-launch QA
- A/A test to validate randomization and metric stability (p-value uniformity, variant share ≈50/50 within strata).
- Event parity test: Each core event within 1–2% relative between arms prior to treatment exposure; missingness <1%.
- Synthetic flows to validate error_code coverage and timestamps ordering.
- Ramp and rollout
- Ramp: 1% → 10% → 50% → 100% with real-time guardrails; auto-halt thresholds (e.g., error rate +50% over baseline or p95 latency +25%).
- Post-rollout: 7–14 days of monitoring; regression alerting; re-verify activation and retention stability.
Data and reporting package required for sign-off
- Baseline and variant metrics:
- Login completion: control vs variant (point estimate, absolute pp, relative lift, 95% CI, p-value).
- Activation within 24h, Day-1 retention: same stats.
- Stepwise funnel drops and error_code distribution deltas.
- Guardrails: crash/5xx, p95 latency, support tickets, privacy events.
- Data quality:
- Event coverage %, null/dup rates, clock skew, identity linkage rate, schema drift report.
- Segmentation:
- Breakouts by device, OS, browser, locale, acquisition channel; interaction p-values.
Next actions
- Provide the experiment output and event quality diagnostics to populate the decision gates above.
- If Variant meets rollout criteria, schedule a progressive ramp with guardrails and begin P0 fixes to instrumentation and top friction step concurrently.
- If results are inconclusive, prioritize P0 instrumentation, re-run with CUPED/stratification, and test the most plausible friction fix as Variant C.
If you share the A/B results (counts per arm, baseline rates, stepwise funnel counts, and guardrail metrics), I will compute effect sizes, confidence intervals, power, and provide a concrete go/no-go recommendation with a tailored iteration plan.
