A/B Testing (Search & Merchandising)

A/B testing compares two variants to see which performs better. In search, it validates ranking and UX changes with real users.

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What is A/B Testing?

A/B testing randomly splits traffic between control and treatment (e.g., new ranker, different facets) and compares outcomes like CTR, add-to-cart, conversion, revenue.

How It Works (quick)

Bucketing: Assign users/sessions deterministically.
Metrics: Define primary/guardrail metrics (latency, zero-results).
Run: Until power thresholds; monitor anomalies.
Decide: Ship, iterate, or roll back.

Why It Matters in E-commerce

Replaces guesswork with evidence.
Catches regressions (e.g., faster but less relevant SERPs).

Best Practices

Bucket at session/user level to avoid cross-exposure.
Pre-register hypotheses and duration; avoid peeking.
Segment by category/locale; compute CIs.
Pair with interleaving for faster early reads.

Summary

Use A/B tests to prove impact before wide rollout—clean bucketing, clear metrics, and disciplined decisions.