๐Ÿงช 5 Ready-to-Run Tests

A/B Tests That Answer Real Questions

Stop designing experiments from scratch. Pre-built A/B tests come with hypotheses, metrics, and split ratios โ€” launch in one click and get statistically significant results.

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5 Pre-Configured A/B Tests

Each test comes with a clear hypothesis, primary metric, traffic split, and recommended duration. Results feed directly into your revenue attribution dashboard.

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Margin vs. Relevance

Does pushing margin hurt clicks?

Control: default weights. Variant: margin at 30%. 50/50 split, 14 days. Primary metric: revenue per visitor.

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Bundle Discount Sweet Spot

10% vs 15% vs 20% โ€” which wins?

3-arm test at 33% each, 21 days. Tests which discount maximizes total bundle revenue, not just conversion.

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Network Recs Impact

Do cross-store recs help or hurt?

Control: internal only. Variant: 25% network ratio. 50/50 split, 14 days. Measures attributed revenue change.

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Widget Placement

Below description vs sidebar vs floating cart

3-arm placement test. Measures CTR and add-to-cart rate per position. 14 days.

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Chat vs. Static Recs

Does conversational discovery convert better?

Grid widget vs AI chat finder. 50/50 split, 21 days. Compares conversion rate and AOV.

Why Pre-Built A/B Tests Beat Custom Experiments

Most ecommerce teams know they should be running A/B tests on their product recommendations, but designing a statistically valid experiment takes expertise most small teams don't have. You need to define a hypothesis, choose the right metric, calculate sample sizes, set traffic splits, and run for long enough to reach significance.

SellerZoom's pre-built experiments encode all of this into one-click templates. Each test comes with a clear hypothesis, a primary metric that actually matters (revenue per visitor, not just CTR), and a recommended duration based on typical store traffic levels.

The Margin vs. Relevance Test

This is the test every store should run first. It answers the foundational question: can you push higher-margin products without hurting engagement? The control group sees default recommendation weights. The variant sees margin weight boosted to 30%. After 14 days, you compare revenue per visitor โ€” not clicks, not conversions, but actual dollars generated per person who saw a recommendation.

Multi-Arm Tests for Bundle Pricing

The Bundle Discount Sweet Spot test uses a 3-arm design to test 10%, 15%, and 20% discounts simultaneously. This is faster than running three sequential A/B tests and avoids the "peeking problem" that invalidates results when merchants check dashboards daily and stop tests early.

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