Category Ranking A/B Testing

Test Category Ranking Changes With Data-Driven Confidence

Transform guesswork into guaranteed results by testing different category ranking strategies against real customer behavior. For example, test whether boosting seasonal colors like autumn oranges drives more engagement than traditional bestseller rankings. By testing up to three variants, you’ll discover which product placements truly resonate with your audience and make optimization decisions backed by performance data.

Channel

Products:

Autonomous Search

Industries:

Ecommerce Retail

Goal:

Acquire Customers

Metrics:

Conversion Rate Revenue Per Visitor

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Why Marketers Use Bloomreach for Confident Category Ranking Optimization

Category merchandising decisions often feel like educated guesses, risking revenue when changes don’t perform as expected. With Bloomreach Discovery’s A/B testing functionality, you eliminate this uncertainty by testing ranking strategies against real customer behavior before full implementation. The platform’s commerce-first AI and streamlined testing workflow let you quickly identify what drives engagement and sales, while built-in performance tracking ensures every category change is backed by concrete data rather than assumptions.

Run A/B Tests on Category Rankings

A/B Testing

Confidently Make Category Ranking Changes With A/B Testing Functionality

Unlock the potential of A/B testing by experimenting with different category ranking changes and measuring the performance of up to three unique variants. This approach helps you identify which ranking strategies resonate most with your customers, allowing you to uncover patterns in what drives engagement and sales. Using these insights, you can make data-backed decisions on product placements and promotions, creating a shopping experience that feels both intuitive and tailored for your audience. With a flexible and straightforward testing workflow, you’ll be able to refine your strategy with confidence and precision.

Recommended Products

AI-Driven Site Search

How It Works

Set up your ranking experiment by defining up to three different category sorting strategies — for example, boost seasonal products, prioritize bestsellers, or highlight new arrivals.

Configure your test parameters, including traffic split, duration, and success metrics like click-through rate, conversion rate, or revenue per visitor.

Launch the experiment where customers are automatically assigned to different ranking variants as they browse your category pages.

Analyze results to identify winning patterns — such as whether seasonal color boosting outperforms traditional bestseller rankings.

Implement the winning variant site-wide with confidence, knowing your decision is backed by actual customer behavior data.

Apply insights to future category optimization strategies, building a data-driven merchandising playbook that continuously improves performance.

Grocery Food and Beverage +8.4% Revenue Per Visitor

Jumbo Supermarkten Produces Exceptional Results Using the AI Behind Bloomreach Discovery

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Campaigns Built Using A/B-Powered Category Ranking Strategies

See how Jumbo, a major supermarket brand in the Netherlands and Belgium, used Bloomreach Discovery’s A/B testing capabilities to confidently optimize category rankings and drive measurable improvements. By leveraging Loomi AI to test the relevance and performance of different search and category listing configurations — such as adjusting product visibility for terms like “cheese” or “fresh produce” — Jumbo was able to validate changes with data-backed confidence.

The results included an 8.4% lift in revenue per visitor and a 4.5% increase in average order value. If you’re looking to make impactful category ranking decisions using A/B testing and tangible insights, Jumbo’s success story is a powerful example of what’s possible.

Target Audience for Category Ranking A/B Testing Solutions

Retail brands with extensive product catalogs, fashion retailers testing seasonal merchandising strategies, home goods companies optimizing category layouts, and beauty brands experimenting with product placement. Particularly valuable for businesses with diverse inventory, seasonal product lines, or complex category hierarchies requiring data-driven merchandising decisions.