--- title: "A/B Testing for Recommendation Performance" slug: "ab-testing-recommendation-performance-mapp-fashion" status: "update" updated: 2026-06-01T12:37:20Z published: 2026-06-01T12:37:21Z canonical: "docs.mapp.com/ab-testing-recommendation-performance-mapp-fashion" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.mapp.com/llms.txt > Use this file to discover all available pages before exploring further. # A/B Testing for Recommendation Performance ## Introduction A/B testing is used to compare different variants of a website or feature in order to evaluate their impact on user behavior and business outcomes. In the context of recommendation systems, A/B testing is commonly used to compare different recommendation strategies, placements, or providers. A/B test tracking and analysis are performed using **Mapp Intelligence**. **Mapp Fashion delivers recommendation content but is not responsible for tracking.** Typical use cases include: - Comparing Mapp Fashion with a competitor solution - Testing different recommendation strategies (for example, Similar Items vs. Outfits) - Optimizing recommendation placements and widgets - Measuring impact on conversion rate and revenue --- ## Core Concept A/B testing consists of two parts: - **Variant assignment** Each user is assigned to a specific variant (for example, Variant A or Variant B). - **Tracking and analysis** User interactions and conversions are tracked and analyzed per variant. Tracking is always handled by Mapp Intelligence. This includes: - Page views - Product interactions - Orders and revenue - (Optional) recommendation interactions When testing recommendation performance, Mapp Fashion provides the recommendation content, while Mapp Intelligence ensures that all interactions and outcomes are measured and attributed correctly. --- ## Tracking Approaches Mapp Intelligence supports three approaches for tracking A/B tests. They differ in implementation effort, level of detail, and attribution capabilities. --- ### 1. Integrations with A/B Testing Tools This approach is based on passing test and variant information from the A/B testing tool to Mapp Intelligence. While Mapp Intelligence provides direct integrations for selected tools, the same principle can be applied to any A/B testing solution that exposes variant information. In general, the A/B testing tool assigns a variant to the user and makes this information available (for example via JavaScript or a data layer). This value can then be included in Mapp Intelligence tracking. **How it works** - The integration reads test and variant information from the A/B testing tool - Variant data is automatically sent to Mapp Intelligence - Data is available in dedicated A/B test dimensions **Advantages** - Low implementation effort - Standardized data structure - Automatically linked to all interactions and conversions **Limitations** - Only available for supported tools - Not applicable to custom or proprietary testing setups **When to use** - A supported A/B testing tool is already in use - Fast and low-risk implementation is required For setup instructions, see: - [Optimizely integration](/v1/docs/optimizely) - [AB Tasty integration](https://docs.mapp.com/docs/ab-tasty) - [Kameleoon integration](/v1/docs/kameleoon) --- ### 2. Recommendation Tracking (Mapp Fashion context – recommended) This approach uses Mapp Intelligence recommendation tracking to measure interactions with recommendation content. Variant information is included in the tracking context (for example, as part of a parameter value). **How it works** - Recommendation interactions (such as clicks) are tracked via Mapp Intelligence - Variant information is included in the tracking data - Product interactions and orders are linked to prior recommendation interactions **Advantages** - High level of detail - Direct attribution from recommendation interaction to purchase - Suitable for comparing recommendation strategies or providers **Limitations** - Requires implementation effort - Tracking may be limited when content is rendered inside iframes **When to use** - Recommendation performance needs to be analyzed in detail - Different recommendation engines or strategies are compared - Item-level attribution is required For implementation details, see: - [Recommendation Tracking documentation](/v1/docs/recommendation-tracking-plugin-intelligence) --- ### 3. Custom Parameters This approach works in all technical environments and is often used as a simple and reliable way to run A/B tests. It is based on passing test and variant information using custom parameters. Depending on the implementation, different parameter types can be used, such as page parameters, event parameters, or e-commerce parameters. However, this approach provides less detailed analysis compared to recommendation-based tracking. **How it works** - The website assigns the active test and variant - Values are sent as parameters (for example, ABTest and ABTestVariant) - Analysis is performed using filters and segments in Mapp Intelligence **Advantages** - Works in all technical environments - Simple and robust implementation **Limitations** - Low level of detail - No direct attribution to recommendation interactions - Requires consistent parameter handling **When to use** - Simple A/B tests where ease of implementation is a priority - Other approaches are not feasible - A high-level comparison of variants is sufficient - A third-party solution cannot be tracked directly For implementation guidance, see: - How-to: [Run an A/B Test Using Custom Parameters](/v1/docs/run-an-a-b-test-using-page-parameters-mapp-intelligence) --- ## Comparison Overview | **Approach** | **Granularity** | **Purchase Attribution** | **iframe Compatibility** | **Implementation Effort** | | --- | --- | --- | --- | --- | | A/B Testing Tool Integrations | High | Limited | Limited | Low | | Recommendation Tracking | Very high | Yes | Limited | Medium | | Custom Parameters | Low | No | Full | Low | > [!NOTE] > Note > > iframe compatibility becomes relevant when content is rendered inside iframes, as interaction data may not be accessible. In such cases, detailed tracking may be limited. --- ## Choosing the Right Approach - Use integrations with A/B testing tools when available - Use recommendation tracking for detailed recommendation performance analysis - Use page parameters when no other option is feasible --- ## Related Content - [A/B Testing – Implementation (Mapp vs Competitor)](/v1/docs/ab-testing-implementation-mapp-vs-competitor) - [A/B Testing – Tracking & Analysis](/v1/docs/ab-testing-tracking-analysis-mapp-fashion)