---
title: "A/B Testing – Tracking & Analysis"
slug: "ab-testing-tracking-analysis-mapp-fashion"
tags: ["AB Testing", "Tracking Requirements"]
updated: 2026-03-31T15:14:55Z
published: 2026-03-31T15:14:55Z
canonical: "docs.mapp.com/ab-testing-tracking-analysis-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 – Tracking & Analysis

## Introduction

This guide explains how A/B tests are tracked and analyzed in Mapp Intelligence.

It builds on the concepts described in: [A/B Testing (Recommendation Performance)](/v1/docs/ab-testing-recommendation-performance-mapp-fashion)

---

## Tracking Requirements

To evaluate A/B tests reliably, tracking must include the following data:

**Variant Information**

- Test name
- Assigned variant

This information must be present in all relevant tracking events.

**User Interactions**

- Page Impressions (page views)
- Product views
- Add-to-basket actions
- Orders

**Recommendation Interactions (if applicable)**

- Clicks on recommended products
- Interaction with recommendation elements

**Revenue Data**

- Orders and revenue
- Product-level information (if available)

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## Attribution

A/B test analysis depends on how interactions are linked to downstream conversions.

**With recommendation tracking**

- Recommendation interactions (such as clicks) are recorded
- These interactions can be linked to product views, basket actions, and orders
- This enables direct attribution from recommendation to purchase

**Without recommendation tracking**

- Only session-based analysis is possible
- Conversions can be analyzed per variant
- Direct attribution to specific recommendation interactions is not available

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## Key Metrics

Evaluate each variant using consistent metrics:

- Conversion Rate (CR)
- Revenue per Visit (RPV)
- Average Order Value (AOV)
- Units per Transaction (UPT)

---

## Analysis in Mapp Intelligence

Use the following analysis approaches to compare variants.

**Variant Comparison**

Compare overall performance:

- Page Impressions
- Conversion Rate
- Order Value

![](https://cdn.document360.io/554b9b98-6720-4d8b-9919-c7b203d72648/Images/Documentation/CleanShot 2026-03-31 at 17.10.45@2x.png)

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**Recommendation Performance**

Analyze recommendation-specific metrics:

- Clicks
- Click-through rate (CTR)
- Revenue (if attribution is available)

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**Process Analysis (conversion funnel)**

Analyze the conversion process:

- Product view → Add to basket → Purchase

Segment by variant to identify differences in user behavior.

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**Device Analysis**

Compare performance across device types to identify inconsistencies or tracking issues.

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## Validation Checks

Before interpreting results, validate data quality:

- Variant distribution is as expected (for example, equal split)
- Users remain in the same variant within a session
- Tracking is consistent across all variants
- All relevant events include variant information

---

## Related Content

- [A/B Testing for Recommendation Performance](/v1/docs/ab-testing-recommendation-performance-mapp-fashion)
- [A/B Testing – Implementation (Mapp vs Competitor)](/v1/docs/ab-testing-implementation-mapp-vs-competitor-1)