Multivariate Test Analyzer

Multivariate Test Analyzer MCP Connector for Claude

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Perform 2k factorial analysis to identify optimal element combinations and interaction effects in multivariate experiments.

3 tools Official Updated Jun 29, 2026 Official Vinkius Partner

The Multivariate Test Analyzer is a statistical engine designed for simplified 2k Factorial Design of Experiments (DOE). It allows AI agents to analyze complex multivariate tests by evaluating how different elements (factors) and their variants impact conversion rates. By using tools like calculate_main_effects, analyze_interaction_effects, and identify_winning_combination, you can isolate the direct impact of individual elements, detect dependencies between pairs of factors, and pinpoint the statistically optimal configuration for your experiments.

multivariatefactorial-designab-testingconversion-optimizationstatistical-analysis

3 tools expose this connector's capabilities to your AI agent.

analyze_interaction_effects

Analyates interaction effects between pairs of factors

calculate_main_effects

Each combination must have levels, conversions, and visits. Calculates main effects for each factor in an experiment

identify_winning_combination

Identifies the winning combination in an experiment

See how to talk to your AI agent using Multivariate Test Analyzer.

Which headline variant performed best in my experiment?

The 'Sale' headline variant resulted in a 5% increase in conversion rate compared to the baseline.

Are there any interactions between button color and text size?

Yes, a significant interaction was detected: the 'Large' text size performs significantly better when paired with the 'Green' button color.

What is the winning combination for my latest multivariate test?

The optimal configuration is: Headline 'Summer Sale', Button 'Green', and Text Size 'Large', with an expected conversion rate of 12.5%.

This tool answers which individual elements or variants are driving the most significant change in conversions by calculating the average performance impact of switching a factor from its baseline to its variant level.

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