Smarter A/B Testing with Azure ML–Powered Experimentation

Smarter A/B Testing with Azure ML–Powered Experimentation

Airtable

Experimented with Azure Machine Learning to build a modular A/B testing pipeline that predicts UI performance and accelerates iteration for B2B products

Overview

Overview

Curious about how machine learning could support product decisions, I ran a self-initiated experiment using Microsoft Azure Machine Learning to build a predictive A/B testing pipeline based on sample web experiment data comparing two UI versions.

In B2B environments, A/B testing often fails to reach statistical significance due to small sample sizes and tight timelines. To address this, I used historical test data to train a Boosted Decision Tree Regression model in Azure ML Studio, enabling UI performance prediction without running full experiments.

As a Product Designer, this project deepened my technical fluency in ML pipelines—equipping me to bridge experimentation, data, and design in faster, more scalable ways.

A/B testing is essential for validating design decisions—but in B2B contexts, small sample sizes and tight timelines often limit statistical confidence.

To address this, I experimented with Microsoft Azure Machine Learning to build a predictive A/B testing pipeline that learns from historical test data. This approach surfaces actionable insights even with limited inputs, enabling faster, evidence-based decisions.

As a Product Designer, I built this ML-powered system using Azure ML Studio and the Boosted Decision Tree Regression algorithm. Trained on real-world e-commerce interaction data, the model compares UI variants and predicts conversion outcomes—empowering teams to prioritize high-performing designs without waiting on full test cycles.

A/B testing is essential for validating design decisions—but in B2B contexts, small sample sizes and tight timelines often limit statistical confidence.

To address this, I experimented with Microsoft Azure Machine Learning to build a predictive A/B testing pipeline that learns from historical test data. This approach surfaces actionable insights even with limited inputs, enabling faster, evidence-based decisions.

As a Product Designer, I built this ML-powered system using Azure ML Studio and the Boosted Decision Tree Regression algorithm. Trained on real-world e-commerce interaction data, the model compares UI variants and predicts conversion outcomes—empowering teams to prioritize high-performing designs without waiting on full test cycles.

Why this Matters

Why this Matters

🚫 The Problem with Traditional A/B Testing

  • High Cost: Enterprise A/B platforms can cost $5K–$10K per month

  • Slow Iteration: A single test may take 2+ weeks to complete

  • Data Demands: Requires large samples to reach significance

🤖 The Advantage of AI-Powered Testing (via Azure ML)

  • Data-Efficient: Delivers accurate predictions from small datasets

  • Historical Forecasting: Leverages past engagement to model performance

  • Lower Overhead: Reduces dependency on full-scale live experiments

✅ The Product Impact

  • Faster, Smarter Design Decisions: Predicts top-performing UI variants before launch

  • Actionable Insights: Enables confident product choices—without waiting

  • Scalable for B2B: Ideal for teams with limited traffic, budget, or time constraints

Highlights

Highlights

Pipeline flow architecture

Azure Machine Learning Studio Pipeline

“Split Data” component was configured so that 70% of the data is used for training purpose,

while the 30% of the data is used to test how well my model functions.

In the “Train Model” step, I specified the label column “Conversion Rate”

in the imported CSV file as the column used to train the model.

In the “Train Model” step, I specified the label column “Conversion Rate”

in the imported CSV file as the column used to train the model.

Results

Results

✅ Version A is a better choice as it outperformed Version B, showing higher conversions and greater predicted user engagement.

  • Conversion Rate (Avg):
    0.1108 (Version A) vs. 0.1009 (Version B)
    → +9.8% improvement in conversions for Version A

  • Avg Scored Label (Predicted Engagement):
    0.1270 (Version A) vs. 0.1062 (Version B)
    → Users are predicted to be more engaged with Version A

The successful pipeline gave the above results for the “Score Model” component,

including the predicted engagement (Scored Labels) of the Conversion Rate column

Connected Azure ML outputs to Power BI to visualize model results as a graph —

it shows that Version A outperformed Version B in both predicted engagement and conversion rate