A/B
Aim
Modern businesses rely on experimentation and simulation to make data-driven decisions. Here we showcase how A/B testing, experimental design, and Monte Carlo simulations are used to validate assumptions, measure outcomes, and forecast results. The goal is to improve business performance by testing what works and modeling what could happen under uncertainty.
Result
The results are delivered through interactive visualizations and simulation dashboards. These outputs include experiment matrices, outcome metrics (e.g., conversion lift, duration probabilities), and actionable insights. Management can use this evidence to confidently launch new features, adjust strategies, or allocate resources more efficiently.
Project Duration
Depending on the scope—such as the number of variants tested or complexity of simulations—project timelines typically range from 2 to 8 weeks. Timely access to clean and structured data is essential. Collaboration with data and product teams is often necessary during the experimental setup, data extraction, and metric definition phases.
A/B Testing
Our A/B testing methodology follows industry best practices to ensure statistically valid results. We determine appropriate sample sizes, run experiments for sufficient duration, and analyze results using appropriate statistical tests.
Case Study: E-commerce Checkout Optimization
An online retailer wanted to reduce cart abandonment and increase conversion rates. We designed an experiment with two checkout page variants.
Variant A (Control)
Traditional multi-step checkout
Variant B (Experimental)
Simplified one-page checkout
Result: Variant B showed a statistically significant 27.4% improvement in conversion rate (p < 0.01). The new checkout design was rolled out to all users, resulting in an estimated $1.2M annual revenue increase.
Monte Carlo Simulation
Our Monte Carlo simulations model uncertainty by running thousands of iterations with randomized inputs based on probability distributions. This approach provides a comprehensive view of possible outcomes and their probabilities.
Case Study: Project Timeline Risk Analysis
A client needed to assess the likelihood of completing a complex project within deadlines. We modeled task durations with appropriate probability distributions. We then developed a dashboad that allows the client to test how changes would influence project completion.
Below is a sample functionality of the dashboard:
Recommendation
Based on the simulation, we recommended adding 5 days as a buffer to the project timeline to increase on-time completion probability to 92%. This balanced risk mitigation with resource constraints.
Experimentation Design
We design experiments that balance scientific rigor with practical constraints. Our approach includes proper randomization, control groups, pre-experiment power analysis, and methods to avoid common biases.
Multi-variant Testing Framework
For a SaaS client, we implemented a multi-variant testing framework to simultaneously test multiple features and their interactions.
Outcome
Using the multi-variant approach we identified two winning features that increased user engagement by 18% when implemented together, while avoiding three features that showed negative interaction effects.
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