What is A/B Testing? - Shipshape Data

A/B testing is a controlled experiment used to compare two versions of a variable to determine which performs better. In practice, it involves testing two variants, A (the control) and B (the variation), to measure which delivers a desired outcome such as higher conversions, engagement, or revenue.

A/B testing is widely used in marketing, product design, and Artificial Intelligence (AI)-driven optimisation. It provides data-backed insights that guide better decision-making and continuous improvement. Rather than guessing which idea works, teams can test hypotheses and let the data decide.

How A/B testing works

The process begins with identifying a goal, such as increasing click-through rates, reducing churn, or improving conversion rates. Two or more versions of an element (e.g. email subject line, web page layout, or pricing model) are shown to different audience segments, and performance is measured statistically.

Step 1: Define the objective and hypothesis.
Step 2: Create the control (A) and variation (B).
Step 3: Randomly split your audience.
Step 4: Collect performance data and analyse results.
Step 5: Implement the winning variant.

A/B testing in AI and data analytics

In modern analytics and machine learning, A/B testing plays a crucial role in model validation and optimisation. It helps teams evaluate which model, feature, or algorithm configuration performs best under real-world conditions. AI-driven platforms can even automate this process, continuously testing and adapting in real time.

AI-powered optimisation: Automated systems can run multiple A/B tests simultaneously to refine content or pricing dynamically.
Model experimentation: Compare different ML models or hyperparameter settings using controlled tests.
Personalisation: Test personalised recommendations generated by predictive algorithms.

Benefits of A/B testing

Data-driven confidence: Replace assumptions with measurable proof.
Optimised performance: Identify the highest-performing option quickly.
Reduced risk: Test changes on a small scale before full deployment.
Improved customer experience: Refine user interfaces and content based on real feedback.
Increased ROI: Ensure marketing spend and product updates deliver measurable value.

Common use cases

Marketing campaigns: Test ad copy, email subject lines, or landing pages.
Product design: Compare layouts, colours, or call-to-action buttons.
AI applications: Evaluate which model version performs better before full rollout.
Pricing strategy: Measure customer response to different pricing tiers.

Challenges in A/B testing

Despite its simplicity, A/B testing requires careful setup and data management. Poor experimental design or low-quality data can produce misleading results. Reliable insights depend on proper sampling, statistical significance, and data quality management.

Sample bias: Ensure randomisation and representativeness.
Small sample sizes: Can lead to inconclusive or inaccurate findings.
External variables: Time, seasonality, or marketing overlap can distort results.
Misinterpreted data: Use proper statistical thresholds to determine significance.

A/B testing vs Multivariate testing

While A/B testing compares two variants at a time, multivariate testing analyses multiple changes simultaneously. A/B tests are ideal for simple, focused experiments, while multivariate testing suits complex web experiences where several elements change at once.

A/B testing is closely linked with Predictive Analytics, Attribution Modelling, and Machine Learning optimisation. Together, these techniques help organisations make evidence-based decisions that maximise performance and minimise risk.

Learn more: Shipshape Data helps organisations design, run, and interpret A/B testing frameworks to improve model performance, digital experiences, and campaign ROI through AI-enabled insights.