Experimentation and A/B-testing

Our Experimentation and A/B-testing service empowers you to make informed decisions by validating hypotheses through controlled experiments. We help you design, implement, and analyze tests to ensure that every change you make is backed by solid evidence.

Benefits

• Evidence-Based Decisions: Make decisions based on data rather than intuition.
• Optimize Performance: Continuously improve your digital experiences through iterative testing.
• Risk Mitigation: Test hypotheses before full implementation to avoid costly mistakes.
• Actionable Insights: Gain clear insights into what works and what doesn’t.

Key Components

• A/B Testing: Compare different versions of a webpage or feature to determine which performs better.
• Personalisation: Find effective personalisation strategies, targeted using behaviour data and proved by individual A/B-tests.
• Experiment Design: Develop robust test plans and hypotheses.
• Result Analysis: Use statistical methods to interpret test results and derive actionable insights.

Our Approach

Our Experimentation and A/B-testing service is designed to provide a systematic and reliable way to validate ideas and optimize your digital experiences. By focusing on evidence-based decisions, we ensure that every change you implement is supported by solid data and proven results.

We assist in forming strong hypotheses through collaborative processes, including HEAT meets, where cross-functional teams develop hypotheses based on presented insights. These workshops foster creativity and ensure that the hypotheses are grounded in real data and observations.

Setting up experiments is a meticulous process that follows a structured experiment design framework. This includes synthesis of insights, hypothesis formulation, targeting specific user segments, performing power analysis to determine sample sizes, and mapping hypotheses to behaviour and visual design. This comprehensive approach ensures that every experiment is well-planned and targeted for maximum impact. Moving on to analysing experiments involves rigorous statistical methods to ensure accurate interpretation of results. We provide detailed reports that highlight what worked, what didn’t, and why, giving you actionable insights to inform future decisions.

While individual experiments need to be set up and analysed correctly, getting value from experimentation is all about running lots of experiments through an experiment program. In our experiment programs, we focus on enhancing the quality of hypotheses to increase their average impact and improving the efficiency of the test-building process to boost testing velocity. This means that not only do you get better insights, but you also get them faster, enabling quicker decision-making and adaptation.

Experiment programs form the basis for continuous optimisation through iterative testing. By constantly refining your digital experiences based on test results, we help you achieve ongoing improvements and drive better results over time.

In summary, our Experimentation and A/B-testing service integrates robust hypothesis formation, meticulous experiment design, and thorough analysis to deliver actionable insights. Whether through individual tests or comprehensive experiment programs, we help you make data-driven decisions that enhance user experience and drive business growth.