How can you scale A/B testing for large websites?

1 Choose a robust platform

The first step to scale A/B testing is to choose a platform that can handle the volume and variety of your experiments. You need a platform that can integrate with your website, analytics, and other tools, and that can support different types of testing, such as multivariate, split URL, or server-side. You also need a platform that can ensure the quality and accuracy of your data, and that can provide features such as segmentation, targeting, personalization, and reporting. Some examples of platforms that can scale A/B testing are Google Optimize, Optimizely, VWO, and Adobe Target.

2 Prioritize your experiments

The second step to scale A/B testing is to prioritize your experiments based on their potential impact and feasibility. You can use a framework such as PIE (Potential, Importance, Ease) or ICE (Impact, Confidence, Ease) to score and rank your experiments. Potential and impact refer to how much improvement you expect from your experiment, importance and confidence refer to how relevant and reliable your experiment is for your business goals, and ease refers to how simple and fast your experiment is to implement and run. By prioritizing your experiments, you can focus on the ones that will deliver the most value and avoid wasting resources and time on low-priority ones.

3 Automate and streamline your workflow

The third step to scale A/B testing is to automate and streamline your workflow as much as possible. You can use tools and scripts to automate tasks such as creating variants, setting up goals, tracking events, analyzing data, and reporting results. You can also use templates and guidelines to standardize your experiment design, execution, and documentation. By automating and streamlining your workflow, you can reduce human errors, increase efficiency, and save time.

4 Optimize your sample size and duration

The fourth step to scale A/B testing is to optimize your sample size and duration. You need to ensure that your experiment has enough traffic and conversions to reach statistical significance and validity. You can use calculators and formulas to estimate your required sample size and duration based on your baseline conversion rate, minimum detectable effect, significance level, and power. You can also use techniques such as sequential testing or Bayesian statistics to monitor and adjust your sample size and duration dynamically. By optimizing your sample size and duration, you can avoid running underpowered or overpowered experiments that can lead to false positives or false negatives.

5 Coordinate and communicate your experiments

The fifth step to scale A/B testing is to coordinate and communicate your experiments with your team and stakeholders. You need to have a clear and transparent process for planning, launching, monitoring, and evaluating your experiments. You need to have a central repository and dashboard for your experiment data and results. You need to have regular meetings and updates to share your learnings and insights. And you need to have a feedback and review system to improve your experiment quality and performance. By coordinating and communicating your experiments, you can align your team and stakeholders on your A/B testing goals and outcomes, and foster a culture of experimentation and optimization.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?