How do you optimize and scale your online experiments for different markets and channels?

1 Define your experiment

Before you launch any online experiment, you need to have a clear hypothesis, a measurable outcome, and a target audience. A hypothesis is a statement that predicts how a change in your online environment will affect your outcome. For example, "Adding a testimonial to the landing page will increase conversions by 10%". An outcome is a metric that you want to improve, such as conversions, retention, or revenue. A target audience is a segment of your online visitors that you want to test your hypothesis on, such as new users, returning customers, or specific geographies.

2 Choose your experiment type

There are different types of online experiments that you can use depending on your hypothesis, outcome, and target audience. The most common ones are A/B tests, multivariate tests, and factorial designs. A/B tests compare two versions of your online environment, such as a landing page, a headline, or a button color, and measure which one performs better on your outcome. Multivariate tests compare multiple variations of different elements of your online environment, such as a combination of headlines, images, and calls to action, and measure which combination performs best on your outcome. Factorial designs compare the effects of different factors and their interactions on your outcome, such as the impact of price, product features, and channel on sales.

4 Analyze your experiment results

To analyze your experiment results, you need to use statistical methods and tools that help you to evaluate the validity, reliability, and significance of your experiment. Validity means that your experiment measures what it intends to measure, and that it is not affected by external factors, such as seasonality, technical issues, or user behavior changes. Reliability means that your experiment results are consistent and repeatable, and that they are not affected by random errors, such as sampling bias, measurement errors, or outliers. Significance means that your experiment results are not due to chance, and that they show a meaningful difference between your variations. You can use tools and platforms that provide built-in statistical analysis, such as the ones mentioned above, or you can use other tools and methods, such as Excel, R, or Python.

5 Optimize your experiment

To optimize your experiment, you need to use the insights and learnings from your experiment results to improve your online environment and your outcome. You can do this by implementing the winning variation, testing new variations, or refining your hypothesis. You can also use the insights and learnings from your experiment to inform your other marketing strategies, such as your content, your messaging, or your segmentation. Optimizing your experiment is an ongoing process that requires continuous testing and learning.

6 Scale your experiment

To scale your experiment, you need to expand your experiment scope and reach to other markets and channels. You can do this by replicating your experiment on different websites, apps, or platforms, or by adapting your experiment to different languages, cultures, or preferences. Scaling your experiment can help you to increase your impact, to validate your generalizability, and to discover new opportunities. However, scaling your experiment also requires careful planning, coordination, and communication, as well as respect for the ethical and legal implications of online experimentation.