What are the best practices for using factorial designs with repeated measures?

2 Why are they useful for marketing research?

Factorial designs with repeated measures are useful for marketing research, as they enable testing multiple hypotheses in one experiment, reducing sample size and cost. For example, you can use them to compare the performance of different versions of websites, landing pages, or email campaigns on metrics such as click-through rate, bounce rate, or conversion rate. Additionally, factorial designs with repeated measures can be used to evaluate the impact of different pricing strategies, discounts, or incentives on customer behavior, loyalty, or satisfaction; assess the influence of different product features, attributes, or benefits on customer preferences, perceptions, or attitudes; and measure the changes in customer responses over time or across different contexts such as seasons, locations, or devices.

3 How to apply them correctly for your CRO and A/B testing goals?

Factorial designs with repeated measures are a powerful tool for marketing research, but they require careful planning, execution, and analysis. To get the most out of them for your CRO and A/B testing goals, you should first define your research question and hypothesis clearly, specifying the factors, levels, and response variable you want to test. Then choose a suitable design type and structure for your experiment - such as a within-subjects design, a mixed design, or a split-plot design - based on the number of factors that are repeated and how many are between-subjects. Calculate the sample size and power of your experiment, taking into account the number of factors, levels, repetitions, and interactions. Randomize and balance your experimental conditions and treatments to avoid confounding effects and biases. Control external variables that may affect your results - like participant characteristics, motivation, fatigue, or learning effects. Analyze your data using appropriate statistical tests like ANOVA or MANOVA. Finally, interpret and report your results clearly and accurately, emphasizing the main effects and interactions of the factors as well as the practical implications and limitations of your findings.

4 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?