How do you use A/B testing and multivariate testing to compare different ad creatives?

2 What is multivariate testing?

Multivariate testing, also known as MVT, is a method of comparing multiple variations of an ad creative to see which combination of elements produces the best results. For example, you can test four different headlines, three different images, and two different colors at the same time. To run a multivariate test, you need to create all the possible combinations of the elements and show each one to a segment of your audience. Then, you need to measure and compare the results using a statistical tool or software.

3 When to use A/B testing or multivariate testing?

Both A/B testing and multivariate testing can help you optimize your ad creatives, but they have different advantages and disadvantages. A/B testing is simpler and faster to set up and run, and it requires less traffic and data to reach statistical significance. However, it can only test one element at a time, and it may miss out on the interactions and synergies between different elements. Multivariate testing is more complex and time-consuming to set up and run, and it requires more traffic and data to reach statistical significance. However, it can test multiple elements at once, and it can reveal the optimal combination of elements.

4 How to design and run effective tests?

To design and run effective tests, you should follow some best practices and tips. Before you start testing, it's important to define a clear and measurable goal, such as increasing your click-through rate by 10%, and a hypothesis, such as "Using a red button will increase the click-through rate more than using a blue button". Additionally, you need to choose elements and variations that are relevant to your goal and hypothesis, and make sure to avoid testing too many elements or variations. It's also important to ensure that your audience is randomly and equally divided into groups or segments, and each group or segment is representative of your target market. Further, make sure to run the test for a long enough time to collect enough data and reach statistical significance. It's also important to avoid ending the test too early or too late, as this will affect accuracy and reliability.

5 How to analyze and interpret the results?

To analyze and interpret the results, you need to use a statistical tool or software that can calculate and compare the key metrics, such as click-through rate, conversion rate, or return on ad spend. You also need to use a statistical tool or software that can determine the confidence level, which is the probability that the results are not due to chance. A common confidence level is 95%, which means that there is a 5% chance that the results are due to chance. You also need to use a statistical tool or software that can account for the margin of error, which is the range of possible values around the estimated value. A common margin of error is 5%, which means that the true value is within 5% of the estimated value.

6 How to avoid common pitfalls and biases?

To avoid common pitfalls and biases, you must be aware of them and how to prevent them. For instance, confirmation bias is the tendency to favor information that confirms your existing beliefs or hypotheses. To counteract this, you need to be objective and open-minded, and to test multiple hypotheses and alternatives. Selection bias is the tendency to choose a sample that is not random or representative of the population. To avoid this, you need to use a random and representative sample, and to control for any external factors that may affect your results. Additionally, the novelty effect is the tendency for new or different things to perform better than old or familiar things, at least temporarily. To counteract this, you should run the test for a sufficient duration and compare the results with your baseline or control group.

7 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?