Insights from our database: Monadic versus Sequential testing

Ad testing is a crucial step in the ad creation process, as it allows brands to understand how their ads will perform in the real world. But with so many different testing methodologies available, it can be difficult to know which one to choose. In this blog post, we'll take a closer look at two popular ad testing methodologies – Monadic and Sequential – and explore the pros and cons of each, as well as how they can impact the effectiveness of your ads.

Monadic testing is when respondents are exposed to and evaluate only one ad. This methodology is considered the gold standard for ad testing, as it eliminates potential biases that could be introduced by a Sequential methodology. However, it can be more time-consuming and costly, as it requires a larger sample size to ensure that each ad is evaluated by the same number of people.

Sequential testing, on the other hand, is when respondents are exposed to and evaluate all ads in random order. This methodology is more cost-effective and efficient, as it requires a smaller sample size. However, it can introduce biases, as repeated exposure to an ad can impact a respondent's perception of it.

To better understand the impact methodology has on assessing the effectiveness of ads, we analyzed 290 ads from our database. Our analysis showed that overall, there are no significant differences in metrics between Sequential and Monadic testing. However, there is one directional difference with Sequential testing; it is directionally higher than Monadic on the metric of “New Information” (does the ad provide new information). This could be due to the repeated exposure of ads in Sequential testing which could help in identifying the information the ads are trying to communicate.

Responses by Methodology

* a/b=Significant difference at 90% confidence interval

When it comes to advising clients on which methodology to use when testing assets, it ultimately comes down to budget and what the client is trying to achieve. If budget is a major concern and Monadic methodology does not seem feasible, but you still want to gain the best understanding of which ad to move forward with - use sequential.

Though the Sequential methodology has its drawbacks, it is still a valid way to test, as long as you set up the test and analyze the data correctly:

  1. Ensure the ads are randomly shown to the respondents and if possible ensure that you show each ad in the number 1 position equal times.

  2. This test will also tell us which of the assets tested was the most effective as we are comparing them to each other – there would be no reason to believe that testing using sequential methodology vs. monadic would give us completely different results.

  3. Knowing that some metrics may be slightly inflated, compare the results to other sequential tests from your database to understand if the ads are under/overperforming vs. those benchmarks.

  4. If there is an existing ad (either from your brand or a competitor) that you know performed well, put that ad in the test as a second benchmark.

After exploring the options available for ad testing, you may have a better understanding of the pros and cons of Monadic and Sequential methodologies. Choosing the right approach comes down to aligning your strategy with the client's budget and goals. To get the most out of your ad testing efforts, it's crucial to have a well thought out plan for setting up and analyzing the test data. If you're looking for more information on Ad Testing, don't hesitate to reach out to athos@lab42.com or book a call with us using Calendly. We're here to help you achieve your research goals.

Jon Pirc

Jon has spent his professional career as an entrepreneur and is constantly looking to disrupt traditional industries by using new technologies. After working at Sandbox Industries as a ‘Founder in Residence’, Jon founded Lab42 in 2010 as a way to make research more accessible to smaller companies. Jon has a Bachelor’s of Science in Psychology from Northern Illinois University.

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