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Research Toolkit: Decoding A/B Testing, Unlocking Success for Luxury Marketing Campaigns

In the ever-evolving landscape of luxury marketing, data-driven decision-making plays a pivotal role in driving success.

A/B testing, also known as split testing, originated from randomized control trials in Statistics, it is one of the most popular ways for Businesses to test and understand if something new should be implemented or not.

It is a powerful technique that enables marketers to optimise their campaigns, websites, and strategies by comparing two variations and measuring their performance. However, conducting AB tests for luxury markets requires careful considerations to ensure accurate results and actionable insights.

Here we discuss why testing is important, how to test, and the how to avoid the biggest mistakes marketers make when trying to implement and A/B test.

Why A/B Testing is Important

A/B testing is a strategic way to optimize your marketing channels using data, instead of guessing and basing ideas on human bias.

The idea behind A/B testing is that you show the variated version of the product to a sample of customers (the experimental group) and the existing version of the product to another sample of customers (the control group).

Then, the difference in product performance in experimental/treatment versus the control group is tracked, to identify the effect of this new version of the product on the performance of the product.

So, the goal is then to track the metric during the test period and find out whether there is a difference in the performance of the product and what type of difference is it.

The main motivation behind A/B testing is to test new product variants which will improve the performance of existing product(s) and will make the product(s) more successful and optimal.

The advantages of A/B testing is that businesses receive direct feedback from actual users by presenting them with existing versus variated products/feature options. This allows businesses and marketers to quickly test new ideas.

If the variated version proves ineffective, it proves as a lesson to be learned from, and allows for further developments.

Questions to ask before any A/B test

Since an A/B test requires a significant amount of resources and might result in product decisions with a significant impact, it is important to to ask these questions before starting the test...

  • What does a sample population look like?

  • What are the customer segments for the target product?

  • Do we want to test single or multiple variants of the target product?

  • Does the test contain a truly randomized control and experimental group? Are both samples an unbiased and true representation of the true user population?

  • Can we ensure the integrity of the treatment vs control effects during the entire duration of the test?

Choosing the primary metric for an A/B test

Choosing the primary metric is one of the most important steps in running an A/B test due to the fact that this metric will be used to measure the performance of the product or features for the experimental and control group. It will be used to identify if there is a statistically significant difference between these two groups.

The choice of the metric depends on the underlying hypothesis being tested. This is one of the most critical steps in setting up an A/B test because it determines how the test will be designed and how well the idea performs.

Poor metric choice can disqualify a large amount of work or can result in incorrect conclusions.

Since revenue is not always the end goal for A/B tests, it is important to connect the primary metric to the direct and higher-level goals of the product or feature.

One of the best ways to test the accuracy of a chosen metric in your A/B test, is to go back to the exact problem you want to solve. And ask this question:

If this chosen metric were to increase significantly while everything else stayed constant, would we achieve our goal and address the problem?

Even though it is necessary to have a single primary metric for an A/B test, it is still paramount to watch the remaining metrics to make sure all the metrics are showing change and not only the target one. Having multiple metrics in your A/B test will lead to false positives since you will identify many significant differences while there is no effect, which is something you want to avoid.

Common A/B test metrics

Popular performance metrics often used in A/B testing are the Click Through Rate (CTR), Click Through Probability (CTP) and Conversion Rate (CR).

1: Click-Through Rate (CTR) - to understand usage Click-Through Rate is when the number of total views or sessions is taken into account. This number is the percentage of people who view the page (impressions) compared with those that actually click on it (clicks).

2: Click-Through Probability (CTP) - to understand impact Unlike the CTR, the CTP does take into account the duplicate clicks a user might make. This means that if a user has performed multiple clicks, in a single session, on the same item, this multiple clicks is counted as a single click in CTP.

3: Conversion Rate (CR) Conversion rate is defined as the proportion of sessions ending up with a transaction.

Stating the hypothesis of the test

It is necessary that A/B test should always be based on a hypothesis. The idea behind a hypothesis is that a hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested.

When creating a hypothesis it is important to prioritize problems and ideas to test. However, a hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable

Do not merge multiple ideas into one hypothesis. Limit the variables introduced in the test so that you can understand their individual impact. Otherwise, you’ll be left with many questions and few answers at the end of your test.

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables.

  • An independent variable is something the researcher changes or controls.

  • A dependent variable is something the researcher observes and measures.

After identifying the variables, write a prediction in an if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

If research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H0, while the alternative hypothesis is H1 or Ha.

For example:

H0: If there is no testimonial on the Hair Salon booking page, then this will lead to more bookings. H1: If there is a testimonial on the Hair Salon booking page, then this will lead to more bookings.

Designing the A/B test

A/B testing is about learning. Following are the steps you need to take to have a solid design for your A/B test.

Step 1: Set up a Hypothesis

Ensure your hypothesis has a clear and define independent and dependent variable.

Step 2: Significance Level

To make sure that results are able to be repeated and can be generalized to the entire population, it is necessary to ensure real statistical significance and to avoid biased results. Therefore, it is important to collect enough observations and run the test for a minimum predetermined amount of time.

Before running the test it is important to determine the sample size of the control and experimental group, and to determine the duration of the test.

Significance level of the test

The significance level is the probability that the null hypothesis will be rejected.

Generally, we use the significance value of 5% which indicates that we have a 5% risk of concluding that there exists a statistically significant difference between the experimental and control variant performances when there is no actual difference.

So, we are fine by having 5 out of 100 cases detecting a treatment effect while there is no effect. It also means that you have a significant result difference between the control and the experimental groups with a 95% confidence.

Step 3: Calculating minimum sample size

Another very important part of A/B testing is determining the minimum sample size of the control and experimental groups. It is important that the two groups are equal in size and randomness. Calculation of the sample size depends on the underlying primary metric that you have chosen for tracking the progress of the control and experimental versions.

Step 4: Determining A/B test duration

As mentioned before, this question needs to be answered before you run your experiment not during and trying to stop the test when you detect statistical significance. To determine the baseline of a duration time, it is best to look at your key KPIs and resources.

Often marketers make encounter two common mistakes when their test is not optimized for the right duration of time: the Novelty Effect and the Maturation Effect

Novelty Effect: test duration is too short

Users tend to react quickly and positively to changes. This positive effect to the experimental version is referred to as novelty effect and it wears off in time and is thus considered “illusory”.

Therefore, when picking a test duration it is important to make sure the test is not run for too short a time period.

Maturation Effect: test duration is too long

When planning an A/B test it is usually useful to consider a longer test duration for allowing users to get used to the new feature or product. That way, it will be able to observe the real treatment effect by giving more time to returning users to cool down from an initial positive reaction.

This should help to avoid novelty effect, however, the longer the test period, the larger is the likelihood of external effects impacting the reaction of the users and possibly contaminating the test results, also known as the maturation effect.

Therefore, running the A/B test for too long is is also not recommended and can better be avoided to increase the reliability of the results.

Step 5: Running the A/B test

Once the preparation has been finished, it is time to start running the A/B test. Implement the variations and launch your A/B test!

Ensure that the test is set up accurately, and the data collection mechanism is in place. Run the test for a sufficient duration to gather enough data and account for any potential variations due to external factors. Avoid premature conclusions by allowing enough time for the test to reach a reliable conclusion.

Step 6: Analyze the Results & Draw Conclusions

Once you have collected enough data, it's time to analyze the results. Compare the performance of each variation against your defined objective and metrics. Use statistical analysis to determine if the differences observed are statistically significant or due to random chance.

By evaluating the data, you can draw meaningful conclusions about which variation performed better and whether it supports or disproves your hypothesis.

Step 7: Implement the Winning Variation and Iterate

Based on the results of your A/B test, identify the winning variation and implement it as part of your marketing/business strategy. A/B testing is an iterative process, as you implement the winning variation, continue testing and refining other variables to maximize your results.


A/B Testing is an effective marketing tool for luxury businesses, data-driven decision-making plays a pivotal role in driving success for brands. Although A/B Testing originated from statistics, it is one of the best ways for Businesses to test and understand if something new should be implemented or not. It is a powerful technique that enables marketers to optimize their campaigns, websites, and strategies by comparing two variations and measuring their performance.

Tips for Successful A/B Testing:

  • Test one variable at a time to isolate its impact on performance.

  • Ensure that your test sample is representative of your target audience.

  • Conduct tests across different segments of your audience to identify variations in behavior and preferences.

  • Continuously monitor the test to identify any anomalies or external factors that may skew the results.

  • Document your findings and insights from each test to inform future iterations and strategies.

If you are interested in learning more about research methods specifically for the luxury marketing sector, check out our articles:

If you are interested in learning more about our research methods, check out Ad-Hoc Research or Research the Affluent Luxury Tracker (RTALT) - a powerful tool and continuous database for luxury sector research

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