A/B testing is a fundamental strategy in marketing, as it allows you to maximise conversion rates and solve previously undetected problems. In this article we explain what A/B testing is and how to carry out an A/B test in 8 steps.
As digital marketing gains ground over traditional marketing, A/B testing has become a fundamental methodology for optimising campaigns.
Within a marketing strategy, A/B testing offers the opportunity to boost conversion rates, detect potential disruptions, identify the customer journey and transform hypotheses into certainties.
- Discover the 10 tools to build an effective customer journey.
The most important benefit of A/B testing is that it helps companies to apply the strategies that work best with their audience, thus helping to increase conversion rates and the overall performance of targeted business actions.
Another great advantage of this methodology is that it provides highly specific results, so its transformation into improvement actions is almost inevitable.
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What is A/B Testing?
An A/B test, also known as A/B Testing or split testing, is a research methodology in the field of marketing and user experience (UX).
It consists of performing a test that measures the performance of two versions of the same element (represented by the variables A and B) and compares their results. The test result usually indicates which of the two samples (A or B) is more efficient.
- Version "A" of an A/B test is the original or "control" version, while "B" represents the modified version, also called the "treatment variable".
- At the end of the test, the version with the best results is known as the "winning version".
A/B testing has many areas of application. However, it is a widely used process in the field of digital marketing, omnichannel customer experiences and the user experience. Marketers use A/B testing to find out which actions work best and to analyse their audience's response to different inputs.
A/B Testing is one of the main research methodologies in content marketing. While it can be used for any type of content, it is especially common for email marketing campaigns.
How does A/B Testing work?
A/B testing provides a systematic methodology to evaluate the effectiveness of any marketing strategy. In an environment where traffic acquisition is becoming increasingly challenging and costly, ensuring an optimal user experience on the website becomes essential to achieve fast and efficient conversions. In marketing, these experiments allow to maximise the value of existing traffic and increase profits.
A/B testing is an essential tool in the conversion rate improvement (CRO) process, helping to gather both qualitative and quantitative information. Its results are valuable in understanding user behaviour and knowing customers better, the degree of customer engagement, obstacles that may arise, and even the level of satisfaction with different aspects of the website, such as new functionalities or revamped sections.
A well-structured A/B testing approach helps to identify problem areas that require optimisation, which in turn improves business results.
In short, the application of A/B testing is essential to avoid missing significant profit opportunities for the company.
The key concepts of an A/B Test
Before learning how to carry out an A/B test, it is important to familiarise ourselves with a number of concepts that we may not have heard before.
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Control variable: Original version of the element to be tested. It corresponds to variable A in the A/B test.
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Treatment variable: This is the modified version of the item being tested. It corresponds to variable B of the test.
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Winning variable: This is the version that performed best according to the test results.
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Control group: The control group is the sample of our experiment. That is, the group of users on which we base the test and on whom the final results depend.
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Significance: In the context of A/B Testing, significance is the importance we attach to the results obtained. In other words, the significance level reflects our level of confidence in the results obtained. The significance level of an A/B test is expressed as a percentage and usually ranges from 95% to 99%.
How to carry out an A/B Test?
The operation of an A/B test is simple: two versions of the same content are created (version A and version B), changing only one element between them. In other words, the two versions must be exactly the same and only the specific element that we want to measure must change. For example, in the case of an email, if we want to measure what type of subject line works best for us, we will use a different subject line for version A and B of the email, but everything else will be the same in both versions.
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The most common mistakes (and the ones you should avoid) when carrying out an A/B Test:
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Lack of planning in terms of the optimisation roadmap.
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Creating invalid hypotheses: It is essential to formulate strong hypotheses before conducting A/B tests, as these hypotheses guide the entire process. An incorrect hypothesis can significantly decrease the chances of success.
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Relying on the success of others: Do not apply the results of others to your own website, as each case is unique and what works for one may not work for another due to differences in traffic, target audience and strategy.
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Running too many tests simultaneously: Testing too many elements at once can make it difficult to identify which specific changes influenced the results, requiring a significant amount of traffic to obtain statistically relevant results. Prioritisation of items to test is essential.
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Overlooking statistical significance: Relying too much on instinct or opinion when formulating hypotheses or setting goals increases the chances of failure. It is critical to allow a test to run for as long as it takes to reach statistical significance, regardless of whether the results are good or bad. These results will provide valuable information for planning future tests.
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Not getting the duration of tests right: Tests should have an appropriate duration based on factors such as traffic and objectives. Either too long or too short a run time can lead to non-meaningful or failed results.
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Not following a repetition-based approach: A/B testing is an iterative process where each test builds on the results of previous tests. Abandoning A/B testing after a first failed attempt reduces the chances of future success. Moreover, even when successful results are obtained, repeated tests must be performed to constantly optimise.
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Ignoring external factors: Tests should be conducted over comparable periods to obtain meaningful results. Do not compare data from days with very different traffic due to external factors, as this can lead to erroneous conclusions.
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Over-caution in creating A/B tests: While starting with small A/B tests can be useful, over-caution can limit the long-term potential. Depending on the objectives, it is important to choose between simple A/B tests or more complex multivariate tests to get the best results.
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Not using the right tools: Using the wrong tools can negatively affect data quality and website speed. It is essential to choose appropriate A/B testing tools that are integrated with the necessary qualitative tools.
The main advantadges of A/B Testing
Nowadays, conversion is one of the focal points of digital marketing strategies. Conversion is what measures the results of marketing actions and how they influence lead generation.
Increasing the conversion rate is usually one of the main objectives of the vast majority of organisations, but it is also among the most difficult objectives to achieve.
A/B testing is one of the most effective ways to increase conversion rates. Because it is a type of testing that provides very specific results that translate into improvements, A/B testing often leads to easily observable performance improvement.
Other advantages of carrying out an A/B test include:
1. Better data-driven decisions: An A/B test allows you to make decisions based on data rather than relying on assumptions or intuition. This helps reduce the risk of making costly and erroneous decisions.
For example, an e-commerce company might conduct an A/B test to determine whether changing the colour of the "Buy Now" button on its website increases conversions. If version B (red button) shows a significant increase in conversions compared to version A (green button), a decision will be made to implement the change based on real data.
2. Optimising the user experience: A/B testing identifies which elements or features of a website or application are most effective in attracting, retaining and converting users.
3. Increased conversion and ROI: One of the most obvious benefits of A/B testing is the ability to increase conversion rates, which translates directly into increased return on investment (ROI). For example, a software company might test two different copies of its pay-per-click (PPC) ad and find that version B generates 20% more clicks. This can lead to more leads and ultimately an increase in sales and ROI.
4. Effective personalisation: An A/B test is also useful for creating personalised customer experiences. For example, a news platform can test two different headlines for a news story and display the headline that attracts more readers based on click-through and dwell time metrics.
5. Reducing assumptions and internal conflicts: Rather than relying on internal opinions or discussions, A/B testing provides an objective basis for making decisions. This helps reduce conflicts and ensures that decisions are made based on what really works for the target audience.
6. Resource savings: By testing variants before implementing large-scale changes, companies can avoid spending time and resources on changes that do not have a positive impact. For example, an online shop can test different navigation structures before redesigning the entire website, which could be costly and time-consuming.
The importance of time in an A/B Test
The A/B testing process asks for continuous evaluation over time. It is common for significant performance improvement to occur after multiple A/B tests. Rarely will you get clear results from a single test.
It is possible that the first A/B test we perform does not produce statistically significant results that support our hypothesis. However, this should not be interpreted as a failure. On the contrary, this situation provides an opportunity to generate new hypotheses and improve our tests as we implement them. For example, it is advisable to test the same variable with different modifications to assess whether it produces a noticeable impact. We can also explore more radical changes if previous adjustments have not yielded the expected results.
Sometimes, we may wonder whether the variable we are analysing has no influence on the conversion rate. However, it is possible that other elements on the page are influencing the conversion rate and, therefore, affecting the results obtained. Proper A/B testing involves experimenting and discovering insights over time.
What matters is that by applying A/B Testing we are evaluating our marketing strategies in order to improve them, so we are moving in the right direction.
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