As any software development company will attest to the fact, businesses often have to test the waters of their market or audience before committing to any major design decisions. After all, it is in every startup’s best interest to not only discover how to attract consumers, but also how to provide them with better services. In order to do so, most companies rely making decisions based primarily on suppositions, instead of employing objective data and analysis. Thankfully, improving this situation is easy, as an in-house or dedicated development team can be called upon to implement A/B testing, a controlled experimentation technique which we would like to unpack today.
What Is A/B Testing?
In a nutshell, A/B testing enables businesses to optimise their digital strategies by making use of data analytics and metrics to identify user preferences on the fly. This is done by comparing multiple versions of a particular graphical element or arrangement. These elements could be anything from different images on a home page or application to a slight (or significant) modification to a call to action.
By showing different versions of a digital asset to various segments of your target audience and measuring the difference in behaviour between each segment, you can uncover which version leads to more conversions, clicks, and any other desired outcome.
Not Only A/B Testing: Other Types of Tests
When analysing web page elements to ensure your company is moving in the right direction, in addition to A/B testing, there are different testing types to bear in mind. The most notable of these are:
MVT (Multivariate Testing)
Multivariate Testing, or MVT, is a popular testing technique that involves evaluating multiple versions of various elements on a single website. By testing a large number of variables simultaneously, MVT can quickly identify the best possible combination of content and thus maximise user engagement and conversions. MVT is often employed when a large number of variables exist, including multiple headlines, images and CTAs, and is able to provide companies with invaluable insight. Note that MVT is more complex than a simple A/B test and it is better suited for advanced professionals in both marketing and product development.
Split URL testing
Though often confused with A/B testing, Split URL testing is a fundamentally different concept management by assumption. It refers to the process of creating two or more versions of landing pages, checkout flows or product pages (each with their own individual URLs) and directing traffic to each separate website to determine which version performs best. For instance, if a company were to put out three different versions of their brand new website (e.g. the first with a focus on audiovisual media, the second with a text-heavy interface, and the third with a mixture of both), users could then be redirected at random to any one of these individual pages — while the company would be free to identify which website leads to better engagement.
MPT (Multipage Testing)
Multipage Testing, or MPT, is a testing technique involving multiple versions of an entire user flow, such as an onboarding flow or a multi-step checkout process, to also determine the most effective version. It can help companies uncover which design or content changes are most effective in driving users through the entire user flow and shine a valuable light onto the user experience. More importantly, MPT may also be more time-consuming than other testing methods, but it can provide a more comprehensive understanding of the user experience and help businesses find opportunities for improvement throughout the user journey.
Two Main Approaches in A/B Testing: Frequentist vs. Bayesian
On the subject of A/B testing, two types of statistical approaches exist: Frequentist and Bayesian. Each of these will have their own pros and cons.
Frequentist
The frequentist approach is a statistical method that is used to determine the effectiveness of changes made to a product or service. It involves dividing users into two equal groups, one that is exposed to the new feature or design change (known as the treatment group) and another that is not (known as the control group). The number of users who take a particular action (such as clicking a button or making a purchase) is then measured in both groups, and the results are compared with a statistical test to determine if the difference between the groups is statistically significant. The approach is often employed when there is a need to measure the impact of specific changes, and it relies on repeated experiments to not only gather more data, but make more accurate decisions over time. It is important to mention that using the frequentist method in A/B testing requires a lot more data than other methods (a function of more visitors tested over longer periods of time) to obtain the correct results.
Bayesian
The Bayesian approach, on the other hand, is a statistical method used to update our beliefs about the effectiveness of changes made to a service or product. It involves starting with an initial belief (known as a prior belief) about the effectiveness of the change, and then allowing the results of an A/B test to update that belief. The Bayesian approach uses a special maths formula called the Bayes’ theorem to calculate the probability of a hypothesis being true based on the evidence observed in the A/B test. It is often used when there is already some prior knowledge or expertise about the problem being addressed, and it enables more nuanced decision-making based on the probabilities of different outcomes. The Bayesian approach is also useful when there is limited data available, as it makes use of prior knowledge to inform decision-making.
A/B Testing Guideline: Three Pillars
1. Collect and research
The first pillar of A/B testing demands collecting and researching data to identify potential areas for improvement. This includes understanding user behaviour, gathering feedback, and analysing key performance indicators to determine where changes can be made that will improve the user experience and increase conversions. In order to effectively do so, it is important to establish key metrics to track and set up data tracking and analytics tools that will accurately measure the impact of changes.
2. Detect and prioritise
The second pillar of A/B testing requires asking yourself which elements of a design or marketing campaign are the most problematic. You can then prioritise changes based on the data collected in the first pillar. In other words, you set clear a hypothesis for every test, identify the control and treatment groups, and calculate the sample size needed to see a significant difference between each group. Prioritising changes should also involve assessing the potential impact of every change as well as the effort required to implement it.
3. Improve and deploy
Finally, the third pillar of A/B testing demands implementing the changes found in the second pillar to have the highest potential impact. This includes designing and developing the new variations, testing them against the control group, and lastly analysing the results to determine which version performs better. The improvements can then be deployed to the live website or application, and the impact of the changes can be tracked over time. It’s important to continue to test and refine the changes to ensure they have a lasting positive impact, and to continue to A/B test as needed to identify new areas for improvement.
What Are Some Other Benefits of A/B Testing?
Ultimately, experimentation is all about learning, and A/B testing is a prime example of how well-employed tactical learning can result in tangible benefits. These include:
- Better content
- Improved user engagement
- Reduced bounce rates
- Higher conversion rates
- Testable assets
- Ease of analysis
- Increased sales
- Lower cart abandonment
- Reduced risks
- Quick results
Why do we need A/B Testing?
A/B testing is often employed for the important reasons outlined below:
- Product decisions should be based on facts, not mere assumptions
- When making assumptions, we tend to also make a lot of mistakes
- Ultimately, our products will only be profitable with our end user’s final approval
- A/B testing enables companies to put customers in the driving seat
Who Should Use A/B Testing?
Internet giants
Despite these incredible advantages, it is important to realise that not everyone stands to benefit from A/B testing. While larger companies can easily A/B test new features, design changes and product improvements in a controlled and reliable manner, by most estimates, you would require at least 30,000 users to start to truly benefit from this practice. Nevertheless, A/B testing is an essential tool for companies like Facebook, Amazon and Google, as it enables them to make data-driven decisions that will increase revenue, encourage engagement and optimise the user experience.
E-commerce
Because most e-commerce companies rely heavily on websites and mobile apps to drive sales and generate revenue, they can utilise A/B testing to improve the design and functionality of their products. This is done by comparing checkout flows and testing multiple sales and graphic elements on the page — buttons, menu, cart view, etc. As a result, they are able to uncover which design and content changes are most effective in driving users through the conversion funnel.
Email marketing
A/B testing is also important for companies involved in email marketing, as it enables them to test different subject lines, body text and graphical content. By running A/B tests on email campaigns, businesses can optimise their email marketing strategy and ensure that they are communicating effectively with their target audience at appropriate times.
Conclusion
A/B testing is a powerful technique for many companies and organisations looking to optimise their digital products. For businesses lacking the expertise that still stand to gain from A/B testing — particularly those with a large amount of users — implementation may still be accomplished via an experienced software development company with a capable dedicated development team.
