What Is A/B Split Testing and Why Should You Do It? (Plus Examples)

A/B testing is an experiment where two versions of a page are shown to users at random and statistical analysis is used to identify the best-performing version for a given conversion goal. It compares different available options to learn what customers prefer. Running an A/B test takes out the guesswork and enables data-informed decision-making, shifting conversations from “we think” to “we know”. You can test website or app layouts, email subject lines, product designs, ad creatives, and CTA button text and colours. 

Conducting A/B testing can help solve visitor pain points, increase conversions or leads, and decrease bounce rates. If you’re not A/B testing, you’re losing out on a lot of potential business revenue. Keep reading to understand what A/B testing is, how it works, its benefits, mistakes to avoid, and tips on how to conduct A/B testing.

Introduction to A/B split testing

Data is an invaluable asset in decision-making, providing valuable insights into customer behaviours and preferences and market trends. Proper data analysis can help businesses to better understand their target audience, tailor products and services to their needs, and identify areas for optimisation. It also helps prevent the repetition of past mistakes by identifying emerging threats early on. 

Never before in history have we operated with as much data as we do today. It’s all about data these days. Businesses don’t want to make decisions unless they have evidence. Fortunately, there are ways to get the required data-backed information without relying on one’s instincts. One of the ways is to conduct A/B testing.  

A/B testing, also known as split testing or bucket testing, refers to a randomised experimentation process for comparing two versions of a variable or even more against each other to determine which one performs best. Essentially, A/B testing eliminates all the guesswork out of the process and enables businesses to make data-backed decisions. 

A/B testing originated in the 1920s with Ronald Fisher, a statistician and biologist. He discovered the most important principles behind A/B testing and randomised controlled experiments in general. In the 1960s and 1970s, the concept was adopted by marketers to evaluate direct response campaigns. In the year 2000, Google engineers ran their first A/B test to determine the best number of results to display on its search engine results page. However, this first test was unsuccessful due to glitches that resulted from slow loading times. In 2011, 11 years after Google’s first test, Google ran over 7,000 different A/B tests. 

Kaiser Fung, founder of the applied analytics program at Columbia University and author of Number Sense: How to Use Big Data to Your Advantage, says that the maths behind A/B testing hasn’t changed at all. It’s the same core concepts, however, it’s now done online, in a real environment, and on a different scale in terms of the number of participants and the number of experiments.      

Importance of A/B testing in ecommerce and dropshipping

Whether you’re a B2B or B2C business, A/B testing is crucial for ecommerce and dropshipping businesses aiming to optimise their operations, boost conversion rates, and increase profitability. It can help deal with common problems such as unqualified leads, high cart abandonment rates, payment page drop-offs, and conversion funnel leaks. 

In a highly competitive ecommerce landscape, small changes can make a significant impact on user behaviour. Every improvement or optimisation should be in the interest of your target audience. A/B testing can provide you with information about your customer preferences and needs and what works for your business. This allows you to make data-driven decisions to optimise your online store and potentially reduce marketing and redesign risks.

A/B split test involves comparing two versions of a webpage, product page, email, or advertisement to determine which variable performs better based on specific business metrics, such as click-through rates, conversion rates, or sales. By testing a single variable, such as the headline, call-to-action, layout, or even the colour of a button, ecommerce and dropshipping businesses can identify what resonates best with their audience. This helps minimise guesswork, providing solid evidence on what drives customer engagement and conversions.

Margins can be thin and customer acquisition costs are high for ecommerce and dropshipping businesses. A/B testing can be an invaluable strategy for these businesses, enabling them to optimise product pages, pricing strategies, and marketing campaigns to ensure they remain effective. For example, by testing different product descriptions or images, a dropshipping business can determine which version leads to better conversion and sales. Similarly, testing various pricing models or promotional offers can reveal the most attractive options to customers, thereby maximising revenue.

A/B testing is an ongoing process that fosters continuous improvement. The ecommerce landscape is constantly evolving, with customer preferences and market trends shifting regularly. Businesses can stay ahead of the curve by adapting their strategies to meet the changing needs of their target audience through regular A/B testing. This iterative approach enhances the customer experience and builds a competitive advantage by consistently refining and optimising every aspect of the business.

The A/B split test is a powerful tool for ecommerce and dropshipping businesses, offering a methodical way to improve performance and drive growth. By leveraging data to make informed decisions, businesses can fine-tune their business and marketing strategies, reduce risks, and ultimately achieve better results in a competitive market.

How A/B split testing works

A/B testing helps businesses determine which variable performs better. Its goal is to enable businesses to make data-driven decisions, which can improve user engagement, conversions, or other key metrics. Here’s the detailed breakdown of how A/B testing works.

Setting up a hypothesis

A hypothesis is a clear, testable statement that predicts how changes to a landing page or any other element will affect user behaviour. It’s a proposed explanation or solution to a problem. 

The hypothesis is made up of two variables - the cause (action we want to test) and effect (the outcome we expect). It sets the foundation for your A/B test and guides the entire process. A well-defined hypothesis should be specific, measurable, and focused on a particular outcome. 

Your hypothesis will include three key components: a problem statement, a proposed solution, and anticipated results.

For instance, your hypothesis could state that changing the colour of the ‘Buy Now’ button on your website to a brighter shade will increase the click-through rate (CTR) by 15 per cent. 

The hypothesis should be based on user research, historical data, or a logical assumption about your user behaviour. It helps in defining the focus of the A/B test and sets expectations about the outcomes.  

Identifying variables (e.g. headlines, images, CTA)

Variables are the elements of your website, app, or marketing assets that you want to test.

In A/B testing, there are generally two types of variables - independent variables and dependent variables. The variables that you want to test are independent variables. For example, headlines, images, colours, CTAs, and layouts. The metrics you'll measure to determine the effect of the independent variable are dependent variables. For example, click-through rates, conversion rates, bounce rates, or time on page.

Some of the common variables to test include CTAs, headlines, subject lines, images, layouts, text size or font size, button colours and placement, content length, and word choice. 

Running the test and analysing results

To run the A/B test, randomly divide your audience into two (or more) groups using the A/B testing tool: the control group and the experimental group. Then, create two versions of the selected variable, with one variable changed in version B. The control group sees the original version A, while the experimental group sees the variation B. You'll need to run the test for a sufficient duration to gather meaningful data. 

After running the A/B test for a predefined period, analyse the results by comparing key metrics, such as bounce rates and conversion rates, between the two versions to determine the better-performing version. The version with a statistically proven significant improvement in key metrics is the successful one. For instance, if version B outperforms version A, you can conclude that the change positively impacts your goal. After identifying the successful versions, implement the winning variables and run further tests for continuous optimisation. Ongoing testing and improvement are crucial for enhancing metrics over time.

Benefits of A/B split testing

In the rapidly evolving ecommerce landscape, businesses are constantly seeking ways to optimise their websites, marketing campaigns, and overall user experience to stay competitive. One of the most effective ways to achieve this is A/B testing. It involves comparing two versions of a webpage, email, or advertisement to determine which version performs better based on a specific metric, such as conversion rates, bounce rates, or user engagement. A/B split tests offer several significant benefits that can drive the success of an ecommerce business. Let’s look at some of the benefits of A/B split tests.  

Optimising conversion rates

As an ecommerce entrepreneur, you always look for ways to increase conversion rates and boost sales. A/B testing is a powerful tool that can help you achieve these goals. One of the primary benefits of A/B testing is its ability to optimise conversion rates, whether those conversions are sales, sign-ups, downloads, or other key performance indicators (KPIs). Conversions are the lifeblood of any ecommerce business. Many factors can affect conversion rates, such as the web page’s design, copy, and CTA. 

By leveraging A/B testing, businesses can experiment with different elements of their web pages or marketing materials, such as headlines, call-to-action buttons, product descriptions, and images. This can help businesses find the combination that works best for their target audience and is the most effective at converting visitors into customers. 

For example, testing two versions of a product page can reveal which layout or wording is more likely to lead to a purchase. This data-driven approach allows businesses to make informed decisions and implement changes that directly impact their bottom line. Over time, even small improvements in conversion rates can lead to significant increases in revenue and customer acquisition.

Reducing bounce rates

Another benefit of A/B testing is its ability to reduce bounce rates. Bounce rates are a common metric to measure how engaging and relevant your website is for your visitors. It’s influenced by multiple factors such as website design, content, navigation, speed, and relevance to your audience. A high bounce rate occurs when visitors leave a website shortly after arriving, often because they didn't find what they were looking for or were not engaged by the content, which can hurt your conversion goals and SEO ranking. A/B testing can help identify the factors contributing to high bounce rates and provide solutions for improvement. Using A/B testing, businesses can test different variations of a specific variable to see which variation reduces bounce rates and implement it. For instance, a business might test different landing page designs, content arrangements, or even loading speeds to see which version keeps visitors on the site longer.

By reducing bounce rates, businesses can increase the chances of converting prospects into customers and improve their site's overall performance in search engine rankings. Lower bounce rates often indicate a more engaging and relevant experience for users, which can lead to higher customer satisfaction and loyalty.

Enhancing user experience

One of the key benefits of A/B testing is its ability to improve the user experience (UX). UX is a crucial component of any successful ecommerce business. A good UX attracts customers and encourages them to return. A/B testing is a powerful tool for enhancing UX by allowing businesses to fine-tune their websites or apps to meet user needs and preferences. 

Through A/B testing, businesses can test different design elements, content layouts, and navigation structures to identify which version provides a more intuitive and enjoyable experience for users. For example, testing different menu structures or checkout processes can help identify the most user-friendly options, leading to a smoother and more enjoyable experience for customers. 

A better UX not only increases customer satisfaction but also encourages longer website visits, higher engagement rates, more conversions, and increases word-of-mouth referrals, which contribute to the long-term success of the business. A/B testing helps ensure that any changes made to a website or app enhance the overall user experience rather than detracting from it.

Data-driven decision making

Making informed decisions is crucial to the success of your ecommerce business. A/B testing allows businesses to understand customer preferences and make decisions based on empirical data rather than intuition or assumptions. 

By testing two different versions of a webpage or ad campaign, businesses can see which version performs better based on objective metrics such as click-through rates, conversion rates, or sales. This approach provides businesses with real user data to understand the impact of their campaigns. So, instead of relying on intuition or assumptions, businesses can make data-informed decisions, which can in turn reduce the risks associated with making changes based on subjective opinions or unverified theories. 

Data-driven decision-making leads to more consistent, repeatable results, and enables organisations to optimise their strategies continuously. By methodically testing and implementing the test results, businesses can significantly improve the effectiveness of their optimisation efforts. 

Lower risk in implementing changes

Implementing changes to a website or marketing campaign always carries some inherent risks. These risks can range from minor setbacks, such as reduced engagement or conversion rates, to significant losses, like damaged brand reputation or decreased revenue. 

A/B testing mitigates these risks by allowing businesses to test changes on a small scale before rolling them out more broadly. This approach means that if a new change performs poorly, it can be quickly identified and reverted without significant impact. This minimises the risk of revenue loss, poor user experience, and damage to brand reputation. 

By reducing the risk associated with making changes, A/B testing encourages a culture of experimentation and innovation, where teams feel confident to try new ideas, refine their strategies, and make incremental adjustments that enhance user experience and drive growth over time.

Informed content strategy

Content is a significant driver of user engagement and conversion in digital marketing. A/B testing plays a crucial role in shaping content strategy by providing actionable insights. It enables businesses to determine which type of content resonates best with their audience by systematically testing different versions of content from blog posts and videos to product descriptions and promotional messages. 

Through A/B testing, businesses can identify which elements drive higher engagement, click-through rates, and conversions. For instance, a business might test two different headlines for a blog post to determine which one attracts more clicks or test different types of social media posts to see which generates more engagement. 

By continuously testing different content formats, tones, and lengths, businesses can refine their content strategy to better meet the needs and preferences of their audience, driving greater engagement and more effective communication.

Better audience segmentation and personalisation

A/B testing can help businesses better understand their audience segments and tailor their offerings accordingly. By testing different variations of content, design elements, or promotional strategies with distinct audience segments, businesses can gain deeper insights into the specific preferences, behaviours, and needs of each group. This process involves creating multiple versions of a webpage, email, or ad, each tailored to different segments, and then measuring which version resonates most effectively with each audience. This knowledge enables more effective segmentation and personalization, allowing businesses to deliver targeted content and offers that resonate with specific audience groups. Personalisation, in turn, enhances user satisfaction and loyalty, driving long-term growth.

By leveraging these benefits, businesses can optimise their digital strategies, improve user satisfaction, and ultimately drive higher revenue growth. A/B testing provides a systematic, data-driven approach to continuous improvement, making it an indispensable tool.

Common mistakes to avoid in A/B testing

A/B testing is a crucial tool in data-driven decision-making, enabling businesses to test different variations of a webpage, email, or ad to determine which performs better. However, to derive meaningful insights from A/B tests, it’s essential to avoid common mistakes that can skew test results or lead to incorrect conclusions. 

The following are the common mistakes to avoid in A/B testing.

Testing too many variables at once 

One of the common mistakes in A/B testing is testing too many variables at once. It’s also known as multivariate testing. Without proper planning, conducting a multivariate test can lead to confusion and inconclusive results. When multiple variables are tested simultaneously, it becomes difficult to determine which specific variable change caused any difference in performance. 

For instance, if a business tests a new headline, image, and CTA button colour on a webpage all at once, and the new version performs differently, it would be unclear whether the improvement or decline was due to the headline, image, button colour, or a combination of these factors. 

The key to effective A/B testing is simplicity: test one variable at a time. This approach isolates the impact of each individual change, making it easier to draw clear and actionable conclusions from test results. A focused A/B test allows for more precise optimisation and helps avoid the potential confusion of conflicting test results.

Insufficient sample size

Conducting A/B tests with an insufficient sample size is another common mistake that can lead to unreliable results. A small sample size increases the margin of error and reduces the statistical significance of the test, meaning the observed differences between versions might simply be due to random chance rather than a genuine effect. Without enough data, the test results may not be statistically significant, leading businesses to make decisions based on false positives or negatives. This can result in businesses implementing changes that may not actually benefit their performance. 

For instance, an online retailer ran an A/B test with only 100 visitors per variant. Due to the small sample size, the observed conversion rate difference was not statistically significant, yet they concluded that one variant was better, leading to misguided marketing strategies.

To ensure reliable and accurate test results, it’s crucial to calculate the required sample size before conducting the test, depending on the desired level of statistical significance. This ensures that the test has a sufficient number of participants to detect the real effect and avoid making decisions based on unreliable data. 

Ignoring statistical significance

Ignoring statistical significance is a critical mistake in A/B testing. Statistical significance is a measure of confidence in the results of a test. It helps determine whether the observed differences between two versions are likely due to the changes made or merely a result of random chance. 

Many online retailers are tempted to end tests early when they see favourable test results or ignore statistical significance thresholds, leading to premature conclusions. Acting on results without ensuring statistical significance can lead to incorrect assumptions about what works and what doesn’t. 

For instance, if a website variant shows a higher conversion rate after just a few hours or days, it may be tempting to implement the change immediately. However, if the sample size is too small or the test hasn’t run long enough to reach statistical significance, the observed improvement may not hold up over time.

Without ensuring statistical significance, you risk interpreting random variations as meaningful. To achieve statistically significant results, it’s essential to predetermine the level of statistical significance (which is often set at 95 per cent) and only implement test results once this threshold is reached.

Running tests for too short or too long

The duration of an A/B test is critical to get reliable test results. Running a test for a shorter period of time or stopping the test prematurely can result in a lack of reliable data or incomplete data. It’s essential to run tests for enough time to gather sufficient data across different times and user behaviours.  

For instance, seasonal traffic fluctuations, marketing campaigns, or day-of-week variations can impact user behaviour. This may cause misleading results if a test doesn’t run long enough to account for these factors. 

Running a test for too long can also pose problems. Extended testing durations increase the likelihood of external factors affecting the test, such as changes in user behaviour due to new marketing tactics, competitive strategies, or even economic shifts. Furthermore, running the tests for a longer duration may delay its implementation. 

To achieve statistically significant results, you should calculate the optimal test duration depending on traffic volume, the conversion rate, and the desired level of confidence. This ensures that the test is long enough to gather valuable data. 

Not accounting for external factors

Failing to account for external factors is a significant oversight in A/B testing. External factors, such as seasonality, economic shifts, holidays, competitor strategies, or concurrent marketing campaigns, can influence user behaviour and affect the outcome of A/B tests. For instance, a spike in website traffic due to a seasonal promotion could make a variant appear more successful than it actually is under normal conditions. Similarly, economic shifts can affect consumer behaviour, making test results less reliable. So you should consider these factors when planning tests and interpreting their results. 

To achieve statistically significant results, it’s important to conduct A/B testing during times that are as representative as possible of typical user behaviour. Additionally, businesses should consider segmenting data to control for known external variables or conducting tests across different times to see if results remain consistent. By accounting for external factors, businesses can ensure that their A/B test results are more accurate and reflective of long-term performance.

Poorly defined hypothesis and goals

Furthermore, without unclear objectives and goals, the test may focus on the wrong metrics, leading to incorrect decisions that don’t align with the overall business strategy. To ensure you get reliable results, it’s essential to define a clear hypothesis and set specific, measurable goals for the A/B test before it begins. This helps ensure that the test is aligned with business objectives and provides actionable insights that drive meaningful improvements.

A/B testing should be hypothesis-driven, with clear goals and metrics for success. A good hypothesis is specific, measurable, and testable. It outlines what change is expected to lead to a particular outcome. Without a clear hypothesis and goals, it's difficult to determine what the test aims to achieve and how to measure its success. This lack of clarity can lead to unclear results, making it challenging to derive actionable insights. 

Furthermore, without clear objectives and goals, the test may focus on the wrong metrics, leading to incorrect decisions that don’t align with the overall business strategy. To ensure you get reliable results, it’s essential to define a clear hypothesis and set specific, measurable goals for the A/B test before it begins. This helps ensure that the test is aligned with business objectives and provides actionable insights that drive meaningful improvements.

For instance, an online retailer runs an A/B test on their homepage with the goal of improving click-through rate. Version A remains the original homepage, while version B shows a complete redesign. Without a hypothesis, the retailer won’t know how or why specific parts of the redesign will positively affect customer behaviour. Additionally, if they did not set specific and measurable KPIs, it will be difficult to determine which version was successful. Even if version B outperforms version A when it comes to click-through rates, the retailer may struggle to determine whether changes to the page were beneficial.

Biased test audience selection

Not all user groups react the same way to changes. Biased test audience selection can distort the results of an A/B test and result in misleading conclusions. The audience being tested should be the representative of the broader target group, so the test results generalise well to all users. Additionally, it's important to analyse test results across different user segments to get a more comprehensive understanding. 

For instance, testing a new web page design on a segment of highly engaged users might show a positive outcome that wouldn’t be replicated with less engaged or new visitors. To avoid such circumstances and misleading outcomes, it’s crucial to ensure that the test participants are representative of the overall audience. This can involve random sampling or segmenting the audience based on relevant criteria to ensure a balanced and unbiased sample.

By selecting a representative audience, businesses can ensure that the results of their A/B tests are more accurate and applicable to their entire user base.

Ignoring long-term impact

A common pitfall in A/B testing is focusing solely on short-term metrics, such as immediate clicks or conversions, without considering the long-term impact of the changes implemented. Sometimes, a test variant may show an immediate improvement in a key metric, however, it might have negative consequences in the long run, such as reduced customer satisfaction, increased churn rate, or a decline in customer loyalty.  

For instance, a pop-up ad may boost conversions for a short period, but it may annoy users over time, resulting in higher bounce rates or lower customer retention. This can be avoided by considering both short-term and long-term metrics when evaluating A/B tests. This approach ensures that any changes made drive immediate improvements and contribute to sustained growth and customer satisfaction.

Examples of successful A/B split tests

Now, we’ll look at some of the best examples of A/B testing in ecommerce. 

True Botanicals

True Botanicals transform digital experience to achieve $2m ROI increase in 12 months.

Founded in 2015 by Hillary Peterson, True Botanicals is a luxurious, consciously crafted skincare brand on a mission to deliver clean and sustainable products that are clinically proven to work at the highest standards. 

The company’s vision was to turn its website into a best-in-class luxury storefront that was a conversion-driving machine on mobile. However, the biggest challenge was navigating Apple’s sweeping privacy changes as it impacted ads and campaigns. Therefore, they needed A/B testing to move forward. 

The team leveraged Optimizely’s Web Experimentation platform for its ability to execute AI-powered personalisation along with web A/B testing. The team went from running singular tests to multiple tests, which helped them achieve statistically significant results to make data-driven decisions. The company’s vision of turning its website into a mobile conversion-driving machine was spearheaded by a 3-pronged CRO testing philosophy:

• Increase mobile conversions by 25 per cent.

• CRO is a game of inches. It takes a series of tests to improve conversion rates over time. 

• Prioritise tests closest to conversion and use the PXL method to focus on the highest impact tests.

True Botanicals’ 2022 CRO Program crushed expectations. The team exceeded its 4.8 per cent CVR goal with a sitewide CVR of 4.9 per cent. The team also greatly exceeded their win rate goal with a 66 per cent test win rate, accomplishing this through a combination of redesigns, qualitative studies and testing efforts. These efforts led to an estimated $2m ROI increase in the first year alone. 

True Botanicals went from opinion-based decision-making to an internal culture of data-driven strategies and data-validated decision-making after leveraging Optimizely’s Web Experimentation platform. The team was also encouraged to A/B test for granular changes, driving its website conversion. You can read the detailed case study here.

Varnish & Vine

Varnish & Vine increased their revenue by 43 per cent with product page optimisation.

Varnish & Vine, a US-based ecommerce store is the go-to destination for premium cactuses and tropical plants. Their online catalogue boasts an expansive array of plants, each with its own unique charm and character. But that’s exactly what caused a challenge when it came to product page optimisation.  

Product pages have huge potential as they receive the majority of the website traffic. A significant majority of website visitors bypass the homepage, arriving directly on the product pages from ads. Varnish & Vine has more than 70 product pages, so it can be a big challenge to optimise all product pages at once. 

OptiMonk’s Smart Product Page Optimizer has been a game-changer, helping businesses optimise thousands of product pages effortlessly in minutes. This tool creates compelling headlines, captivating descriptions, and persuasive benefit lists, then automatically runs A/B tests to create the ideal product page. It tailors each product page, optimising its content to resonate with your target audience, and doing it quickly and at scale. 

Varnish & Vine’s original product pages had been using the product names as the main headline and didn’t really offer any useful information in the above-the-fold section. They wanted to add new headlines and benefit lists to their product pages. The tool analysed Varnish & Vine’s product pages and crafted captivating headlines, subheadlines, and lists of benefits for each product page automatically. These new additions were designed to resonate with the target audience and supercharge conversions. 

After the changes were made, the tool started running A/B tests automatically to compare the results. Based on the A/B tests, the company saw that the AI-optimised product pages resulted in a 12 per cent increase in orders and an impressive 43 per cent increase in revenue. You can read the detailed case study here.

HubSpot Academy

HubSpot projected that variant B would lead to about 375 more sign ups each month.

HubSpot Academy is the worldwide leader in inbound marketing, sales, and customer service/support training. From quick, practical courses to comprehensive certifications, individuals can learn everything they need to know about the most sought-after business skills. 

Most websites have a homepage hero image that inspires users to engage and spend more time on the site. Any changes made to the hero image can impact user behaviour and conversions. 

Based on previous data, HubSpot Academy found that out of more than 55,000 page views, only .9 per cent of those users were watching the video on the homepage. Of those viewers, almost 50 per cent watched the full video. Chat transcripts also highlighted the need for clearer messaging for this useful and free resource. That’s why the HubSpot team decided to test how clear value propositions could improve user engagement and delight.  

HubSpot used three variants for this test, using HubSpot Academy conversion rate (CVR) as the primary metric. Secondary metrics included CTA clicks and engagement. 

Variant A was the control. For the variant B, the team added more vibrant images and colourful text and shapes. It also included an animated ‘typing’ headline. Variant C also added colour and movement, as well as animated images on the right-hand side of the page.  

As a result, HubSpot found that variant B outperformed the control by 6 per cent. In contrast, variant C underperformed the control by 1 per cent. From those numbers, HubSpot was able to project variant B would lead to about 375 more sign ups each month.  

How to get started with A/B split testing

Getting started with A/B testing involves several key steps, including understanding the basics, choosing the right tools, and following best practices to ensure reliable and actionable results. 

Here’s a detailed guide to help get started with A/B testing.

Best practices for beginners 

To ensure the success of your A/B testing efforts, follow these best practices:

Start with a clear hypothesis

Before running an A/B test, define a clear hypothesis based on data or observations. For instance, if you notice that a significant number of visitors are abandoning their shopping carts, your hypothesis might be, "Changing the checkout process from a multi-page form to a single-page form will reduce the cart abandonment rate by at least 15 per cent." 

A well-defined test hypothesis helps focus your test and provides a basis for measuring success. Forming a test hypothesis can be complicated. Craig Sullivan’s Hypothesis crafting formula can be extremely helpful when writing a hypothesis.

Focus on one variable at a time

The importance of testing only one variable at a time cannot be stressed enough. This approach is also known as “isolated testing”. It helps identify what’s driving changes in your performance metrics accurately. For instance, suppose you test a new headline and a different video simultaneously in your marketing campaign. If you notice an improvement in conversion rates, it would be impossible to determine whether any changes in performance are due to the new headline, the different video, or a combination of both.  

To accurately determine which change impacts performance, you should test only one variable at a time, such as headline, CTA, or visuals. This ensures that the observed results can be attributed to that one particular variable. Testing multiple variables simultaneously can make it difficult to identify which specific change led to the observed results.

Ensure sufficient sample size

Sample size refers to the number of participants we include in A/B testing. It’s the main component of A/B testing that helps obtain statistically significant and reliable results. Sample size determines the sensitivity of your A/B test to detect meaningful effects. 

It’s quite crucial to get the right sample size for your A/B testing. Imagine you have a jigsaw puzzle with a thousand pieces and want to understand the complete picture. If you randomly select a few pieces of the puzzle, you would have a limited representation of the complete picture. And, it would be challenging to accurately determine the entire puzzle’s details, patterns, and colours based on just a few pieces.    

To achieve statistically significant results, calculate the required sample size before starting your A/B test. Using tools like an online sample size calculator can help determine the appropriate number of participants needed to detect meaningful differences between variations.

Run tests for an appropriate duration

One of the most popular questions when starting with A/B testing is, how long should an A/B test run before you can draw conclusions from it? The answer depends on the relevance of the analysis to your own conversion goals. 

According to Neil Patel, marketers should stick to the 95 per cent plus rule and not call off their test before reaching that level of significance or higher. You ideally want to run your A/B tests for at least two weeks to achieve statistically significant results.    

Sometimes, the initial results may seem promising. However, even in this case, you should avoid ending tests too early. Tests should run long enough to account for variations in user behaviour across different times of the day, week, or month. Running tests for an appropriate duration ensures more reliable and representative results.

Use reliable metrics

Metrics are the guard rails of any A/B test. Without the right metrics, A/B testing would be haphazard and a waste of resources and time. Choosing metrics that can be consistently tracked and measured over time is crucial to ensure your KPIs are valid and accurate.  

Choose the right metrics to measure the success of your test based on your goals. For example, if your goal is to increase conversions, focus on conversion rate rather than secondary metrics like bounce rate or time on page. Defining key performance indicators (KPIs) in advance helps keep the test focused and meaningful.

Segment your audience

To gain deeper insights, segment your audience based on demographics, needs, priorities, common interests, and other psychographic or behavioural criteria. A/B testing can be most effective when you target specific audience segments. You can analyse how different user groups respond to the test variations, which provides insights into optimisation and personalisation opportunities.

Audience segmentation delivers more relevant experiences and gathers more actionable insights. It can also reveal patterns or preferences that are not immediately apparent in the aggregate data and can inform more targeted optimisations.

Document and analyse results

After completing the test, you must perform careful analysis of the results to make sure the data is valid and the difference between the variants is truly significant. You must review the sample size, significance level, test duration, number of conversions, and other important metrics to ensure the accuracy of the results.  

Furthermore, you must also investigate the factors contributing to the win to understand what worked and why. Document the test process, results, and any learnings. This documentation helps build a knowledge base for future tests and ensures continuous improvement.

Iterate and learn

A/B testing is an iterative process. Use the insights gained from each test to inform future A/B tests. Even if a test does not yield the desired results, it provides valuable information about user behaviour and preferences, which can help you refine your approach.

Tools and resources

Choosing the right tools and resources is crucial for successful A/B testing. Here are some popular A/B testing tools and resources for beginners:

AB Tasty

AB Tasty is an A/B testing and automated testing tool that helps businesses leverage machine learning and artificial intelligence technologies to track audience engagement, monitor content interest, and manage data collection. It builds end-to-end experiences that drive growth across all digital channels. You can test your website and app with a low, no-code approach.

The tool features excellent personalisation capabilities, allowing you to personalise experiences for different audience segments, thereby significantly improving conversion rates. The interface is easy-to-use, and it also provides in-depth analytics and reporting tools that integrate with Google Analytics 4. 

Furthermore, AB Tasty has a library of online resources, including blogs, case studies, and webinars to assist you in your optimisation efforts.  

Features

• A/B testing

• Multivariate testing

• Multipage testing

• Split testing

• Server-side testing

• Mobile app testing 

• Advanced audience segmentation

• Experience optimisation with AI 

Pricing

You can request a custom quote on their website.

Optimizely

Optimizely, a comprehensive A/B testing platform, allows you to run omnichannel experiments, generate insights, and continually optimise experiences across the board. It provides a user-friendly interface, detailed reporting and analytics capabilities, and advanced features like multivariate testing and personalisation. This tool is designed to scale with your business. It integrates seamlessly with various third-party tools and enables you to not only optimise experiences on your website, but mobile apps, email campaigns, and other digital touchpoints. 

Features

• A/B testing

• Launch experiments

• Multipage testing

• Multivariate testing

• Server-side testing

• App testing

• Personalisation

Pricing

You can request pricing on their website for the services you need for your ecommerce business.

VWO

VWO is a comprehensive experimentation platform that offers end-to-end optimisation of entire digital user journeys to deliver exactly what your customers want. It helps businesses optimise digital experiences and maximise conversions. With its advanced features, you can decode the evolving behaviours of your customers, fine-tune with robust experimentation, and personalise experiences. 

Furthermore, the platform helps businesses boost conversions across their websites and mobile apps through data-driven UI and server-side enhancements. It provides a visual editor for creating test variations without coding. VWO also offers features like heatmaps, session recordings, on-page surveys, and a range of integrations, making it a good choice for those looking to enhance their testing strategy.

Features

• A/B testing

• Multivariate testing

• Multipage testing

• Cross-platform tests

• Server-side testing

• Mobile app testing

• Customisation for each audience segment 

Pricing

VWO offers different plans for businesses of all sizes. You can try VWO for free to identify if it works for your business. For detailed pricing information, check out their pricing plans.

Adobe Target

Part of the Adobe Experience Cloud, Adobe Target provides everything you need to tailor and personalise your customers’ experiences. It offers advanced AI-powered testing, personalisation, and automation features, so you can find that one customer out of a million and give them what they want. Adobe Target integrates well with other Adobe tools for comprehensive analysis. 

Features

• Omnichannel personalisation

• A/B testing 

• Multivariate testing

• AI-powered automation and scale

• Personalisation

Pricing

Different businesses have different needs, so Adobe Target offers flexible licensing and configuration options. Businesses can get a solution customised for their organisation's needs. View Adobe Target pricing here.

Crazy Egg

The Crazy Egg A/B testing tool keeps it simple. No complicated set-up here. Simply select an element you want to test ideas on, and get testing. A/B testing conversion tracking and reporting can be a nightmare. With Crazy Egg, you can easily define goals. Furthermore, this tool combines A/B testing with heatmaps and session recordings, providing insights into user behaviour and enabling users to optimise their website based on real user data.

Features

• AI automated split testing or manual traffic split

• Multivariate testing

• Works well with Google Tag manager

• Super simple A/B testing

• Records the entire user session 

• AI generated text suggestions

Pricing

Crazy Egg offers three different pricing plans: Plus, Pro, and Enterprise. Each plan comes with a 30-day free trial. Read more about what each plan includes here.

Online resources

To learn more about A/B testing and how to get started, beginners can explore online resources such as blogs, tutorials, and courses on platforms like Coursera, Udemy, or HubSpot Academy. These resources provide foundational knowledge and practical tips for conducting effective A/B tests. You can even read case studies to know how a specific tool helped a business achieve its goals.   

Summary

A/B testing is an essential practice for any ecommerce or dropshipping business looking to optimise its performance and drive growth. By systematically testing and refining different aspects of their digital presence, businesses can optimise conversion rates, reduce bounce rates, and enhance the overall user experience. These improvements may lead to immediate gains in revenue and customer engagement and help build a stronger, more competitive business in the long run. In the fast-paced world of ecommerce, where customer preferences and market trends are constantly evolving, A/B testing provides the insights needed to stay ahead and continue delivering value to customers.