How to Run AB tests

What is AB Testing?

At its core, AB testing, also known as split testing, is a method that allows you to compare two versions of a webpage, app, or any digital asset to discern which performs better. It's a scientific approach to decision-making that involves presenting different versions (A and B - or sometimes more) to distinct segments of your audience and closely analysing user behaviour. The goal? To make informed decisions that positively impact your business objectives.

For small businesses, AB testing can be a game-changer. Imagine being able to test different variations of your website or app to see which resonates best with your audience, leading to higher conversion rates, increased engagement, and, ultimately, improved business outcomes.

Who Popularised AB Testing?

The roots of AB testing trace back decades, but its meteoric rise to popularity occurred in the early 2000s, thanks to tech giants like Google. Google's unwavering commitment to data-driven decision-making made AB testing a cornerstone of its optimisation strategy. By rigorously experimenting with various elements of its search engine algorithms and user interfaces, Google set a benchmark for businesses globally.

This trend wasn't limited to Google. As businesses witnessed the transformative power of AB testing, it quickly became a standard practice, evolving into an indispensable tool for those looking to fine-tune their digital strategies.

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What Are The Steps to Running AB Tests?

The success of AB testing lies in a structured and systematic approach. Let's break down the essential steps that will guide you through the process:

1. Define Your Goal and Hypothesis

Before diving into an AB test, clearly defining your goal is crucial. What specific outcome are you aiming for? Whether increasing sales, boosting click-through rates, or enhancing user engagement, having a well-defined objective sets the stage for a focused experiment.

Formulate a hypothesis that outlines your expectations. This hypothesis serves as the foundation for your test, stating the changes you anticipate will positively impact your chosen metrics.

Example: Imagine you run an e-commerce site, aiming to increase the conversion rate on your product pages. Your hypothesis could be that changing the colour of the "Buy Now" button to green will lead to a higher conversion rate.

2. Identify Key Metrics

With your goal in mind, identify the key metrics that align with it. These metrics serve as the yardstick for measuring the success of your test. Whether you track purchases, clicks, sign-ups, or any other relevant KPI, selecting the right metrics is critical for accurate evaluation.

Example: In our e-commerce scenario, key metrics could include conversion rate, average order value, and time spent on the product page.

3. Randomly Assign Users

To ensure the validity of your test results, it's essential to randomly assign users to either the control group (version A) or the variant group (version B). Randomisation helps eliminate biases, ensuring that both groups are comparable.

Example: Your e-commerce website may have a pool of 10,000 visitors. Using a random assignment method, 5,000 visitors see the original product page (version A), while the other 5,000 see the variant with the green "Buy Now" button (version B).

4. Implement Changes

With your groups established, it's time to implement the changes based on your hypothesis. Make the necessary alterations to your webpage or app, ensuring the changes are consistent with your goals.

Example: In our example, the green "Buy Now" button is implemented for the variant group.

5. Run the Test

Launch your AB test and let it run its course. It's important to be patient and allow sufficient time for the test to gather statistically significant results. Rushing the process might lead to inconclusive or misleading outcomes.

Example: The test runs for two weeks, with both versions garnering data on user interactions.

6. Analyse Results

Once your test has concluded, it's time to analyse the results. This involves thoroughly examining the data collected and comparing the performance of version A with version B. Statistical analysis is crucial at this stage to determine the significance of the observed differences.

Example: The analysis reveals that the variant with the green "Buy Now" button has a 15% higher conversion rate than the original version.

7. Draw Conclusions

Based on the results, conclude the effectiveness of the changes. This is the moment of truth where you decide whether to implement the variant or stick with the control. Remember, the data should guide your decision-making process.

Example: Given the positive impact on conversion rates, you implement the green "Buy Now" button across all product pages.

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What Tools Can Be Used For AB Tests?

The effectiveness of your AB tests often hinges on the tools you choose. Fortunately, there are several user-friendly and powerful options available. Let's explore a few:

1. Convert.com

If you're looking for a simple tool with a user-friendly interface, Convert.com is an excellent choice. It empowers you to create and run tests without extensive technical knowledge.

2. Optimizely

Known for its versatility, Optimizely supports a variety of experiment types. Its robust analytics provide valuable insights to help you interpret your results effectively.

3. Unbounce

For businesses focusing on optimising landing pages, Unbounce is a specialised tool that simplifies creating and testing landing page variants. Its intuitive interface allows you to make changes without extensive coding knowledge.

If you have heard of Google Optimize, note that Google no longer offers this product, and it was sunsetted in 2023.

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How To Pick AB Test Winners?

Determining the winner of your AB test involves more than a simple comparison of raw numbers. Here are key considerations:

1. Statistical Significance

Before making any decisions, ensure that your results are statistically significant. This means that the observed differences are unlikely to have occurred by random chance. A standard threshold for statistical significance is 95% or higher.

2. Practical Significance

While statistical significance is crucial, assessing practical effectiveness is equally essential. Ask yourself whether the observed differences have a meaningful impact on your business goals. Sometimes, a statistically significant result may translate into something other than a significant real-world effect.

3. Consider the Long-Term

Avoid the temptation to focus solely on short-term gains. Assess the long-term effects of your changes to ensure they contribute to sustained success rather than providing temporary boosts.

Example: In our e-commerce scenario, while the green "Buy Now" button led to a statistically and practically significant increase in conversion rates, it's essential to consider whether this change will have a lasting impact or a short-term trend.

Challenges With AB Tests

Despite the benefits, AB testing comes with its set of challenges that small businesses may encounter:

1. Limited Sample Size

Small businesses may need help accumulating a sufficiently large sample size for their tests. A small sample size can impact the test's statistical power, making it challenging to draw reliable conclusions.

2. External Factors

External factors, such as seasonality or unexpected events, can influence test results. It's crucial to be aware of these factors and, when possible, account for them in your analysis to isolate the impact of the changes you're testing.

3. Interpreting Results

Correctly interpreting results requires a sound understanding of statistical concepts. Misinterpretation can lead to misguided decisions, emphasising the importance of statistical literacy when engaging in AB testing.

4. Resource Constraints

Small businesses may need more resources or expertise to conduct tests effectively. Outsourcing or utilising user-friendly tools can mitigate this challenge, allowing enterprises to leverage the power of AB testing without extensive investments.

Key Takeaways

AB testing is a valuable strategy for small businesses aiming to refine their online presence and achieve better results. The ability to test and optimise based on data-driven insights is a powerful tool in the digital era.

Remember, success in AB testing is not just about running tests but learning from them. Each test provides an opportunity to gain valuable insights into your audience's preferences and behaviours. Use these insights to continuously optimise your digital strategy, ensuring that your online presence evolves alongside the ever-changing landscape of the digital world. Happy testing, and here's to unlocking the full potential of your small business through the AB tests.