Creating a well-structured hypothesis is a cornerstone of successful A/B testing. A clear hypothesis not only guides the experiment toward relevant and tangible results but also provides a solid framework for analysis. This article will guide you through the process of creating impactful hypotheses using the If-Then-Because format, provide powerful examples relevant to eCommerce, and demonstrate how to organize and collaborate on hypotheses with your team utilizing Compose’s planning tools.
What is a Hypothesis in A/B Testing?
In general, a hypothesis is a proposed idea that serves as a starting point for experimentation. In the context of A/B testing, a hypothesis is a specific change you can make to your website or app to improve a measurable outcome. The If-Then-Because format helps articulate this change, its expected impact, and the rationale behind it.
The If-Then-Because Format
If [change or variation is implemented], Then [expected outcome will occur], Because [rationale or reasoning behind the expected outcome]
This format ensures that your hypothesis is clear and concise, detailing the specific change being tested, the anticipated impact on user behavior or metrics, and the underlying rationale driving the hypothesis. Your hypothesis should focus on a singular change to isolate its impact accurately. Multiple variations of the change can be tested, but if the hypothesis is too broad, it will be challenging to determine which specific change caused the outcome.
How to Construct a Hypothesis
When constructing a hypothesis, it’s important to consider your business’s primary goals and how you could solve customer pain points simultaneously. For example, if your goal as a company is to increase AOV then ask yourself questions such as… How can we make it easier for customers to continue shopping after adding to the cart? Or, how can we more clearly communicate our free shipping threshold to our customers? The goal of A/B testing is to improve the customer experience, so it’s important to keep them at the forefront of ideation while keeping your business goals top of mind as well.
To gather the most impactful insights, you should use knowledge gathered directly from your customers. These insights can come from past experiments, customer feedback via surveys or focus groups, and analytics tools such as GA4, heatmaps, session recordings…etc.
Example Hypotheses for eCommerce Websites
Below are a few real world eCommerce examples of how you can combine your company’s goal with consumer insights to generate an If-Then-Because hypothesis statement.
Example 1: Increasing average order value (AOV):
We’re an affordable apparel brand and our primary goal is to increase AOV. After reviewing our order history data we realize that the current AOV is below $50, but we offer free shipping on orders over $50. We also notice that there is a significant number of customers abandoning checkout which is the first time our shipping fees are displayed.
Hypothesis: If we add messaging regarding the free shipping threshold to the cart, then we anticipate a rise in the average order value and an uptick in the overall revenue conversion rate, because more customers will see the free shipping incentive and be encouraged to add more items to their cart to qualify for the offer.
Example 2: Getting more email sign-ups
We’re a skincare brand with high-performing email marketing and are looking to incorporate more personalization to the campaigns, but our email list is not growing quickly. Through customer surveys, we have also learned that a big barrier for our customers is that they aren’t sure what product will work best for their skin.
Hypothesis: If we incorporate a product match quiz into our site, then we will collect more email sign-ups and helpful customer data for personalization, because customers want to receive tailored product recommendations.
Example 3: Increase add-to-cart and revenue
We’re a snack company with a large selection of flavors. Our product pages currently have dropdowns to select your flavor, but after reviewing our heatmaps in Hotjar there are very few clicks on the dropdown.
Hypothesis: If we switch from a dropdown to variant selectors on the PDP, then we expect a lift in add-to-cart clicks and PDP engagement, because customers will be made aware of our large flavor catalog right away without the extra click.
Example 4: Decrease bounce rate
We’re a home goods eCommerce store with an enormous catalog and lots of product categories. The bounce rate on the homepage of our website is much higher than we would like, and after implementing an exit-intent survey we learn that customers don’t see what they are looking for. Our search bar is underutilized and hidden within the hamburger navigation on mobile and is very small on desktop, and we feature only our best-selling product category in the homepage hero. For this scenario, we’ve provided two potential hypotheses based on the same insights.
Hypothesis 1: If we redesign our homepage hero in a grid layout, then we hope to decrease bounce rate, because we can showcase 4-5 categories in place of just one.
Hypothesis 2: If we make the search bar in our navigation more prominent, then we expect to decrease bounce rate, because customers will be able to quickly find what they are looking for.
How to organize hypotheses and collaborate with your team
Now that you have the knowledge to create an impactful hypothesis, you need to form a strategic experimentation strategy alongside the rest of your team to ensure that A/B testing is as efficient as possible. With Compose, we offer a collaborative hypothesis list tool as well as a roadmap to help keep your team aligned on what and when to test.
You start by creating a hypothesis, where you can place your If-Then-Because statement in the objective along with any additional information regarding the idea or insight. You will also be able to add visual media and set a PIE score to designate the potential, importance, and ease of implementation for the idea. This will help you quickly identify what tests you may want to focus on in your list as it grows. The hypothesis list will be universal for all team members in the project, so it’s a perfect way to collaborate.
Once you’ve decided on the hypotheses you would like to test over the coming months, you can add them to your team’s roadmap. This highly flexible roadmap will provide everyone on the project with a quick overview of what’s coming up ensuring that no valuable time is wasted and that interfering tests don’t overlap. Any time that you do not have an experiment running you are missing an opportunity for insights and growth, so having this roadmap can be invaluable for building a more effective experimentation program.
Conclusion
Crafting effective hypotheses using the If-Then-Because format can significantly enhance the clarity and impact of your A/B testing efforts. By focusing on specific changes, expected outcomes, and the rationale behind them, you can ensure your experiments are well-structured and based on sound reasoning. This methodology allows you to systematically address customer pain points and align your tests with overarching business goals, leading to more informed decision-making and better outcomes.
On top of that, using the If-Then-Because format promotes a culture of evidence-based experimentation within your team. It encourages critical thinking and helps in documenting and communicating your assumptions and expectations clearly, making it easier for stakeholders to understand the purpose and potential impact of each test.
With Compose, you can collaborate more effectively with your team, maintain a comprehensive list of hypotheses, and use a roadmap to strategically plan and execute your experiments. This organized approach not only prevents overlap and ensures continuous testing but also maximizes the potential for gaining valuable insights and driving growth.
By adopting the If-Then-Because format and leveraging tools like Compose, you can create a robust framework for A/B testing that drives meaningful results. Because, ultimately, the key to successful A/B testing lies not just in the execution of tests, but in the thoughtful planning and hypothesis generation that precedes them. With a clear, methodical approach, you can transform your experimentation process into a powerful engine for growth and optimization, continually enhancing the user experience and driving your business forward.