Incomplete, irrelevant or poorly formulated A/B test hypotheses are at the root of many neutral or negative tests.
Often we imagine that doing A/B tests to improve your e-commerce site’s performance means quickly changing the color of the “add to cart” button will lead to a drastic increase in your conversion rate, for example. However, A/B testing is not always so simple.
Unfortunately, implementing random changes to your pages won’t always significantly improve your results – there should be a reason behind your web experiments.
This brings us to the following question: how do you know which elements to experiment with and how can you create an effective AB test hypothesis?
Determine the problem and the hypothesis
Far too few people question the true origins of the success (or failure) of the changes they put in place to improve their conversion rate.
However, it’s important to know how to determine both the problem and the hypothesis that will allow you to obtain the best results.
Instead of searching for a quick “DIY” solution, it’s often more valuable in the long term to take a step back and do two things:
- Identify the real problem – What is the source of your poor performance? Is it a high bounce rate on your order confirmation page, too many single-page sessions, a low-performing checkout CTA or something more complex?
- Establish a hypothesis – This could show the root of the problem. For example, a great hypothesis for A/B testing could be: “Our customers do not immediately understand the characteristics of our products when they read the pages on our e-commerce site. Making the information more visible will increase the clicks on the “add-to-cart” button.”
The second step may seem very difficult because it requires a capacity for introspection and a critical look at the existing site. Nevertheless, it’s crucial for anyone who wants to see their KPIs improve drastically.
If you’re feeling a bit uncomfortable with this type of uncertainty around creating an effective hypothesis, know that you’ve come to the right place.
What is an A/B test hypothesis?
Technically speaking, the word hypothesis has a very simple definition:
“A proposal that seeks to provide a plausible explanation of a set of facts and which must be controlled against experience or verified in its consequences.”
The first interesting point to notice in this definition is “the set of facts to be explained.” In A/B testing, a hypothesis must always start with a clearly identified problem.
A/B tests should not be done randomly, or you risk wasting time.
Let’s talk about how to identify the problem:
- Web analytics data – While this data does not explain digital consumers’ behavior exactly, it can highlight conversion problems (identifying abandoned carts, for example) and help prioritize the pages in need of testing.
- Heuristic evaluation and ergonomic audit – These analyses allow you to assess the site’s user experience at a lower cost using an analysis grid.
- User tests – This qualitative data is limited by the sample size but can be very rich in information that would not have been detected with quantitative methods. They often reveal problems understanding the site’s ergonomics. Even if the experience can be painful given the potential for negative remarks, it will allow you to gather qualified data with precise insights.
- Eye tracking or heatmaps – These methods provide visibility into how people interact with items within a page – not between pages.
- Customer feedback – As well as analyzing feedback, you can implement tools such as customer surveys or live chats to collect more information.
The tactics above will help you highlight the real problems that impact your site’s performance and save you time and money in the long run.
A/B test hypothesis formula
Initially, making an A/B test hypothesis may seem too simple. At the start, you mainly focus on one change and the effect it produces. You should always respect the following format: If I change this, it will cause that effect. For example:
Changing (the element being tested) from ___________ to ___________ will increase/decrease (the defined measurement).
At this stage, this formula is only a theoretical assumption that will need to be proven or disproven, but it will guide you in solving the problem.
Here are two examples of hypotheses phrased according to the formula explained above and that can apply to e-commerce:
- Changing our CTA from “BUY YOUR TICKETS NOW” to “TICKETS ARE SELLING FAST – ONLY 50 LEFT!” will improve our sales on our e-commerce site.
- Shortening the sign-up form by deleting optional fields such as phone and mailing address will increase the number of contacts collected.
In addition, when you think about the solution you want to implement, include the psychology of the prospect by asking yourself the following:
What psychological impact could the problem cause in the digital consumer’s mind?
For example, if your problem is a lack of clarity in the registration process which impacts the purchases, then the psychological impact could be that your prospect is confused when reading information.
With this in mind, you can begin to think concretely about the solution to correct this feeling on the client side. In this case, we can imagine that one fix could be including a progress bar that shows the different stages of registration.
Be aware: the psychological aspect should not be included when formulating your test hypothesis.
Once you have gotten the results, you should then be able to say whether it is true or false. Therefore, we can only rely on concrete and tangible assumptions.
Best practice for e-commerce optimization based on A/B hypotheses
There are many testable elements on your website. Looking into these elements and their metrics can help you create an effective test hypothesis.
We are going to give you some concrete examples of common areas to test to inspire you on your optimization journey:
- The header/main banner explaining the products/services that your site offers can increase customers’ curiosity and extend their time on the site.
- A visible call-to-action appearing upon arrival will increase the chance visitors will click.
- A very visible “about” section will build prospects’ trust in the brand when they arrive on the site.
- Filters save customers a lot of time by quickly showing them what they are looking for.
- Highlighting a selection of the most popular products at the top of the sections is an excellent starting point for generating sales.
- A “find out more” button or link under each product will encourage users to investigate.
- Product recommendations create a more personal experience for the user and help increase their average shopping cart
- A visible “add to cart” button will catch the prospect’s attention and increase the click rate.
- An “add to cart and pay” button saves the customer time, as many customers have an average of one transaction at a time.
- Adding social sharing buttons is an effective way of turning the product listing into viral content.
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- The presence of logos such as “Visa certified” enhances customer confidence in the site.
- A very visible button/link to “proceed to payment” greatly encourages users to click.
- A single page for payment reduces the exit rate.
- Paying for an order without registration is very much appreciated by new prospects, who are not necessarily inclined to share their personal information when first visiting the site.
- Having visibility over the entire payment process reassures consumers and will nudge them to finalize their purchase.
These best practices allow you to build your A/B test hypotheses by comparing your current site with the suggestions above and see what directly impacts conversion performance.
The goal of creating an A/B test hypothesis
The end goal of creating an A/B test hypothesis is to identify quickly what will help guarantee you the best results. Whether you have a “winning” hypothesis or not, it will still serve as a learning experience.
While defining your hypotheses can seem complex and methodical, it’s one of the most important ways for you to understand your pages’ performance and analyze the potential benefits of change.