A/B testing is a vital tool for product optimization
A/B testing compares success rates
User experience research helps create the ideal experience for your product. It aligns both customer and business goals, reveals strengths and weaknesses, and ultimately saves you money.
By testing your digital product with users, you increase your return on investment (ROI) through thoughtful design. When it comes to improving the efficiency of a digital experience, there are many common tools you can use.
Today we will cover the benefits of one of those tools — A/B testing.
What is A/B testing?
A/B testing — also known as split testing — is a way to test two versions of the same page to understand which has a higher chance of success.
This method of preference testing helps to make a data informed decision regarding which page experience is more successful at reaching the page’s overarching goal. Typically, the page that performs better is deployed, which ultimately saves budget because the two pages are tested in a controlled environment.
Rolling out changes and waiting to see if it works in a live environment may result in a waste of time and budget if it proves to be unsuccessful. A/B testing ensures, with data as proof, that a change will be effective. By recording data with A/B testing, you can measure success for websites, mobile applications, web applications, or any digital experience.
All common elements typical to a digital product’s design essentially influence the overall experience for the user. Design choices that are general best practices create a pleasant enough experience. However, design choices, even seemingly small ones, that are informed through data can make users feel not only catered to, but your product becomes unique compared to competitors.
How does A/B testing improve customer experience?
Companies use A/B testing to maximize the effectiveness of a digital product
Typically, the differences between the two tested pages are fairly minor. It can be as simple as a different call-to-action button, the spatial layout of a component, or even minor content adjustments. By continually testing, you can prolong a product’s lifespan through informed adjustments.
Over time, users’ behaviors and needs may change, so the product also needs to adapt to grow. Goals and milestones that a company wants to meet may need to change strategies to keep up with the customer base.
With clear goals in mind, modifications to the product can be applied in a cost-effective and low-risk manner.
Common goals of A/B testing include:
- Decreased spend
- Increased revenue
- Improved user engagement
- Reduced bounce rates
- Increased customer conversion
How to track A/B testing metrics
Tracked data can show concrete success of an A/B test
Resources can analyze page views, the percentage of new users, and even the average number of daily visitors to a page. This data provides insights into which areas of the product need some revision.
To illustrate, a small business may find that, while they have a steady number of new users coming to their website, they have a low conversion rate. Therefore, their goal is to get new users to engage so they can expand their business.
After determining what meeting that goal will look like, they will develop a hypothesis on these new users’ behaviors, and design a solution. The data collected before the testing now works as a control to measure the success of an A/B test.
Analytics useful for A/B testing can include:
- Number of clicks
- User movement
- Duration of the average visit
- Form completion
- Checkout completion
The previously mentioned business may find that in order to raise their user conversion rate, they need to change their current inbound marketing strategy. A pop-up offering the user benefits in exchange for user information sounds like a good idea. However, launching a new strategy that only hypothetically works is risky. Without any feedback as to why the current solution doesn’t meet their goals, and no guarantee that a new solution will, changing the product could backfire. The A/B test can determine how effective the new solution will be.
How A/B testing works
With the original site as version A, and the new variation as version B, users are split into two groups. One group navigates using version A, while the other group tests on version B. After gathering data, the relevant statistics are compared. Generally, a more effective variation will have positive results. In this way the business can say with confidence that the increased conversion rates from the testing will be reflected upon launch.
While A/B testing is an effective way to gain understanding of which page functions better, it’s still important to understand why
Following up testing with user research can pinpoint the “why” A or B works. Even if one version performs better than another, you still need to consider the overall usability of a page. If a design choice performs better according to metrics, but has a poor user experience, your results may vary over the long term.
In a controlled testing environment, users can explain the logic behind their browsing habits. Without qualitative data, you may lose key insights that explain why one version performs better. You may even lose information that could improve the variant’s quality or notice new trends in your user base. A variation may work well due to reasons unrelated to your hypothesis.
In the case of the small business, the group testing version B may say that the pop-up is effective because the copy offering benefits is enticing. Meanwhile, group A’s version does not have the same visual display to emphasize the great deals, and thus goes largely unnoticed. Overall, an A/B test backed by thorough research is the most effective way to meet your goals.
In conclusion
Implementing regular A/B testing takes the guesswork away, saves time, and budget. It can also be used to trial a new feature before launching, or pitch two different designs against each other.
Testing creates a safe space for new layouts or features to be tested in a controlled environment. This method eliminates the risks associated with deploying new changes by collecting data first. With this style of testing, you can fine tune design choices to effectively track growth and meet both short and long-term goals.
Want to learn more about A/B testing? Learn more through these resources:
