what is A/B Testing
Essentially, A/B testing eliminates all guesswork in website optimization and allows UX optimizers to make data-backed decisions. In A/B tests, ‘A’ refers to the “control” or the original test variable, while ‘B’ refers to the “variation” or a new version of the original test variable.
The version that positively drives your business metrics is known as the “winner.” Implementing changes from this winning variation to your tested page(s) or element(s) can help optimize your website and increase the business’s ROI.
Conversion metrics are unique to each website. For instance, in e-commerce, it might be product sales. Meanwhile, for B2B, it could be generating qualified sales leads.
A/B testing is one of the components of the overall Conversion Rate Optimization (CRO) process, using which you can gather both qualitative and quantitative user insights. Then, you can use these collected data to understand user behavior, engagement rate, pain points, and even satisfaction with website features, including new features, revamped page sections, etc. If you’re not performing A/B tests on your website, you’re missing out on a significant sales potential.
¿Why Do A/B Testing?
Your website’s specific goals do matter… And a lot!
Conversions don’t happen by magic, and this requires dedication, time, and effort. Essentially, that’s what a digital marketing company dedicates all its time to.
So, investing time in creating A/B tests helps you identify changes you need to make on your website to trigger those calls to action.
Why you should do A/B testing:
1. Resolve Visitor Pain Points
Visitors come to your website with a specific goal in mind. It could be to understand more about your product or service, purchase a particular product, read/learn more about a specific topic, or simply browse. Whatever the visitor’s goal, they may face some common pain points while achieving it. It could be confusing copy or difficulty finding the CTA button like buy now, request a demo, etc.
Failing to achieve their goals leads to a poor user experience. This increases friction and eventually affects your conversion rates. Use data collected through visitor behavior analysis tools like heat maps, Google Analytics, and website surveys to resolve your visitors’ pain points. This is true for all businesses: e-commerce, travel, SaaS, education, media, and publishing.
2. Get Better ROI from Existing Traffic
As most experience optimizers have realized, the cost of acquiring quality traffic to your website is huge. A/B testing allows you to make the most of your existing traffic and helps increase conversions without spending additional dollars on acquiring new traffic. A/B testing can provide you with a high ROI, as sometimes, even the smallest changes on your website can result in a significant increase in overall business conversions.
3. Reduce Bounce Rate
One of the most important metrics to measure your website’s performance is the bounce rate. There could be many reasons behind your website’s high bounce rate, such as too many options to choose from, different than expected expectations, confusing navigation, use of technical jargon, and much more.
Since different websites have different goals and cater to different audience segments, there’s no one-size-fits-all solution to reducing bounce rate. However, running an A/B test can be beneficial. With A/B testing, you can test multiple variations of a website element until you find the best possible version. This not only helps you find frictions and visitor pain points but also improves the overall experience of your website’s visitors, making them spend more time on your site and possibly even converting into a paying customer.
4. Make Low-Risk Modifications
Make minor and incremental changes to your webpage with A/B testing instead of redesigning the entire page. This can reduce the risk of jeopardizing your current conversion rate.
A/B testing allows you to focus your resources for maximum output with minimal modifications, resulting in a higher return on investment. An example of this could be changes in product descriptions. You can conduct an A/B test when planning to remove or update your product descriptions. You don’t know how your visitors will react to the change. By running an A/B test, you can analyze their reaction and determine which way the balance might tip.
Another example of low-risk modification can be the introduction of a new feature change. Before introducing a new feature, launching it as an A/B test can help you understand whether or not the new change you are suggesting will please your website audience.
Implementing a change on your website without testing it may or may not succeed in the short and long term. Testing and then making changes can make the outcome more certain.
5. Achieve Stat
istically Significant Improvements**
Since A/B testing is completely data-based with no room for guesses, instincts, or feelings, you can quickly determine a “winner” and a “loser” based on statistically significant improvements in metrics like time spent on the page, the number of demo requests, cart abandonment rate, click-through rate, etc.
6. Redesign Website to Increase Future Business Gains
Redesign can range from minor text or color adjustments on CTA buttons of particular web pages to a complete website overhaul. The decision to implement one version or another should always be data-driven when it comes to A/B testing. Don’t stop testing after finalizing the design. As the new version rolls out, test other webpage elements to ensure the most appealing version is served to visitors.
How to Conduct an A/B Testing
A/B testing involves making partial or complete changes to the website or application you wish to optimize. When changes are made to several elements, it is also known as multivariate testing.
These changes can include:
- CTA (Call to Action) buttons.
- Site-wide color schemes.
- Layout (web page structure).
- Placement and description of images.
- Any other element that seeks user interaction.
- Checkout process.
Testing can also be done between two completely different versions, which are randomly presented to users to determine what attracts them more, thus continuously improving the user experience (UX).
A/B Testing Process
Here is an A/B testing framework you can use to start testing:
1. Collect Data
Your analytics tool (like Google Analytics) often provides insights on where to begin optimizing. It’s helpful to start with high-traffic areas on your site or app to gather data quickly. For conversion rate optimization, look for pages with high bounce or abandonment rates that can be improved. Also, consider other sources like heat maps, social media, and surveys for new areas of improvement.
2. Identify Goals
Your conversion goals are the metrics you’re using to determine if the variation is more successful than the original version. Goals can range from clicking a button or link to product purchases.
3. Generate Testing Hypotheses
Once you’ve identified a goal, you can start generating A/B test ideas and hypotheses for why you believe they will be better than the current version. Once you have a list of ideas, prioritize them in terms of expected impact and difficulty of implementation.
4. Create Variations
Using your A/B testing software (like Optimizely Experiment), make the desired changes to an element of your website or mobile app. This could be changing the color of a button, swapping the order of elements on the page template, hiding navigation elements, or something entirely custom. Many top A/B testing tools have a visual editor that will make these changes easy. Ensure you QA test to make sure the different versions are functioning as expected.
5. Run the Test
Start your experiment and wait for visitors to participate. At this point, visitors to your site or app will be randomly assigned to either the control version or the variation of your experience. Their interaction with each experience is measured, counted, and compared against the baseline to determine how each performs.
6. Wait for Test Results
Depending on how large your sample size (target audience) is, it may take some time to achieve a satisfactory result. Good experiment results will tell you when the results are statistically significant and reliable. Otherwise, it would be hard to say whether your change truly made an impact.
7. Analyze the Results
Once your experiment is complete, it’s time to analyze the results. Your A/B testing software will present the experiment data and show you the difference between how the two versions of your page performed and if there is a statistically significant difference. It’s important to get statistically significant results so you can be confident about the test outcome.
If your variation is a winner, congratulations! Try applying the learnings from the experiment to other pages on your site and continue iterating on the experiment to improve your results. If your experiment results in a negative or no result, don’t worry. Use the experiment as a learning experience and generate new hypotheses that you can test.
Whatever the outcome of your experiment, use your experience to inform future tests and continue iterating on optimizing your app or website’s experience.
Challenges of A/B Testing
Regular A/B Testing is determined based on the needs of your website, but the ROI generated by such a test can have a very positive impact. It helps you identify the exact problem areas and thus direct your marketing efforts towards the most valuable elements of your page. Let’s look at some challenges you should consider when conducting A/B Testing.
1. Deciding What to Test
It’s crucial to have a plan when conducting A/B testing. It’s not just about saying, “I’m going to change this today.” Small changes can be easy to implement, especially when you’re looking to improve the reach of your business goals, but they sometimes yield insignificant results. That’s why data analysis obtained from the website and visitors is so important. Analytics is one of the key factors that must be constant when aiming to optimize a website.
Therefore, you should choose elements that have a greater impact on the conversion rate or focus on those pages that have higher traffic.
2. Formulating a Hypothesis
Data obtained from web analytics play a very important role here. The results from A/B testing will provide useful information to generate a hypothesis, and this can only be achieved with proper testing process and planning.
3. Determining the Sample Size
When developing an A/B testing program, it’s important to consider the time and sample size that will truly indicate the results. The sample size is chosen based on the website’s traffic and must be large enough to trust the obtained results.
4. Maintaining a Culture of A/B Testing
One of the most important characteristics of testing programs (whether A/B or multivariate, etc.) is that they are iterative processes. To ensure that the effort you invest in optimization has a long-term impact, programmable cycles for testing should be created.
Therefore, the stages of web analytics data collection, identification of collected and analyzed elements, hypothesis generation, implementation of A/B test, plan for site changes and development and deployment of the winning version should be repeated in cycles.
Tools for A/B Testing
After conducting the test, information will be gathered through analytics to determine if this experience positively or negatively changes the interaction with Test A and Test B, which will facilitate evidence-based decision-making about what works best.
Some of the most used tools are:
- Google Optimize
- Heatmaps, site maps, and click maps.
Juan Esteban Yepes