A/B testing is a method of stacking up two versions of a webpage against each other to determine which one shows better performance. Essentially, A/B testing is an experiment where users see two or more versions of a web page randomly, and data analysis helps determine which version performs better for a particular conversion purpose.
This testing allows companies to make changes to their user experience by collecting data from the results. Some end-result considerations to make when editing your site might be the overall ease of use, design engagement, and optimization of site forms and interactive features.
For example, a life insurance company may want to improve their sales of life insurance packages. To achieve that, the team would try implementing A/B testing changes to elements such as headline, form fields in the application, or overall layout of the page.
Steps for Running Successful Data-Driven A/B Tests
If you are a marketing manager at a DTC life insurance company looking to boost the conversion on your website, the first important step is to examine your web pages in Google Analytics. This can be as simple as compiling a list of the top landing pages on your website and analyzing the conversion rates, levels of traffic and engagement metrics, such as bounce rate, etc.
These statistics will help you determine what exactly needs modification for A/B testing. Moreover, beyond the basic Google Analytics results, detailed data and metrics need to be analyzed. Strategically tag and closely monitor the pages of your life insurance agency’s website with a particular research goal in mind.
After you collect the stats, you may want to ask your website visitors the following questions to better understand their user experience/feedback, their motivations, and needs.
⮚ Why did they visit your agency’s website/ customer portal/agent portal?
⮚ Why did they not convert?
⮚ Were they able to find what they were looking for on your website?
⮚ What don’t they like about your digital platform?
There are a variety of tools available to conduct this research and collect user insights. Applications such as Qualaroo and Crazy Egg can help with website user data collection, however, the best data comes directly from the backend of your website. Having correct metrics and tags in place is essential to making informed changes. Furthermore, you can add exit surveys to the web page right before the point of final conversion, or to web pages that have high bounce rates. This will enable you to comprehend better what is causing your visitors to drop off.
Moreover, even if visitors do not fill out an exit survey, there are other ways you can draw assumptions about their experience by "reading the data." For example, what actions did they take in version A of the website but not in version B? Are there any other descriptive factors that made certain users act a certain way?
By now, you have successfully been able to identify the core areas to test and improve by conducting relevant research. Examples of improving user experience could run the gamut from shortening the customer application and updating the copy to be less technical to reducing clutter by focusing on the primary goal of your website. Now, it is time to lay the groundwork and run your A/B tests.
The key to successful A/B testing is that you start small. In other words, first, run the tests that you can set up in a few minutes. You could try changing an image or headline copy on any of the web pages of your website or insurance portal. The objective here is to run a test that can help in validating the process for your team.
Once you have completed the validating process, you can then run the A/B tests for impact. These are bold changes made on a bigger scale. Sometimes, minor changes do not have much of an effect because the tests are too small to impact user behavior. In such a scenario, you have to take things up a notch and go for A/B testing on a larger scale. An example of a significant change is a complete redesign of your insurance company website to create a more engaging user experience.
At Exclamation Labs, we recommend a mixed approach by coupling smaller tests with a few significant changes to yield the best results.
The final step is the post-test analysis. Once you have run the test and collected statistical significance against the original version of your web page, you can end your test. Reaching statistical significance could take a couple of weeks or a couple of months, depending on how much traffic your variations receive. You should then compile all results and analyze them side by side. If the test has been successful, meaning the newer version of the web page shows better results than the previous version, you can permanently update the web page. On the other hand, if the results are not up to par, you should identify the key learning points from the test so that you can implement these in any future optimization.
There is no denying that the technicality of an A/B test may be overwhelming. Nevertheless, it is important to know the reasons why these tests are a powerful part of your agency’s marketing strategy, and why they must be done correctly. If the process feels outside of your comfort zone, you are not alone. Working with a professional partner can make a critical difference in the effectiveness of the project. Our team at Exclamation Labs has the experience to set up the testing properly, gather results, and make recommendations based on the data. Plus, our efficiency in implementation is likely to save you a lot of time.
If you are looking for a professional agency with 20+ years of experience in the insurance and financial industries, give us a shout! We’re here to help!