What is A/B split testing?
In a world as digital as the one we live in, the number of visitors to a website equals the number of opportunities you have to grow your business by acquiring new customers and building relationships by meeting their needs.
Businesses want visitors to perform a specific action (also called a "click-through"). conversion) on their website. The more optimized your conversion funnel is, the higher the conversion rate.
One of the most important ways to optimize your website's funnel in digital marketing is A/B split testing. Typically, in an A/B split test, the variant that yields higher conversions is the winner, and that variant can help you optimize your website for better results.
For e-commerce a desired conversion may be selling products, while for B2B companies it may be generating qualified leads.
Why you should A/B split test
Mens B2B companies may be unhappy with the unqualified leads they get per month, e-commerce stores struggle with a high proportion of visitors who leave their items in the cart and thus don't proceed to checkout. Meanwhile, media and publishers also have the problem of low viewer engagement. These core conversion actions are affected by some common problems in the conversion funnel, site abandonment on the payment page, etc.
Solve visitor problems by split testing
Visitors to your website come because they want to perform a specific action, such as downloading your free guide/e-book, learning more about your product or service, purchasing a product, etc. Regardless of the user's purpose for visiting your website, they may face some common issues.
It could be that they have difficulty finding a CTA-button - like "buy now", "download now". When the user can't immediately figure out what you want them to do, it leads to a poor user experience. This lowers your conversion rate.
Use data collected through analytics tools like Google Analytics and create surveys to find out where your visitors are dropping off.
Get a better ROI from existing traffic with A/B split testing
As most marketers realize, the cost of acquiring quality traffic can be high. A/B testing allows you to get the most out of your existing traffic and helps you increase conversion without having to spend time acquiring new traffic. A/B testing can give you a high return on investment as even the smallest changes can result in a significant increase in conversions - for example, the color of your CTA.
Reduce your bounce rate with A/B split testing
One of the most important things to keep an eye on is your website's bounce rate - also called rejection rate. There can be many reasons why your website has a high bounce rate, such as too many options. Since different websites serve different purposes and target different audiences, there is no easy way to lower your bounce rate. One way to do this is through split testing, by A/B testing you can test multiple variations of an element on your website until you find the best possible version. This improves your user experience, making visitors spend more time on your website and reducing your bounce rate.
How do you do an A/B split test?
A/B testing is a very systematic way of finding out what works and what doesn't work in a particular marketing campaign. Most marketing efforts are aimed at driving more traffic. As it becomes harder and more expensive to generate this traffic, it becomes paramount to offer the best experience to the users coming to your website. A/B split testing allows you to make the most of your existing traffic.
A/B split testing can increase your ROI by identifying the most crucial issues your website has and thus needs optimization. A/B testing goes from being a one-off, one-time activity to a more structured and continuous activity.
- Step 1: Research. Before designing a plan for your A/B split test, it's important to first thoroughly examine how the website is currently performing.
- Step 2: Observe and hypothesize. Get closer to reaching your business goals by logging research observations and creating hypotheses aimed at increasing conversions.
- Step 3: Make variations. Next, create a variation based on your hypothesis and A/B test it against the existing version (control).
- Step 4: Results analysis and implementation. Although this is the final step in finding your best performing variant, analyzing the results is extremely important because A/B testing requires continuous data collection and analysis.
