The Importance of A/B Testing for Your Blog

If the goal of your website is to drive your visitors towards a specific action (also known as a conversion) then you’ll need to measure at what rate they convert (conversion rate). One of the best ways to constantly improve your conversion rate is to A/B test. Otherwise known as a “split test”, A/B testing has been a staple in the marketing world forever.

Perfectly suited for the online world, it is an experiment which determines which of two options performs best when those options are the only changes. The goal is to constantly be challenging your original assumptions by letting your audience tell you what they prefer through their behavior on your website. This applies to not just pages that have yet to be tested but also revisiting earlier tests as your audience grows and changes.

An Example of A/B Testing

A perfect example of an A/B test would be comparing the performance of a blue CTA button versus an orange button. You might have 50% of your audience see only the blue button and the other 50% only the orange. After a statistically significant number of users have participated the higher click-through rate of the two would determine the winner. In the below example there was a 20% conversion using the blue button and 50% conversion for the orange button. Result: the orange button is the winner and will be used in the future.

Improved User Engagement

A/B testing reveals what content the audience best responds to. If you are able to identify and incorporate these preferences into your website, your visitors will inevitably have a better user experience. This in turn, creates loyalty and repeat business.

Improved Content

As your testing improves your existing content also serves as a guidepost for additional content. Exponentially this should reduce your learning curve as you create any future content. Every step of the way you learn what your audience wants and how best to deliver it to them.

Reduced Abandonment Rates

While abandonment rates are usually associated with virtual shopping carts, they can also refer to when a visitor comes to your website but leaves without taking your desired action. That could mean they didn’t make a purchase, download a freebie or listen to a podcast. Regardless, you want to reduce the number of visitors that don’t convert. Once you have identified where you are losing your audience, A/B testing can help you try alternatives to keep them engaged. For instance, if a visitor reads an article but does not click on the CTA (Call-To-Action) button embedded in the post then test a different CTA. This could involve testing different language, colors and/or positioning on the page.

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Increased Conversion Rates

Not only will the pages successfully tested help you to increase your conversion rates, the lessons learned each time can be applied to the next A/B test. You’ll find over time that certain trends start to develop as to the preferences of your audience. If you pay attention, their behavior will show you what’s important to them and what they want you to provide for them. Give them what they want and they will keep coming back.

Quick Return

If your visitor volume is sufficient and diverse enough you should get results rather quickly. This allows you to be nimble and react in near real-time to improve performance. But be cautious not to declare a winner too quickly. See the Some Tips section below for some best practices.

Increased Profits

As you optimize each webpage your visitor’s user experience will improve, your pages and forms will convert better which will all lead to increased sales. And you can accomplish it without spending a dime. You just need to trust the data and be patient and honest about the results. If you are diligent and pay attention, increased sales will be inevitable.

A Few Tips . . .

While A/B testing isn’t difficult there are some best practices to improve your decision-making. Consider each of the following when preparing for a test.

Start small and simple. A good place to start is on a sign-up form. A/B test two different colors on your Submit button.
Have a goal in mind before starting each test. Make sure you know what you want to accomplish with each test. For instance, if you have a high bounce rate on an important post or page then start with the headline, A/B test different words, and see if that helps. If not then move to your content. Maybe a picture or visual above the fold could hold their attention or better help define the page’s content.
Start in an area where you aren’t performing well but need to. If you have a high margin product not selling then start there.
Make the A and B distinctively different. Don’t test a dark blue vs. a light blue. Instead test the dark blue vs. red. Make sure your audiences are truly choosing between very different choices. This will bring the most significant improvements.
Only change one thing on a page at a time. If you change multiple things on a page you’ll have no idea which change was significant. This is referred to as a Control element.
Make sure both the A and the B are tested equally. This means both must be tested at the same time, over the same time period and concluded at the same time. It also means each must be tested by the same approximate number of visitors. Make sure each A and B are comparing “apples-to-apples”.
Make sure you test during average times. By this we mean don’t test during times when your audience isn’t being fully represented. A good way to discipline yourself is to always test a minimum of 7 days. This way you will cover both weekdays and weekends. However, avoid testing on long weekends or holidays as patterns of behavior could be different than the rest of the year.
Only make changes if the results are meaningful. Just because A beats B doesn’t necessarily mean you should make the change. The difference must be meaningful. Have a goal in mind before you begin and only declare a winner if that goal is met.

While there certainly are stand-alone A/B testing tools, keep it simple to start and just use Google Analytics (GA). It is free, easy to use and you really need to get comfortable with GA anyway. We’ll have an article soon with some GA basics and also how to use GA for A/B testing.

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