As a business professional, you know that the one-size-fits-all approach doesn’t work in the world of digital marketing; every company requires a custom approach for their own unique target markets. A/B testing, which is also sometimes called split testing, is a method of comparison between two versions of an email, an SEO keyword, or a paid ad, for example, to determine which one performs better with a target audience. It helps you quickly identify the most effective marketing strategies and land the most engagement.
Here, we’ll discuss in more depth what A/B testing is, as well as what you can accomplish with such testing and how to do it most effectively.
What Is A/B Testing in Marketing?
Think of an A/B test as an experiment. If, for example, you wanted to know which of two call-to-action buttons yields the most clicks or which subject lines cause people to open an email more frequently, you would split the target audience into two random groupings. You would then show one grouping button A/subject line A and the second grouping button B/subject line B and measure which one demonstrated a greater level of engagement.
This experiment can be done with practically any element of your marketing. Some A/B testing examples could include variations in your:
- Web page color scheme
- Button copy
- Headings and subheadings
- Body copy formatting
- eCommerce store layout
- Social proof
- Paid ad design
- Site navigation structure
- And much more
Why Is A/B Testing Important?
A/B testing is important because many businesses have more than one target audience for their products, and these various target audiences exhibit different online behaviors and priorities. So, what works for one target audience may not work for another.
For example, let’s say you sell athleisure, a type of fitness clothing that can be used for exercise and/or lounging. The female group of your target audience is looking for different product qualities than the male group, and your offerings must be presented to them where they each shop and in a way that appeals to them. As an example, the men might prioritize cooling and moisture-wicking properties, while women may favor style, squat-proof, and stretch qualities. According to a 2017 study, men are more utilitarian in their online shopping behaviors while women prioritize the lifestyle that a brand sells and how it makes them feel. A/B testing can guide your search for the strongest calls to action, the best copywriting voice, and so on for the males and females in your target audience so you can reach them most effectively.
Once you know what works for each target audience through A/B testing, you can decrease bounce rates, increase conversion rates, garner more website traffic, and decrease instances of cart abandonment. It’s important to be cautious, however, as making the wrong assumptions can lead your campaign astray. Below, we talk about how to set up an A/B test so you can avoid outside influences and statistically insignificant test results.
How to Run a Proper A/B Test
1. Invest in the right tools
A/B testing tools have come a long way over the last five years. Today, there are more than two dozen platforms you can choose from to run your tests. Choosing the best one for your needs will save you time, money, and effort.
The best one for you comes down to your skills, budget, current traffic, and the number of tools you require within the platform. If you don’t have an in-house web developer or you aren’t experienced in statistics, you may want to opt for a more inclusive A/B testing tool that has all the capabilities you need within the platform. Below are some of our favorite options:
- Google Optimize
2. Establish a variable and a goal
A marketing campaign has lots of moving parts, and to put together a reliable A/B test, you need to isolate a single variable for testing. Narrowing down a specific area of focus and measuring its performance ensures that you’re only testing the performance of that variable.
You can decide on a variable to test by determining where there are holes in your marketing strategy. Consider whether the design and layout of a web page, the wording of a paid ad, the presence of a name in an email subject line, or the size of a call-to-action button is impacting your leads and/or sales, for example. Google Analytics is a great resource that can show you where people are dropping off in the conversion funnel, which can guide you to potential changes. Even the slightest changes can yield significantly different results; you’d be surprised how changing a single word on a button or the location of an image on a webpage can alter user behavior.
Every marketer wants to increase conversions and sales, but your goal should be more specific and measurable than this. An example of a strong goal would be to decrease the bounce rate on a home page by X percent using A/B testing to determine the strongest tagline in the hero section. Your goal should stretch you but should not be unrealistic and exceed your current capabilities.
3. Decide on a challenger
A challenger is the alternative email subject line, button text, home page tagline, online store layout, or other test variables that will compete against what you already have in place (your control variable). For example, if you want to see if a client’s “Buy Now” button leads to more conversions when placed higher on the webpage, design a copy of that page with the button higher up. The copy becomes your test page (Option B), and the original is your control (Option A).
If you’re having trouble deciding on a challenger, it may help to do some market research on the variable you’re testing to see what strategies your competitors are using.
Bonus Tip: Avoid creating double-barreled tests! Test only one outcome for a single input. (Will Landing Page header with our key value proposition [X] text improve sign-ups for our monthly email newsletter [Y].) You can test multiple inputs, but it’s confusing to try and measure improved sign-ups for our newsletter while also testing if our bounce rate improves and sales increase. Optimize for a single outcome per test, and you will WIN!
4. Set up two sample groups
For an A/B test to produce actionable results, you need to divide your traffic between the challenger and control group randomly. Truly random sampling is achieved by ensuring that anyone who visits your client’s site has an equal chance of seeing one of your A/B variations. This will help you avoid false positives and negatives — instead, you’ll obtain only valid results.
The way you split your audience will depend on the platform you use. Some testing tools will automatically divide your traffic for you. You may need to manually calculate a minimum sample size for your tests before you launch them, but again, this depends on the platform you’re using. You should only begin to analyze the results once the minimum sample size has been achieved.
5. Run the test for at least two weeks and decide on a significance level
There are three main factors used to determine the validity of a test: variability (acceptable error), confidence level(statistical significance), and deviation (statistical intervals). The unscientific way to say this is that a test must predict reliable results (confidence) within an acceptable range of numbers (variability) defined by your requirements (deviation)
A common practice is to gather a large sample size to produce statistically significant results with a confidence level of at least 95%. Statistical significance means how confident we are that an outcome will fall within our sampled range – 95% confidence means out of 100 tests, 95 are expected to fall inside our expected range.
Generally speaking, two continuous weeks of running your A/B test will produce reliable results. However, keep in mind that time isn’t the factor that determines your test’s reliability; The size of your sample increases the accuracy of your test.
It is recommended that you do not continue running your test beyond four weeks, as longer-running tests can be influenced by extraneous factors that will impact your data.
6. Interpret your results and make improvements accordingly
Now that you have data from several weeks of exposure to both variables, it’s time to measure the significance and find out whether or not a change should be made. Even if there is statistical significance with a high level of confidence in favor of the challenger, make changes slowly so you can monitor the long-term impact and avoid unintended consequences.
Not Feeling Confident in Your A/B Test Skills? Let us Help!
A/B testing is a skill that can be challenging to master. There are so many external factors that can potentially influence the success of a marketing variable, and without the right help, you could end up with invalid data that throws off your entire campaign.
Avalaunch Media is home to marketing experts with years of A/B testing experience. Get in touch with us for professional guidance that will launch your business!