5 Uncomfortable A/B Testing Questions to Ask When Starting a Testing Program

Graphic representation of AB testing

A/B testing is a powerful and incredibly lucrative part of your digital strategy. Though 70% of companies are running at least two tests a month, many midmarket organizations hesitate to lean into their data, or find it difficult to kickstart and manage a testing program. This type of data science can help your team understand exactly how users interact with your content. But only if you are ready to push aside assumptions to make room for greater customer experiences.

The uncomfortable truth is that you’ll need to be willing to ask tough questions and make difficult decisions to truly connect with and serve your target market. 

1. Have you put your boots on the ground and conducted thorough market research?

If you can’t say ‘yes’ with vigor, then the answer is no. Be sure to conduct the proper market research needed to lay the foundation for your testing program. This means that you have a thorough understanding of exactly where you sit within your marketplace. You know what your competitor’s strengths and weaknesses are. It also means that you have a defined target audience, and you understand how your ideal customer’s hopes and dreams play into your products or services. Discovery is vital. Without it, you're risking wasted resources, missed opportunities, and even *alienating* your customers!

Pro Tip: Conduct a Cohort Analysis which will give you a deeper understanding of your customers behavior, allowing you to create more personalized campaigns.

Related: Customer Journey Podcast

2. Ok, so what are you actually planning on testing?

A/B testing is an experiment. First, you come up with a hypothesis based on research. Then, you run a test and examine the results. It's best to start by testing just one thing at a time. You could try multi-variant testing, but each test should focus on a single hypothesis. Take the time to test each component separately - whether it's headlines, copy, CTA buttons, navigation changes, banners, or images. By testing each piece individually, you can figure out exactly which changes led to the best results.  Every test should have a clear outcome. Did you get more newsletter sign-ups? Did you notice an increase in revenue per visitor or longer page visits? Each iteration should be aimed at optimizing the overall customer experience

Reminder: A 20% increase in test results does not mean that you were going to see a 20% increase in sales. The value of each element and web page is only one part of your overall conversion rate. 

3. And how dialed in is your data tracking game?

Make sure you dive into the details of your datasets. To get the full picture of how your variants impact visitor behavior, be sure to integrate your testing program with analytics or business intelligence platforms. Testing is validated learning - and it's essential for success. For each test, make sure to separate your traffic sources and run a small stable base to uncover the true cost of an action. How does it affect users coming from paid ads versus organic channels? By properly tracking value-based metrics, you'll be better equipped to make informed decisions and drive meaningful results.

Pro Tip: Test as long as you possibly can to rule out seasonality and ensure that you collect enough traffic to make a definitive conclusion. 

4. Do you get enough traffic to make the impact you want?

Generally, the more traffic you have, the faster you'll be able to get statistically significant results. To determine if your website has enough web traffic for A/B testing, you need to know your sample size. This is the number of visitors you need to see reliable results. An ideal sample size depends on your conversion rate, the magnitude of the change you're testing, and the statistical significance level you want to achieve. For example, if your website currently has a conversion rate of 2% and you want to detect a 10% increase in conversions, you will need a sample size of 2,814 visitors for each variation. You can absolutely run a testing program with lower traffic, it’s just going to take some time to create statistical significance

5. Are you unintentionally getting in your own way?

Most of us do. Your experience matters, but your market research matters more. You're not in the business of proving anyone (including yourself) right or wrong. Instead, your focus should be on connecting with, engaging, and selling to your ideal customer. The truth is you really never know until you test and collect feedback. Test, edit, and retest again in order to find valuable and actionable change. There will be times when your test yields a winning idea that you absolutely hate. But it's not about you – it's about what works for your users. Testing allows you to fail, learn, and grow faster. And that means more conversions and higher order values (if that's your thing). 

Reminder: Pave your own path. What worked for other companies, even those in the same field, may not work for you. Case studies should offer inspiration on what to test — nothing more. 

Despite the difficulties in kickstarting a testing program, it is vital to collect and *listen to your data* in order to improve performance. Whether your CMS or DXP has a native A/B testing app, or if you are using third-party programs like Optimizely or Unbounce, you will need to first get comfortable with being uncomfortable — the faster you do, the faster you will make powerful incremental changes that boosts revenue and takes your business from good to great! 

It's time to focus on improving your customers' experience, but do you have the resources to do it properly? Give us a call and let us show you how an A/B testing program can seamlessly fit into your digital ecosystem.