A/B Testing: Optimizing your Marketing Campaigns

A/B testing is to the marketing world what experiments are to the science world. So throw on your lab coat and bust out the goggles.

Today we are talking about all things A/B testing and how you can use it to maximize the success of your marketing campaigns.

What is A/B testing?

Just to get things straight, let’s go over exactly what A/B testing is. Simply put, A/B testing is when you isolate one component of your campaign, release different variations of your campaign where the only difference is that one component, and then analyze the success of each variation to determine which one works best. 

The results from your A/B test can help your campaign in a number of ways. Depending on the variable that you test, you could improve different areas of your campaign. Here are just some variables you could test with their related improvement areas:

• Headlines, buttons, photos etc: Increased conversions
• Layout, emails, social media posts: Improved customer experience

Overall, these insights can lead to time/cost savings, higher engagement, and will ensure that the decisions you make for your marketing are data-driven.

How to Run your Own A/B Test

To ensure a successful A/B test, take a page from Hubspot's book and consider these 15 things:

Pick the variable to test: In scientific terms, this is your independent variable. This could look like email subject lines, sender names, tone of messages, colors, design, etc. The important thing here is to pick only one variable per A/B test. You can always run more tests!

Identify your goal: This is your dependent variable aka the outcome variable. You need to pick which changes you measure based on the differences in the independent variable. This could be CTR, engagement, conversions, etc. 

Create a control scenario and experimental scenario: These are the two different versions of your campaign with the independent variable changed. 

Split your sample equally and randomly: Beyond isolating the variable from other components of the campaign, you have to make sure to control for any differences in the audiences that see each version of your test. This means that you need to do your best to get an equal and random sample of your audience for each test with no apparent differences that could be caused by exposure to your marketing at different times of day, different days, by different genders, etc.

Determine your sample size: For tests such as email campaigns, it is important to determine how many people you are going to reach out to. You want this to be a number that is large enough to produce reliable results but not too large to drown out any differences that could be due to your test.

Decide on significance:  Here we are talking about statistical significance. This means that you need to determine how much of a chance you want to take that the differences you see in your results are due to chance (your confidence level). Typically you want to set your confidence level to 95% meaning that you are okay with a 5% chance that the differences you find will be due to chance. 

Run only one A/B test at a time: If you try to test more than one variable at a time, you can complicate your results. It will become harder to isolate the effects of the changes that each variable has on the results

Pick an A/B testing tool: A/B testing tools can be a great help to organizing and analyzing A/B tests, some tests include HubSpot Enterprise and Google Analytics.

Test both variations simultaneously: In the name of isolating the variable, you need to make sure that both renditions of your campaign go out at the same time to ensure that there are no unintended differences in the reception of the messages.

Five the test enough time to produce useful data: Think about the normal amount of traffic you get and how many people you are reaching and make sure that you are giving the test enough time to produce results before you end the test.

Ask for qualitative data from real users: By adding a survey to the experience, you can give your audience an opportunity to comment on why they interacted with your campaign the way they did which could give you more insight into their behavior.

Focus on your goal metric: After you get your results, try not to focus on other outcomes beyond the dependent variable you already chose.

Measure the significance of your results: After setting your significance level and getting your results, you will then need to calculate whether or not your results are statistically significant or not. Don’t worry, you don’t have to calculate this all by hand. There are tools you can use to calculate this such as the free calculator on Hubspot.

Take action based on your results: Once you have completed your calculations, it's time to act on them. Go with the more successful campaign and drop the less successful one.

Plan the next test: Just like with scientific experiments, the end is really just the beginning. There are always ways to improve your campaign. So, once you are done with your test, it is time to start planning your next test on a different variable so you can improve your results even more. 

Concluding Thoughts

All in all, A/B testing is a great way to optimize your campaigns and make sure you aren’t wasting your time and resources. There are infinite kinds of tests that you can run. Take some time to think about what makes the most sense for you and your business. If you want some ideas about different kinds of tests for different businesses check on this post from Kameleoon.

As always, Space Bison is here to help! If you want any help setting up and running your A/B test just drop us a line.

Those of you who have run your own A/B test, what things did you find most useful?