A Beginner’s Guide: How an Effective A/B Test is Run
A/B testing is fun. But how do you do it?
8 seconds. That’s the average attention span of people. That’s one second less than a goldfish!
It’s just as you suspected; the information age has changed the general attention span and the way we consume information. In addition to this, people now have a shorter attention span with the proliferation of smartphones.
If you’re still reading this and haven’t also checked your Instagram feed, then thank you!
As people have shorter attention spans, marketers will need to discover ways to increase their conversion rate and prevent their customers from switching over to their competitors.
So, if you had 8 seconds to convince your customers to take action, what would you do about it? How do you draw conclusions about what to change, and what not to change?
Your answer is A/B testing! Making sure you’re doing the right things largely depends upon it.
Ready to boost your conversion rates without having to spend on acquiring new traffic and bring in more revenue through A/B testing?
Then let’s get started.
What is A/B Testing?
A/B testing aka split testing refers to a process of running marketing experiments to determine whether a new design, copy, or UX visual brings improvement and drives more conversions, according to a chosen metric. It’s a great method for figuring out the best online marketing strategies for your business and is one of the clear-cut and efficient ways used in conversion rate optimization. If you’re not A/B testing your site, your email campaign, or whatever marketing asset you use, you’re leaving money on the table.
The idea’s simply to challenge an existing version of your marketing content (A, control) with a new one (B, variation), by randomly splitting traffic and comparing the performance indicators on each of the splits.
Let’s say, one day you wake up and want to find out if changing the color of your call-to-action (CTA) button from red to green can increase the number of your email newsletter subscribers.
For instance, you could design an alternative CTA button with color green that leads to the same landing page as the red CTA button, and after running A/B testing on them the green variation receives more clicks on it you may want to change the color of the call-to-action button to green.
How to Conduct A/B Testing
Here are steps you can take for A/B testing to ensure that you’re closer to reaching your business goals and achieving success by increasing your conversion rates.
1. Identify the problem and choose what you want to test
Start by deciding which element of your page (or your email campaign) you want to improve in order to increase your conversion rate.
Conversions don’t just happen. It takes a significant time investment in studying how visitors perform on your site or click on your email. As leads quickly scroll through your page, you want to stand out to grab their attention in order to convert them into customers.
But on your newly created landing page, people may face some common pain points while taking an action and not being able to achieve their goals leads to bad user experience. This is why the first thing you need to do is identify a list of weaknesses on your site and pick one weakness to test if it changes the interaction of people on your site.
Use data gathered through visitor behavior analysis tools such as heatmaps and Google Analytics to see which pages have high failure rates and poor conversion rates and to understand how users interact with elements of your site.
2. Use hypotheses that are measurable and valuable
“It’s been a while since we changed button color — let’s test it!”
Wait!
You should never run a split test just for the sake of testing. Every visitor to your website provides you a learning opportunity and this is a valuable resource that shouldn’t be wasted.
You listed problems and concerns that your customers struggle with when they’re completing the conversion goal of your website and you’ve decided what to optimize. Now it’s time to think about exactly how you’ll optimize it.
What do you want to achieve with your A/B test? What are your company-wide goals and KPIs? What would you learn if your hypothesis is proven correct in the case of a variation winning?
Make sure your hypothesis is clear, easy to understand, and able to be determined with an A/B test. Your hypothesis should clearly state what change do you want to make, why do you want to so, and what you believe the outcome will be.
You should have a very clear purpose of how the answer can help you so that you can nudge people further toward the conversion goal.
3. Create variations
The next step in your split testing process should be to choose a variation or a “challenger” based on your hypothesis, and A/B test it against the control.
For example, if you’re wondering whether omitting fields that ask for the personal information of your form would make a difference, set up your “challenger” page with a shorter form.
With control and test created, you can split your audience into equally or randomly sized, randomized groups for testing. Sample size will depend on the nature of the test you want to run.
Remember — it’s better to focus on a single metric at first.
If you concentrate on one thing at a time, you’ll get more valuable data and better results and clearly conclude what’s affecting your conversion rate.
4. Statistical significance needs to be achieved
A/B testing is a means to determine which page’s most appealing to your visitors, and which implementation results in the highest conversions.
Changing one single element on your page is likely to play a huge role that can result in significant increases in your performance.
Because the results eventually give you information about your customers, whether the test “wins” or “loses.”
But how do you determine how significant your results need to be to justify choosing one variation over another?
Statistical significance is the best evidence that challenger is actually better than control and this is why the test should be run long enough to reach statistical significance.
The higher the percentage of your confidence level, the surer and safer you can be about your results as the change has a noticeable impact on conversion rate. I recommend this blog on A/B testing statistics to set the desired statistical significance right.
5. Run that test!
This is the wait-and-see phase.
If you’re doing a good job choosing tests based on data and prioritizing them for impact, then running the A/B test should be easy.
During this process you can view the progress of your test at any time, and when the test concludes, you’ll get data in order to determine a new way to make your marketing content more effective and to figure out which marketing strategies work best for your business.
6. Analyze, learn, repeat
You now can analyze the results by considering metrics like percentage increase which is extremely important for the split testing process.
If one variation performed statistically better than the other, deploy the winning variation.
If there’s no winner stick with the original variation or run another test.
And don’t stop there.
You can start this 6-step process over again with a new challenger.
Don’t be afraid to test and test again; because A/B testing call for continuous data gathering, and repeated efforts can only help to improve optimization
Which Are the Best Elements to A/B Test?
There are several elements that you can split test:
· Your call-to-action (CTA) — different size, colors, placement, and copy
· Social media sharing buttons — different placement and size
· Headings — color, copy and size
· Content — font size, style, placement, and structure
· Images — different composition and placement
· Any on-page elements requiring user interaction
· Email subject lines — questions versus statements, subject lines with and without emojis
· Product descriptions
Conclusion
A/B testing is invaluable to any digital marketer making decisions in an online environment and is crucial when it comes to improving conversion rates.
So happy A/B testing!
Hope you enjoyed the post as much as I enjoyed writing it! Comments, suggestions, corrections are much appreciated.
Sources: HubSpot, Search Engine Journal
