Making Sense of A/B Testing for Marketing
Improving Your Conversion Rates with Empirical Experimentation
Introduction to A/B Testing
A/B testing, also known as split testing or bucket testing, is a method of comparing two versions of an experience to determine which one more often leads to a desired result.
In a world where the waters of digital trends and interaction are muddied by millions of companies, bots, and memers, figuring out if a new feature, ad, or communication copy works is hard.
A/B testing shows you what actually works to modify the behavior of your customers.
Why Would I Need A/B Testing?
It’s a sunny day in the spring of March 2016. You’ve recently been promoted to the Director of Digital Marketing for a direct to consumer (DTC) brand that sells swimsuits.
Knowing that the summer is quickly approaching, you want to get the word out about your product and build excitement.
You normally get about 100 sales an hour and are hoping to bump that to 150 with the new campaign.
After extensive research into your existing customers and potential lookalike audiences, you choose a few demographics on Facebook to advertise to.
With the ads ready to go and a heart full of hope, you publish your campaign to Facebook the night of March 21.
When the sun rises the next morning, you wait with bated breath to see who buys your stylish new suits.
8:00 AM: 20 sales
9:00 AM: 70 sales
10:00 AM: 150 sales
With a tear in your eye, you tell the team to turn off the campaign and you plan to try a new approach next week.
Can you tell what you missed?
Marketing Tests Need Controls
An important thing that you missed was that nearly the entire internet went down for the morning of March 22, 2016.
React, what Facebook used at the time, was broken during a disagreement which resulted in an NPM code contributor un-publishing his packages.
Your smaller, self-hosted website was able to still make a few sales from people who already knew to go directly to your site to make a purchase.
Seeing a few dozen purchases per hour made you think that things were still being measured correctly.
Because you didn’t split your test group, you didn’t know that the issue wasn’t your new advertisement campaign.
Implementing Successful A/B Tests for Conversion Optimization
Getting started with A/B testing should not be a herculean task. Once you know why you need A/B testing, the process to set up your first tests should not be too difficult.
By conducting controlled experiments, you can identify the changes that lead to whatever you want to change: Higher click-through rates, More purchases, Increased sign-ups.
How to Get Started
Define Your Goal: Before you start an A/B test, clearly define your objective. Are you looking to increase newsletter subscriptions, boost product sales, or enhance user engagement? Having a specific goal will help you measure success with clear eyes.1
Identify Key Variables: To conduct an effective A/B test, you must identify the key variables that may impact your conversion rates. These variables could include headlines, call-to-action buttons, page layouts, images, colors, or even pricing. By testing these elements individually, you can identify the factors that have the most significant impact on your conversions.2
Create Variations: Once you have identified the variables to test, create variations of your webpage or marketing element. For example, if you're testing a landing page, you might create two versions: one with a red call-to-action button and another with a blue one. Keep the changes focused and limited to one variable modifications between each version and the control.
Split Your Traffic: To conduct an A/B test, you need to split your website traffic between the control (original) version and the variation. Ensure that you normalize your measured variables to rates rather than just totals if you’re splitting un-evenly between control and variation. Using an A/B testing tool or software can help you easily manage and distribute traffic to the different versions.
Analyze and Iterate: Once your A/B test is running, it's crucial to monitor and analyze the results. Use analytical tools like Google Analytics or other A/B testing platforms to measure the performance of each variation. Pay attention to metrics like conversion rate, bounce rate, time on page, and any other relevant data. Based on the results, iterate and make adjustments to further improve your conversion rates.
Conclusion
A/B testing is a valuable tool for optimizing your user experience and achieving better results in your marketing campaigns. By conducting controlled experiments and analyzing the data, you can make informed decisions to enhance product performance.
Remember to define your goals in advance, identify key variables, create variations, split your traffic, and analyze the results to continuously improve.
Start experimenting today and unlock the potential for increased success in your digital marketing efforts.
Keep reading about testing design by following up with this article about Multi-Arm Bandit testing.
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In a future post, I’ll be addressing advanced variation testing such as multivariate regressions and multi-armed bandit testing
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Pre-Defining, or pre-registering your goals and measurement processes will ensure that you don't subconsciously change how you measure things to give your favorite variations preferential treatment
Note that A/B testing does not have to be just an A and B version. Many versions can be tested at the same time. Just make sure you have enough volume to accurately test the results if you split among many variations!