Michael Epps Utley {kind=image/jpg}
A/B testing has been an effective way for businesses to optimize their promotional campaigns since the dawn of modern marketing. This simple approach is designed to quickly reveal which marketing efforts are working and which are not (and why this is the case). Ideally, A/B testing should yield fair and accurate data while helping you find ways to optimize your campaigns – but is this always the case?
Not exactly. Like many things in today’s complex omnichannel marketing world, the myth of A/B testing may be just that: A myth.
Before you throw out the baby with the bathwater, however, there’s a lot you should know about A/B testing in the modern marketing world. This article examines whether A/B testing is still a valid testing method and explores whether better options could be available.
A/B Testing: The Basics
A/B testing, also referred to as split testing, is a randomized experimentation process where two or more versions of a variable are shown to different groups in a target audience over the same period. The goal here is to figure out which version of a particular strategy or campaign makes the maximum impact and generates better business metrics. When more than two versions of something are tested, additional letters are added – for instance, A/B/C testing (we’ll stick with “A/B” in this article as a catch-all).
A/B testing can be used for almost any type of marketing, including social media, email, landing pages, and more. What’s important is that you only test a single element at a time in an A/B test. Consider, for example, the subject line in an email or photo in a social media post or color on a landing page. Maintaining this constant is the only way to know which variable improves or worsens results. If you change two or more details, you can’t be sure which is influencing results or whether it’s a combination of things.
A/B Testing: Why It’s So Popular
A/B testing data typically holds the most weight when making marketing decisions and setting future direction. It’s a scientifically controlled testing method that should yield meaningful, understandable, and usable data sets. Marketers have historically had many reasons to leverage A/B testing in building their marketing strategies and improving their tactics, including:
It’s easy to use. A/B testing is a relatively simple concept. It’s easy to implement, too. Even the smallest marketing operation (including solo ones managed by small business owners) can use this method to test their marketing efforts. The barrier to entry is low.
You’re able to prove valid results. Model validity is a major concern for marketers, business owners, and managers. Everyone today must be able to justify marketing decisions and spending by accurately tracking the performance of content, ads, landing pages, emails, social posts, and more. Because A/B testing has been used for so long, its results are considered valid by many.
It works (sometimes). A/B testing of singular variables presented to a random selection of a target audience should be expected to yield meaningful data and insights about relative marketing performance. However, the way A/B testing is done today – especially on major platforms driven by algorithms – may provide limited or less meaningful results.
Let’s look at why A/B testing may not be serving you well these days.
A/B Testing: Is it Still Valid?
In theory, A/B testing should provide valid results. However, in many situations, it does not. Here’s why:
No control over the testing environment. It’s impossible to control everything in a third-party media platform like Facebook, Instagram, or Google Ads. You don’t own the logic that runs them. And unless you can genuinely control every aspect of your testing, you can’t trust your A/B testing results. The same A/B test run at another time could likely deliver extremely different metrics. It’s almost like trying to conduct a double-blind medical study in a lab you don’t control. Would you trust the results?
Most A/B testing isn’t random. Every social media post, digital ad, or other marketing asset is distributed through algorithms that choose the ideal viewers for the content. That means your A/B tests aren’t random – they’re optimized to deliver the best results.
Algorithms have their reasons for being. A/B testing can’t override algorithms. Corporations built platforms like Google Ads, Twitter, and Facebook to generate income. Their algorithms always try to figure out how to show your content to the people most likely to take the desired action. Media platforms make money when people click, and they do anything possible to make that happen, including running over your A/B tests.
Many seemingly benign channels like email may have optimization logic built in that could skew your A/B test results. The same is true when testing landing page variations. You can’t be sure that the platforms driving traffic to your pages deliver identical types of people to them.
This means that any platform’s profit goal usurps your goal of testing variations of your marketing assets on random viewers. Any data you collect comes back skewed and essentially meaningless (misleading, even).
If you act on your A/B test results, you could end up diminishing your overall campaign performance. Because of the optimization done by algorithms, you could eliminate all the clicks earned by the losing creative because the people who responded to it may never choose the winning option.
One other critical issue: A/B testing may be completely missing the point. Even in its purest form, for instance, testing email subject lines in a completely randomized distribution test, you’ll find out which subject line performs better. However, this won’t tell you whether email is the best marketing channel for you — or even close.
An Alternative to A/B Testing
So, what can you do if A/B testing is no longer valid? Consider shifting from total reliance on single-platform testing to a unified marketing measurement solution.
Unified marketing measurement takes the data and insights from each of your marketing platforms and combines them into one complete view. The single view presents you with a more accurate picture of overall marketing performance. It’s a more comprehensive and efficient form of analysis.
While A/B testing may never go away entirely, united marketing measurement provides significantly more benefits, including:
Omnichannel optimization. Shifting from single platform, single element A/B testing to unified measurement testing allows you to optimize everything you do across your entire marketing plan. This moves beyond the incremental improvements realized through channel-by-channel A/B testing and analysis. Shifting from a one-off strategy to an omnichannel one increases efficiency exponentially and allows you to optimize your overall marketing strategy.
Reduce optimization time and increase scope. When you implement unified marketing measurement, you collect data from all of your platforms in real time. This single view of all your data gives you an accurate picture of how marketing campaigns are performing. It can also reduce the lag time between data analysis and acting on it. With everything tied together, you can move faster. Instead of acting on a single image, headline, or color choice, you have the power to amp up one channel and turn off another if the relative results merit it.
Viewing this another way, A/B testing is like changing out the wallpaper in a single room in your home – it can improve things, but you’re essentially living in the same place. Unified marketing analysis allows you to determine whether the rooms in your home (and your home itself) are the best they can be.
Benefits of Unified Marketing Measurement
We’ve made a case for why leveraging omnichannel measurement to gain a big-picture view of all your marketing activities is a much more effective way to improve your efforts than A/B testing. In short, a unified approach allows you to:
Simplify optimization. There’s no need to set up ongoing individual tests with omnichannel measurement. You simply need to optimize efforts by identifying new opportunities through the data. You may be able to leverage insights across all channels – not just a single one.
Increase data analysis. With omnichannel measurement, you never have to wait for results from a single A/B test to make something better. You can analyze your data anytime, looking for insights to optimize your efforts.
See significant improvements in results. Instead of the incremental improvement typically achieved by A/B testing, you can expect more meaningful optimization through omnichannel measurement. You can leverage insights from and across all your promotional tactics and strategies.
Be aware. Unified marketing measurement may seem like a big jump up from A/B testing. Don’t worry about getting overwhelmed. Unified measurement solutions can yield a much bigger perspective on your marketing. You can start in a small and manageable way and then gradually build up. You’ll experience improvement early on (likely better than from A/B testing) and even more significant growth in the future.
Getting Started With Unified Marketing Measurement
The simplest way to leverage the potential of unified marketing measurement is via tools like Measured, Adtriba, or Activate. These systems help you collect all your promotional data in a single place so you can begin conducting analysis across all your marketing. Your improved marketing results will quickly cover your investment in it. Most platforms come with training that can get even the most data-adverse business owners, managers, and marketers up-to-speed quickly.
If you’re not ready for that, start small. Transfer your individual campaign data to a single spreadsheet so you can compare and contrast it to see what’s working and what’s not.
No matter which approach you take, what’s critical is that you move on from the micro world of A/B testing to the broader, multidimensional universe of unified marketing management. The results will speak for themselves.
Learn more about marketing return on investment (ROI).
