How to Scale A/B Testing for Digital Marketing | Guide, Framework & Examples

Adam Hencz

February 13, 2024

If A/B testing is causing you a headache sometimes and you just wish to speed up the process, then look no further.

This is the guide for you.

In just a few minutes you’ll learn a simple framework to scale your A/B testing without data overload.

We’ll cover:

  • How to run A/B tests for tight deadlines & high volume
  • The downside of A/B testing & common mistakes to avoid
  • Rapid A/B testing examples for landing pages, social media, and more

Here’s how to get the most out of A/B testing.

What is A/B testing? How does it work?

Before we dive into some practical A/B testing strategies, let’s spell out what A/B testing really is.

If you’re looking for an encyclopedic definition of A/B testing, then hit up Google again. Here, we’ll cover the gist of the concept, so you can tweak the process for your own good like better stakeholder management, higher conversions, you name it. So here it is:

A/B testing is comparing two ideas to see which one works better for a specific goal.

A/B testing works by showing version A and version B (hence the name A/B testing) to an audience and then analyzing the reactions to make data-informed decisions.

A/B testing font-weight, colors, headlines, and CTAs can dramatically boost attention & content performance.

A/B testing can be valuable because you can:

  • Test subjective ideas and find evidence to back them up
  • Save resources by eliminating ideas that won’t work
  • Balance intuition with insights

However, it's important to see that A/B testing has both its bright and dark sides, too. Sure, running A/B tests on ads, for example, can be a game-changer. You can fine-tune your ads to hit specific targets—like tweaking page layouts or ad copy to boost clicks, rack up more purchases, and collect higher-quality leads.

But, as we will unpack this later, A/B testing has its share of pitfalls, especially without the right strategy and insight. It can easily be a waste of your time and money.

We’ll talk about a simple framework for a quick and effective A/B testing process in just a bit.

But first, let’s take a look at why A/B testing can easily become a financial black hole.

What are the common A/B testing mistakes?

Despite the global A/B testing market's projection to hit $1.08 billion by 2025, only 28% of marketers are satisfied with their testing performance.

Here's where many of us fail with A/B testing:

  • Getting lost in a sea of metrics and variables
  • Taking too little time to run tests to produce good results
  • Skipping retesting rounds and then missing out on golden insights

This is mainly because of the inherent challenges of A/B testing like:

  • Crafting an easily testable hypothesis
  • The need for substantial sample size or traffic
  • Having sufficient time to achieve high-confidence results

And let's face it—not every digital marketer has the luxury of large traffic volumes, especially when working with niche markets or new brands. However, small sample sizes and tight deadlines don't have to become roadblocks in your A/B testing efforts.

But the biggest challenge of A/B testing is landing on results that truly stand out.

Often, after all that testing, the data you end up with can seem frustratingly similar, leaving you stretching your head on what to do next. You don’t want to go too broad with your A/B tests, but on the flip side, you also don’t want to zoom in too close, because that can muddle the process, too. Finding that right balance can make all the difference in arriving at conclusions that actually move the needle.

And this leads us to our framework for quick A/B testing.

Quick A/B testing: A practical framework for digital marketing

Many digital marketers find themselves stuck in a cycle of gathering data, crunching numbers, and waiting for results that sometimes don't even lead to actionable insights. But, as Alex M. H. Smith brilliantly points out, the secret sauce of successful brands isn't in their data analysis prowess but in their creative process.

Let’s break it down.

Here’s how we usually think how A/B testing gives the big players real solutions:

  1. They gather the data
  2. They crunch the numbers
  3. Data leads them to a rational conclusion

But in the real world, it rarely works this way.

Here’s what really happens:

  1. They look at the situation
  2. They come up with a hypothesis
  3. They check it, and if it makes sense, they dig deeper

Lead with imagination and end with the analysis, Smith suggests.

Analysis is great for testing your hypothesis, but first, you need to check your thinking and select a few ideas you will test out.

When it comes to ads, checking your thinking can be a quick chat with your team, your designer, other employees, or your boss to set a direction. Then you sketch a few versions and can automate the next testing phase with AI tools that do the testing for you. This will also remove most of the guesswork from your process.

Outdated A/B testing drains resources and is slow to give results, while rapid A/B testing with AI is highly scalable and delivers one-click validation.

Some specialized AI tools like Predict give you instant feedback on your creatives. Predict lets you compare multiple variations, and you can run unlimited tests with the tool. It’ll show you performance gaps and ways to improve your designs. It's almost like working with an experienced art director who
gives instant feedback on your creative choices.

This is rapid A/B testing, or pre-testing if you will.

Remember that this isn't just about saving time—it's about boosting the quality of your marketing efforts. This isn't just rapid testing; it's smart testing, designed for marketers who value both speed and substance.

The most effective type of A/B testing: pre-testing with AI

Pre-testing is the process of evaluating designs without risking your budget and spending a dime on testing designs in the wild.

Pre-testing with AI can completely replace your current A/B testing process. Or if you are into more nuanced A/B testing, it will give you an unbeatable head start on the process.

Standing out visually is the brunt of the work and this approach is all about making your visuals "pop" with (almost) no guesswork. Predict allows you to scrutinize your designs in seconds, from color schemes to layout, making it a breeze to choose the best options and eliminate underperforming designs early in the design process. With one-click AI recommendations, it gives you critical feedback for improving designs for attention and maximum engagement.

Stop wasting budget on inefficient A/B testing and speed up the process with AI.

Running pre-tests with Predict lets you:

  • See how small changes affect performance
  • Improve designs to reach goals like CTRs
  • Save time and budget from A/B testing

Now, let's dive into where this approach really makes waves.

Rapid A/B testing examples

With AI, A/B testing becomes less of a guessing game and more about making informed choices quickly. Here are some examples where rapid A/B testing changes the game:

  • Display ads
  • Landing pages
  • Social media posts

Let’s look at some examples.

A/B testing display ads in the easy & smart way

When it comes to A/B testing display ads, the crux is to recognize your product's market position and the customer journey that precedes a purchase.

In short, A/B testing depends on the nature of your product.

  1. For products that naturally draw interest, A/B testing can focus on nurturing that interest over time. This might involve testing the positioning of different branding and product elements or value propositions that keep the product top of mind.

See how soft-drink producer CO-RO gave a 20% Boost in Brand Awareness with Predict AI.

  1. For products considered mundane or transactional, the goal is to make the customer’s decision as easy as possible. A/B testing can help sharpen your design and messaging to effectively communicate value without unnecessary complexity.

See how Tre Kronor Media agency improved CTR by 73% with a swift analysis and simple adjustments, done in just 30 minutes.

If you’d also like to set a clear path for your display advertising campaigns then read these quick display ad improvement tips on our blog.

How to run faster A/B tests for landing pages

To run faster A/B tests for landing pages use A/B testing tools like Predict, which can provide insights into which version of your A/B test "won," making it easy to choose from multiple versions.

Since you can run unlimited predictions with Predict, you can continuously test ideas and iterate on your landing page design to optimize for maximum clicks and conversions.

If you’d like to boost your landing page with a few simple tricks, hit up our blog post with 7 easy tips to improve your landing page CTRs.

A/B testing for attention can boost your landing page metrics.

How to do rapid A/B testing for social media

For this one, we did the heavy lifting for you.

First, our science team trawled through data from selected neuroscience studies with our clients. Then we distilled their findings into an actionable guide you can use to speed up your A/B testing process for social media.

If you want to save hours of guesswork and create more effective ads then don’t forget to get your copy of our Social Media Ad Success Guide.

Scalable A/B testing tool & software for marketing & design

Are you still struggling with inefficient A/B testing?

See how Predict can help your team scale your A/B testing process while saving time and budget. Get your free session now!

How to Scale A/B Testing for Digital Marketing | Guide, Framework & Examples

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