Why A/B Testing Is An Effective Strategy For Understanding Your Users
Multivariate testing is an attractive framework for building out experimentation workflows. The ability to test multiple variables simultaneously has its benefits, but this strategy is easy to get wrong. Smart optimizers know that A/B testing is a more effective strategy for understanding users.
A/B testing forces your team to narrow their focus on specific aspects of the product or user experience because it requires more in-depth knowledge of customers. This focus ensures you spend time and resources on making changes that have a real impact.
Refining the product or user experience through subsequent iterations also helps your team forge stronger relationships with customers. You’ll learn their preferences, what drives their engagement, and what retains them longer. When you combine all this together, it helps you create experiments that make an actual impact on your business goals.
What Is A/B Testing?
A/B testing is a process for evaluating the impact of individual changes on your product or user experience. You take a control variant, whether it’s a page on your website or a mobile app experience, and change one thing about it to create a test variant. This structure helps you determine which variant is better by gathering real-world data from users and analyzing the results.
Understanding how to build these types of experiments, what makes them valuable, and any potential issues you’ll encounter help your team create more effective tests for mobile and web.
Pros of Split Testing
- Allows for more control
- Easy to set up and analyze
- Minimal traffic requirements
When you create an A/B test, you’re only able to change one element of the page at a time. While you might think of that as a constraint, narrowing down experiments to a single aspect of the product or user experience actually helps streamline the process for your team. And it forces them to think critically about the impact each test has on your target audience.
This forcing function also leads to easier setup and execution, as it’s always clear what parts of the experience you’ve changed. Instead of guessing what elements had the most impact on customers, you can spend your teams’ valuable time analyzing why your specific change did or didn’t work. This helps you gain a better understanding of each experiment’s impact on your audience.
Creating an effective A/B test also requires significantly less website traffic than a multivariate test. You can run your test for two weeks instead of the three to four weeks required for multivariate testing and easily capture enough data to either prove or disprove your hypothesis.
Cons of Split Testing
- Requires multiple tests for more important experiences
- Takes time to run multiple tests
- Relies on a comprehensive understanding of the product/user experience
Most of the time, refining product or user experiences requires a series of multiple A/B tests. This increases the time it takes to reach a concrete hypothesis, as each experiment needs at least two weeks to gather enough data, especially when you’re testing out a critical aspect of the experience.
The constraint of testing one element at a time means your team has to have a clear sense of what they want to evaluate before you create this experiment. So the person creating your experiments needs a comprehensive understanding of the experience as well as enough data to make decisions confidently.
What Is Multivariate Testing?
Multivariate testing is an experimentation framework that works in much the same way as A/B testing but with significantly more variations per test variant. Instead of drilling down on a specific element of your product or user experience, multivariate testing changes multiple aspects of that product or experience at the same time.
Pros of MVT
- Can test multiple elements at the same time
- Cuts down on single test duration
- Helps refine “experiences” at scale
Multivariate testing has its place when you’re interested in testing subtle changes to existing content. If you’re time- or bandwidth-constrained, multivariate testing does help you create and run your tests on multiple aspects of the user or product experience at the same time. This means you can quickly refine large-scale “experiences” without the need for a series of subsequent tests over weeks or months.
These tests also help you build entirely different pages or features based on your specific target audience’s needs. If you’re looking to change an outdated landing page or experiment with your company branding across a number of different experiences, you can create multiple versions of the test for each audience. This flexibility helps you make significant changes faster than you would have using an A/B testing framework.
Cons of MVT
- Significantly larger traffic requirements
- Difficult to set up, document, and control
- Multiple variations easily skew results
While multivariate testing does help your team make changes on a larger scale, that increased testing infrastructure requires a considerable amount of website traffic to garner actionable results. Each element you test needs approximately the same amount of traffic as a single A/B experiment to surface actionable results. This means you’ll only be able to run these tests if you know you’ll have enough traffic to support large-scale changes.
Creating the specific test variants for these experiments also takes a lot more time and resources from your team. You’ll need to set up each page or feature, catalog the differences between it and other variants, and differentiate them on your experimentation platform. This can easily lead to confusion if not appropriately documented, which is another constraint of this framework.
Not only is there additional work during the setup stage, but analyzing multivariate test results is much more difficult as well. Making multiple changes to the user or product experience means you have to take everything into consideration when you analyze your tests, and you’ll never know what single element impacts your target audience most.
Why A/B Testing Is Better
Both A/B testing and multivariate testing help teams solidify their understanding of customers, but only A/B testing provides the level of insight required to make smart decisions for your product. A/B testing not only allows for more control than multivariate testing but also helps you target specific audiences and their unique behavior.
Narrowing the focus for each experiment acts as a forcing function for your team—making them consider the impact each element of the product or user experience has on a deeper level. It focuses your team on the fundamental aspects of the product or user experience that customers actually engage with.
Every experiment you create needs to add value to these experiences. Each test variant builds on the previous test to refine your team’s understanding of customer behavior and highlight opportunities to develop stronger relationships at scale.
A/B tests take considerably less time to create as well, especially with the right tools. When you combine this with the smaller traffic requirements and more straightforward analysis, it helps you build experiments faster and maximize your impact on the experience at scale.
Ease of use and analysis also help create a team culture that’s engaged with experiments throughout the organization. This helps boost productivity and minimize strain on your team. A/B testing is also less resource-intensive than multivariate testing. It provides actionable data on customer preferences without the time and money required to set up more complex experiments.
And while the name alludes to a one-to-one change for every test, you can create multiple variants for your A/B tests without increasing complexity for your team or skewing results.
Successful Experiments Start with Deep Customer Understanding
Building an effective experiment, whether it’s for your product or user experience, relies on your team’s understanding of specific customer behaviors. While multivariate testing helps highlight nuanced changes in this behavior, it doesn’t provide the same level of particular insights. By forcing your team to narrow their focus on individual aspects of the customer experience, A/B tests make it easier for your team to dive deeper into what drives certain behaviors throughout the customer journey.