You’ve probably heard the saying in marketing circles that goes, “Eighty percent of my advertising is wasted, but I’m not sure which eighty percent.” This concept is also pertinent to the web design world. Imagine if we could pinpoint which aspects of our design aren’t performing to their full potential—wouldn’t that be invaluable?
Discovering what truly resonates with users in terms of design can seem like hitting the jackpot. That’s the lure of A/B testing—it offers solid, data-backed insights on which design alternative triumphs in achieving a particular goal. However, this methodology, while powerful, isn’t a cure-all. Overusing it can cloud your creative judgment and ironically lead to choices that are less optimal than they could be.
In this discussion, we’ll explore some of the challenges associated with A/B testing and how it should be wielded judiciously within a designer’s arsenal, rather than being the primary strategy.
A/B testing, also known as split testing, has gained popularity in web design thanks to the capability to dynamically serve web pages and the rise of sophisticated analytics tools like Google Analytics. It’s straightforward to administer such tests: one group of site visitors sees one version of a layout, while another group sees an alternative. The goal, judged by the analytics, is to see which layout drives more of a specific action—perhaps clicking a ‘Buy Now’ button or filling out a sign-up form. These outcomes, or ‘goals’, must be measurable events that analytics software can track. However, while it can measure the click of a link, it can’t gauge whether the user actually reads the content after clicking.
Further insights into A/B testing can be found here, along with a collection of influential testing case studies.
The nature of A/B testing is inherently reductive, focusing on evolving the most ‘fit’ design. Testing two completely different designs reveals which is superior for your targeted goal. You could iterate this process indefinitely. Yet, to advance beyond this stage, it’s necessary to vary elements within the winning design to enhance its effectiveness. Immediately, this shifts the focus from comparing two distinct designs to fine-tuning a single solution. Statisticians refer to this as seeking out the local maximum as opposed to the global maximum—the risk is getting trapped optimizing for a small win while overlooking potentially greater improvements. When introducing multiple variations, known as multivariate testing or bucket testing, complexity increases, often accompanied by a higher cost.
Moreover, split testing can optimize only one goal at a time. On a narrowly-focused site, like an e-commerce platform, optimizing for one primary goal may suffice. However, if your site serves multiple purposes, your design must perform well across all objectives.
Excessive optimization can make designers hesitant to adopt new designs, due to the investment in the current version. The challenging question remains whether you’re simply improving a mediocre design when a better option might exist that could achieve even higher success rates.
User behavior evolves, and a design that was effective one month might not perform as well the next, creating a cycle of continuous testing and adjustment. The role of a designer then shifts from creator to what might be termed a ‘quant-a automaton,’ obsessed with the reassurance testing provides rather than trusting creative instincts and design expertise.
First impressions are crucial. Research from Ontario University and others has shown that web visitors rapidly, often within milliseconds, form an unconscious decision about whether they like a website or not. This instantaneous response, known as the ‘halo effect’, influences their further perception and even their judgment of the site’s credibility. It’s always been surprising how quickly people will leave a website (‘bounce’), frequently due to the frustration of slow loading times. Focusing on technical optimization and reducing page weight can be more impactful than UX testing, as slow rendering drives people away regardless of a website’s aesthetic appeal.
However, you can only begin A/B testing after your site is live. You need actual users engaging with real goals to effectively perform split testing. Testing on a pre-launch private beta can also be misleading without a significant number of beta users. Reliable results also lean on a large sample size, meaning you must commit to a design initially—another realm unaided by A/B testing.
There’s a fine line between following and leading your audience. A/B testing risks handing over the reins of your design decisions to the collective judgment of your users. Pursuing creative innovation leaves room for the spontaneous ignition of truly original and fresh ideas. With many sites adopting familiar designs out of safety, the courage to be different can set you apart, as exemplified by this thought-provoking talk. Although a unique design may initially underperform, its distinctiveness could gradually attract and engage a new audience.
A/B testing can refine a design, but it can’t pioneer revolutionary ideas. To truly understand if your audience enjoys your website, qualitative feedback is essential—raw numbers don’t tell the whole story.
Ultimately, designing with intent and confidence, coupled with your unique design perspective, is what allows a website to stand out. A website crafted with consistency and a clear vision will outshine a site that’s been built through a series of hesitant, constantly tested tweaks.
This is not to dismiss the value of A/B testing; it is excellent for testing specific elements, not entire layouts. It’s most effective for comparing different versions of a button’s text or exploring which signup form arrangement works best. For a deep dive into practical testing examples and e-commerce focused UX testing, have a look at Which Test Won.
In general, it is often more fruitful to devote the time you’d spend on A/B testing to other site improvements that directly enhance user experience, such as cross-browser rendering consistency and mobile optimization. Regular tuning, typos correction, and aesthetic adjustments can be made without relying on A/B testing to affirm each decision.
In conclusion, A/B testing can be a part of the design process, but it shouldn’t become a crutch for design decisions. Trusting your judgment, augmented by user feedback and clear objectives, can often lead your website to greater success.
How much do you rely on A/B testing? Can web designers excel without it? Share your perspective below.
Featured image/thumbnail, decision image courtesy of Shutterstock.
