Experimentation in Web Design

We’ve long departed from the days when website design was a static endeavor. Today, design decisions are increasingly informed by data and feedback from users through conversion and engagement metrics, using techniques from A/B testing to advanced multi-armed bandit (MAB) strategies.

‍

In the fast-paced era of short-form content and rapid trend cycles, user preferences are more fluid than ever. Experimentation, then, isn’t a one-and-done deal; it’s an ongoing strategy essential for keeping marketing efforts at pace with ever-evolving demographics, trends, and preferences.

Data-Driven Decision-Making

While intuition can inspire creative ideas—like a catchy tagline or striking layout—relying solely on gut feeling can be risky. What feels like the best option might not stand up in an online environment where user behavior is unpredictable: a design that looks great on the drawing board might not perform well in the real world.

‍

Moreover, even a proven design can become outdated quickly as preferences shift. If experimentation and adaptation are driven by human monitoring, you’re always reacting rather than proactively adapting. By the time you come up with a good replacement for an old variant, you may have already lost engagement to a suboptimal version of your site. Not only is identifying what needs adjusting hard, but so is coming up with something new: the creative process takes valuable time and effort.
‍

However, with data-driven experimentation, each experiment builds on the positive effects of prior ones. Not only are you improving incrementally, but each change is based on real-world performance data, so different parts of your website can be continuously validated and, if needed, refined.

How Experimentation Gets You Ahead

Experimentation does more than provide an immediate boost in site performance. It also helps you adapt quickly and stay ahead of the curve, by remaining agile to the latest trends. By keeping your site up-to-date through testing and refining, your site not only attracts new users but also can improve the retention of an existing user base. Similarly, experimentation offers insights beyond what works best overall. It can reveal what works best for each group, which may differ across different customer segments. So, investing in experimentation has dual benefits: overall better performance and also the potential for personalizing your site.

Continuous Experimentation

With experimentation, each increase in conversion adds up over time. Let's say each test you run leads to a modest 5% boost in conversion on average. So, if you run one experiment per quarter, you'd have a total boost of 22% over a year. If you were to bump this up to once a month, you'd have an increase of 79%. But if you were constantly experimenting, these returns are compounded continuously. Plus, while running separate experiments requires ideation and manual effort to run and monitor, continuous experimentation can run in the background, without any human involvement.

‍

Suppose your baseline revenue was $1M/month, so a 5% lift would mean an additional 87k/month. So, if you chose to experiment every quarter, you'd get $2.64M more at the end of the year. If you experimented every month, the resulting boost to your revenue would be $9.48M. But continuous experimentation allows you to outperform even that, all without having to expend extra effort setting up and monitoring experiments monthly.

‍

Experimentation not only results in more conversions and data-backed insights about your customer base, but it also results in less manual effort overall: using MAB-based approaches for continuous experimentation takes the human error and turnaround time out of experimentation and frees up those efforts for use elsewhere.

‍

Continuous optimization is where Coframe’s MAB-inspired methods shine. Rather than separating the experimentation process into distinct brainstorming, experimentation, and implementation phases, the MAB approach allows these stages to operate simultaneously, so traffic isn’t lost to an underperforming variant during a ‘testing’ phase. And unlike traditional MAB methods, Coframe’s methodology also allows you to test new variants over time, so you’re not just picking a winner from a pre-defined set of options.

‍

Testing is like tasting—don’t serve it up until you know it’s good. Learn more about Coframe.