Multivariate Testing and Usability: Designing User-Friendly Interfaces
Multivariate testing is a technique used to optimise websites, apps, and other digital interfaces by testing different versions against each other.
The goal is determining which version leads to the desired outcome, like more sales or signups.
Usability and user-friendly design refer to how easy and intuitive an interface is to use. A user-friendly interface has clear navigation, and natural workflows, and reduces friction for users trying to accomplish tasks.
Multivariate testing is an invaluable tool for improving usability and creating more user-friendly interfaces. By testing different versions of an interface, you can gain insights into how real users interact with and respond to design changes.
The results make it clear which changes create a smoother, more intuitive experience. This empowers designers and developers to create interfaces optimized for usability.
With a strategic testing methodology, multivariate testing provides the data needed to iteratively enhance interfaces over time. The result is digital products that are highly intuitive and frictionless for users.
This article will explore how to leverage multivariate testing and usability to design user-friendly interfaces.
Following proven tactics and examples, you’ll learn how to craft and test interface variations that elevate the user experience.
Table of Contents
How Multivariate Testing Improves Usability
1. Different types of tests reveal user preferences and behaviours.
A/B testing allows designers to test simple variations between two versions of a page, such as different headlines, call-to-action button colours, or even the presence or absence of certain elements.
This reveals how even minor changes impact user engagement and conversion rates.
Multivariate testing expands on this by testing multiple page elements simultaneously across myriad combinations.
For example, a multivariate test could test completely different page layouts, navigation schemes, content variations, and visual styles all at once. The breadth of these tests exposes detailed insights into how users respond to many aspects of the interface and design.
Both testing methods illuminate the underlying psychology and motivations guiding user behaviour. They help designers understand what truly engages visitors and propels them to convert.
2. Quantifiable data guides design decisions.
A major benefit of multivariate testing is the concrete data it produces on how different versions of a page perform.
There is no guesswork involved – the tests make clear which variations compel more visitors to convert at higher rates. Designers can analyse the results to identify the specific page elements that are most effective at driving conversions, whether that’s the layout, call-to-action placement, image choice, or content structure.
Having granular data on the impact of each design element allows designers to optimize page composition, navigation, workflows, content, and visuals in a far more precise way.
Rather than assumptions or opinions, multivariate testing offers unbiased data to inform design decisions.
3. It enables an iterative optimisation process.
The data and insights from multivariate testing fuel an ongoing process of iterative optimization.
Designers can use what they learn from tests to refine and enhance the interface through multiple optimization cycles. As new versions are tested, the improvements compound over time.
Optimizing in cycles aligns with agile and user-centric philosophies of continually gathering user feedback to improve products incrementally. Even small conversion rate gains of a few percentage points add up significantly over months of optimization.
Multivariate testing gives designers the feedback loop necessary to keep advancing the design and user experience. In this way, usability matures through gradual refinements driven by user data.
Conducting Tests to Improve Interfaces
1. Selecting test parameters.
This requires thoughtful decisions about which elements to test and how to set up meaningful variations.
Designers first need to determine the key aspects like page layout, interactions, workflows, content structure, visuals, and features to test. For example, they may want to test how positioning the primary CTA button impacts conversions. Or they may want to test how different content structures and lengths influence engagement.
For each element, designers will define specific permutations to test – such as testing layout A versus B or headline version 1 against versions 2 and 3.
Defining focused test goals and purposeful, strategic variations that align with key hypotheses is critical. Trying to test too many elements or variations at once can dilute insights and learnings. Keeping test parameters clear, concise, and driven by important research questions leads to actionable data.
2. Analyzing and applying results.
This involves understanding statistical significance and confidence levels. The test duration and amount of traffic/samples determine how statistically conclusive the data is.
Designers must discern which findings are definitive versus more tentative. They also must balance hard data with design judgment and experience.
Not every test result should directly dictate design decisions. However, significant data should inform changes to page elements and flows. Meanwhile, smaller gains can guide incremental improvements over time. Seasoned designers know how to integrate data-driven and intuitive thinking to optimize interfaces.
3. Iterating based on feedback.
This means using initial test results as a starting point for further refinements and follow-up testing.
Optimization is an ongoing process, not a one-time event. Designers can drill down on inconclusive results by retesting those elements with larger sample sizes. After launching redesigned pages, they should monitor performance to ascertain if more testing is needed.
Multivariate testing is concluded when the data satisfies design goals or incremental gains diminish. At that point, it’s best to shift focus and budgets to new optimization opportunities. Approached systematically over time, each cycle of testing, analysis, and iteration compounds interface improvements.
Frequently Asked Questions.
1. Q: What is the difference between A/B testing and multivariate testing?
A: A/B testing compares just two versions of a page element against each other, like two headlines or two page layouts. Multivariate testing expands on this by testing multiple variations of multiple elements simultaneously.
2. Q: How do you determine the elements and variations to test?
A: Base your tests around key hypotheses and questions about how changes may impact user behavior. Focus on testing elements that matter most to your goals like conversions. Avoid testing too many variations at once.
3. Q: How large should the sample size be for multivariate tests?
A: Aim for sample sizes large enough to achieve statistical significance. The required size varies based on your traffic levels and test duration. Use power analyzers to estimate minimums.
4. Q: How do you analyze and interpret multivariate test results?
A: Focus on changes that reach statistical significance. But also observe smaller gains that may guide incremental improvements. Consider results in context with other data and UX insights.
5. Q: When should you conclude testing and implement a design?
A: When data indicates clear winners, incremental gains decrease, or you need to shift resources to new tests. Monitor after launching to confirm optimizations.
6. Q: What are some common mistakes with multivariate testing?
A: Too many variations and elements. Confusing or overlapping test objectives. Short test durations. Changes that go against other UX research.
7. Q: How can you maximize the impact of multivariate testing?
A: Align tests to key business goals. Test early and often. Build a testing culture on your team. Share insights across the organization.
8. Q: What tools are available for running multivariate tests?
A: Dedicated platforms like Optimizely, VWO, Adobe Target, etc. Or analytics tools like Google Optimize.
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