.jpeg)
Given the rapid proliferation of data of every kind, gathering actionable insights has never been more critical. Furthermore, more data implies more variables-and this is where multi-variate testing comes in.
Multi-variate testing (MVT) is a relatively more sophisticated approach in the field of analytics. This is because of how it uncovers intricate relationships between different elements. Its complexity lies in the simultaneous examination of multiple variables, each holding distinct characteristics and relationships.
Unlike A/B testing, in which a single element is changed to compare two versions (A and B), MVT involves changing multiple elements at once and studying the various permutations and combinations that arise from these changes.
For example, consider a webpage in which you want to test three elements: the headline, the call-to-action button, and an image. In A/B testing, you might only test two different headlines. With MVT, you could test multiple versions of all three elements simultaneously, leading to numerous combinations. The result is a richer, more complex test that can uncover interactions between these elements that might not be apparent when only testing them one at a time.
For example, Ubisoft (the gaming giant behind Assassins Creed and Far Cry to name a couple), revamped their game For Honor’s ‘buy now’ page entirely, and saw a 12% uptick in completed purchases. Different from a conventional A/B test, the Ubisoft team collected visitor data using ‘heatmaps, scrollmaps, clickmaps, and on-page surveys’.
Basically, they used multiple variables. The data that was collated in this process was then used to design a tentative new ‘Buy Now’ page. This page was then A/B tested against the previous one, after which it was apparent that it was superior.
In this article, we'll explore the what, why, and how of multi-variate testing, delving into its application in product development, highlighting real-world examples, and even prognosticating future trends. Whether you're a seasoned PM or a bootstrapped founder just getting into analytics, this deep dive promises to help you understand how and why you should implement multi-variate testing into your workflow.
You might discover that a specific headline works exceptionally well with a particular call-to-action button's color or text. For instance, an inspiring headline may drive more clicks when paired with a ‘Get Started Now’ button, while a more informational headline may perform better with a ‘Learn More’ button. Understanding this relationship can lead to a more cohesive and effective page design.
Testing different images alongside various headlines may reveal insights into visual and textual harmony. Perhaps a humorous headline aligns well with a playful image, leading to higher engagement, while a more serious image may demand a factual headline. This interaction can help in creating a visual narrative that resonates with the target audience.
Beyond pairwise interactions, MVT can uncover how all three elements interact together. A certain combination of headline, image, and call-to-action button might synergize perfectly to create a compelling user experience, where changing just one of these elements disrupts the balance. This level of insight is something that more simplistic testing methods could miss.
The result of this richer, more complex test is an understanding of how these elements do not just function individually, but how they collectively contribute to user perception and behavior. By identifying these nuanced interactions, you're better equipped to craft a webpage that not only looks aesthetically pleasing but is also optimized for conversions or other desired actions.
The example we spoke about in the beginning of this article, Ubisoft, does a great job of displaying all three factors dicussed here visually:
Before diving into testing, it is imperative to define the study's specific objectives clearly. Whether aiming to boost conversion rates or enhance user engagement, the selected variables must align with these goals.
This involves a rigorous evaluation of different variables that could impact outcomes. For a website, it might include color schemes, text fonts, or page layouts.
The goal is to steer clear of randomly choosing variables. Selection must be rooted in understanding the product and its target audience. Arbitrarily chosen variables can lead to confusing or misleading results.
Creating the test matrix involves deciding on the various combinations of variables that will be tested. It's like creating a roadmap for how the test will proceed.
This pertains to mixing and matching different elements. Understanding how variables interact with each other is vital. An alteration in one variable might affect how another performs.
While not a step strictly speaking, having a solid grounding in statistics helps in designing a test that is both scientifically robust and practically meaningful. Knowledge of principles like variance, correlation, and regression can guide the creation of a well-balanced matrix.
Selecting the correct multi-variate test is a nuanced decision that depends on your unique project and objectives. Here's a detailed guide to help you navigate this crucial decision-making process:
This approach investigates every possible combination of factors and levels in a system, creating a thorough matrix of experimental conditions. It ensures no interactions are missed, offering a detailed and comprehensive understanding.
(In this context, "factors" refer to the individual elements or variables that you want to test, such as color, font size, or layout within a webpage. "Levels" denote the different variations or settings of each factor, such as different shades of color or different types of fonts.)
Suitable for the exploratory phase of a product, whenunderstanding the underlying mechanics and relationships is crucial. It's labor-intensive and can be costly with many factors, but the investment pays off in deep insights, especially in complex systems like app interfaces.
A streamlined version of full-factorial, this design tests only a fraction of possible combinations. It's like taking a representative sample, allowing for meaningful insights without overwhelming complexity.
This design is apt for refining existing products, when broad insights are more valuable than granular details. It saves time and resources, enabling quicker decision-making in fast-paced environments, such as startup product iterations.
A specialized fractional design, Plackett-Burman focuses on quickly identifying the primary influences among a large set of variables. It doesn't capture interactions but excels in screening phases.
Excellent for early-stage product development when a broad understanding of influential factors is more important than deep interaction analysis. It's particularly valuable in tight deadlines and budget constraints.
What It Is: RSM visualizes complex, multi-variable relationships in 3D plots, revealing hidden patterns and interactions. It not only identifies these relationships but helps optimize them for desired outcomes.
When & Why to Use It: In a complex product landscape, like a software platform with numerous interlinked features, RSM offers a way to navigate and optimize these interconnections, driving performance, usability, or other essential factors.
The Taguchi Method employs pre-arranged orthogonal arrays, which are mathematical constructs designed to balance the effects of multiple factors to study complex multi-variable systems. This method allows you to conduct fewer experiments compared to a full factorial design by carefully selecting a subset of combinations that still capture the interactions between variables.
Orthogonal arrays enable you to analyze the impact of several factors simultaneously while controlling for other influencing factors. In essence, these arrays are organized in a way that the effect of each factor can be isolated and studied without the interference of others, even when testing them all at once.
Ideal for later-stage product development, when you need to refine and fine-tune specific features or components. Rather than testing every possible combination, which may be impractical and expensive, the Taguchi Method provides a robust yet efficient way to optimize the system without compromising on quality. This approach can save both time and cost, particularly in situations where extensive testing is not feasible.
By leveraging orthogonal arrays, it allows for deep insights into the interplay between various factors, such as user interface elements, without needing to test each permutation exhaustively.
This method might be employed when you've already built a solid foundation for your product and are looking to optimize specific aspects. Suppose you're working on a user interface and want to understand how font size, color scheme, and button placement interact to influence user engagement. In that case, the Taguchi Method allows you to study these interactions with fewer overall experiments, leading to faster insights and implementation.
MVT allows businesses to pinpoint how users react to specific combinations of factors like color, text, and layout. For example, do users click more when a red button is paired with a specific headline? MVT's approach of testing multiple variables simultaneously uncovers such complex interactions, offering a more nuanced view of user behavior.
The level of analysis MVT enables helps businesses tailor experiences for the purpose of personalization.By identifying how and why certain combinations resonate with users, MVT helps companies create experiences that cater to individual preferences, transforming generalized findings into actionable, personalized strategies.
The true power of MVT lies in its ability to uncover the interactions between disparate elements of a product, and how these relationships shape the overall user experience. For instance, it doesn't just test how a particular feature performs; it explores how that feature works in harmony with different pricing models, user interfaces, and marketing messages.
The holistic understanding MVT provides supports a cohesive design in which every component serves the goal of building a seamless user journey. Rather than isolated observations, MVT shows the sum of these observations' parts, in which each part contributes to a satisfying user experience. It guides decision-making to not only meet user expectations but to exceed them, resulting in an engaging, delightful experience that resonates with the target audience.
Multi-variate testing is more than a tool; it's a gateway to unlocking profound insights that shape the user experience, drive engagement, and foster satisfaction. By intertwining numerous variables, MVT crafts a tailored narrative that speaks to individual users, ensuring that every decision is data-driven and user-focused.
Nevertheless-it’s yet another aspect of product management that could greatly benefit from improved ease of use and UX; perhaps especially so.
That’s where Crunch comes in.
By bringing Generative AI to analytics, we accomplish exactly that.
With our Magic Canvas, you can talk to your data in plaintext, with our model helping you narrow down your thoughts in a conversational mind map.
Ready to transform the way you approach product decisions? To gain early access to our insights, features, and the future of data-driven decision-making, join our waitlist now.
