A year ago, I wrote a research paper titled Identification and Measurement of Hierarchical Layout Patterns on User Experience. This paper documents a study that spanned over several months (September of 2019 until April of 2020), in which I performed a two-part analysis & experiment on the most popular site layouts and how user experience is affected. While this paper barely scratched the surface of what is possible, it documented a novel approach to studying user experience and how it may be possible to measure "success" through objective metrics — both quantitative and qualitative. Below is the abstract of that paper and a link to continue reading.
Abstract
User experience continues to grow in importance as web applications become increasingly widespread and utilized. Further, high-quality user experience often leads to increased user retention, product usage, and revenue for businesses and applications alike. Though user experience is a focus for some, there is often ambiguity in what an optimal or effective layout looks like, and how components within the hierarchy should be structured to accomplish a set goal. This study proposes a potential approach to objectively analyzing and identifying layouts from the highest indexing sites on a global scale, and methods of implementation to assess whether or not certain metrics representing user experience are impacted by layout. After analyzing 908 screenshots and four clusters from the globally highest ranking sites using a K-means algorithm and weighted subclustered images, three common layouts, or variants, were identified and created for use in a web application. Users were randomly assigned one of three layouts, and they were instructed to navigate through the layout to register or purchase the product while their session lengths and mouse movements were recorded. The results from this study found that while the differences in variances between the mean session lengths from 55 participants were statistically insignificant, interesting patterns were found from a manual qualitative analysis when comparing interface hierarchies to data. Subsequently, it was found that various avenues exist for improvement of the model, such as an objective, mathematical metric to understand how layout impacts user experience metrics.
Read the paper here.
More information
Title: Identification and Measurement of Hierarchical Layout Patterns on User Experience
Written: April 22, 2020
Keywords: user experience (UX), K-means clustering, Keras VGG16, AWS Alexa
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