Perceived Performance of Top Retail Webpages In the Wild

Abstract

Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the \emph{Speed} of a page. In this paper we present \emph{SpeedPerception}, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process. In Phase-1 of our \emph{SpeedPerception} study using Internet Retailer Top 500 (IR 500) websites, we found that commonly used navigation metrics such as \emph{onLoad} and \emph{Time To First Byte (TTFB)} fail (less than 60\% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with $87 \pm 2\%$ accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its \emph{visualComplete} event.