On Design of Problem Token Questions in Quality of Experience Surveys

Abstract

User surveys for Quality of Experience (QoE) are a critical source of information for application developers. In addition to the common “star rating” used to estimate Mean Opinion Score (MOS), more detailed survey questions (problem tokens) about specific areas provide valuable insight into the factors impacting QoE. This paper explores two aspects of problem token questionnaire design. First, we study the bias introduced by fixed question order, and second, we provide a methodology to manage the size of the survey while keeping it informative. Based on 900,000 calls gathered using a randomized controlled experiment from Skype, we find that token selections can be strongly biased due to token positions and display design. This selection bias can be significantly reduced by randomizing the display order of tokens. It is worth noting that users respond to the randomized-order variant at levels that are comparable to the fixed-order variant. The effective selection of a subset of tokens is achieved by extracting tokens that provide the highest information gain over user ratings. This selection is known to be in the class of NP-hard problems. We apply a well-known greedy submodular maximization method on our dataset to capture 94% of the information using just 30 % of thequestions.