AI

To Smiley, Or Not To Smiley? Considerations and Experimentation to Optimize Data Quality and User Experience for Contextual Product Satisfaction Measurement?

Abstract

Happiness Tracking Surveys (HaTS) at Google are designed to measure satisfaction with a product or feature in context of actual usage. Smiley faces have been added to a fully-labeled satisfaction scale, to increase discoverability of the survey and response rates. Sensitive to the potential variety of effects from images and visual presentation in online surveys (Tourangeau, Conrad & Couper, 2013), this presentation will describe research designed to inform and optimize Google's use of smileys in Happiness Tracking Surveys across products and platforms:

1) We explore construct alignment by capturing users' interpretations of the various smiley faces, via open-ended responses. This data shows meaningful variation across potential smiley images, which informed design decisions. 2) We assess scaling properties of smileys by measuring each smiley independently on a 0-100 scale, to calculate semantic distance between smileys in order to achieve equally-spaced intervals between scale points (Klockars & Yamagishi, 1988). 3) We describe considerations and evaluative metrics for a smiley-based scale with endpoint text labels, to be used with mobile apps and devices.