Measuring User Engagement
Together with Heather O’Brien and Elad Yom-Tov, we will be giving a tutorial at the International World-Wide Web Conference (WWW), 13-17 May 2013, Rio de Janeiro. Here is a description of our tutorial. We will add slides and a bibliography soon.
Introduction and Motivations
In the online world, user engagement refers to the quality of the user experience that emphasizes the positive aspects of the interaction with a web application and, in particular, the phenomena associated with wanting to use that application longer and frequently. User engagement is a key concept in the design of web applications, motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and it must satisfy both their pragmatic and hedonic needs and expectations. Measurement is key for evaluating the success of information technologies, and is particularly critical to any web applications, from media to e-commerce sites, as it informs our understanding of user needs and expectations, system design and functionality. For instance, news portals have become a very popular destination for web users who read news online. As there is great potential for online news consumption but also serious competition among news portals, online news providers strive to develop effective and efficient strategies to engage users longer in their sites. Measuring how users engage with a news portal can inform the portal if there are areas that need to be enhanced, if current optimization techniques are still effective, if the published material triggers user behavior that causes engagement with the portal, etc. Understanding the above is dependent upon the ability to measure user engagement. The focus of this tutorial is how user engagement is currently being measured and future considerations for its measurement.
User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential approaches for its measurement, both objective and subjective. Common ways of measuring user engagement include: self-reporting, e.g., questionnaires; observational methods, such as facial expression analysis, speech analysis, desktop actions, etc.; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms, etc.; and web analytics, online behavior metrics that assess users’ depth of engagement with a site. These methods represent various tradeoffs between the scale of data analyzed and the depth of understanding. For instance, surveys are small-scale but deep, whereas clicks can be collected on a large-scale but provide shallow understanding.
Part I – Foundations
Approaches based on Self-Report Measures
Questionnaires are one of the most common ways of gathering information about the user experience. Although self-report measures are subjective in nature, they have several advantages, including being convenient and easy to administer, and capturing users’ perceptions of an experience at a particular point in time. The fundamental problem is that questionnaires are seldom subjected to rigorous evaluation. The User Engagement Scale (UES), a self-report measure developed by O’Brien and colleagues in 2010 will be used to discuss issues of reliability and validity with self-report measures. The UES consists of six underlying dimensions: Aesthetic Appeal, Perceived Usability, Focused Attention, Felt Involvement, Novelty, and Endurability (i.e., users’ overall evaluation). It has been used in online web surveys and user studies to assess engagement with e-commerce, wiki search, multimedia presentations, academic reading environments, and online news. Data analysis has focused on statistically analyzing the reliability and component structure of the UES, and on examining the relationship between the UES and other self-report measures, performance, and physiological measures. These findings will be shared, and the benefits and drawbacks of the UES for measuring engagement will be explored.
Approaches based physiological measures
Physiological data can be captured by a broad range of sensors related to different cognitive states. Examples of sensors are eye trackers (e.g., difficulty, attention, fatigue, mental activity, strong emotion), mouse pressure (stress, certainty of response), biosensors (e.g., temperature for negative affect and relaxation, electrodermal for arousal, blood flow for stress and emotion intensity), oximiters (e.g., pulse), camera (e.g., face tracking for general emotion detection). Such sensors have several advantages over questionnaires or online behaviour, since they are more directly connected to the emotional state of the user, are more objective (measuring involuntary body responses) and they are continuously measured. They are, however, more invasive and, apart from mouse tracking, cannot be used on a large-scale. They can nonetheless be highly indicative of immersive states through their links with attention, affect, the perception of aesthetics and novelty – all of which are important characteristics of user engagement. A particular focus in this tutorial will be the usage of mouse pressure, so-called mouse tracking, because of its potential for large-scale measurement. The use of eye-tracking to measure engagement will also be discussed, because of its relationship to mouse movement.
Approaches based on web analytic
The most common way that engagement is measured, especially in production websites, is through various proxy measures of user engagement. Standard metrics include the number of page views, number of unique users, dwell time, bounce rate, and click-through rate. In addition, with the explosion of user-generated content, the number of comments and social network “like” buttons are also becoming widely used measures of web service performance. In this part we will review these measures, and discuss what they measure vis-à-vis user engagement, and consequently their advantages and drawbacks. We will provide extensive details on the appropriateness of these metrics to various websites. Finally, we will discuss recent work on combining these measures to form single measures of user engagement.
Part II – Advanced Aspects
Measuring User Engagement in Mobile Information Searching
Mobile use is a growing area of interest with respect to user engagement. Mobile devices are utilized in dynamic and shifting contexts that form the fabric of everyday life, their portability and functionality make them more suited to some tasks than others, and they are often used in the presence of other people. All of these considerations – context, task, and social situatedness – have implications for user engagement. The Engagement Lab at the University of British Columbia is exploring user engagement with mobile devices in a series of studies. In this section of the tutorial, we will explore the ways in which mobile engagement may differ from engagement with other devices and what the implications of this are for measurement. We will describe both lab and field-based work that we are undertaking, and the measures that we are selecting to capture mobile engagement.
Networked User Engagement
Nowadays, many providers operate multiple content sites, which are very different from each other. For example, Yahoo! operates sites on finance, sports, celebrities, and shopping. Due to the varied content served by these sites, it is difficult to treat them as a single entity. For this reason, they are usually studied and optimized separately. However, user engagement should be examined not only within individual sites, but also across sites, that is the entire content provider network. Such engagement was recently defined by Lalmas et al. as “Networked User Engagement”. In this part of the tutorial we will present recent findings on the optimization of networked user engagement. We will demonstrate the effect of the network on user engagement, and show how changes in elements of websites can increase networked user engagement.
Combining different approaches
Little work has been done to integrate these various measures. It is important to combine insights from big data with deep analysis of human behavior in the lab, or through crowd-sourcing experiments, to obtain a coherent understanding of engagement success. However, a number of initiatives aiming to combine techniques from web analytics, existing works on user engagement coming from the domains of information science, multimodal human computer interaction and cognitive psychology, are emerging. We will discuss work emerging in these directions, and in particular studies related to mapping mouse tracking and qualitative measurement of user engagement, and the challenges in designing experiments, and interpreting and generalizing results.
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