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Data-Driven Personas by
This book traces the techniques that have enabled the development of data-driven personas and how they can be leveraged as tools for empathizing and understanding users. Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools--data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user-understanding functionalities for anyone needing such insights.
Publication Date: 2021-02-05
Mobile Search Behaviors by
With the rapid development of mobile Internet and smart personal devices in recent years, mobile search has gradually emerged as a key method with which users seek online information. In addition, cross-device search also has been regarded recently as an important research topic. As more mobile applications (APPs) integrate search functions, a user's mobile search behavior on different APPs becomes more significant. This book provides a systematic review of current mobile search analysis and studies user mobile search behavior from several perspectives, including mobile search context, APP usage, and different devices. Two different user experiments to collect user behavior data were conducted. Then, through the data from user mobile phone usage logs in natural settings, we analyze the mobile search strategies employed and offer a context-based mobile search task collection, which then can be used to evaluate the mobile search engine. In addition, we combine mobile search with APP usage to give more in-depth analysis, such as APP transition in mobile search and follow-up actions triggered by mobile search. The study, combining the mobile search with APP usage, can contribute to the interaction design of APPs, such as the search recommendation and APP recommendation. Addressing the phenomenon of users owning more smart devices today than ever before, we focus on user cross device search behavior. We model the information preparation behavior and information resumption behavior in cross-device search and evaluate the search performance in cross-device search. Research on mobile search behaviors across different devices can help to understand online user information behavior comprehensively and help users resume their search tasks on different devices.
Publication Date: 2018-03-19
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