ABSTRACT

Data-driven persona development unifies methodologies for creating robust personas from the behaviors and demographics of user segments. Data-driven personas have gained popularity in human-computer interaction due to digital trends such as personified big data, online analytics, and the evolution of data science algorithms. Even with its increasing popularity, there is a lack of a systematic understanding of the research on the topic. To address this gap, we review 77 data-driven persona research articles from 2005–2020. The results indicate three periods: (1) Quantification (2005–2008), which consists of the first experiments with data-driven methods, (2) Diversification (2009–2014), which involves more pluralistic use of data and algorithms, and (3) Digitalization (2015–present), marked by the abundance of online user data and the rapid development of data science algorithms and software. Despite consistent work on data-driven personas, there remain many research gaps concerning (a) shared resources, (b) evaluation methods, (c) standardization, (d) consideration for inclusivity, and (e) risk of losing in-depth user insights. We encourage organizations to realistically assess their data-driven persona development readiness to gain value from data-driven personas.

摘要

数据驱动的角色开发统一了根据用户细分的行为和人口统计数据创建强大角色的方法。由于数字化趋势，如拟人化大数据、在线分析和数据科学算法的发展，数据驱动的角色在人机交互中越来越受欢迎。尽管它越来越受欢迎，但对该主题的研究缺乏系统的理解。为了弥补这一差距，我们回顾了 2005 年至 2020 年的 77 篇数据驱动的角色研究文章。结果表明三个时期：（1）量化（2005-2008），包括第一次数据驱动方法的实验，（2）多元化（2009-2014），涉及更多元化地使用数据和算法，以及（ 3)数字化（2015 年至今），以丰富的在线用户数据和数据科学算法和软件的快速发展为标志。尽管在数据驱动的角色方面有一致的工作，但在 (a) 共享资源、(b) 评估方法、(c) 标准化、(d) 考虑包容性和 (e) 失去深入用户的风险方面仍然存在许多研究空白见解。我们鼓励组织现实地评估其数据驱动的角色开发准备情况，以从数据驱动的角色中获取价值。