Data-Driven Personas

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. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. 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.

Bernard J. Jansen is a Principal Scientist in the social computing group of the Qatar Computing Research Institute. He is a graduate of West Point and has a Ph.D. in computer science from Texas A&M University. Professor Jansen is Editor-in-Chief of the journal, Information Processing and Management (Elsevier).

Joni Salminen is a research scientist at Qatar Computing Research Institute, Hamad Bin Khalifa University, and at Turku School of Economics. His current research interests include automatic persona generation from social media and online analytics data, the societal impact of machine decision making (#algoritmitutkimus), and related social computing topics.

Soon-gyo Jung is a software engineer focused on news/data analytics and implementing related systems at Qatar Computing Research Institute. He received a B.E. degree in computer software from the Kwangwoon University, Seoul, Korea, in 2014, and an M.S. degree in electrical and computer engineering from the Sungkyunkwan University, Suwon, Korea, in 2016.

Kathleen Guan is a graduate student with the University College London. She has a Bachelor of Science in Foreign Service in International Law from Georgetown University, and research training in Public Health from Johns Hopkins University. In addition to research consulting for industry, Kathleen is currently a research student in Neuroscience and Psychopathology through a joint graduate program between University College London and Yale School of Medicine.