With user experience (UX) research and market analysis, the development of data-driven, algorithmically-created personas stands at the forefront, marking a significant leap in understanding diverse population segments. These algorithmically-created personas, crafted through a synergy of big data, machine learning (ML), and artificial intelligence (AI), are revolutionizing our approach to understanding audiences, customers, and users by seamlessly bridging hard logic with soft logic with human computer interaction (HCI) for a human-centric approach.
Hard Logic: At the core of these algorithmically-created personas is hard logic, the foundation of any data-driven approach. Numbers and rationality! Here, vast amounts of data serve as the raw material. This data, collected from various sources such as social media, browsing habits, transaction records, shopping carts, and more, represents critical information about audiences, customers, and users. AI and ML algorithms sift through these extensive datasets to identify patterns and correlations. The process involves statistical analysis and data analytics, ensuring that the conclusions drawn are not just mere coincidences but are statistically significant. This quantitative approach allows for segmenting the population into distinct groups based on measurable criteria such as purchasing habits, online behavior, demographic information, and more.
Soft Logic: The added benefit is not just presenting the numbers but humanizing this information as personas, which is the integration of soft logic. Qualitative empathy and feelings! Despite their foundation in hard data, these personas are not cold, lifeless statistics. AI and advanced algorithms imbue them with a qualitative depth that resonates with human empathy and understanding. Through natural language processing, Large Language Models (allowing for direct interaction with personas), and sentiment analysis, AI can interpret and understand the emotions, preferences, and motivations behind user actions. This understanding allows the creation of personas that are not just demographic segments but are representative of real human stories and experiences. These data-driven personas provide insights into the why behind the what, offering a glimpse into the emotional and psychological drivers of behavior.
Hard Logic & Soft Logic: Synergy and holistic understanding are the true power of these algorithmically-created personas, as they integrate hard and soft logic. By combining the objective, measurable insights provided by hard data with the subjective, empathetic understanding of human behavior, you achieve a more holistic view of your audiences, customers, and users. This synergy allows for the creation of algorithmically-created personas that are both accurate in their representation of user segments and deeply empathetic in their understanding of the user experience. Algorithmically-created personas help organizations and businesses not only to know who their users are in a statistical sense but also to understand them on a human level.
The implications of this algorithmically-created personas approach are far-reaching. For businesses, it means more effective and empathetic marketing strategies, improved product design, and better customer service. For researchers, it opens new avenues for understanding human behavior and social dynamics. This is why we are building systems such as Acua (https://acua.qcri.org) and Survey2Persona (https://s2p.qcri.org).
As we continue to refine these systems, the potential for these algorithmically-created personas to become even more nuanced and representative of the complexity of human experience is immense. The future of UX research and market analysis is not just data-driven but empathetically informed, offering a richer, more comprehensive understanding of the people you seek to serve. The development of data-driven, algorithmically-created personas represents a groundbreaking approach to bridging the gap between hard logic and soft logic. By doing so, algorithmically-created personas offer a more complete and empathetic understanding of your audiences, customers, and users, paving the way for more effective and human-centric strategies in business and research.
Read more about algorithmically-created personas.
Jansen, B. J., Aldous, K, Salminen, J., Almerekhi, H. and Jung, S.G. (2023). Understanding Audiences, Customers, and Users via Analytics – An Introduction to the Employment of Web, Social, and Other Types of Digital People Data. Springer Nature.
Jansen, B. J., Salminen, J., Jung, S.G., and Guan, K. (2021). Data-Driven Personas. Synthesis Lectures on Human-Centered Informatics,1 Carroll, J. (Ed). Morgan-Claypool: San Rafael, CA., 4:1, i-317.
Jansen, B. J., Jung, S.G., and Salminen, J. (2023) Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels, Information Systems Frontiers. https://doi.org/10.1007/s10796-023-10395-5
Jansen, B. J., Salminen, J., Jung, S.G., and Almerekhi, H. (2022) The Illusion of Data Validity: Why Numbers About People Are Likely Wrong. Data and Information Management. 6(4), 100020.
Salminen, J., Jung, S.G., Kamel, A. M., Santos, J. M., Kwak, H., An, J., and Jansen, B. J. (2022) Using Artificially Generated Pictures in Customer-facing Systems: An Evaluation Study with Data-Driven Personas. Behaviour & Information Technology. 41:5, 905-921. DOI:10.1080/0144929X.2020.1838610