CHI — or ACM Human Factors in Computing Systems — is one of the most relevant conferences for persona research, and the largest in the field of human-computer interaction (HCI).
This year, our team had three persona papers at CHI, published as extended abstracts.
Persona research at CHI2019:
Jung, S.-G., Salminen, J., and Jansen, B. J. (2019). Personas Changing Over Time: Analyzing Variations of Data-Driven Personas During a Two-Year Period. In Extended Abstracts Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2019), Glasgow, UK.
Jung, S.-G., Salminen, J., and Jansen, B. J. (2019). Creating Manageable Persona Sets from Large User Populations. In Extended Abstracts Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2019), Glasgow, UK.
Salminen, J., Jung, S.-G., & Jansen, B. J. (2019). Detecting Demographic Bias in Automatically Generated Personas. In Extended Abstracts Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2019), Glasgow, UK.
In this blog post, we’ve gathered some commonly asked questions about personas. Got more questions about personas? Just send us a message and we’ll address your question!
Frequently Asked Persona Questions:
Continue reading “Common Questions About Personas (We’ve Got the Answers!)”
Personas are humanlike descriptions of customer segments. Just like customer segments, you can use the persona technique to divide your overall market into smaller subset that you can then examine individually.
In practice, the best way of using personas for customer segmentation is to apply a technique of data-driven persona creation, such as automatic persona generation. Using this approach, you can create an arbitrary number of personas from your source data; e.g., 5, 10, 50, or 100 personas.
If you are interested in learning more, just contact the persona team!
Personas and market segments are higly similar in the sense that they both are based on real data about the customers. However, the main difference is that personas individualize that data in a representation that has a name, face, and human attributes. In conclusion:
Personas = humanlike representations of customer groups
Market segments = nameless, faceless descriptions of customer groups
Personas provide alternative to numbers. Therefore, you can use personas to present your online analytics data as people instead of nameless, faceless target groups. This can help decision makers to “get into the shoes” of customers, offering a more immersive understanding of the customers than the “cold”, raw numbers.
Background: We investigate if and how more photos than a single headshot can heighten the level of information provided by persona profiles. We conduct eye-tracking experiments and qualitative interviews with variations in the photos: a single headshot, a headshot and images of the persona in different contexts, and a headshot with pictures of different people representing key persona attributes. We conduct the within-subject experimental study with 29 participants. The participants were selected to reflect the staff working with news content on a daily basis and formed a diverse pool of individuals originating from 19 different countries (e.g., Egypt, Georgia, Germany, Syria, UK, USA, etc.). Continue reading “Is More Better?: Research Concerning the Impact of Multiple Photos on Perception of Persona Profiles”
Introduction to Data-Driven Personas
Automatic Persona Generation (APG) is a system developed by the persona research team at Qatar Computing Research Institute. APG is defined both as a methodology and a system for automatic creation of personas from online analytics data.
Automatic Persona Generation has specifically been developed to address the limitations of manual persona creation. This blog post details the main benefits of data-driven personas compared to manually created personas. Continue reading “9 Benefits of Data-Driven Personas”