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Personas

Data-Driven Personas: Glossary Terms

Our book, Data-Driven Personas, has a comprehensive glossary of persona and related terms. Great reference for teaching, workshops, and courses!

Glossary Terms

A/B testing: using personas as a part of a split testing process, e.g., to increase team members’ empathy towards users before creating test versions that target said users.

 Ad-hoc personas: personas created based on one’s current understanding of the users (without performing additional data collection). (See also ‘assumption-based personas’.)

Adoption: the real use of personas in an organization for tasks and/or decision making.

Agile development: a framework that advocates adaptive planning and continual development to define requirements and develop a (software) product.

Agile personas: personas used in agile development. When new knowledge suggests agile personas should be changed, the personas are modified. This modifying can happen several times during the agile development process.

Algorithmic bias: the influence of an algorithm or algorithms on the deviation of the created persona set from a persona set that is regarded as ideal.

Analytics: generation of user information and insights from computational processing of data.

Assumption-based personas: created based on one’s rough guess about target user type rather than actual user data. Assumption-based personas can be seen as a starting point for more thorough persona research. (See also ‘ad-hoc personas’.)

Automated persona generation: the creation of personas from analytics data using statistical algorithms.

Card Sorting: a technique that organizes information into logical groups, frequently used to explore architecture, workflow, and navigation of systems.

Consistency: the degree to which the personas have the same or similar attributes when applying an identical algorithm and dataset to their creation over a number of iterations.

Curse of dimensionality: as more attributes (dimensions) are added to a persona, the number of users represented by the persona decreases.

Data: numerical or qualitative information about users. Qualitative data may need to be converted to numbers before used in data-driven persona creation.

Data-driven persona development: creating personas based on trends and patterns identified via quantitative analysis on actual user data.

Data-driven persona readiness: the degree to which an organization is ready for the creation and employment of data-driven personas.

Data-driven personas: humanized user segments of a given user population generated as the output of a data-driven persona development process.

Decision making: using personas to make decisions concerning users of products (e.g., choosing or prioritizing features to develop).

Design: using personas for creating tangible or intangible offerings (e.g., products, systems, services, campaigns, messages, tools).

Desk-drawer personas: personas that, for some reason, are not used by stakeholders in a meaningful way (i.e., they are created and placed into a mental or actual desk drawer and remain there).

Diversity: the variability of personas by an attribute of interest (e.g., a persona set with more personas from different ages would be more diverse than a persona set with personas centered around a narrow age range).

Empathy: the ability to understand others (a central benefit of using personas).

Fairness: the degree to which the personas correspond with the distribution of persona attributes in the user data from which they were created.

Flat-file personas: static personas, represented in the form of paper-like medium, and pose limited interactivity to persona users.

Full-stack personas: personas that serve as interactive interfaces making the persona, related analytical information, and the underlying data easily accessible

Gap analysis: the process of comparing the current performance of some objective with a future or projected performance for that objective. From this comparison, one ascertains the gaps and identifies the corrective measures to close the gap.

Goal-directed personas: personas created to target a common goal of a specific set of users.

Goodness of data-driven personas: an idea implying that persona creation should be driven by certain desiderata.

Interaction: how persona users engage with data-driven personas and/or persona systems (e.g., clicking, typing, voice direction).

Interactive persona system: a tool that comprises user interface, functionalities, and data. Users can interact with the personas via different interaction techniques in the system (see ‘Interaction’).

Layered information: the presentation of persona information at multiple levels (e.g., using data breakdowns to provide additional details on the persona or tooltip definitions to provide explanations on how the information was generated).

Metric-driven personas: personas that are based on specific design goals (e.g., diversity, accuracy, impact, revenue) rather than particular patterns in user data.

Mixed methods approach: collecting and analyzing qualitative and quantitative data to create personas.

Negative personas: personas that represent the less-than-ideal users. They are composed of a collection of behaviors, demographics, and real-life scenarios that separate them from the ideal users.

Participatory design: a cooperative approach that actively involves different stakeholders such as customers, partners, and employees in the design of a system.

Persona analytics system: creating personas using analytics data and algorithmic methods and presenting them via an interactive interface.

Persona analytics: the process of analyzing data to create data-driven personas based upon interpretation and communication of meaningful patterns in the user data.

Persona attribute: one of the information pieces in a persona profile (e.g., the persona’s age).

Persona cast: an organized collection of personas created from the same underlying user data. (See also ‘persona set’.)

Persona choice: the factors that influence which persona a persona user selects for a given task.

Persona end user (aka ‘persona stakeholder’, ‘user’): an individual that uses data-driven personas for decision-making

Persona information design: the selection of information elements (attributes, characteristics) that the finalized persona profiles will communicate to persona users.

Persona narrative: a textual description of the persona.

Persona number preference: the number of data-driven personas an end user wants to see.

Persona Perception Scale (PPS): a survey instrument for evaluating how individuals perceive data-driven personas.

Persona Performance Monitoring (PPM): a formal document explaining how the personas will be used after their creation and how the results will be measured.

Persona profile: typically, a one or two ‘page’ description of the persona, usually containing a name, photo, demographic, behaviors, and other information about the persona.

Persona subset: a collection of data-driven personas to which end users refer. (See also ‘persona cast’.)

Persona template: a layout used to create a persona profile, including, for example, name, picture, and demographics of the persona in a certain position and size.

Persona viewing behavior: the style and manner in which a user perceives the persona profile and processes its information (e.g., the order and duration of viewing different information elements).

Persona-as-an-interface: the idea that data-driven personas are an alternative interface to user data, similar to graphs, figures, and tables that also act as interfaces to user data.

Personas: fictional characters created to represent the target users/customers/audience to help stakeholders enhance their user understanding.

Qualitative approach: a method for persona data collection focused primarily on words. Such an approach can be useful to understand concepts, thoughts and/or experiences of people. This approach enables the collection of in-depth insights on topics that are not well understood.

Quantitative approach: a method for persona data collection using primarily numbers and statistics. This data can be presented using charts and graphs, and mathematical or statistical knowledge is needed to carry out the analysis.

Role-based personas: personas that describe the demands, challenges, and context of the (social) role of a user segment.

Rounded persona: a persona that contains all the necessary information for persona users to complete a task or a range of tasks.

Scenarios: stories that describe the context behind a specific user group behavior and help designers understand their motivations, needs, and challenges.

Segmentation: the approach of dividing a target market population into smaller, homogeneous categories based on shared characteristics.

Serious games: games designed with a focus on pedagogical value or decision-making scenarios (e.g., strategy formulation) as opposed to pure entertainment.

Skeleton persona (AKA, ‘skeletal personas’): prototypes of personas containing the bare basic information but not all the characteristics typical of personas, such as names and images.

Stakeholder: an individual either using personas or being affected by the use of personas.

Target groups: potential users to which a system or product is directed.

Transparency: the degree to which the persona user is made aware of how the data-driven personas were created (e.g., by providing explanations of the algorithms).

User interface: a visual presentation of the persona that enables user actions such as filtering, selecting, refreshing, comparing, and so on.

User stories: short statements that describe user goals. (See also ‘scenarios’.)

User studies: user research conducted to understand persona users’ reactions, behaviors, and perceptions concerning personas. User studies are conducted towards creating more useful personas.

User-centricity: a focus on understanding the users better so that user-friendly systems can be designed for them.

Users: either the people who will interact with personas (“use personas”), or the people the created personas are based on.

Willingness to use: a persona user’s implicit or explicit desire to use personas for a task.

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.

By Jim Jansen

Dr. Jansen is a Principal Scientist in the social computing group of the Qatar Computing Research Institute, and a professor with the College of Science and Engineering, Hamad bin Khalifa University, and an adjunct professor with the College of Information Sciences and Technology at The Pennsylvania State University. He is a graduate of West Point and has a Ph.D. in computer science from Texas A&M University, along with master degrees from Texas A&M (computer science) and Troy State (international relations). Dr. Jim Jansen served in the U.S. Army as an Infantry enlisted soldier and communication commissioned officer.