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UX Exploration
Personas
Personas are data-based, fictional user profiles that represent typical members of a target group and help teams consistently align design decisions with real user needs. They make abstract target-group data tangible in everyday work, reduce the risk of misguided developments and sustainably increase the effectiveness of user-centered product development.
Aligning design drafts with user needs: How to avoid undesirable developments at an early stage
Designers and developers constantly have to make design decisions in their day-to-day work without always having suitable primary data from user research. Nevertheless, design drafts should always be oriented as closely as possible to the needs of the target group in order to prevent possible undesirable developments as early as possible and to improve the effectiveness of user-centered development.
Personas: How to develop practical target group profiles for an improved user experience
Personas help the members of your team to visualize the different requirement profiles of their target groups and internalize them for their daily work. As a central heuristic in day-to-day work, clarity and practical relevance are of great importance. We support you in developing personas that your team can actually work with – whether anecdotal or data-based, whether based on qualitative or quantitative data, whether as real-typical or ideal-typical personas. Our personas help you to improve the user experience, increase customer satisfaction and be more successful as a company.
Our approach to bringing our personas to life
We create customized personas for you that match the requirements and issues of your team.
By using scientific research methods, we create realistic profiles that accurately reflect the behavior and needs of your target groups.
Our personas allow you to better align your product development with the needs of your target groups in everyday life.
The risk of undesirable developments in product development is reduced by well-founded insights.
Together with our colleagues at SKOPOS ELEMENTS, we offer you an AI-supported chatbot that allows you to ask your personas questions about your ideas.
FAQs
What are personas and what is the difference between anecdotal and data-based personas?
Personas are fictional but reality-based user profiles that represent typical members of a target group. Anecdotal personas are created on the basis of team knowledge and assumptions – fast but subjective. Data-based personas emerge from real user research, such as interviews or surveys, and are therefore more valid and more reliable for strategic decisions. The difference is relevant: anecdotal personas can cement existing assumptions rather than break them up.
What types of personas does SKOPOS NOVA develop?
We develop different persona types depending on need, from anecdotal to fully data-based personas, from qualitatively grounded to quantitatively validated profiles, as well as realistic and idealized personas. Together with you, we choose the approach that best fits your questions and resources.
Who are personas suitable for?
Personas are suitable for all teams that regularly make design decisions, from UX designers and product managers to marketing teams and developers. They are particularly valuable in companies where primary research is not possible for every decision.
How long does it take to develop personas?
Anecdotal personas based on existing data can be developed in one to two days. Data-based personas, based on qualitative or quantitative research collected specifically for the purpose, take two to six weeks depending on the study. Personas validated quantitatively on representative samples (n ≥ 150 per segment) can take correspondingly longer. We recommend measuring the effort against the decision weight of the personas – not every project needs fully validated profiles.
How do personas help avoid misguided developments?
By making the needs, goals and behaviors of the target group tangible, personas allow teams to check early in the development process whether their decisions match real user requirements. This significantly reduces the risk of costly corrections in later development phases.