Personas - User Focused Design

You are now reading the second edition of Personas—User Focused Design. Since the first edition, I have done a lot more research, been manager of Center for Persona Research and Application, closed the centre, coached a lot of companies, been in connection with Automated Persona Generation, and experienced how difficult it is to do international personas.

The starting point when working with personas is knowledge about the users. Step 1: Collection of data covers how you get information about the users, what data is, and how you analyse the data. These are some of the questions you must deal with when designing surveys. You must also consider, whether the data material is of sufficient quality to deduct assumptions on what makes users different from each other.

One of the most critical criteria for well-founded personas is sound material. When you have concluded step 1, and the relevant data is collected, you need to work out what differentiates the users to find both the number of personas and what constitutes their differences. Step 2 ‘You Form a Hypothesis’ describes how you form the first analysis of the data and from this formulate a hypothesis concerning what makes the users different—within your specific area of focus.

When preparing persona descriptions, you need to keep in mind that they must be usable for the many different professional competencies that are involved in a project. This could be: The management group where the descriptions are used for strategic decisions about present and potential users. The project group who uses the personas for general decisions about the direction of the project. Designers, both those who design product features and those who design the shaping or graphics, who must be able to identify with the descriptions and use them for decisions on details. Finally, test managers who need the persona descriptions to recruit users for product tests. The many and varied usages create a number of requirements for the descriptions as well as considerations of the relationship between the personal descriptions that create empathy and the technical descriptions that give insights.

Whether you work with user journeys, user stories, use cases, scenarios, wire frames, or any other tool that helps you imagine a future system or product, your aim is to understand how different people are going to interact with it.

To manage software development, many companies have over the past 20 years been applying agile processes. The waterfall model contains an iterative process, but makes it difficult to change during development, as it defines the final product from the beginning.

In recent years, discussions about interface design and evaluation methods have brought cultural aspects in play, and this goes for personas too. Organisations are developing personas for global markets, but the cultural issues of personas are not clearly defined, and there is very little information on how to account for cultural differences when creating personas (Seidelin et al. in Implementing personas for international markets: a question of UX maturity, 2014).

As the quantity of social and online analytics data has drastically increased, a wide variety of methods are deployed to make sense of this data, typically via computational and algorithmic approaches. However, in many cases, these approaches trade one form of complexity for another by ignoring the principles of human cognitive processing. In this perspective manuscript, we propose an approach of employing Personas as an alternative form of making large volumes of online user analytics information useful to end users of the user and customer analytics, with results applicable in software development, business sectors, communication industry, and other domains where understanding online user behavior is deemed important. Toward this end, we have developed a system that automatically generates data-driven Personas from social media and online analytics data, capable of handling hundreds of millions of user interactions from tens of thousands of pieces of content on YouTube, Facebook and Google Analytics, while retaining the privacy of individual users of those channels. Our approach (1) identifies and prioritizes user segments by their online behavior, (2) associates the segments with demographic data, and (3) creates rich Persona profiles by dynamically adding characteristics, such as names, photos, and descriptive quotes. This chapter characterizes the currently open research problems in automatic Persona generation, such as de-aggregation of data, cross-platform data mapping, filtering of toxic comments, and choosing the right information content according to end-user needs. Addressing these problems requires the use of state-of-the-art techniques of computer and information science within one system and benefits greatly from inter-disciplinary collaboration. Overall, the research agenda set in this work aims at achieving the vision for automatic user profiling using diverse online and social media platforms and advanced data processing methods for the end goal of making complex analytics data more useful for human decision makers, especially those working with online content.

Your persona project is well underway, and the descriptions are used for: ideation, supporting dialogue, recruiting users for product tests, and communicating with the users etc. You now have to consider how to, in the long run, get the method to maintain focus, what should happen if you obtain new knowledge about the users, when to update information, and how to communicate the personas to new employees.