Personas

Personas for your non-customers – the Edge, the Refuse, and Unknown non-customers

Personas can be a force multiplier in reaching your three categories of non-customers – the Edge Non-customers, the Refuse Non-customers, and Unknown Non-customers. Algorithmically-Generated Personas are great for humanized representation of your existing customers presented in understandable chunks of information. These data-driven personas are great for team communication while creating content and designing systems, along …

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Ideas for Persona Research Using Quantitative User Analytics

What Are Personas and Why Should We Care? Personas, commonly applied in human-computer interaction (HCI) (Cooper, 1999), design (Aoyama, 2007), and business domains such as marketing and sales (Salminen et al., 2018), are fictional depictions of end-users, patients, customers, or other groups of interest (Cooper, 1999). Personas are cited to have many benefits, at least …

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Creating More Personas Improves Representation of Demographically Diverse Populations: Implications Towards Interactive Persona Systems

Personas represent distinct user types. However, while online user data can be demographically and behaviorally heterogeneous, most studies generate less than ten personas, regardless of how heterogeneous the data is. Because all persona creation efforts need to assign a number of personas to create, assigning this number evokes a fundamental question, How many personas to …

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Big Data, Small Personas: How Algorithms Shape the Demographic Representation of Data-Driven User Segments

Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from …

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Personas as a Exploratory Data Analysis Technique

Exploratory data analysis (EDA) explores and analyzes a data set using descriptive statistics and visualizations to learn about the dataset. The main motivations for EDA are: detecting data errors testing assumptions about the data selecting appropriate models determining relationships among the explanatory variables accessing the direction and magnitude relationships between explanatory and outcome variables. However, …

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Algorithmically-Generated Personas paired with AI-Generated Pain Points Detection

One does not often hear personas, AI, and pain points mentioned together. However, the combination can provide better situational awareness and better brainstorming on addressing customer pain points. It is analogous to a mixed-methods approach!   Artificial intelligence carries a high potential to automatically detect customers’ pain points, which is a particular concern the customer …

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Personas: a data visualization technique

Although often touted as a design technique, personas are more accurately viewed as a data visualization option, similar to tables, charts, or graphs. Personas were proposed to represent perceived users, customers, or audience members. In practice. personas are often used to represent current users, customers, or audience members. Personas can be a valuable data visualization …

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