Data-driven Personas: Advantages, Challenges, and Strategies to Address Challenges

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Data-driven personas are considered a novel contribution to the field of human-computer. A data-driven persona represents a group of technology users as a fictional person. Different types of users’ data can be retrieved from social media platforms (e.g., Facebook, YouTube), digital analytics services (e.g., Adobe Analytics, Google Analytics), or CRM data. Then, state-of-the-art data-driven persona systems, such as APG, can be developed based on algorithms to create the personas.

 

Example of a data-driven persona from the Automatic Persona Generation (APG) system. The System shows different persona’s attributes such as name, picture, gender, age, and location

 

Advantages of data-driven personas:

Data-driven personas have several advantages, including:

  1. Data-driven personas can help develop empathy and understand users’ demographics, interests, and needs.
  2. Data-driven personas can demonstrate user segmentation that is generally modeled based on computational methods.
  3. Data-driven personas can be created and updated expeditiously after employing automatic data collection and persona-generation algorithms.

Challenges of data-driven personas & Strategies to address these challenges:  

On the opposite side, creating data-driven personas can also have some challenges, including:

  1. Complexity: Creating data-driven personas might require complex algorithms to collect and analyze the data.
    To address this challenge: Successful data-driven personas engineers might be willing to share source code and documentation to enable other researchers to freely access, reuse, modify, and redistribute the data-driven personas algorithms. 
  2. Disconnection: Users’ segmentation may not reflect the goals and objectives of the end-users.
    To address this challenge: conducting users’ studies on data-driven personas might contribute to creating a better understanding of the needs, demands, choices, and motivations of the users. 
  3. Outliers: data-driven personas usually represent the mainstream users and might hide outliers or minorities.
    To address this challenge: Analyzing user’s attributes gaps and low representation might contribute to creating data-driven personas systems that engender diversity and inclusion. 

 

Reference:

Jansen, B., Salminen, J., Jung, S.g., & Guan, K. (2021). Data-Driven Personas. Springer International Publishing.

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