big data, small personas: research agenda for automatic persona generation
TRANSCRIPT
Big Data, Small PersonasResearch Agenda for Automatic Persona Generation
Joni Salminen, Soon-gyo Jung, Bernard J.
Jansen, Jisun An, Haewoon KwakQatar Computing Research Institute
Hamad Bin Khalifa University
What is a persona?
• ‘Persona’ is a fictive person (picture, name, age…) describing a core user group.
• Simplifies numerical data into easy-to-understand representation: another human being
• Helps communicate user information in the organization, so that content creation, marketing, and product development can be done keeping the users in mind at all times.
Automatic Persona Generation
Methodology and system for automatically creating personas from online social media data.
Currently:
a. processing hundreds of millions of user interactions fromYouTube, Facebook, and Google Analytics.
b. stable and robust system using Flask framework, PostgreSQL database, and Pandas/scikit-learn data analysis library.
c. deployed in Al Jazeera English, AJ+ Arabic, Qatar Foundation, AJ+ San Francisco, and Qatar Airways.
Why automate personas?
Personas are usually created with manual methods (e.g., interviews).The methods are expensive, slow, and the personas quickly become outdated. Therefore, even after creation, organizations cannot be certain the personas accurately represent their true customers.
Our solution:
1. Real data better personas
2. Faster creation better personas
3. Updates each month better personas
Better personas better decisions better results.
Information
architecture: Choosing
the correct information
elements and layout for
a given user or industry.
Comments: Finding
representative, relevant
and non-toxic comments
describing the persona.
Evaluation: Validating
accuracy, consistency,
and usefulness of
personas for individuals
and organizations.
Topics of interest:
Classifying topics of online
content and discovering
probable interests across
social media platforms.
APG: Platform for research
Description: Generating
fluent text descriptions of
the personas.
Discovering better ways to computationally process
and choose useful representations from vast
amounts of online data (”giving faces to data”).
Image: Using neural
networks to generate
persona profile pictures.
Story selection: predicting
and choosing content for
personas or content
creators.
Temporal analysis:
Observing change in
personas over time.
Determining the right information content and layout
Research Objective: There is an extremely high number of potential information elements (e.g., demographics, psychographics, interests, political affiliation…) and types (image, text, sizes, colors…) that can be chosen and shown for a given user. However, the screen real-estate is limited, and obviously some information is not relevant in all use cases, as user preferences and contexts vary a great deal. Therefore, how to ensure a user receives the right information at the right time?
Determining the right information content and layout
Plan: Conduct theory-driven A/B and/or multivariate experiments with real users of APG (e.g., journalists, digital marketers). Measure reaction with Web analytics metrics (time-on-site, number of clicks) and mouse tracking (AOIs, heatmaps), and analyze it according to known information on the users.
Persona Evaluation Scale
Research Objective: Personas are notoriously difficult to validate and evaluate. As fictive user representations based on real data, they can be seen subject to interpretations from end-users of personas. These interpretations can be multi-dimensional and include elements, such as empathy, perceived accuracy and liking. We develop an evaluation scale and validate it among end users of personas.
Persona Evaluation Scale
Plan: Develop the scale, validate with pilot study, and then conduct a large scale survey for persona users.