Big Data, Small Personas: Research Agenda for Automatic Persona Generation

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Big Data, Small Personas Research Agenda for Automatic Persona Generation Joni Salminen, Soon-gyo Jung, Bernard J. Jansen, Jisun An, Haewoon Kwak Qatar Computing Research Institute Hamad Bin Khalifa University

Transcript of Big Data, Small Personas: Research Agenda for Automatic Persona Generation

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usually created with manual methods (e.g., interviews).The methods are expensive, slow, and the personas quickly become outdated.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.

Which one do you prefer?

vs.

“Personas give faces to data.”

Case exampleAl Jazeera

1.First, retrieve data from YouTube Analytics.

2.Second, generate personas and show them to

end users.

3.Third, show individual personas.

…of course, a lot more is happening in the background.

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.

Thank you!