Part 1. Setting the stage -- part 2. Getting ready -- 1. The data-driven persona revolution -- 1.1. What are personas? -- 1.2. What has changed since the 1990s? -- 1.3. Traditional personas, data-driven personas, and traditional analytics -- 1.4. A short history of data-driven personas -- 1.5. Relating data-driven personas to segmentation -- 1.6. Data-driven personas from APG -- 1.7. Chapter take-aways -- 1.8. Discussion and assessment questions -- 1.9. References 2. Getting your organization data-driven persona ready -- 2.1. Why is the onboarding of data-driven personas needed? -- 2.2. The organization's data-driven persona readiness -- 2.3. Are data-driven personas right for your organization, even if the organization is ready for them? -- 2.4. Three-step process for deploying data-driven personas in the organization -- 2.5. Educate-invest-employ is a cyclical process -- 2.6. Illustration using APG -- 2.7. Chapter take-aways -- 2.8. Discussion and assessment questions -- 2.9. References Part 3. Developing data-driven personas. 3. Getting meaningful data -- 3.1. Data-driven persona users' information needs -- 3.2. Persona information design -- 3.3. Data collection strategies -- 3.4. Data challenges -- 3.5. Conclusion -- 3.6. Chapter take-aways -- 3.7. Discussion and assessment questions -- 3.8. References 4. Creating data-driven personas -- 4.1. Multiple methodologies for creation -- 4.2. Multiple methodologies for validation -- 4.3. Design principles for data-driven personas -- 4.4. Challenges of data-driven persona development -- 4.5. The automatic persona generation approach -- 4.6. Cross-domain contributions to data-driven persona development -- 4.7. Chapter take-aways -- 4.8. Discussion and assessment questions -- 4.9. References 5. Data-driven personas as interfaces for persona analytics system -- 5.1. Introduction -- 5.2. Flat personas leading to interactive data-driven personas -- 5.3. APG system overview -- 5.4. APG : interaction with data-driven personas -- 5.5. The road ahead for theory and practice -- 5.6. Chapter take-aways -- 5.7. Discussion and assessment questions -- 5.8. References Part 4. Using data-driven personas. 6. Challenges of applying data-driven persona development -- 6.1. Common challenges of applying data-driven persona development -- 6.2. Ethics in data-driven persona development application -- 6.3. APG illustration -- 6.4. Chapter take-aways -- 6.5. Discussion and assessment questions -- 6.6. References 7. Use cases for data-driven personas -- 7.1. Toward use cases of data-driven personas -- 7.2. Analytical use cases -- 7.3. Interactive use cases -- 7.4. Team-centered use cases -- 7.5. Chapter take-aways -- 7.6. Discussion and assessment questions -- 7.7. References 8. Using data-driven personas alongside other human-computer interaction (HCI) techniques -- 8.1. Use of data-driven personas alongside other user-experience research techniques -- 8.2. Using data-driven personas with scenarios -- 8.3. Using data-driven personas with participatory design -- 8.4. Using data-driven personas with card sorting -- 8.5. Using data-driven personas with serious games -- 8.6. Using data-driven personas with agile development -- 8.7. Chapter take-aways -- 8.8. Discussion and assessment questions -- 8.9. References Part 5. Evaluating data-driven personas and the road ahead. 9. Evaluating data-driven personas -- 9.1. The need for evaluation -- 9.2. Overview of evaluation approaches for data-driven personas -- 9.3. General evaluation approaches for data-driven personas -- 9.4. What of the persona being evaluated? -- 9.5. What is a good data-driven persona? -- 9.6. Evaluating the "real" impact of data-driven personas -- 9.7. Implementation of evaluation practices for data-driven personas -- 9.8. Evaluation APG data-driven personas -- 9.9. Summary -- 9.10. Chapter take-aways -- 9.11. Discussion and assessment questions -- 9.12. References 10. Selecting the appropriate persona creation method -- 10.1. Introduction -- 10.2. Primary methods of persona creation -- 10.3. Literature collection and analysis -- 10.4. Strengths and weaknesses -- 10.5. Discussion and implications -- 10.6. APG illustration -- 10.7. Conclusion and the road ahead -- 10.8. Chapter take-aways -- 10.9. Discussion and assessment questions -- 10.10. References Part 6. Data-driven personas and the future. 11. Conclusion : dispelling myths and laying out the grand challenges of data-driven personas -- 11.1. Data-driven persona myths -- 11.2. The grand challenges for data-driven personas -- 11.3. Vision of the perfect data-driven persona -- 11.4. Future considerations -- 11.5. References.
Summary:
Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools--data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights.
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