There is a need for personas usage to shift from focusing on one persona at a time to Multi-Persona Design. In the context of global design and development using personas, the traditional approach of utilizing a limited set of personas, often 10 or fewer, is a widely accepted guideline. This ‘small persona’ mindset assumes a…… Continue reading Designing With Multiple Personas in Mind: Multi-Persona Design for Large Target Populations
Algorithmically generated personas have emerged as quintessential tools for understanding audiences, customers, and users, bridging data analytics and human-centric experiences. These algorithmically-generated personas (APGs) are at the intersection of user experience research, marketing strategies, and product development, and APGs are realistically crafted identities that mirror the varied facets of a target audience, customer, or user…… Continue reading What Information to Include in a Persona Profile?
Personas inform design by representing diverse user needs. Since their initial application in commercial technology contexts, personas have been adopted in several research domains for the public good, such as health, accessibility, politics, civic society, education, sustainability, cybersecurity, and criminology. In this review paper, we analyzed 58 research studies that created personas in these domains,…… Continue reading Leveraging Personas for Social Impact: A Review of Their Applications to Social Good in Design
If your organization does cross-platform posting (i.e., posts the same or similar content on multiple social media platforms). As part of our Acua system, we’ve developed a feature that takes a generic post and automatically optimizes it for 6 social media platforms. See below. We have already tested the feature in a 900-person user study,…… Continue reading StoryOptimizer – Leveraging AI for better cross platform social media content creation
SegmentSizeEstimator estimates the segment size based on language, country, gender, age range, and topical interest. Moreover, it finds the most likable topical interest based on the given story and provides related segment sizes. This is powered by Facebook Ads API and (if available) Twitter Ads API. Give it a try! SegmentSizeEstimator Read more! Jansen, B.…… Continue reading SegmentSizeEstimator estimates the segment size based on language, country, gender, age-range, and topical interest
Text2Toxicity predicts a text’s toxicity. The tool is based on the machine learning classifier reported in the research article mentioned below. Give it a try! Text2Toxicity Salminen, J., Hopf, M., Chowdhury, S. A., Jung, S., Almerekhi, H., & Jansen, B. J. (2020). Developing an online hate classifier for multiple social media platforms. Human-Centric Computing and Information Sciences,…… Continue reading Text2Toxicity predicts a text’s toxicity
GAN2Name provides the most likely names for the given demographic. “GAN” comes from Gender, Age, and Nationality. Give it a try! GAN2Name Read more! Jung, S.G., Salminen, J., and Jansen, B. J. (2021) All About the Name: Assigning Demographically Appropriate Names to Data-Driven Entities. 54th Annual Hawaii International Conference on System Sciences (HICSS 2021) Koloa, Hawaii, United States. 5-8…… Continue reading GAN2Name provides the most likely names for the given demographic
Name2GAN is a service of Acua used to predict a name’s most likely demographics. “GAN” comes from Gender, Age, Nationality. Give it a try! Name2GAN Read more about the service! Jung, S.G., Salminen, J., and Jansen, B. J. (2021) All About the Name: Assigning Demographically Appropriate Names to Data-Driven Entities. 54th Annual Hawaii International Conference on System…… Continue reading Name2GAN Predict a name’s most likely demographics
What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we conduct a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles…… Continue reading How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation
For this blog posting, we experiment with different ChatGPT prompts and glimpse gender biases in output. The motivation of this experiment is to inspect if ChatGPT propagates gender biases. Gender biases in ChatGPT have been noticed before. For example, Ivana Bartoletti, Director of Women Leading in AI, asked Chat GPT-4 to write “a story about…… Continue reading Is There a Gender Bias in Personas Generated by ChatGPT?