Audience – a group of people who are the intended recipients a particular message (i.e., content). Content is transmitted to a group of people who receive and interpret the message. Audience Description involves considering demographics, interests, knowledge, & expectations to tailor the message. Audience Analytics, including reactions to the content, can provide valuable insights to…… Continue reading What is an audience?
To understand the “target market”! The target market is colloquially defined as a group of people identified as ‘right’ for an offering. The target market should receive the majority of a firm’s time, resources, and attention. The rationale is is that it is better to be focused than to be dispersed. Depersonification process to generate…… Continue reading What is the motivation for analytics?
People data are datasets describing people, including but not limited to behaviors, demographics, pain points, or attitudes. Dimension People Data Cleanliness Typically has a high percentage of missing values and (depending on the data collection) errors. Depending on the data collection methods can have a very low error percentage. Interaction Typically edited and added to…… Continue reading What is people data?
It a two-step process of 1.Depersonification – the representation of real people by numerical, text, or other data, creating quantitative proxies for representing a set of people 2.Personification – the algorithmically generated representation of numbers, text, metrics, and other user data in the form of fictitious humans. See more about using analytics to make impactful…… Continue reading What is the concept of analytics?
Here is the cover for our book, Understanding Audiences, Customers, and Users vis Analytics. Jansen, B. J., Aldous, K, Salminen, J., Almerekhi, H. and Jung, S.G. (2023). Understanding Audiences, Customers, and Users via Analytics – An Introduction to the Employment of Web, Social, and Other Types of Digital People Data. Springer Nature.
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
In today’s interconnected digital landscape, cross-platform shared content has emerged as a powerful tool for creators and businesses. With the ability to transcend platform limitations and reach a wider audience, cross-platform shared content has become a game-changer, enabling seamless experiences and fostering engagement on multiple fronts. However, while the benefits are substantial, it’s important…… Continue reading The Power and Challenges of Cross-Platform Shared Content: Breaking Barriers and Expanding Reach
The concept of “Intelligent Personas” or “AI personas” presented here builds on extant research on data-driven personas and interactive persona systems. With this conceptualization, we particularly focus on outlining a vision for an intelligent end-to-end advertising system that supports human marketers’ decision making by deploying data-driven personas throughout the stages of the online advertising process.…… Continue reading How Can Intelligent Persona Features Support Online Advertising Work?
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?
When analyzing data, one may come across two main types of machine learning models: classification and regression. Both models are used to predict outcomes based on input variables, but they differ in their objectives and output formats. In this blog post, we will compare the classification and regression results to understand how they differ in…… Continue reading Comparing the Results: Classification vs. Regression Models in Machine Learning