Generating personas based entirely on data has gained popularity. Personas describe characteristics of a user group in a human-like format. This project presents the persona creation process from raw data to evaluated personas for Zapiens’ knowledge management system. The objective of the personas is to learn about customer behavior and aid in customer communication. For the described methodology, platform log data was clustered to group the users. The quantitative approach is, thereby, fast, updatable, and scalable. The analysis was split into two different features of the Zapiens platform. Persona sets for the training component and the chatbot component of Zapiens were tried to be created. The group characteristics were then enhanced with data from user surveys. This approach proved to be only successful for the training analysis. The collected data is presented in a web-based persona template to make the personas easily accessible and sharable. The finished training persona set was evaluated using the Persona Perception Scale. The results showed three personas of satisfying quality. The project aims to provide a complete overview of the data-driven persona development process.