The paper discusses the CRISP-DM (CRoss Industry Standard Process for Data Mining) project, which proposes a comprehensive and industry-independent process model for data mining projects. The authors argue for the importance of a standard process model in the data mining industry, highlighting its benefits for planning, communication, and documentation. They share practical experiences from applying the CRISP-DM methodology in a response modeling application project, emphasizing the model's flexibility and adaptability to different skill levels and project contexts. The generic CRISP-DM process model is useful for initial planning and communication, while specialized process models are necessary for detailed, practical guidance. The authors also discuss the challenges and lessons learned, such as the need for iterative planning, quality assurance, and flexible documentation. They conclude that CRISP-DM is effective for large, repeatable processes and future work will focus on adapting and refining the specialized process model.The paper discusses the CRISP-DM (CRoss Industry Standard Process for Data Mining) project, which proposes a comprehensive and industry-independent process model for data mining projects. The authors argue for the importance of a standard process model in the data mining industry, highlighting its benefits for planning, communication, and documentation. They share practical experiences from applying the CRISP-DM methodology in a response modeling application project, emphasizing the model's flexibility and adaptability to different skill levels and project contexts. The generic CRISP-DM process model is useful for initial planning and communication, while specialized process models are necessary for detailed, practical guidance. The authors also discuss the challenges and lessons learned, such as the need for iterative planning, quality assurance, and flexible documentation. They conclude that CRISP-DM is effective for large, repeatable processes and future work will focus on adapting and refining the specialized process model.