Utilizing data analytics to gain customer insights in a real-world company

This thesis addresses a practical application of data analytics, specifically the development of an advanced customer segmentation solution based on the RFM model, for a company that provides information systems to businesses in the gastronomy industry. The primary objective of this work is to create a customer segmentation algorithm tailored to the specific needs of the company. The CRISP-DM methodology, encompassing business understanding, data comprehension, data preparation, modeling, evaluation, and deployment, was employed to achieve the main goal. The modeling phase was customized to align with the unique requirements of the company and the task at hand. To ensure the successful completion of the task, the main goal was further supported by literature review and exploration of relevant sources and literature on key theoretical topics. The thesis commences with an introduction to the fundamental theoretical aspects related to its thematic focus. It then provides an overview of methodologies used in data analytics. The practical part of the work initially focuses on business understanding of the task, presenting the business and system context of the company. It also introduces the initial data set and specific technological elements utilized for the implementation of the solution. Finally, it elaborates on the creation of a customer segmentation algorithm, the verification process of the algorithm's outputs, the development of an API prototype for data exposure, and a comprehensive evaluation of the solution's implementation and its benefits. The outcome of this study is the mentioned algorithm for customer segmentation and an API prototype, which the company will be able to deploy in practice, potentially impacting hundreds of real businesses.