Browsing by Subject "customer segmentation"

The primary concern of the companies working with many customers is proper customer segmentation, i.e., division of the customers into different groups based on their common characteristics. Customer segmentation helps marketing specialists to adjust their offers and reach potential customer groups interested in a specific type of product or service. In addition, knowing such customer segments may help search for new look-alike customers sharing similar characteristics. The first and most crucial segmentation is splitting the customers into B2B (business to business) and B2C (business to consumers). The next step is to analyze these groups properly and create more through product-specific groups. Nowadays, machine learning plays a vital role in customer segmentation. This is because various classification algorithms can see more patterns in customer characteristics and create more tailored customer segmentations than a human can. Therefore, utilizing machine learning approaches in customer segmentation may help companies save their costs on marketing campaigns and increase their sales by targeting the correct customers. This thesis aims to analyze B2B customers potentially interested in renewable diesel "Neste MY" and create a classification model for such segmentation. The first part of the thesis is focused on the theoretical background of customer segmentation and its use in marketing. Firstly, the thesis introduces general information about Neste as a company and discusses the marketing stages that involve the customer segmentation approach. Secondly, the data features used in the study are presented. Then the methodological part of the thesis is introduced, and the performance of three selected algorithms is evaluated on the test data. Finally, the study's findings and future means of improvement are discussed. The significant finding of the study is that finely selected features may significantly improve model performance while saving computational power. Several important features are selected as the most crucial customer characteristics that the marketing department afterward uses for future customer segmentations.