Rfm Based Customer Segmentation for a Mobile Application - Baykan, Ozan Barış (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021)
In this project, customer segmentation was made for Doggo, a mobile application that brings together trained dog walkers for people who are not able to provide daily needs of their dogs. The data was organized by obtaining the columns of recency, frequency, monetary and tenure, and RFM-based customer segmentation was made using machine learning algorithms such as K-means and Gaussian Mixture Model (GMM). Then, the model was built with the part of the dataset that includes recency, monetary and tenure columns using K-means. In addition, with a function developed, the RFM and tenure will be repeated at intervals determined by the Doggo operation team, and this tool is used to monitor the customer condition changing. Various marketing campaigns have been proposed according to the current situation and the transitions they have made.
Mobil bir uygulama için GSP tabanlı müşteri segmentasyonu
Keywords: Marketing, Customer Segmentation, RFM, Clustering, Machine Learning, K-means clustering, GMM clustering