Cluster analysis of high-dimensional customer data from a subscription-based business

Cluster analyses are an established method for identifying natural groupings of customers for customer segmentation. However, the unsupervised nature of clustering algorithms and the high-dimensionality of customer data complicate the analysis at all stages. This project presents the results from a cluster analysis of high-dimensional customer data from a subscription-based software company. The analysis tested multiple dimensionality reduction methods, outlier and noise detection methods, and clustering algorithms (including deep neural networks). The results and models from the analysis can be used to inform strategy around customer support and feedback, and can serve as the basis from which additional analyses can be conducted.Master of Science in Information Scienc

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details