A first version of this work was a "Customer Segmentation and Analysis in the Hospitality sector with customized recommendations", however, starting from the original dataset, the work has been revised and refined, resulting in a complete. "Framework for the calculation of Customer Lifetime Value in the hotel industry with customer segmentation".
The first project provided an overview of an hotel customer base, clustered through a K-Means algorithm (on Dataiku) based on specifically-computed RFM (Recency, Frequency, Monetary) values for each customer. After segmenting customers in three different segments based on their RFM scores, specific recommendations were drafted for each of the three cases. The prospect also provided an overview on the evolution of the study and a brief glance on the feature engineering part. (This first version is labeled as "1 - Customer Segmentation and Analysis in the Hospitality sector with recommendations")
This first part of work has been supervised by Professor Daniel Tapiador, PhD, Partner at BCG X.
During the coursework of my Msc. in Data Science, together with my colleagues, I had the opportunity to work again on the original dataset and build from scratch an analytical pipeline, useful for the data engineering part and the analysis of the data itself. The framework delivers both a segmentation through K-Means clustering based on specifically-computed RFM (Recency, Frequency, Monetary) values for each customer, a Customer Lifetime Value for each of the customers and a related budget for the Acquisition Cost (AC). While the technical (hard-coded) part is included in the attached Jupyter Notebook, a descriptive pdf guides the user through the qualitative part of the process. (The two sections of this final version are labeled respectively as "2 - Framework for the calculation of Customer Lifetime Value in the hotel industry" and "3 - CustomerProcessing.ipynb")
This second part of work has been supervised by Professor Jesus Martin Calvo, Data Privacy and Measurement Lead at Google.