A carregar...
Publicação
Smooth Skies Ahead: Predicting Early Signs of Churn Among Ultra-HighNet-Worth-Individuals with Machine Learning
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.08 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
In today's competitive market, many companies are realizing the importance of customer-oriented
strategies for sustaining their market share while maintaining stable profit levels. Among many
customer relationship management strategies, retention of existing customers is the least expensive
compared to others, which is why companies have been investing in customer attrition analysis.
Although churn is an unavoidable phenomenon, its early detection is of paramount importance as it
allows for timely intervention, potentially saving substantial assets and safeguarding the longstanding
trust between the client and the company. This study delves into the realm of predictive analytics and
machine learning, aiming to construct a robust framework for anticipating churn among Ultra-HighNet-Worth Individuals (UHNWIs) in the scope of a service provider. Through meticulous data
collection, preprocessing, and model development, this research seeks to uncover hidden patterns
and signals that precede client attrition. Moreover, it endeavours to design and implement a machine
learning model capable of processing diverse data sources, from service interactions to market trends.
Lastly, it aims to evaluate the efficacy of the developed model through testing and validation,
providing actionable insights for wealth managers. This research bridges the gap between traditional
consumer behaviour studies and data-driven predictive modelling through the implementation of
advanced feature selection techniques. The findings presented in this study underscore the potential
for technology-driven solutions to impact customer retention in the luxury market significantly. These
findings can be used to inform retention strategies, such as targeted marketing campaigns and
personalized customer experiences, to improve customer retention and business growth.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Machine Learning Churn Ultra-High-Net-Worth-Individuals Luxury SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure