Unsupervised Learning Applied to the Customer Lifetime Value (CLV)
September 21, 2021
09:00 - 10:00 EDT (click here to check your local time)
The core business of Insurance Companies is to enable individuals and firms to protect themselves against rarely events paying a small premium compared to the eventually damage incurred.
Customer Lifetime Value (CLV) evaluates the value of the customer for the Company, in other words, it’s the Net Present Value of the cash flows ascribed to the relationship with a customer.
In this work, from the collection of portfolio contracts by one insurance year, will be predicted the Customer Lifetime Value of the last three months of the year, also looking at the effects coming from the use of Unsupervised Learning.
Unsupervised Learning describes tasks that involves using a model to discover a good internal representation of input data useful for subsequent Supervised Learning. In this job Unsupervised Leaning are used to provide a low-dimensional representation of inputs and clustering numerical variables to provide a better portfolio analysis of customers. In both situations, Unsupervised Learning can be used as feature engineering to improve Machine Learning performances.
Keywords: Unsupervised Learning, Supervised Learning, Machine Learning, Gradient Boosting Machine, Generalized Linear Models, Random Forest, Neural Networks, Principal Component Analysis, Autoencoders, ISOMAP, t-Distributed Stochastic Neighbor Embedding, K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models.
T. Hastie, R. Tbishirani, J. Friedman, “The Elements of Statistical Learning”, 2017, Springer
M. V. Wutrich, S. Rentzmann, “Unsupervised Learning: What is a sport car?”, 2019, SSRN.
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, 2016, MIT Press
Yoon Hyup Hwang, “Hands-On Data Science for Marketing”, 2019, Packt
G. Bonaccorso, “Hands-On Unsupervised Learning with Python”, 2019, Packt
Claudio Giorgio Giancaterino
Claudio is an actuary and a data science enthusiast in his free time. He likes to play on hackathons, and he’s delighted to attend projects about data science topics.
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