Authors :
Lavina Anand Parulekar; Sanika Abasaheb Sardesai; Prajkta Shriram Jamsandekar; Sampada Sanjay Parkar; Sawant S.P
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/3bs4hm6r
Scribd :
https://tinyurl.com/nhek7z8r
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1246
Abstract :
In today’s highly competitive business
landscape, customer retention revenue preservation,
customer experience improvement, and marketing
optimisation are critical factors for sustained growth and
profitability. Customer churn predictionis discontinuing
their services or purchases, which presents asignificant
challenge for businesses across various industries. This
project focuses on developing a predictive model to expect
customer churn in the banking sector using machine
learning techniques. The project involves the collection
and analysis of historical customer data, confined account
activity, transaction history, demographic information,
and customer service interactions. By extracting the right
features from this data, a machine learning model is
trained to forecast which bank customers are at the
highest risk of churning. A critical step in this study
was the selection of relevant features that influence
customerchurn. Feature selection was guided by domain
knowledge and feature importance analysis. The different
classifiers were used and then trained on the training
dataset further ensuringthe model’s optimal performance.
The model’s performance is assessed through various
evaluation metrics, including accuracy, precision, and
recall. Additionally, the project explores a model
illustration to uncover the influential factors contributing
to customer churn within the banking context. This
project’s outcomes can empower banks to take proactive
measures in retaining customers, enhancing their overall
experience, and thereby preserving revenue streams. By
addressing customer churn, banks can foster long-term
relationships, reduce customer acquisition costs, and boost
their competitiveness in the financial industry. The results
of this project are expected to assist businesses in
proactively retaining customers by targeting those at the
highest risk of churning. Ultimately, reducing customer
churn can lead to increased customer satisfaction, revenue,
and long-term business sustainability.
In today’s highly competitive business
landscape, customer retention revenue preservation,
customer experience improvement, and marketing
optimisation are critical factors for sustained growth and
profitability. Customer churn predictionis discontinuing
their services or purchases, which presents asignificant
challenge for businesses across various industries. This
project focuses on developing a predictive model to expect
customer churn in the banking sector using machine
learning techniques. The project involves the collection
and analysis of historical customer data, confined account
activity, transaction history, demographic information,
and customer service interactions. By extracting the right
features from this data, a machine learning model is
trained to forecast which bank customers are at the
highest risk of churning. A critical step in this study
was the selection of relevant features that influence
customerchurn. Feature selection was guided by domain
knowledge and feature importance analysis. The different
classifiers were used and then trained on the training
dataset further ensuringthe model’s optimal performance.
The model’s performance is assessed through various
evaluation metrics, including accuracy, precision, and
recall. Additionally, the project explores a model
illustration to uncover the influential factors contributing
to customer churn within the banking context. This
project’s outcomes can empower banks to take proactive
measures in retaining customers, enhancing their overall
experience, and thereby preserving revenue streams. By
addressing customer churn, banks can foster long-term
relationships, reduce customer acquisition costs, and boost
their competitiveness in the financial industry. The results
of this project are expected to assist businesses in
proactively retaining customers by targeting those at the
highest risk of churning. Ultimately, reducing customer
churn can lead to increased customer satisfaction, revenue,
and long-term business sustainability.
