Authors : Joseph Kobi; Dr. Brian Otieno

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/ysen68av

Scribd : https://tinyurl.com/3zh7fdem

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1148

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Predictive analytics applications have a lot of potential to help the telecommunications business keep customers and make more money. However, more studies are needed to use industry data to build and test solid predictive models for important customer relationship management tasks. The study tries to create models that can predict customer churn, lifetime value, and segmentation by using a dataset from a prominent telecom provider that includes demographic, usage, transactional, and survey response data. Descriptive statistics will be used to describe the group and find the most critical customer traits that affect retention. The research will use logistic models, decision trees, and neural networks to see how well they can predict churn. Using regression methods, different ways of keeping a customer will be used to figure out how much they are worth over their career. Customers will be put into groups by clustering algorithms based on how likely they are to stay as customers. When the results come in, they will show how well different types of predictive modeling keep people. We will look at the best models to find out more about how the things about a customer affect their likely to stick with a business. For each segmented group, a customer profile will be made, and specific ways to keep customers will be offered. People will talk about the data in terms of past studies and methods. We will also talk about what happens when you use predictive analytics to make data-driven plans to keep customers and make the most money throughout the customer journey. The main point of this study is to make predictive analytics work better in the telecoms business to keep customers. By building and testing predictive models on a real-world industry dataset, we can learn more about how to use customer data and analytics carefully to make relationships better, decide where to help users, and make more money from them over time.

Keywords : Predictive Analytics, Customer Retention, Churn Prediction, Lifetime Value, Customer Relationship Management, Telecommunications Industry, Logistic Regression, Decision Trees, Neural Networks, Clustering

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