Hybrid Telecom Churn Analysis

Customer Churn is one of the biggest problems facing by most Telecom  Companies. Especially, the industries that the user acquisition is expensive, it's crucially necessary for one company to scale back and ideally build the client churn to zero to sustain their revenant revenue. If you co

Project Title

Hybrid Telecom Churn Analysis

Project Area of Specialization

Artificial Intelligence

Project Summary

Customer Churn is one of the biggest problems facing by most Telecom  Companies. Especially, the industries that the user acquisition is expensive, it's crucially necessary for one company to scale back and ideally build the client churn to zero to sustain their revenant revenue. If you concentrate on client retention is often cheaper than client acquisition and customarily depends on the info of the user (usage of the service or product), it poses a great/exciting/hard downside for machine learning. It is very important for a company to know such customers who are about to abandon their company, also a prediction of such customer churn is a part of a strategy that is aiming to reduce the probability of such customer churn in future based on the past knowledge base.

Therefore, the ability of this prediction model is of main concern to managers through which researchers can help those making better decisions for their business. Based on this customer churn prediction model has good predictive performance leading to actionable insights. There is a lot of Machine Learning technique to predict customer churn. The purpose of the project is to reduce false prediction and improve the accuracy by using a hybrid approach.

Project Objectives

Telecommunication Industry in the last few years has been involved in many changes and economic growth. The growth in telecom provider increases the competition in the market now companies more focused toward customer retention rather than customer acquisition. The offers, day by day are better and more competitive for the customer, today are a lot of opportunities and this is good for the customer. These reasons and others cause massive churn. Hence, the objective of this project is to accurately estimate the customor churn.

Project Implementation Method

I had improved the churn prediction accuracy by applying Hybrid Classification Approach using LLM (Logit Leaf Model) Technique. LLM contain a dual approach to classify and predict churning customer, In the first step,  Decision tree is used for classification and then Logistic Regression is applied to predict churning customers.

Benefits of the Project

• It can be used in the telecom sector to predict customer churn.
• Telecom companies can provide offers to predicted churning customer to hold them with the company.
• Companies can compete with other service providers to find customers by churning behavior.
• Revenue enhancement from an existing customer.
• Future Economic Impact.

Technical Details of Final Deliverable

The Accuracy of the prediction model is 87 percent. That very optimal because if accuracy increases more model become overfit.

Final Deliverable of the Project

Software System

Type of Industry

Telecommunication

Technologies

Artificial Intelligence(AI), Big Data

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com