TELECOM CUSTOMER CHURN PREDICTION SYSTEM

Abstract

ABSTRACT A telecom customer churn prediction system (100) comprising a plurality of data sources configured to collect one or more parameters associated with customer behavior; a communication module coupled with the plurality of data sources, wherein the communication module is configured to upload the one or more parameters to a cloud database; a processor communicatively coupled with the communication module, configured to receive the one or more parameters from the cloud database, analyze the one or more parameters, apply machine learning and ensemble learning techniques to predict customer churn, and generate actionable insights regarding at-risk customers.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of telecommunications and, in particular, to a system and method for predicting customer churn using machine learning and ensemble learning techniques.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The telecommunications industry faces significant challenges in retaining customers, as customer churn-defined as the loss of clients or customers-is a persistent and costly issue. With the proliferation of service providers and competitive , Claims:1. A telecom customer churn prediction system (100) comprising:
a plurality of data sources configured to collect one or more parameters associated with customer behavior;
a communication module coupled with the plurality of data sources, wherein the communication module is configured to upload the one or more parameters to a cloud database;
a processor communicatively coupled with the communication module, wherein the processor is configured to:
receive the one or more parameters from the cloud database,
analyze the one or more parameters,
apply machine learning and ensemble learning techniques to predict customer churn, and
generate actionable insights regarding at-risk customers

2. The telecom customer churn prediction system (100) as claimed in claim 1, wherein the plurality of data sources includes, but is not limited to, customer usage patterns, service quality metrics, and customer support interactions.

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