Gupta / Ghosal / Kaushik AI-Driven Computational Engineering for Sustainable Development

1. Auflage 2026
ISBN: 978-981-5324-03-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 292 Seiten

AI-Driven Computational Engineering for Sustainable Development explores the convergence of Artificial Intelligence (AI), Natural Language Processing (NLP), and computational engineering in shaping the transition toward Industry 5.0. The book focuses on human-centric, sustainable, and data-driven approaches that enhance collaboration between humans and machines while improving productivity and environmental outcomes.
It begins by outlining the evolution from Industry 4.0 to Industry 5.0, followed by core concepts in AI, NLP, and computational engineering. The chapters then examine real-world applications in smart manufacturing, intelligent automation, and sustainable industrial systems. The book also addresses key challenges such as data management, ethics, system integration, and the need for resilient technological frameworks, offering insights into future research and innovation directions.
Key Features:
-Covers AI, NLP, and computational engineering applications in Industry 5.0
-Focuses on sustainable, human-centric, and data-driven industrial systems
-Real-world applications and cases in smart manufacturing and intelligent automation
-Discusses emerging research trends and future technological directions
-Insights into challenges such as data governance, ethics, and system integration

Autoren/Hrsg.

Gupta, Subir

Ghosal, Indrajit

Kaushik, Keshav

Dandotiya, Monika

Leseproben

AI-Driven Churn Management Portal for Managing Churn and Retention

Hemlata Jain1, *, Rohit Khatri1, Anshul Bhardwaj1

1 Computer Science and Engineering, Poornima University, Jaipur-303905, India

Abstract

Client retention is now just as important as client acquisition in today's fiercely competitive business climate. Since it is frequently more expensive to acquire new clients than to retain current ones, churn management is an essential corporate priority. Artificial Intelligence (AI) has become an increasingly powerful tool as companies seek more efficient methods to reduce client attrition. Traditional approaches cannot match the capacity to anticipate, evaluate, and react to customer behaviour that AI-driven churn management solutions provide. By foreseeing churn threats and offering useful insights, advanced analytics and machine learning algorithms play a crucial part in this process. Telecom firms take a multifaceted strategy to churn management. To build client loyalty, this includes: 1) personalised offerings, which entail developing service plans and incentives based on unique consumer demands and usage patterns; 2) customer engagement, which includes establishing enduring relationships with customers by being proactive in communication, giving excellent customer service, and responding to issues right away; 3) predictive analytics, which involves using predictive models to pinpoint potential customers who might leave and putting retention plans in place; 4) network quality, which entails ensuring a dependable and high-quality network to reduce reasons for churn connected to services; 5) competitive pricing, which includes drawing and keeping clients that are price sensitive, offering competitive pricing structures while preserving profitability; 6) data security, which inludes protecting consumer information and privacy that is important for maintaining trust and reducing turnover and lastly, 7) value-added services, which includes enhancing the client experience with cutting-edge features and services. The topic of telecom churn management is one thatis continually evolving due to the changing technologies and evolving customer expectations. Successful churn management can have a long-lasting positive impact on a telecom company's bottom line by not only preserving revenue but also fostering customer loyalty and advocacy. This study emphasises the significance of telecom churn management in a highly competitive market and the continual innovation and adaptation required to suit customers' shifting needs. This study implements a paradigm for managing telecom churn, which will assist telecom companies in maintaining low churn rates. To achieve this, the framework forecasts churners with a likelihood percentage, predicts their churn behaviors and causes,

recommends consumers who exhibit similar behaviors, and suggests retention strategies for customers based on their behaviors. The administrator of the telecom company can efficiently manage telecom clients through this framework's user-friendly interface. This framework includes: 1) a comprehensive list of all customers, 2) a predictive list of potential churners along with their reasons for leaving, identified using the Random Forest algorithm, 3) detailed customer profiles that highlight individual service usage patterns, and 4) most importantly, an AI-powered retention solution that suggests targeted actions for customers likely to churn, based on behavior predictions generated by the TCCMR framework. The entire system is implemented using the Python programming language. This framework also suggests that similar consumers assist telecom firms in offering the same retention solutions to similar customers. The model performed exceptionally well in predicting churners, achieving outcomes with up to 100% accuracy.

Keywords: Churn management, Churn prediction, Machine learning, Recommendation system, Retention solutions, Telecommunications.

* Corresponding author Hemlata Jain: Computer Science and Engineering, Poornima University, Jaipur-303905, India; E-mail: [e-mail]

Introduction

One of the most pressing problems service providers face in the fast-paced and fiercely competitive telecommunications sector is customer churn. Customers are the primary source of income for all industries, making them a valuable resource for any business [1].

Customer churn, also known as “churn,” occurs when subscribers stop using a certain telecom service provider's products or switch to another one. Churn can be expensive for telecom firms because it typically incurs significant costs to acquire and retain new subscribers. Long-term client loss can also have a significant negative effect on sales and profitability.

Rapid technological advancements, evolving customer preferences, and intense competition characterize the telecommunications sector. These factors make it imperative for telecom companies to adopt proactive churn management strategies to stay competitive and sustainable in the marketplace.

A wide range of activities, such as data analysis, customer segmentation, predictive modeling, and targeted retention initiatives, are included in effective telecom churn management. Churn management seeks to reduce churn by implementing various retention techniques, such as offering new products or services, to keep customers from cancelling subscriptions. For retention strategies to be effective, businesses must gain insights into their customers' traits and behaviors to identify those most likely to leave [2]. It entails understanding the causes of customer turnover, identifying at-risk clients, and implementing strategies to reduce churn rates. The following are some of the main causes of telecom churn:

1.1. Customers are frequently sensitive to price fluctuations and may switch to suppliers that offer more affordable options.

1.2. Quality of Service: Customer satisfaction and retention are greatly impacted by network dependability, call quality, and data speed.

1.3. Consumer service: Whether a consumer decides to stay or depart depends on how well they are taken care of and how quickly they respond to problems.

1.4. Competitive Environment: The telecom industry is highly competitive, with competitors' better deals frequently drawing in customers.

1.5. Technology advancements: As consumers seek the latest features and capabilities, new technologies and services can influence their purchasing decisions.

1.6. Contract Conditions: Contract conditions, early termination fees, and adaptable plans all affect client retention.

1.7. Customer Experience: Churn rates can be impacted by the general experience, including billing procedures and the simplicity of account management.

1.8. Market Saturation: In developed countries, there is a single solution that works for all cases of managing telecom churn. It necessitates a data-driven, customer-focused approach that can adapt to the demands of the market and the needs of the client. Companies that succeed in understanding their consumers, anticipating churn, and implementing successful retention strategies are better positioned to thrive in the telecom sector in this constantly changing environment. This study delves further into the tactics, techniques, and technologies that telecom firms use to lower churn rates, boost customer satisfaction, and ultimately achieve sustainable development in this fast-paced and fiercely competitive industry. The telecom admin user can view all current customers, potential churners, and related customers, as well as retention strategies for potential churners, categorized by their potential cause for leaving.

Businesses seek sustainable techniques that complement their entire retention goals to maintain client loyalty over the long run. This approach aims to foster long-term client loyalty, rather than merely reactive strategies. That is precisely what AI-powered churn management portals offer. They enable them to create data-driven plans. These personalized engagement plans foster client satisfaction and promote long-term retention.

For instance, a telecom business may examine call centre interactions, billing information, and service consumption trends using an AI-powered churn management solution.

In a more realistic scenario, AI can be used by a subscription-based streaming service to track metrics related to user engagement, including viewing duration, user preferences, and account activity. The technology may automatically launch client retention campaigns by identifying trends that indicate possible churn, such as a decline in logins or a move toward free competition. To help re-engage users before they quit their membership, consider providing free trial extensions or delivering targeted content recommendations.

Businesses can gain a thorough understanding of consumer behavior and implement proactive and predictive retention strategies by incorporating AI into their churn management. With this strategy, resources can be effectively distributed to the...