Relevant bibliographies by topics / Artificial Intelligence based Predictive Analysis of Customer Churn

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Academic literature on the topic 'Artificial Intelligence based Predictive Analysis of Customer Churn'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Artificial Intelligence based Predictive Analysis of Customer Churn"

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Vaani Gupta and Aman Jatain. "Artificial Intelligence Based Predictive Analysis of Customer Churn." Formosa Journal of Computer and Information Science 2, no. 1 (2023): 95–110. http://dx.doi.org/10.55927/fjcis.v2i1.3926.

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Customer churn, also known as attrition, occurs when subscribers or customers stop doing business with an enterprise or organization by unsubscribing to a service, discontinuing membership or simply stopping payment. Churn is a critical metric because it is more cost-effective to retain existing customers than it is to acquire new ones. Since churning impedes growth, companies usually use a defined method for calculating customer churn in a given period. By monitoring churn rate and the various factors affecting it, organizations determine their customer retention success rates and identify st
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Jatain, Aman, Shalini Bhaskar Bajaj, Priyanka Vashisht, and Ashima Narang. "Artificial Intelligence Based Predictive Analysis of Customer Churn." International Journal of Innovative Research in Computer Science and Technology 11, no. 3 (2023): 20–26. http://dx.doi.org/10.55524/ijircst.2023.11.3.4.

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Deep learning has been evidenced to be a cutting-edge technology for big data scrutiny with a huge figure of effective cases in image processing, speech recognition, object detection, and so on. Lately, it has also been acquainted with in food science and business. In this paper, a fleeting overview of deep learning and detailly labelled the structure of some prevalent constructions of deep neural networks and the method for training a model is provided. Various techniques that used deep learning as the data analysis tool are analyzed to answer the complications and challenges in food sphere t
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Gramegna, Alex, and Paolo Giudici. "Why to Buy Insurance? An Explainable Artificial Intelligence Approach." Risks 8, no. 4 (2020): 137. http://dx.doi.org/10.3390/risks8040137.

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We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical an
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Bakhvalov, Sergey, Eduard Osadchy, Irina Bogdanova, Rustem Shichiyakh, and E. Laxmi Lydia. "Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries." Fusion: Practice and Applications 14, no. 2 (2024): 172–85. http://dx.doi.org/10.54216/fpa.140214.

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Intelligent System for Customer Churn Prediction (CCP) relates to a system or application that controls advanced artificial intelligence (AI), data analysis, and machine learning (ML) methods for anticipating and predicting customer churn in business or service. CCP approach utilizes various data sources comprising customer behavior and historical data, to create predictive method able of categorizing customers who are potential to leave or stop their engagement. By employing intelligent method, this system supports businesses in proactively addressing customer retention and executing manners
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Atay, Mehmet Tarik, and Munevver Turanli. "ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, -NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS." Advances and Applications in Statistics 92, no. 2 (2024): 147–69. https://doi.org/10.17654/0972361725008.

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Customer churn predictions (CCPs) and their comprehensive analysis have become prevalent in the global telecom industry over the last five years, driven by advancements in machine learning (ML) technologies. In addition, AI (artificial intelligence) and ML-based predictive methods are currently employed for CCP applications to enhance customer retention. This predictive CCP methodology streamlines customer management processes and ensures sustainable profit growth. The machine learning models focus on identifying features derived from data that is rich in various types of information. This stu
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Babatunde, Ronke, Sulaiman Olaniyi Abdulsalam, Olanshile Abdulkabir Abdulsalam, and Micheal Olaolu Arowolo. "Classification of customer churn prediction model for telecommunication industry using analysis of variance." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1323. http://dx.doi.org/10.11591/ijai.v12.i3.pp1323-1329.

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Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has
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Chouiekh, Alae, and El Hassane Ibn El Haj. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (2020): 1–16. http://dx.doi.org/10.4018/ijcini.2020010101.

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Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider. The authors use this method to identify new business use case by considering each subscriber as a single input image describing the churning state. Different experiments were
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Mamun, Md Nur Hasan. "ADVANCEMENTS IN MACHINE LEARNING FOR CUSTOMER RETENTION: A SYSTEMATIC LITERATURE REVIEW OF PREDICTIVE MODELS AND CHURN ANALYSIS." Journal of Sustainable Development and Policy 01, no. 01 (2025): 250–84. https://doi.org/10.63125/9b316w70.

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Customer retention has emerged as a critical strategic objective for organizations seeking to sustain profitability and competitive advantage, particularly in highly saturated and dynamic markets. Predictive modeling, driven by machine learning (ML) techniques, plays an increasingly essential role in enabling firms to identify customers at high risk of churn and to implement proactive retention interventions. This systematic literature review provides a comprehensive synthesis of contemporary advancements in ML-based customer retention analytics, focusing on predictive models and churn analysi
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Mandić, Marin, and Goran Kraljević. "Churn Prediction Model Improvement Using Automated Machine Learning with Social Network Parameters." Revue d'Intelligence Artificielle 36, no. 3 (2022): 373–79. http://dx.doi.org/10.18280/ria.360304.

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Due to strong competition in the telecom market, telecom companies are facing customer churn problems. For telecom, it is very important to predict the churn of a user to be able to prevent it. Marketing campaigns can be used to prevent churn and thus prevent a decrease in revenue. Usually, the churn prediction is based on behavioural user data, which describes user activity and general user data. In our prediction model, we added social network attributes that describe the social influence of other users on the user's decision to make a churn. Besides standard centrality measures, we develope
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Prashanthan, Amirthanathan, Rinzy Roshan, and MWP Maduranga. "RetenNet: A Deployable Machine Learning Pipeline with Explainable AI and Prescriptive Optimization for Customer Churn Management." Journal of Future Artificial Intelligence and Technologies 2, no. 2 (2025): 182–201. https://doi.org/10.62411/faith.3048-3719-110.

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This study presents RetenNet, a comprehensive framework for managing customer churn in telecommunications, integrating predictive modelling, prescriptive optimization, and explainable artificial intelligence (XAI) incorporated with Large Language Models (LLMs). The process commences with the IBM Telco dataset, divided in an 80:20 ratio into training and testing sets. Categorical variables are converted by one-hot and label encoding, whilst class imbalance is mitigated using SMOTEENN. Min-max scaling and mutual information-based feature selection guarantee data appropriateness for machine learn
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Book chapters on the topic "Artificial Intelligence based Predictive Analysis of Customer Churn"

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Zhang, Yuyan, Ke Chen, and Ting Chen. "Analysis and Prediction of Bank Customer Loyalty Based on XGBoost Algorithm." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230865.

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At present, the homogenization of banking products and the vigorous development of internet finance have intensified the competition among banks. Customers are the core assets of banks, whose size and loyalty is crucial to any bank. Loyal customer’s repeat purchases or recommending products to people around creates higher value for banks. Therefore, in order to improve customer loyalty, a method of identifying customer loyalty is urgently needed which prioritizes providing more personalized services for loyal customers. Based on bank’s long-term customer resource data, this paper divides customer groups by means of feature selection and data processing, compares the experimental results of multiple machine learning models such as GBDT, and selects the optimal XGBoost model to predict customer’s long-term loyalty to banks, in order to predict potential customer churn for banks, attempt to retain high-value customers as much as possible, and to increase potential revenue.
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Balle Borja, Casas Bernardino, Catarineu Alex, Gavaldà Ricard, and Manzano-Macho David. "The Architecture of a Churn Prediction System Based on Stream Mining." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-320-9-157.

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Churning is the movement of customers from a company to another. For any company, being able to predict with some time which of their customers will churn is essential to take actions in order to retain them, and for this reason most sectors invest substantial effort in techniques for (semi) automatically predicting churning, and data mining and machine learning are among the techniques successfully used to this effect. In this paper we describe a prototype for churn prediction using stream mining methods, which offer the additional promise of detecting new patterns of churn in real-time streams of high-speed data, and adapting quickly to a changing reality. The prototype is implemented on top of the MOA (Massive Online Analysis) framework for stream mining. The application implicit in the prototype is the telecommunication operator (mobile phone) sector.
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Klepac, Goran. "Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries." In Business Intelligence. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch023.

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This chapter represents the business case in the telecommunication company called Veza, in domain of churn prediction and churn mitigation. The churn project was divided into few stages. Due to limited budget and cost optimization, stage one was concentrated on prospective customer value calculation model based on fuzzy expert system. This helps Veza company to find most valuable telecom subscribers. It also helped company to better understand subscriber portfolio structure. Developed fuzzy expert system also helped Veza company in detection of soft churn. Stage two is profiling and customer segmentation based on time series analysis which provided potential predictors for predictive churn model. The central stage was concentrated on developing traditional predictive churn model based on logistic regression. This calculated probability that subscribers will make churn in next few months. The final stage was dedicated to SNA (Social Network Analysis) model development which found out the most valuable customers from the perspective of existing subscriber network. This model gave the answer that subscribers have the greatest influence on other subscribers in a way what is dangerous if they leave Veza company because they will motivate other subscribers to do the same thing. All three stages made complete churn detection/mitigation solution which take into consideration past behaviour of subscribers, their prospective value, and their strength of influence on other subscribers. This project helped Veza company to decrease churn rate and it gave directions for better understanding customer needs and behaviour which were the base for new product development.
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Chiurunge, Panashe, Agripah Kandiero, and Sabelo Chizwina. "Customer Churn Prediction for Financial Institutions Using Deep Learning Artificial Neural Networks in Zimbabwe." In Theoretical and Conceptual Frameworks in ICT Research. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-7998-9687-6.ch010.

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The research was conducted to develop a customer churn predictive modelling using deep neural networks for financial institutions in Zimbabwe using a local leading financial institution. This was based on a need to perform a customer churn analysis and develop a very high accurate and reliable customer churn predictive model. In this era, every customer counts, hence once acquired a business should do everything in its power to keep that customer because the cost of acquiring a new customer is far greater than the cost of keeping an existing one. Therefore the need to ascertain customers who have churned and also be at a position to anticipate those who are churning or are about to churn then take corrective measures to keep such customers on board. The study followed one of the data science research methodologies called CRoss industry standard process for data mining (CRISP-DM) which involves understanding the business, understanding the data, data preparation, modelling, validating the model then deployment of the model.
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Rajalakshmi, K., C. Hemachandran, and K. Guru. "Enhancing Kiosk Efficiency With Artificial Intelligence." In Practical Applications of Self-Service Technologies Across Industries. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-4667-0.ch004.

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This paper explores the integration of Artificial Intelligence (AI) in self-service kiosks, focusing on how customer profiling techniques enhance their efficiency and effectiveness. AI-powered kiosks use algorithms to analyze user behavior, preferences, and demographics, delivering personalized experiences. Methods include purchase history analysis, Wishlist integration, behavioral analytics, and demographic insights, allowing kiosks to recommend tailored products. Sentiment analysis from social media and predictive analytics anticipate customer needs, while context-aware personalization adapts recommendations based on time, location, and weather. These AI innovations transform kiosks into intelligent systems, improving user satisfaction, fostering loyalty, and driving sales. Through data-driven insights and real-time adjustments, AI-powered kiosks create a seamless, engaging self-service experience.
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Klepac, Goran, Leo Mršić, and Robert Kopal. "Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques." In Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8686-0.ch016.

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The chapter will propose a novel approach that combines the traditional machine learning approach in churn management and customer satisfaction evaluation, which unite traditional machine learning approach and expert-based approach, which leans on event-based management. The core of the proposed framework is hybrid fuzzy expert system, which can contain a variety of data mining predictive models responsible for some specific areas as additions to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. The chapter will introduce how revealed patterns can be applied for continual portfolio management improvement. The proposed solution unites advanced analytical techniques with the decision-making process within a holistic self-learning framework.
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Sarkar, Dr Archana. "Improved Customer Experience and Cost-Savings Through AI." In Decision Strategies and Artificial Intelligence Navigating the Business Landscape. San International Scientific Publications, 2023. http://dx.doi.org/10.59646/edbookc4/009.

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By enhancing corporate marketing’s personalisation and enabling data extraction from client calls to the contact centre, AI strengthens the consumer journey. Data analysis and collection, in particular, are areas where Artificial Intelligence (AI) technology shines when it comes to gathering customer experience metrics. Chatbots, predictive analytics, voice help, sentiment analysis, and so forth may all learn from interactions with customers and become better over time. These portals use AI to provide recommendations based on a user’s past behaviours or frequently asked questions throughout the whole website, allowing them to detect and resolve issues before a client ever has to contact support. To save money and boost productivity, this chapter explains how to handle a high number of queries without hiring more people.
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Chandok, Madhu, Rachna Chandan, Dharna Shukla, Sandhya Anilkumar, and Nishi Priya. "Artificial Intelligence (AI) Technology-Enabled Housekeeping." In Advances in Hospitality, Tourism, and the Services Industry. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7447-4.ch019.

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The integration of Artificial Intelligence (AI) into housekeeping is transforming task management across industries. This research provides a roadmap for implementing AI-enabled housekeeping, focusing on process optimization, service enhancement, and operational efficiency. AI technologies like automated cleaning predictive maintenance streamline operations, improve guest satisfaction, and reduce costs. The roadmap explores smart devicesdata analytics in housekeeping, addressing workforce adaptations, and technological infrastructure in hotels, retail, hospitals, and airports. It offers insights into AI's benefits and integration strategies. Facility management leads in AI adoption, favoring limited AI assistance with human interaction. This research highlights AI's transformative impact on housekeeping, promoting efficiency and customer-centricity. The methodology includes descriptive analysis, reliability tests, and correlation analysis based on 472 expert responses. Future research could explore national vs. international scope, larger samples, and sector-specific objectives.
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Allal, Zouhair, Hajar Kobi, Lamyae Khattabi, Laila Zamzami, and Elmahdi Lemrami. "Systematic Analysis of the Role of Artificial Intelligence in Customer Experience in the Service Sector." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0918-7.ch001.

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The integrated use of artificial intelligence (AI) is rapidly transforming the service industry and fundamentally reshaping the customer experience (CX) through automation, personalization, and predictive analytics. With global AI adoption projected to exceed $500 billion by 2025, service organizations are increasingly adopting AI-based solutions to enhance customer engagement and operational efficiency. Yet, the dynamic interplay between AI and CX remains under-researched, particularly with regard to ethical implications, trust drivers, and consumer preferences. To address this knowledge gap, this study employs a systematic literature review to develop an integrative heuristic model for understanding the impact of AI on CX. The results reveal that while AI improves service efficiency and personalization, its effectiveness depends on consumer trust, ethics, and industry-specific characteristics. The study provides a conceptual framework for assessing the role of AI in CX, highlighting both the opportunities and challenges associated with AI deployment in the service sector.
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Dixit, Harsh Vardhan, Ruchi Rayat, and Sadhana Tiwari. "The Impact of Artificial Intelligence on Customer Experience and Personalization." In Future Frontiers: Mastering the Latest in Computer Technology. QTanalytics India, 2025. https://doi.org/10.48001/978-81-980647-0-7-9.

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In today’s dynamic business environment, Artificial Intelligence (AI) is transforming customer experience and personalization. This study explores how AI technologies—machine learning, natural language processing, and predictive analytics—enable companies to analyze big customer data and deliver more tailored interactions. Tools like recommendation systems, chatbots, and virtual assistants enhance engagement, satisfaction, and loyalty. Based on secondary literature, the research highlights AI’s benefits and challenges, including concerns about data privacy, algorithmic fairness, and the risk of depersonalization. The study advocates balancing AI automation with human interaction to preserve empathy and authenticity. It recommends investing in AI-driven personalization, strong data governance, regulatory compliance, and seamless omnichannel experiences. Limitations include reliance on secondary data, with future research encouraged to incorporate primary data, industry-specific insights, and cross-regional analysis.
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Conference papers on the topic "Artificial Intelligence based Predictive Analysis of Customer Churn"

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Xinping, Shang, and Wang Yi. "Research on Bank Customer Churn Prediction Model based on Ensemble Learning Algorithm." In 5th International Conference on Artificial Intelligence and Machine Learning. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141809.

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With the rise of Internet finance, the competition of banking industry is becoming increasingly fierce. To gain more accurate and comprehensive insight into customer needs and improve customer loyalty, it is essential to establish a customer churn analysis model. This kind of model can help banks identify customers who are about to lose, facilitate business decisions, retain relevant users, and ensure that bank interests are not affected. Under this background, this paper establishes a customer churn prediction model using ensemble learning algorithm. Experimental data show that the model can
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He, Hanyue. "Bank Customer Churn Prediction Analysis Based on Improved WOA-SVM." In 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI). IEEE, 2022. http://dx.doi.org/10.1109/iwecai55315.2022.00093.

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Horchuk, Yurii, Mariia Yukhimchuk, and Volodymyr Dubovoy. "ENHANCING DECISION-MAKING IN BUSINESS PROCESS MANAGEMENT WITH PREDICTIVE ANALYTICS BASED ON ARTIFICIAL INTELLIGENCE." In 17th IC Measurement and Control in Complex Systems. VNTU, 2024. https://doi.org/10.31649/mccs2024.2-07.

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This thesis examines the role of artificial intelligence (AI), specifically AI-based predictive analytics, in enhancing decision-making within the framework of Business Process Management (BPM). As organizations strive for increased efficiency and adaptability in their processes, predictive analytics has emerged as a key tool that empowers businesses to make more informed decisions. By leveraging AI models such as ChatGPT, Gemini AI, and others, companies can analyze vast amounts of historical and real-time data to forecast trends, optimize resource allocation, and mitigate risks in their oper
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Minovski, Zoran, Bojan Malchev, and Todor Tocev. "NEW PARADIGM IN ACCOUNTING INFORMATION SYSTEMS – THE ROLE OF THE LATEST INFORMATION TECHNOLOGY TRENDS." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0004.

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The purpose of this paper is to identify the impact and benefits of the latest information technologies on Accounting Information Systems (AIS). Taking into account the numerous papers related to new technologies and their application in the accounting profession within Industry 4.0, and conducted survey about perception of practitioners in Republic of North Macedonia, this paper summarizes the characteristics and key benefits of some of the new technologies for the functioning of AIS in the digital age. First of all, the evolution of AIS is elaborated, based on theoretical and empirical analy
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