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Journal articles 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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1

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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3

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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Nwabekee, Uloma Stella, Ebuka Emmanuel Aniebonam, Oluwafunmike O. Elumilade, and Olakojo Yusuff Ogunsola. "Predictive Model for Enhancing Long-Term Customer Relationships and Profitability in Retail and Service-Based." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 960–870. https://doi.org/10.54660/.ijmrge.2021.2.1.860-870.

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In an increasingly competitive marketplace, fostering long-term customer relationships and sustaining profitability remain paramount for retail and service-based industries. This study presents a predictive model designed to enhance customer loyalty and drive consistent financial performance through an analytics-driven approach. By leveraging advanced data analytics, artificial intelligence (AI), and machine learning (ML) techniques, the model identifies patterns in customer behavior, preferences, and purchasing habits. It integrates key metrics such as customer lifetime value (CLV), churn lik

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Lin, Wei-Chao, Chih-Fong Tsai, and Shih-Wen Ke. "Dimensionality and data reduction in telecom churn prediction." Kybernetes 43, no. 5 (2014): 737–49. http://dx.doi.org/10.1108/k-03-2013-0045.

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Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results.

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Fathian, Mohammad, Yaser Hoseinpoor, and Behrouz Minaei-Bidgoli. "Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods." Kybernetes 45, no. 5 (2016): 732–43. http://dx.doi.org/10.1108/k-07-2015-0172.

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Purpose – Churn management is a fundamental process in firms to keep their customers. Therefore, predicting the customer’s churn is essential to facilitate such processes. The literature has introduced data mining approaches for this purpose. On the other hand, results indicate that performance of classification models increases by combining two or more techniques. The purpose of this paper is to propose a combined model based on clustering and ensemble classifiers. Design/methodology/approach – Based on churn data set in Cell2Cell, single baseline classifiers, ensemble classifiers are used fo

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Kiran Nagubandi. "Leveraging AI to Revolutionize Subscription Business Models." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 649–60. http://dx.doi.org/10.32628/cseit241051052.

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This article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, i

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Nnenna, Ijeoma Okeke, Anne Alabi Olufunke, Ngochindo Igwe Abbey, Chrisanctus Ofodile Onyeka, and Paul-Mikki Ewim Chikezie. "AI-driven personalization framework for SMES: Revolutionizing customer engagement and retention." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2019–35. https://doi.org/10.5281/zenodo.15051414.

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In today's competitive business landscape, Small and Medium Enterprises (SMEs) face unique challenges in building and maintaining strong customer relationships. An AI-driven personalization framework offers a transformative solution by enabling SMEs to deliver highly targeted and individualized customer experiences, improving both engagement and retention rates. This review outlines how artificial intelligence (AI) can empower SMEs by integrating data-driven insights with customer interaction processes to revolutionize business practices. AI-driven personalization leverages machine learning al

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Aman, Jatain, Bhaskar Bajaj Shalini, Vashisht Priyanka, and Narang Ashima. "AI Based Food Quality Recommendation System." International Journal of Innovative Research in Computer Science and Technology (IJIRCST) 11, no. 03 (2023): 20–26. https://doi.org/10.5281/zenodo.8109937.

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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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17

Cheng, Jiajun. "AI-Based Hotel Customer Churn Prediction Model." Journal of Progress in Engineering and Physical Science 3, no. 4 (2024): 15–21. https://doi.org/10.56397/jpeps.2024.12.03.

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This study delves into the application of artificial intelligence technology in predicting hotel customer churn, aiming to reduce churn rates and enhance customer satisfaction through predictive analytics. By employing a comprehensive array of statistical and machine learning models, including logistic regression, random forests, neural networks, and support vector machines, we analyzed key data features such as customer behavior, transaction history, and service interactions. The findings indicate that artificial intelligence technology can effectively predict customer churn, providing a basi

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Zahraa, Zahraa, and Dasha Stablichenkova. "Design of Long Short Term Memory Based Deep Learning Model for Customer Churn Prediction in Business Intelligence." International Journal of Advances in Applied Computational Intelligence 5, no. 1 (2024): 56–64. http://dx.doi.org/10.54216/ijaaci.050105.

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Innovations in business intelligence are crucial in the digital era to staying popular and competitive across the increasing business trends. Businesses have started scrutinizing the next level of data analytics and business intelligence solutions. Customer Churn Prediction (CCP), on the other hand, a crucial for making business decisions, which correctly recognizes the churn customers and acts appropriately for customer retention. Customer churn is an unavoidable consequence when the user is not satisfied with the company’s service for a longer period. Service unsubscription by the user does

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Umozurike, Victor Obioma. "The Churn Dilemma: Why Traditional CRM Fails and How AI Can Fix It." American Journal of Data, Information and Knowledge Management 6, no. 1 (2025): 15–22. https://doi.org/10.47672/ajdikm.2710.

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Purpose: The purpose of this paper is to analyze the limitations of traditional Customer Relationship Management (CRM) systems in their attempts to reduce customer churn and propose that Artificial Intelligence (AI) is a revolutionary solution. Customer churn, especially in the retail industry, lowers profit margins and erodes long-term customer value. Traditional CRMs often lack predictive insights and cannot act in real-time. This article demonstrates how AI-powered CRM systems, with machine learning and predictive analytics, provide anticipatory and personalized approaches to customer engag

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Naveen Reddy Singi Reddy. "Beyond Demographics: How Artificial Intelligence redefines customer segmentation in digital marketing." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1379–86. https://doi.org/10.30574/wjarr.2025.26.1.1121.

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This article examines the transformative impact of artificial intelligence on customer segmentation strategies in contemporary marketing practices. By leveraging advanced machine learning algorithms, businesses can now transcend traditional demographic segmentation to identify nuanced behavioral patterns, preference structures, and predictive purchase indicators. The article synthesizes empirical evidence from multiple industry sectors to demonstrate how AI-driven segmentation enables the development of hyper-targeted campaigns with significantly enhanced engagement metrics. Through analysis o

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Dang Tran, Hoang, Ngoc Le, and Van-Ho Nguyen. "Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models." Interdisciplinary Journal of Information, Knowledge, and Management 18 (2023): 087–105. http://dx.doi.org/10.28945/5086.

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Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effecti

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Shahabikargar, Maryam, Amin Beheshti, Wathiq Mansoor, et al. "ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering." Algorithms 18, no. 4 (2025): 238. https://doi.org/10.3390/a18040238.

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Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of customers’ cognitive status and behaviours, as well as early signs of churn. Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. A

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Babadoğan, Borga. "Harnessing AI and Predictive Analytics to Revolutionize Customer Retention Strategies." Next Frontier For Life Sciences and AI 8, no. 1 (2024): 65. http://dx.doi.org/10.62802/k2a4gf39.

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Artificial Intelligence (AI) and predictive analytics are rapidly transforming how businesses approach customer retention, enabling more proactive and personalized strategies. This research investigates the role of AI-driven predictive analytics in identifying at-risk customers, forecasting churn, and optimizing retention efforts across various industries. By analyzing historical data, machine learning models can accurately predict future customer behavior, enabling businesses to implement targeted retention strategies such as personalized offers, timely engagement, and customized support. Thi

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Jing, Changran. "Data analysis and machine learning in the context of customer churn prediction." Applied and Computational Engineering 2, no. 1 (2023): 914–26. http://dx.doi.org/10.54254/2755-2721/2/20220570.

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Due to the fierce competition in the market, customers are often faced with multiple choices when choosing products and services. So many industries, including banking, are now facing the problem of how to address customer churn. At the same time, in order to improve the quality of service for users, banks and other institutions need to conduct in-depth research on the characteristics of customers. This paper provides solutions to the above two problems by using data analysis and mining technology and machine learning technology in artificial intelligence. The study provides an in-depth explor

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Venkata, Murali Krishna Neursu, Krishna Reddy Vuyyuru Ramya, and Kilaru Kalyan. "From Data to Decisions: AI in SaaS Product Analytics and Customer Experience Optimization." Sarcouncil Journal of Public Administration and Management 4, no. 2 (2025): 1–8. https://doi.org/10.5281/zenodo.15046839.

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The integration of artificial intelligence (AI) into SaaS product analytics and customer experience optimization has emerged as a game-changer in the software industry. This study explores how AI-driven analytics can transform raw data into actionable insights, enabling SaaS companies to enhance user engagement, predict churn, and deliver personalized experiences. Using a mixed-methods approach, we analyzed user interaction data, customer feedback, and predictive modeling outcomes to evaluate the effectiveness of AI in optimizing SaaS products. Key findings reveal that AI-powered tools, such a

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Yurchenko, Viktoriia V., and Hanna V. Telnova. "Optimization of the Management of the Customer Base of a Telecommunications Company Using Artificial Intelligence Methods." Business Inform 9, no. 560 (2024): 101–7. https://doi.org/10.32983/2222-4459-2024-9-101-107.

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The aim of the study is to substantiate the use of machine learning and statistical analysis methods, in particular the CHAID (Chi-squared Automatic Interaction Detection) algorithm, to identify key factors influencing customer churn and telecommunications company revenue. The research is directed towards developing effective strategies for managing the customer base and optimizing business processes in the telecommunications industry. The article conducts a comprehensive analysis of the client base of a telecommunications company using the method of decision trees. The six most important fact

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Loukili, Manal. "Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 50–63. http://dx.doi.org/10.15849/ijasca.221128.04.

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Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adop

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Varun Raj Duvalla. "Human-AI Collaboration in Customer Behavior Research: Personalizing Financial Services." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 106–15. https://doi.org/10.32996/jcsts.2025.7.4.12.

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This article explores the symbiotic relationship between artificial intelligence systems and human researchers in revolutionizing customer behavior analysis within the financial services sector. By examining how AI's computational capabilities complement human contextual understanding, we demonstrate a framework where machine learning models process vast transactional datasets while human experts provide crucial interpretive insights regarding socioeconomic factors and cultural nuances. The resulting collaborative approach enables financial institutions to develop more sophisticated customer s

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Dr. Sonali Nemade, Dr. Sujata Patil, Mrs. Deepashree Mehendale, Mrs. Vidya Shinde, and Mrs. Reshma Masurekar. "To Study and Analyse the Customer Churn Prediction using Machine Learning Algorithm." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 4 (2024): 61–65. http://dx.doi.org/10.32628/ijsrset241143.

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The customer churn prediction (CCP) is one of the challenging problems in the E-Commerce industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular pr

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AL-SULTAN, Sultan Yahya, and Ibrahim Ahmed Al-Baltah. "Enhancement Customer Loyalty Via Data Mining Techniques in Yemeni Banks: Review Study." مجلة جامعة صنعاء للعلوم التطبيقية والتكنولوجيا 2, no. 4 (2024): 348–54. http://dx.doi.org/10.59628/jast.v2i4.1059.

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Artificial intelligence (AI) significantly enhances our daily lives, driving many service and financial institutions to seek optimal utilization of it. This paper addresses the problem, which is the inability of some institutions, such as banks, to satisfy customers using outdated methods and solely focusing on acquiring new customers instead of prioritizing customer retention that would be more effective and profitable to them. The main object of this paper is to enhance the quality of service in banks by proposing an intelligent model that leverages customer data to improve services and fost

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Shafeeq, Ur Rahaman. "Predictive Customer Journeys: Leveraging Data Analytics to Map and Influence Digital Touch points." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 5 (2021): 1–10. https://doi.org/10.5281/zenodo.14352146.

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The Predictive customer journeys map and shape key points in contact throughout the lifecycle of a customer with a brand, using data analytics. These businesses can identify patterns within significant amounts of customer information that reveal what future behaviors will be like and proactively frame marketing strategies based on where and when they are most apt to execute. This not only enhances the personalization of content within the customer experience but also engagement, satisfaction, and loyalty. Predictive analytics tools harness machine learning, artificial intelligence, and big dat

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Anuj, Kumar. "AI-Driven Predictive Analytics: Enhancing Cybersecurity, Seismic Forecasting, Consumer Insights, and Customer Retention in the USA." International Journal of Science and Social Science Research 2, no. 4 (2025): 164–72. https://doi.org/10.5281/zenodo.14961981.

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Artificial intelligence (AI)-driven predictive analytics is transforming several key sectors in the USA, including cybersecurity, seismic forecasting, consumer insights, and customer retention. This study examines the effectiveness of machine learning (ML) models in detecting cyber threats, predicting seismic activities, analyzing consumer sentiment, and forecasting customer churn. Researchers utilized extensive datasets from these areas, including network traffic logs, seismic records, social media sentiment datasets, and customer transaction data. They applied advanced AI techniques, such as

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K. Narasimhulu. "Empowering Smart Cities with AI: Predictive Models for Customer Retention in Banking." Journal of Information Systems Engineering and Management 10, no. 25s (2025): 01–06. https://doi.org/10.52783/jisem.v10i25s.3925.

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Introduction: Smart cities thrive on innovative technologies, and artificial intelligence (AI) plays a pivotal role in enhancing customer-centric services. In the context of the banking sector, customer retention is vital for maintaining competitiveness, especially in the highly dynamic urban environments of smart cities. Objectives: The main objective of this study is to investigate the application of supervised machine learning algorithms to predict customer churn, a critical factor in developing efficient retention strategies. Methods: This work uses a dataset of 10,000 customer records, mo

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Y. Syah, Rahmad B., Rizki Muliono, Muhammad Akbar Siregar, and Marischa Elveny. "An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1547. http://dx.doi.org/10.11591/ijai.v13.i2.pp1547-1556.

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Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer ch

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Shree, Chand Chhimpa. "Predictive Analytics in Financial Forecasting: Methods, Applications, and Challenges." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.10673796.

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Predictive analytics plays a crucial role in financial forecasting, offering organizations the ability to anticipate future trends, mitigate risks, and make data-driven decisions. This paper provides an in-depth exploration of predictive analytics in financial forecasting, covering methods, applications, challenges, and emerging trends. Through case studies and empirical examples, we illustrate the practical applications and tangible benefits of predictive analytics across various industries, including retail, banking, and telecommunications. We discuss key methodologies such as regression ana

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Fatima, Ghulam, Salabat Khan, Farhan Aadil, Do Hyuen Kim, Ghada Atteia, and Maali Alabdulhafith. "An autonomous mixed data oversampling method for AIOT-based churn recognition and personalized recommendations using behavioral segmentation." PeerJ Computer Science 9 (January 2, 2024): e1756. http://dx.doi.org/10.7717/peerj-cs.1756.

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The telecom sector is currently undergoing a digital transformation by integrating artificial intelligence (AI) and Internet of Things (IoT) technologies. Customer retention in this context relies on the application of autonomous AI methods for analyzing IoT device data patterns in relation to the offered service packages. One significant challenge in existing studies is treating churn recognition and customer segmentation as separate tasks, which diminishes overall system accuracy. This study introduces an innovative approach by leveraging a unified customer analytics platform that treats chu

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Matuszelański, Kamil, and Katarzyna Kopczewska. "Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach." Journal of Theoretical and Applied Electronic Commerce Research 17, no. 1 (2022): 165–98. http://dx.doi.org/10.3390/jtaer17010009.

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This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers loca

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Rainy, Tahmina Akter, and Debashish Goswami. "MECHANISMS BY WHICH AI-ENABLED CRM SYSTEMS INFLUENCE CUSTOMER RETENTION AND OVERALL BUSINESS PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL FINDINGS." ASRC Procedia: Global Perspectives in Science and Scholarship 01, no. 01 (2025): 142–65. https://doi.org/10.63125/zva9wb39.

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This systematic literature review investigates how Artificial Intelligence (AI)-enabled Customer Relationship Management (CRM) systems influence customer retention and overall business performance. With increasing digital transformation across industries, AI-powered CRM solutions such as predictive analytics, natural language processing, and intelligent automation are reshaping customer engagement and strategic decision-making. The mechanisms through which AI-enabled CRM systems operate draw on theoretical frameworks spanning information systems, marketing, strategic management, and behavioral

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Dronova, Tetiana, Viktoriya Khurdei, and Dmytro Mishchenko. "ARTIFICIAL INTELLIGENCE IN THE MARKETING STRATEGIES OF LOGISTICS COMPANIES." Economic scope, no. 199 (April 14, 2025): 32–38. https://doi.org/10.30838/ep.199.32-38.

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The purpose of the study is to investigate the use of artificial intelligence in the marketing strategies of logistics companies. Logistics companies face a high level of competition and the need to constantly improve their services. The use of artificial intelligence in marketing strategies helps to increase the efficiency of operations, improve customer interaction, and optimize delivery processes. The article discusses in detail the role of artificial intelligence (AI) in the formation and improvement of marketing strategies of logistics companies. The key aspects of machine learning, big d

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Prasenjeet Mahadev Madare. "AI-driven personalization in cloud marketing platforms: A framework for implementation and ethical considerations." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1818–30. https://doi.org/10.30574/wjaets.2025.15.1.0319.

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This article presents a comprehensive analysis of AI-driven personalization in cloud marketing platforms. It examines this rapidly evolving field's technological foundations, implementation approaches, and strategic implications. The research explores how artificial intelligence has transformed traditional customer segmentation. Modern approaches now leverage dynamic micro-segmentation powered by behavioral pattern recognition algorithms. This enables marketers to create increasingly granular and responsive customer profiles. The article investigates the role of predictive analytics in several

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Guo, Shuangshuang, Linlin Tang, Xiaoyan Guo, and Zheng Huang. "Power Customer Complaint Prediction Model Based on Time Series Analysis." Revue d'Intelligence Artificielle 34, no. 4 (2020): 471–77. http://dx.doi.org/10.18280/ria.340412.

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To improve customer service of power enterprises, this paper constructs an intelligent prediction model for customer complaints in the near future based on the big data on power service. Firstly, three customer complaint prediction models were established, separately based on autoregressive integrated moving average (ARIMA) time series algorithm, multiple linear regression (MLR) algorithm, and backpropagation neural network (BPNN) algorithm. The predicted values of the three models were compared with the real values. Through the comparison, the BPNN model was found to achieve the best predicti

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Aygün, Sultanova Haji gizi. "DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE." Annali d'Italia 66 (April 29, 2025): 83–85. https://doi.org/10.5281/zenodo.15303068.

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Artificial intelligence is a set of technologies that enable computers to perform a variety of advanced functions, including the ability to see, understand, and translate spoken and written language, analyze data, make recommendations, and more.One of the distinguishing advantages of AI is its ability to automate repetitive tasks, thereby freeing up human resources for more strategic efforts. AI-powered tools can handle everything from data entry to complex data analysis and predictive maintenance. AI technology promises significant benefits for businesses, including customer improvement, data

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Anurag Bhatnagar. "Customer Churn Prediction using Machine Learning Approach: A Comprehensive Study." Journal of Information Systems Engineering and Management 10, no. 25s (2025): 80–92. https://doi.org/10.52783/jisem.v10i25s.3944.

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Churn is a term that combines” Change” and” Turn.” The ability to predict customer churn is a signnificant concern for service providers. In today’s market, customers are increasingly discerning and seek to access the best services available in their daily lives. This pursuit of superior services often leads to churn or attrition for organizations. Consequently, forecasting churn has emerged as one of the most formidable challenges faced by service providers. The complexity of churn prediction is heightened by the vast amount of customer data, its sparsity, and the imbalanced nature of this da

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Bhattacharjee, Rajat, and Aruna Dev Rroy. "Artificial intelligence (AI) transforming the financial sector operations." ESG Studies Review 7 (May 13, 2024): e01624. http://dx.doi.org/10.37497/esg.v7iesg.1624.

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Objective: The study aims to explore the potential of artificial intelligence (AI) in enhancing operations within the financial sector. The primary focus is on identifying functions that could be improved through the adoption of AI technologies. Method: The research methodology involves a comprehensive review of existing literature and research on AI applications in the financial sector. The study examines various dimensions where AI can enhance financial operations and proposes a conceptual framework based on the findings. Results: The study finds that AI significantly impacts several areas o

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Oluwafemi, Esan. "ENHANCING SAAS RELIABILITY: REAL-TIME ANOMALY DETECTION SYSTEMS FOR PREVENTING OPERATIONAL DOWNTIME." International Journal of Engineering Technology Research & Management (IJETRM) 08, no. 12 (2024): 466–85. https://doi.org/10.5281/zenodo.15482517.

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The proliferation of Software-as-a-Service (SaaS) platforms has redefined digital service delivery acrossindustries, offering scalable, cloud-native solutions that support critical business operations. However, as relianceon SaaS intensifies, so too does the impact of service disruptions—where even brief periods of downtime can leadto significant financial losses, customer churn, and reputational damage. Ensuring reliability at scale has thusbecome a central operational priority, requiring proactive strategies that move beyond reactive incidentmanagement. This paper explores the emergenc

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Isayev, S. "THE INCREASING ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS OPERATIONS." Slovak international scientific journal, no. 92 (February 14, 2025): 25–32. https://doi.org/10.5281/zenodo.14869942.

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Artificial intelligence technologies are used in various fields today. The use of artificial intelligence in the implementation of business processes affects both market development and process improvement. It is widely used in analyzing customer data, managing the supply chain, and formulating marketing strategies. In addition, it is used in improving competitive strategies, building sales channels, natural language processing, and providing other predictive programs. In this article, we have noted the role of artificial intelligence, its modern aspects, and the importance of its use in the b

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Julker Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850. https://doi.org/10.30574/wjarr.2024.23.2.2523.

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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and pred

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Julker, Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850–65. https://doi.org/10.5281/zenodo.14908920.

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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and pred

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P., K. Agarwal, and Poswal Sourabh. "AI's Influence on Customer Decision-Making: A Comprehensive Examination." RECENT RESEARCHES IN SOCIAL SCIENCES & HUMANITIES (ISSN 2348–3318) 10, no. 3 (2023): 17–26. https://doi.org/10.5281/zenodo.8396524.

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The study examines the significant influence of artificial intelligence (AI) on consumer decision-making in various industries. Based on an extensive literature analysis, this study examines the significant impact of artificial intelligence (AI) on consumer decision-making processes. Specifically, it explores how AI facilitates personalisation, predictive analytics, and automation, influencing consumer choices. However, it also highlights the ethical aspects and potential discriminatory hazards linked to AI-driven decisions. Algorithmic bias, privacy concerns, transparency, and fairness are si

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Jayashree Swapnil Patil. "The Role of AI-Based CRM Systems in Revolutionizing FinTech Customer Experience." Journal of Information Systems Engineering and Management 10, no. 27s (2025): 895–901. https://doi.org/10.52783/jisem.v10i27s.4662.

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The introduction of Artificial Intelligence using Customer Relationship Management systems is redefining the FinTech sector, revolutionizing how organizations communicate with clients and maintain relationships. This paper explores the significant impact of AI-based CRM techniques on enhancing customer experience in the FinTech industry. By leveraging AI-driven insights and automation, FinTech companies can offer personalized financial services, streamline customer support, and ensure regulatory compliance. The integration of AI-based CRM solutions enables real-time data analysis, predictive m

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