Abandono en servicios - Una revisión bibliométrica

[EN] The purpose of this article is to identify the most impactful research on customer churn and to map the conceptual and intellectual structure of its field of study. Data were collected from the WoS database, comprising 338 articles published between 1995 and 2020. Several bibliometric techniques were applied, including analysis of co-words, co-citation, bibliographic coupling, and co-authorship networks. R software and the Bibliometrix/Biblioshiny package were used to perform the analyses. The results identify the most active and influential authors, articles, and journals on the topic. More specifically, through co-citations and bibliographic coupling, it was possible to map the oldest articles (retrospective analysis) and the current research front (prospective analysis). The retrospective analysis, based on co-citations, revealed that the foundations of this research field are constructs such as quality of service, satisfaction, loyalty, and changing behaviors. The prospective analysis, performed through bibliographic coupling, revealed that current research is embedded in predictive analysis, clusters, data mining, and algorithms. The results provide robust guidance for further investigation in this field.[ES] El objetivo de este artículo es identificar las investigaciones más impactantes sobre la pérdida de clientes y trazar la estructura conceptual e intelectual de su campo de estudio. Los datos han sido recogidos de la base de datos WoS, que comprenden 338 artículos publicados entre 1995 y 2020. Varias técnicas bibliométricas fueron aplicadas, incluyendo el análisis de co-palabras, cocitaciones, acoplamiento bibliográfico y redes de coautoría. Para realizar los análisis se utilizaron el software R y el Bibliometrix/Biblioshiny. Los resultados identifican los autores, artículos y revistas más influyentes y activos sobre el tema. Más específicamente, a través de las cocitaciones y el acoplamiento bibliográfico, fue posible mapear los artículos más antiguos (análisis retrospectivo) y la investigación más actual (análisis prospectivo). El análisis retrospectivo, basado en las cocitaciones, reveló que los fundamentos de este campo de investigación son constructos como la calidad del servicio, la satisfacción, la lealtad y el cambio de comportamientos. El análisis prospectivo, realizado a través del acoplamiento bibliográfico, reveló que la investigación actual está inmersa en el análisis predictivo, los conglomerados, la minería de datos y los algoritmos. Los resultados proporcionan una sólida orientación para seguir investigando en este campo

Enhancing bank direct marketing through data mining

The financial crisiscreated pressure on banksdue to credit restriction, increasing competition for deposits retention and demanding efficiency improvements of direct marketing campaigns. Our research conducted a data mining project on direct marketing campaigns for depositssubscriptionsby using recent data of a Portuguese retail bank. We used the Support Vector Machine (SVM) data mining technique for modeling and evaluated it through a sensitive analysis. The findings revealed previously unknown valuable knowledge, such as the best months for campaigns to occur, and optimal call duration. Such knowledge can be used to improve campaign efficiency

Investigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and, after that, end their connection with the bank. Therefore, customer retention is essential in today’s extremely competitive banking market. Additionally, having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele. These factors make reducing client attrition a crucial step that banks must pursue. In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics. In addition, we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis. Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition

Intelligent data analysis approaches to churn as a business problem: a survey

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.Peer ReviewedPostprint (author's final draft

Data Mining Tools and Techniques: a review

Data mining automates the detection of relevant patterns in a database, using defined approaches andalgorithms to look into current and historical data that can then be analyzed to predict future trends.Because data mining tools predict future trends and behaviors by reading through databases for hiddenpatterns, they allow organizations to make proactive, knowledge-driven decisions and answer questions thatwere previously too time-consuming to resolve. The data mining methods such as clustering, associationrules, sequential pattern, statistics analysis, characteristics rules and so on can be used to find out the usefulknowledge, enabling such data to become the real fortune of logistics companies and support theirdecisions and development. This paper introduces the significance use of data mining tools and techniquesin logistics management system, and its implications. Finally, it is pointed out that the data miningtechnology is becoming more and more powerful in logistics management.Keywords: Logistics management, Data Mining concepts, application areas, Tools and Technique

A SLR on Customer Dropout Prediction

Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.info:eu-repo/semantics/publishedVersio

A SLR on Customer Dropout Prediction

Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.info:eu-repo/semantics/publishedVersio