The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Furthermore, economical pressures and competition has led marketing managers to invest on directed campaigns with a strict and rigorous selection of contacts. Such direct campaigns can be enhanced through the use of Business Intelligence (BI) and Data Mining (DM) techniques. This paper describes an implementation of a DM project based on the CRISP-DM methodology. Real-world data were collected from a Portuguese .
See Full PDF See Full PDFThe financial crisis created pressure on banks due 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 deposits subscriptions by 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.
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In this paper, we propose a data mining approach to predict the success of telemarketing. We are applying the algorithms for the first time on the dataset. The dataset obtained from UCI, which contain the most common machine learning datasets. The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The number of the instance is 45212 with 15 input variables and the output variable. Classification is a data mining techniques used to predict group membership for a data instance. we present the comparison of different classification techniques in open source data mining software which consists of a One-R algorithm methods and Naïve-Bayes algorithm The experiment results show are a bout classification sensitivity, specificity, accuracy. The results on bank marketing data discovered that the One-R algorithm is better in classifying the data comparing with the Naïve-Bayes algorithm; where the error rate is lower.
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The Review of Socionetwork Strategies
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ISCTE-IUL, Business Research Unit (BRU-IUL) Working Papers Series 2 13-06
"Due to the global financial crisis, credit on international markets became more restricted for banks, turning attention to internal clients and their deposits to gather funds. This driver led to a demand for knowledge about client’s behavior towards deposits and especially their response to telemarketing campaigns. This work describes a data mining approach to extract valuable knowledge from recent Portuguese bank telemarketing campaign data. Such approach was guided by the CRISP-DM methodology and the data analysis was conducted using the rminer package and R tool. Three classification models were tested (i.e., Decision Trees, Naïve Bayes and Support Vector Machines) and compared using two relevant criteria: ROC and Lift curve analysis. Overall, the Support Vector Machine obtained the best results and a sensitive analysis was applied to extract useful knowledge from this model, such as the best months for contacts and the influence of the last campaign result and having or not a mortgage credit on a successful deposit subscription."
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Today ,Banks to survive and grow it becomes critical to manage customers, build and maintain a healthy relationship with customers. Data Mining in Banks can play a significant role for customer relationship Management. The areas in which Data mining Tools can be used in the banking industry are customer segmentation, Banking profitability, credit scoring and approval, Predicting payment from Customers, Marketing, detecting fraud transactions, Cash management and forecasting operations, optimising stock portfolios, and ranking investments. Various Data Mining techniques for data modeling are Association, Classification, Clustering, Forecasting, Regression, Sequence discovery Visualization etc. Some examples of some widely used data mining algorithms are Association rule, Decision tree, Genetic algorithm, neural networks, k-means algorithm, and Linear/logistic regression. This paper reviews some Data Mining tools and its application in Banks for Customer Relationship Management.
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Telemarketing is a kind of straightforward marketing in which salesman requests the consumer either face to face or telephone request and influence him to purchase the product. Telemarketing achieves most prevalence in the 20th century and still increasing it. Now, the phone has been broadly accepted. It is valued efficient and holds the consumers up to date. In the Banking area, marketing is the backbone to exchange its goods or service. Business promotion and marketing is frequently based on an exhaustive understanding of actual information about the market and the real client demands for the productive bank manner. We recommend a data mining (DM) method to foretell the achievement of telemarketing requests for contracting long-term bank deposits. A local Portuguese bank was labeled, with data gathered from 2011 to 2016, thus involving the effects of the current economic crisis. We examined a comprehensive set of 11 features associated with bank consumer, goods and social-economic characteristics. We also discuss four DM forms with the hybrid model: Naïve Bayes (NB), Decision Trees (DTs), Perceptron Neural Network (NN) and Support Vector Machine (SVM). The four types were tested and compared with proposed hybrid classification methods (Perceptron Neural Network + Decision Tree) on an evaluation set, and we are splitting data into training and testing sets using cross-validation method. The proposed hybrid classification technique presented the best results (Precision 99% and ROC = 97%).
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International Journal of Advanced Research in Computer Science
Data mining is becoming important area for many corporate firms including banking industry. It is a process of analyzing the data from numerous perspective and finally summarize it into meaningful information, so data mining assist the bankers to take concrete decision. This paper is an attempt to analyse the data mining technique and its useful application in banking industry like marketing and retail management, CRM, risk management and fraud detection.
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