Bank Telemarketing Research Articles

A Study on the Method of Predicting the Success of Bank Telemarketing

A Study on the Method of Predicting the Success of Bank Telemarketing

A novel ensemble approach for estimating the competency of bank telemarketing

Having a reliable understanding of bank telemarketing performance is of great importance in the modern world of economy. Recently, machine learning models have obtained high attention for this purpose. In order to introduce and evaluate cutting-edge models, this study develops sophisticated hybrid models for estimating the success rate of bank telemarketing. A large free dataset is used which lists the clients’ information of a Portuguese bank. The data are analyzed by four artificial neural networks (ANNs) trained by metaheuristic algorithms, namely electromagnetic field optimization (EFO), future search algorithm (FSA), harmony search algorithm (HSA), and social ski-driver (SSD). The models predict the subscription of clients for a long-term deposit by evaluating nineteen conditioning parameters. The results first indicated the high potential of all four models in analyzing and predicting the subscription pattern, thereby, revealing the competency of neuro-metaheuristic hybrids. However, comparatively speaking, the EFO yielded the most reliable approximation with an area under the curve (AUC) around 0.80. FSA-ANN emerged as the second-accurate model followed by the SSD and HSA with respective AUCs of 0.7714, 0.7663, and 0.7160. Moreover, the superiority of the EFO-ANN is confirmed against several conventional models from the previous literature, and finally, it is introduced as an effective model to be practically used by banking institutions for predicting the likelihood of deposit subscriptions.

Machine Learning Insights: Identifying Factors for Successful Bank Telemarketing Campaigns

Bank telemarketing campaigns play a pivotal in fostering customer relationships and promoting financial products. However, the factors that contribute to the success of these campaigns are multifaceted and often elusive. This study utilizes a range of machine learning techniques to analyze an extensive dataset of telemarketing campaigns from a Portuguese banking institution, shedding lights on critical determinants of its success. The data underwent the application of several machine learning algorithms, including Decision Trees, Random Forest, Logistic Regression, Gradient Boosting, and Nave Bayes, facilitating the discovery of notable patterns and correlations. Findings revealed that variables such as age, occupation, seasonality, and the number of phone calls exert significant influence on campaign outcomes. By leveraging these insights, banking institutions and marketing strategists can craft more effective, data-driven telemarketing strategies. This in turn stands to enhance marketing efficacy, customer acquisition, and retention, translating into improved business performance.

How to improve the success of bank telemarketing? Prediction and interpretability analysis based on machine learning

Because of the low cost and user-friendliness, telemarketing has become a common way for banks to obtain deposits for a long time. Meanwhile, researchers have been attempting to analyze consumer information in-depth to improve the effectiveness of bank telemarketing and attract deposits through telephone communication. In this paper, we construct bank telemarketing prediction models by three machine learning (ML) methods, i.e., Random Subspace (RS), Multi-Boosting (MB) and Random Subspace-Multi-Boosting (RS-MB), and find the best performing model. Also, we make the interpretability analysis to provide banks with decision information to develop and implement an effective marketing plan. We rank the importance of the original independent variables by the ML method and select those variables whose influence on the prediction results was significant. And we reconstruct the bank telemarketing prediction models based on the selected independent variables. Furthermore, we illustrate the marginal effects of the selected independent variables on the consumers’ subscription of deposits by the Partial Dependence Plots (PDP) to analyze how these selected independent variables affect the success of bank telemarketing campaigns. The empirical results indicate that the RS-MB using selected independent variables achieves the best performance for prediction. It is worth noting that banks would rather contact uninterested customers than miss potential deposit customers. Therefore, when predicting the success of telemarketing campaigns, it is more critical to reduce false negative rate than false positive rate. Moreover, banks using telemarketing should pay more attention to type of job that the customer does, the month that the customer was connected, and contact day of week.

A Machine Learning Framework towards Bank Telemarketing Prediction

The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in every field. For the automatic targeting of customers in a banking telemarketing campaign, the use of ML-based approaches in previous work has not been able to show transparency in the processing of heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision function. These dummy variables are often either suppressed or coded in an arbitrary way in most works without really evaluating their impact on the final performance of the models. In many cases, their coding either favours or disfavours the learning model performance without necessarily reflecting reality, which leads to over-fitting or decreased performance. In this work, we applied the CMB approach to data from a bank telemarketing campaign to build an optimal model for predicting potential customers before launching a campaign. The results obtained suggest that the CMB approach can predict the success of future prospecting more accurately than previous work. Furthermore, in addition to its better performance in terms of accuracy (97.3%), the model also gives a very close score for the AUC (95.9%), showing its stability, which would be very unfavourable to over-fitting.

A dynamic ensemble selection method for bank telemarketing sales prediction

We propose a dynamic ensemble selection method, META-DES-AAP, to predict the success of bank telemarketing sales of time deposits. Unlike existing machine learning-based marketing sales prediction methods focusing only on prediction accuracy, META-DES-AAP considers the accuracy and average profit maximization. In META-DES-AAP, to consider both accuracy and average profit in the framework of dynamic ensemble selection using meta-training, a multi-objective optimization algorithm is designed to maximize the accuracy and average profit for base classifiers selection. Base classifiers suitable for each test telemarketing campaign are integrated according to the dynamic-based base classifiers integration method. Experimental results on bank telemarketing data show that META-DES-AAP achieves the best accuracy and the largest average profit when compared across several state-of-the-art machine learning methods. In addition, the factors influencing telemarketing on the average predicted probability of telemarketing success and average profit obtained by META-DES-AAP are analyzed.

Parameter Tuned Machine Learning based Decision Support System for Bank Telemarketing

Parameter Tuned Machine Learning based Decision Support System for Bank Telemarketing

Performance Enhancement of Hybrid Algorithm for Bank Telemarketing

Telemarketing is an interactive direct marketing system in which telemarketers encourage customers to leverage the resources by notifying, imparting knowledge of online products, latest business offers via direct interaction or through a telephone call. In the contemporary global pandemic spell telemarketing has become dominant backbone to increase the online banking business to withstand for the reducing retail business. It has gained prominance in the banking and financial sector with the enormous adoption and availability of cellular connections amongst customers. The contemporary work has scrutinized conventional classification as well as data mining methods have a problem of ill-fitting with multiple features and are prone to data leakage during re-training of the machine learning model. A local Indian bank were designated, contemplating the current economic slowdown and crisis. A discussion on three machine learning (ML) models is performed along with the Hybrid ML model, Logistic Regression ML model (LR), Naive Bayes ML model (NB), Decision Trees ML model (DTs). The three ML models were tested and analysed with proposed Hybrid ML model on an evaluation set, the data is partitioned as training, validation and test set. The hybrid model first identifies important features of subscribed customers and predicts response for a potential customer, both existing and new who will eventually subscribe again through the direct marketing campaign. The hybrid model is trained to predict the response of new customer who will subscribe to the product or service offered via a direct marketing campaign through transfer learning. The hybrid model API shows new customer response on the front-end screen. To overcome the problem of ill-fitting and data leakage, the model is trained on a large dataset and tuned on a validation set. The proposed hybrid machine learning technique presented the best results (Accuracy 98.69%). Python language is used to develop the model. Financial institutions and organizations can use the hybrid model for predictions of product direct marketing response with customer transaction information.

TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING

Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques. Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resampling aims to balance the unbalanced data resulting in an accuracy value of 90.18% and a ROC of 0.89%. Meanwhile, if the data resampling is not used in the multilayer perceptron (MLP) algorithm, the accuracy value is 88.6 and ROC is 0.88%. The use of resampling data becomes more effective and results in higher accuracy values.

Parameter Tuned Machine Learning based Decision Support System for Bank Telemarketing

In banking sectors, telemarketing is the major support of selling the products or services. Banking advertisement and marketing are mainly depending upon the comprehensive knowledge of objective data regarding the market and the actual client requirements for the bank gainful way. Decision Support Systems (DSS) play a vital part in telemarketing sector, which determines a specific class of automized facts to assist the company to make decisions. Machine learning (ML) is commonly used in the DSS which integrates the data and computer application for precise prediction of results. This paper presents an effective parameter tuned ML based DSS (PTML-DSS) for bank telemarketing sector. The proposed PTML-DSS technique follows a three-level process namely preprocessing, classification, and parameter optimization. Initially, the marketing data is preprocessed to get rid of unwanted information. In addition, gradient boosting decision tree (GBDT) based classifier model is used to classify the data. Besides, firefly algorithm (FFA) is applied for tuning the parameters involved in the GBDT model. In order to verify the improved performance of the PTML-DSS technique, a series of simulations were performed, and the results are inspected under varying aspects. The resultant values reported the improved performance of the PTML-DSS technique over the other techniques.

Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing

Tele-marketing presents a huge challenge in identifying potential customers with lack of effective marketing strategy may led a company to succumbs to problems such as prolonged marketing campaign. Various attempts to improve the performance of binary classification model for bank tele-marketing data. Previous researches indicate that the neural network is the most common algorithms being employed and able to produce commendable results with higher accuracy percentages compared to other algorithms. Despite several attempts to improve the model through treatment of imbalance dataset and features selection, this research argues that they are incomplete. Therefore, this research proposes a data pre-processing algorithm for bank tele-marketing binary classification neural network. Three datasets have been employed (19, 16, and 20 features) to evaluate the performance of the algorithm towards the classification model. The data pre-processing algorithm is divided into three phases; data cleaning, data imbalance treatment and finally data normalization. In this paper, the result indicated that binary classification model complemented with data cleaning techniques such as Missing common (MC) and Tomek Links (TL) shows a better result compared to Ignore Missing (IM). In terms of data normalization, techniques such as MaxAbsScaler (MAS) and MinMaxScaler (MMS) consistently indicated better performance from other normalization techniques. The classification model employed in this paper utilize data pre-processing algorithm combination of MC-TL-MMS. The algorithm using this approach able to record an area of the receiver operating characteristic curve (AUC) of 0.9129 and 0.9464 by using 16 features and 20 features respectively. This result presents the highest figure in terms of performance accuracy compared to other previous researches.

Bank Telemarketing Forecasting Model Based on t-SNE-SVM

As a low-cost marketing model, telemarketing has always been the most important channel for banks to promote wealth management products. Traditional telemarketing has not only brought intrusiveness to many telephone access customers, but also a waste of resources for the bank itself. In order to improve the success rate of bank telemarketing, it is necessary to predict in advance which customers are most likely to purchase the wealth management product, so as to achieve precision marketing. Aiming at the complex high-dimensional nonlinear characteristics of the factors affecting the success rate of telemarketing, a t-SNE (t-distributed stochastic neighbor embedding) feature extraction method, and then take the extracted low-dimensional features as input, use nonlinear support vector machine (SVM) for training and prediction. The empirical results show that the bank phone based on t-SNE-SVM proposed in this paper. The marketing prediction model has good learning ability and generalization ability, which can provide certain decision-making reference for banks and other industries to achieve precision marketing.

Performance Evaluation for Competency of Bank Telemarketing Prediction using Data Mining Techniques

In today's market there is cut throat competition in the banks and struggling hard to gain competitive advantage over each other. The banking industry has undergone tremendous changes in the way business conducted. They realizes the needs and techniques of data mining which is helpful tool to gather, store, capture data and convert into knowledge. The application of data mining enhances the performance of telemarketing process in banking industry. It also provide an insight how these techniques effectively used in banking industry to make the decision making process easier and productive. This work describes a data mining approach to extract valuable knowledge and information from a bank telemarketing campaign data. At this time, the potential of five data mining methods was explored for forecasting of term deposit subscription. The presentation of these techniques was evaluated on fourteen different classifier parameters. The overall better performance achieved by J48 decision tree which reported 91.2% correctly classified with sensitivity, specificity and lowest error rate of 53.8, 95.9 and 8.8 % respectively

Three data mining models to predict bank telemarketing

The bank telemarketing dataset was collected by a Portuguese retail bank when selling a bank long-term deposits product. In order to predict the success of bank telemarketing, an more accurate approach was proposed. Firstly, the dataset was analyzed and processed. The features value of the dataset could be divided into two kinds: continuous variable and categorical variable. The two kinds of variable had been processed with different methods. The continuous variables were transformed to discrete features and then were normalized. The categorical variables were coded with two different methods according to whether they were belong to the ordered categories. The missing value of features had to been processed with two different methods depending on the examples number of the missing value. Secondly, the three data mining models which were support vector machine, neural network and decision trees were adopted to predict the result of the processed bank telemarketing dataset. The classification and regression trees algorithm was adopted in the decision trees model. The BFGS algorithm had been used when the neural network model was programming. The simple sequential minimal optimization (SMO) algorithm was adopted in the support vector machine model. All the three models were executed using python 2.7 and conducted in a Windows7 PC, with an Intel Cerelon G1630 2.80 GHz processor. The results of three models were analyzed and compared. The decision trees presented the best results and cost the least time, and its accuracy, precision, recall value and AUC area of the were all equal to 1.0. It showed that decision trees could predict 100% correct rate. The accuracy, recall value and AUC area of the neural network were better than those obtained by the support vector machine (improvement of 1.5 pp, 2.7 pp and 5 pp respectively), but the neural network cost almost 4.4 times more time than the support vector machine. It showed that not only choosing the data mining models but also processing datasets in advance played a key role in obtaining better prediction results when datasets needed classifying.

Long-term deposits prediction: a comparative framework of classification model for predict the success of bank telemarketing

The long-term deposits product is often offered to prospective customers by the bank through telemarketing strategy. How to maximize customer value through telemarketing strategy is a major focus in this research. Therefore, required a model that can classify potential customers with the potential to increase corporate earnings. The Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), K-Nearest Neighbour (K-NN), Support Vector Machine (SVM), Neural Network (NN), and Logistic Regression (LR) model have been proposed to compare. The comparison of these algorithms evaluated using the Protestal Bank dataset of the UCI Machine Learning repository, for performance algorithms using Area Under Curve (AUC) and Accuracy. From the experiment, the results show that SVM yield promising results with the Accuracy and AUC 97.07% and 0.925 respectively. It can be concluded that SVM is the best choice for classifying prospective customers who have the potential to be interested in time deposit products that are offered by telephone or cellular as distinguished from other classification algorithms.

Using Logistic Regression Model to Predict the Success of Bank Telemarketing

Term deposit is always an essential business of a bank and a good market campaign plays an essential role in financial selling. Nowadays, the telephone marketing, which can assist consulting institution to extract potential clients, has been one of the most general marketing campaigns. Previous research shows that data mining has gradually stood out on the era of Big Data and has been incorporated to deal with massive data precisely. The purpose of this study is to predict the success of bank telemarketing to select the best consumer set. A relationship is observed between success and other factors through constructing logistic regression model. To validate the effectiveness of prediction, some basic classification models have been compared in this study, including Bayes, Support Vector Machine, Neural Network and Decision Tree. As a result, the prediction accuracy and the area under ROC curve prove the logistic regression model performs best in classifying than other models. All of the experiments are implemented by R language software. And the experimental results can provide some suggestions and instructions towards the management of the bank.

A divide‐and‐conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing

AbstractThe discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub‐problem within the original problem. We propose a divide‐and‐conquer strategy for data mining using both the data‐based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate feature. The inbound telemarketing sub‐problem re‐evaluation led to a large increase in targeting performance, confirming the benefits of such approach and considering the importance of telemarketing for business, in particular in bank marketing.

은행 텔레마케팅 예측을 위한 레이블 전파와 협동 학습의 결합 방법

은행 텔레마케팅 예측을 위한 레이블 전파와 협동 학습의 결합 방법

The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data

In this paper the Naïve Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed.

IBMMS Decision Support Tool for Management of Bank Telemarketing Campaign

Although direct marketing is a good method for banks to utilize in the face of global competition and the financial crisis, it has been shown to exhibit poor performance. However, there are some drawbacks to direct campaigns, such as those related to improving the negative attributes that customers ascribe to banks. To overcome these problems, attractive long-term deposit campaigns should be organized and managed more effectively. The aim of this study is to develop an Intelligent Bank Market Management System (IBMMS) for bank managers who want to manage efficient marketing campaigns. IBMMS is the first system developed by combining the power of data mining with the capabilities of expert systems in this area. Moreover, IBMMS includes important features that enable it to be intelligent: a knowledge base, an inference engine and an advisor. Using this system, a manager can successfully direct marketing campaigns and follow the decision schemas of customers both as individuals and as a group; moreover, a manager can make decisions that lead to the desired response by customers.