A Cluster-Based Data Balancing Ensemble Classifier for Response Modeling in Bank Direct Marketing

2015 ◽  
Vol 14 (04) ◽  
pp. 1550022 ◽  
Author(s):  
Mohammad Amini ◽  
Jalal Rezaeenour ◽  
Esmaeil Hadavandi
Keyword(s):  
Prediction Models ◽  
Direct Marketing ◽  
Imbalanced Data ◽  
Response Prediction ◽  
Ensemble Classifier ◽  
Ensemble Method ◽  
Ensemble Classification ◽  
Under Sampling ◽  
The Right ◽  
Response Modeling

The aim of direct marketing is to find the right customers who are most likely to respond to marketing campaign messages. In order to detect which customers are most valuable, response modeling is used to classify customers as respondent or non-respondent using their purchase history information or other behavioral characteristics. Data mining techniques, including effective classification methods, can be used to predict responsive customers. However, the inherent problem of imbalanced data in response modeling brings some difficulties into response prediction. As a result, the prediction models will be biased towards non-respondent customers. Another problem is that single models cannot provide the desired high accuracy due to their internal limitations. In this paper, we propose an ensemble classification method which removes imbalance in the data, using a combination of clustering and under-sampling. The predictions of multiple classifiers are combined in order to achieve better results. Using data from a bank’s marketing campaigns, this ensemble method is implemented on different classification techniques and the results are evaluated. We also evaluate the performance of this ensemble method against two alternative ensembles. The experimental results demonstrate that our proposed method can improve the performance of the response models for bank direct marketing by raising prediction accuracy and increasing response rate.

scholarly journals Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

2021 ◽  
Vol 11 (19) ◽  
pp. 9016
Author(s):  
Fereshteh Safarkhani ◽  
Sérgio Moro
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Prediction Models ◽  
Direct Marketing ◽  
Imbalanced Data ◽  
Bayes Classifier ◽  
Data Prediction ◽  
Feature Selection Approach ◽  
Improved Accuracy

Telemarketing is a widely adopted direct marketing technique in banks. Since customers hardly respond positively, data prediction models can help in selecting the most likely prospective customers. We aim to develop a classifier accuracy to predict which customer will subscribe to a long-term deposit proposed by a bank. Accordingly, this paper focuses on a combination of resampling, in order to reduce the imbalanced data, using feature selection, to reduce the complexity of data computing and dimension reduction of inefficiency data modeling. The performed operation has shown an improvement in the performance of the classification algorithm in terms of accuracy. The experimental results were run on a real bank dataset and the J48 decision tree achieved 94.39% accuracy prediction, with 0.975 sensitivity and 0.709 specificity, showing better results when compared to other approaches reported in the existing literature, such as logistic regression (91.79 accuracy; 0.975 sensitivity; 0.495 specificity) and Naive Bayes classifier (90.82% accuracy; 0.961 sensitivity; 0.507 specificity). Furthermore, our resampling and feature selection approach resulted in improved accuracy (94.39%) when compared to a state-of-the-art approach based on a fuzzy algorithm (92.89%).

Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection

2018 ◽  
Vol 7 (1) ◽  
pp. 57-72
Author(s):  
H.P. Vinutha ◽  
Poornima Basavaraju
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Information Gain ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Chi Square ◽  
Traffic Pattern ◽  
Data Mining Algorithms

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.

Effective customer selection for marketing campaigns based on net scores

2017 ◽  
Vol 11 (1) ◽  
pp. 2-15 ◽  
Author(s):  
René Michel ◽  
Igor Schnakenburg ◽  
Tobias von Martens
Keyword(s):  
Decision Trees ◽  
Direct Marketing ◽  
Real Data ◽  
Business Case ◽  
Added Value ◽  
Scoring Method ◽  
Content Type ◽  
Effective Selection ◽  
Selection For ◽  
Response Modeling

Purpose This paper aims to address the effective selection of customers for direct marketing campaigns. It introduces a new method to forecast campaign-related uplifts (also known as incremental response modeling or net scoring). By means of these uplifts, only the most responsive customers are targeted by a campaign. This paper also aims at calculating the financial impact of the new approach compared to the classical (gross) scoring methods. Design/methodology/approach First, gross and net scoring approaches to customer selection for direct marketing campaigns are compared. After that, it is shown how net scoring can be applied in practice with regard to different strategical objectives. Then, a new statistic for net scoring based on decision trees is developed. Finally, a business case based on real data from the financial sector is calculated to compare gross and net scoring approaches. Findings Whereas gross scoring focuses on customers with a high probability of purchase, regardless of being targeted by a campaign, net scoring identifies those customers who are most responsive to campaigns. A common scoring procedure – decision trees – can be enhanced by the new statistic to forecast those campaign-related uplifts. The business case shows that the selected scoring method has a relevant impact on economical indicators. Practical implications The contribution of net scoring to campaign effectiveness and efficiency is shown by the business case. Furthermore, this paper suggests a framework for customer selection, given strategical objectives, e.g. minimizing costs or maximizing (gross or lift)-added value, and presents a new statistic that can be applied to common scoring procedures. Originality/value Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now. The new χ2-statistic is a straightforward approach to the enhancement of decision trees for net scoring. Furthermore, this paper is the first to the application of net scoring with regard to different strategical objectives.

scholarly journals Analisis Preferensi Ketidaknyamanan Konsumen Terhadap Promosi Dan Personal Selleryang Mempengaruhi Minat Beli Konsumen Serta Pemilihan Merek Pada Asuransi (Studi Kasus di PT. Prudential Life Assurance)

2020 ◽  
Vol 2 (1) ◽  
pp. 16-29
Author(s):  
Putu Dyah Permatha Korry ◽  
Ayu Wendy Widhia Pramesti
Keyword(s):  
Structural Equation ◽  
Direct Marketing ◽  
A Value ◽  
Sense Of Security ◽  
Analysis Technique ◽  
Insurance Agent ◽  
Predicted Model ◽  
The Right ◽  
Insurance Data ◽  
A Company

Health is an important thing to be noticed in everyday life, because humans will not be separated from the dangers that arise suddenly by an event. Insurance is one way to guarantee a sense of security in handling the risks that arise suddenly. No wonder various promotions are carried out by companies to generate consumer buying interest, but one of the promotional techniques of direct marketing through telemarketing is inconvenience, because promotion through this technique consumers feel disturbed when contacted because according to them telemarketers do not think of the right time when offering a product. Personal Sales (Personal Seller) is an insurance agent from a company that can deal directly with consumers so that later there will be buying interest in insurance. To increase consumer buying interest, promotional inconvenience, and seller personalities are expected to be able to influence consumers so that after buying interest arises, consumers will be able to decide on the brand selection on insurance. Data was collected through questionnaires to 85 respondents. The data analysis technique used is structural equation (SEM) with PLS. The results of this study indicate that promotional discomfort has a negative and significant effect on consumer buying interest, while a personal seller has a positive and significant effect on consumer buying interest, and consumer buying interest has a positive and significant effect on brand selection. Based on the results of testing the Q2 model gets a value of 0.774, which shows the predicted model is appropriate.

scholarly journals Direct marketing practice in Hungarian agriculture

2008 ◽  
Vol 53 (No. 5) ◽  
pp. 230-234 ◽  
Author(s):  
I. Fehér
Keyword(s):  
Geographical Origin ◽  
Direct Marketing ◽  
Point Of View ◽  
Marketing Practice ◽  
Sales Channels ◽  
Product Regulation ◽  
Local Product ◽  
Marketing Activities ◽  
Historical Heritage ◽  
The Right

Farm-sale, also known as direct sale, provides major opportunities to farmers in the future. This kind of sale is of increasing popularity in Europe, but farmers have to be familiar with the regulations concerning processing and sales. Mainly small and medium farmers prefer direct sale. In this activity, they must compete with an increasing number of hypermarkets, supermarkets and wholesale markets. When talking about direct sale, it means that farmers sell their products directly to customers. There are more options: (i) sale in their own shop, (ii) through a catalogue and (iii) delivery to restaurants and shops. It has to be mentioned that the development of special local products means the products representing a common local value and principally those that can be associated with a specific village due to their historical heritage or tradition. There is no standard or official definition for special local products that includes all the possible factors. Efforts of marketing and rural development experts are needed to identify and market these special products to the appropriate consumers. Meanwhile it has to be noticed that, mainly in Europe, the definition and the possibilities of product regulation concerning geographical origin, are clearly defined and well-known. However, the “protection of geographical origin” is not the same issue as “special local products” mentioned above. In a wider sense, these can be described from a marketing point of view as “local product, common product” that interconnect and integrate villages, people and approaches, but are not regulated and protected legally. These products mentioned above reach the consumer in relatively small quantities, through direct sale, and they are often attached to the services of rural tourism. The local products are also developed to ensure high quality products for the consumer or to attract tourists. People can be proud of them since they cannot be bought anywhere else. Advisers are also helping farmers choose the right sales channels to diversify their marketing activities.

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