Data Mining Based Product Marketing Technique for Banking Products

Mitik, Merve; Korkmaz, Ozan; Karagöz, Pınar; Toroslu, İsmail Hakkı; Yucel, Ferhat

doi:10.1109/icdmw.2016.0085

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…Apart from the continuous exploration of the above Portuguese direct marketing dataset, Shih et al [65] presented a target marketing model for commercial banks for the personal loan service, and the experiment was conducted with the data from a bank in Taiwan. With the direct marketing data set of a Turkish bank, Mitik et al [66,67] proposed a two step hybrid system and achieved promising accuracy and a huge increase in the overall profit/cost ratio. Another regionally focused research by Wang and Petrounias [68] analyzed the relationships between demographic characteristics and mobile banking in China with big data collected through questionnaires.…”

Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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Section: Customer Development and Customizationmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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“…Another approach to identify the customers who subscribe for the bank services had been discussed in [7] by achieving two objectives, in which the first objective was to predict the response of each customer and the second was to identify the relevant features using clustering. Improving the outcome of marketing campaigns was proposed in [8] by performing clustering of customers based on similarities and then building a classifier. Novel algorithms such as SMOTE [9], Rotation Forest (PCA)-J48 [10] were also proposed to improve the outcome of marketing campaigns.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Krishna¹,

Reddy²

2019

IJRTE

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The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the realworld domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy

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“…Direct marketing is about finding the most potential customers for a certain product based on their characteristics, interests, behavior and needs, then trying to make customized marketing campaigns for these customers. All industries aim to increase their returns of marketing campaigns and their sales consequently through using the right marketing channels and techniques directed to the right customers at the right time [15]. Banks present one of the major sectors which have a great pressure to increase profits and reduce costs through using the right marketing strategies [17].…”

Section: Introductionmentioning

confidence: 99%

“…There are two approaches for promotions: mass marketing and direct marketing. Mass marketing uses the traditional media for promotion such as television, radio, newspapers and broadcast messages to be distributed randomly without any customization [15], [12]. This type of marketing becomes less effective with time because of the great competition and the large number of available products these days along with its high cost.…”

Section: Introductionmentioning

confidence: 99%

“…Data mining techniques, machine learning and business intelligence present important models which can be used for direct marketing since there is a huge amount of available data about customers stored in the databases [4], [13], [29] [32], which makes it impossible to analyze this data manually [15] [2], [20]. This data can be studied and analyzed in order to discover the customers' behavior, interests and pattern of buying.…”

Section: Introductionmentioning

confidence: 99%

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Developing Decision Tree based Models in Combination with Filter Feature Selection Methods for Direct Marketing

Obiedat¹

2020

IJACSA

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Direct Marketing is a form of advertising strategies which aims to communicate directly with the most potential customers for a certain product using the most appropriate communication channel. Banks are spending a huge amount of money on their marketing campaigns, so they are increasingly interested in this topic in order to maximize the efficiency of their campaigns, especially with the existence of high competition in the market. All marketing campaigns are highly dependent on the huge amount of available data about customers. Thus special Data Mining techniques are needed in order to analyze these data, predict campaigns efficiency and give decision makers indications regarding the main marketing features affecting the marketing success. This paper focuses on four popular and common Decision Tree (DT) algorithms: SimpleCart, C4.5, RepTree and Random Tree. DT is chosen because the generated models are in the form of IF-THEN rules which are easy to understand by decision makers with poor technical background in banks and other financial institutions. Data was taken from a Portuguese bank direct marketing campaign. A filter-based Feature selection is applied in the study to improve the performance of the classification. Results show that SimpleCart has the best results in predicting the campaigns success. Another interesting finding that the five most significant features influencing the direct marketing campaign success to be focused on by decision makers are: Call duration, offered interest rate, number of employees making the contacts, customer confidence and changes in the prices levels.

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Data Mining Based Product Marketing Technique for Banking Products

Cited by 8 publications

References 2 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

Developing Decision Tree based Models in Combination with Filter Feature Selection Methods for Direct Marketing

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