Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method

Djurisic, Vladimir; Kašćelan, Ljiljana; Rogić, Sunčica; Melović, Boban

doi:10.1080/08839514.2020.1790248

Cited by 16 publications

(9 citation statements)

References 26 publications

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“…The author combines the status quo of Chinese culture's external dissemination and the process of dissemination and the links between the various communication elements and constructed the audience-centered 6R culture external dissemination ring structure shown in Figure 4; the significance of the ring mode is to emphasize that in the external dissemination of Chinese culture, the communicator chooses the close connection between the transmission path, transmission channel, transmission mode, and other elements. The center of the ring model is audience analysis, and its significance lies in emphasizing every link in the process of foreign cultural dissemination in China; audience analysis plays an important role in improving the ability of cultural communication to the outside world; it is to start from the characteristics of the audience, improve the strategy selection of each communication link, pay attention to the feedback of the audience's communication effect, and constantly innovate the communication method [19].…”

Section: Intelligent Communication Platform and Focusedmentioning

confidence: 99%

The Dissemination Mode of External Intelligence of Archery Culture Based on the Particle Swarm Algorithm

Xue

2022

Wireless Communications and Mobile Computing

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To study the archery culture based on the particle swarm algorithm, the dissemination mode of external intelligence is differentiated. Firstly, the algorithm based on a particle swarm is introduced, and the external intelligence dissemination mode is distributed. Then, taking the spread of archery as the research object, mainly using questionnaires and other methods, from the perspectives of intelligent differentiation, organizational communication, and mass communication, we investigated and analyzed the dissemination activities of archery. Finally, the relevant countermeasures to promote the spread and development of archery are summarized and put forward. The experimental results show the following: the process of interpersonal communication of archery has the characteristics of many channels of information transmission, flexible methods, and strong interaction; the leadership of elite archers and the interpersonal communication of archery played a good role in promoting. Dissemination of external intelligence and mass dissemination, in the process of human-to-human communication of archery, have the same significance.

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Section: Intelligent Communication Platform and Focusedmentioning

confidence: 99%

The Dissemination Mode of External Intelligence of Archery Culture Based on the Particle Swarm Algorithm

Xue

2022

Wireless Communications and Mobile Computing

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“…those that are farthest from each other and between which the margin is the widest, leading to maximum separation of classes. Applying a balanced DT ensemble for rule extraction from such pre-processed data set (SVM-RE ensemble) misclassification rate of the most valuable customer segment is reduced by 66%, which is a much better result than the result obtained in [58], where using standalone SVM preprocessor and standalone DT rule extractor this misclassification rate was reduced by 37%.…”

Section: Discussionmentioning

confidence: 81%

“…In a study by Djurisic et al [58] authors tested how well SVM preprocesses data and enables CRM optimization in banks. The results showed that during the segmentation of credit card users, this method successfully resolves overlapping and unbalanced classes.…”

Section: Class Balancing In Direct Marketingmentioning

confidence: 99%

Class balancing in customer segments classification using support vector machine rule extraction and ensemble learning

Rogić

Kašćelan

2021

ComSIS

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An objective and data-based market segmentation is a precondition for efficient targeting in direct marketing campaigns. The role of customer segments classification in direct marketing is to predict the segment of most valuable customers who is likely to respond to a campaign based on previous purchasing behavior. A good-performing predictive model can significantly increase revenue, but also, reduce unnecessary marketing campaign costs. As this segment of customers is generally the smallest, most classification methods lead to misclassification of the minor class. To overcome this problem, this paper proposes a class balancing approach based on Support Vector Machine-Rule Extraction (SVM-RE) and ensemble learning. Additionally, this approach allows for rule extraction, which can describe and explain different customer segments. Using a customer base from a company?s direct marketing campaigns, the proposed approach is compared to other data balancing methods in terms of overall prediction accuracy, recall and precision for the minor class, as well as profitability of the campaign. It was found that the method performs better than other compared class balancing methods in terms of all mentioned criteria. Finally, the results confirm the superiority of the ensemble SVM method as a preprocessor, which effectively balances data in the process of customer segments classification

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“…It was shown in the literature that the SVM method [17] successfully resolves overlapping and unbalanced classes [18,19] by creating a hyperplane between the examples belonging to different classes, which can discriminate the class to the maximum distance, regardless of the number of instances available to learn from for any class [20]. Hence, SVM resolves data noise, i.e., class overlapping, and complements the minor class with the most relevant examples by moving the margin to the closest and thus most similar examples of the major class and categorising them into smaller classes [21].…”

Section: Introductionmentioning

confidence: 99%

Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine

Rogić

Kašćelan

Bach

2022

JTAER

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Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.

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Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method

Cited by 16 publications

References 26 publications

The Dissemination Mode of External Intelligence of Archery Culture Based on the Particle Swarm Algorithm

The Dissemination Mode of External Intelligence of Archery Culture Based on the Particle Swarm Algorithm

Class balancing in customer segments classification using support vector machine rule extraction and ensemble learning

Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine

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