(CRM) and Artificial Intelligence techniques for Customer Identification (original) (raw)
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Predicting reaction based on customer's transaction using machine learning approaches
International Journal of Electrical and Computer Engineering (IJECE), 2023
Banking advertisements are important because they help target specific customers on subscribing to their packages or other deals by giving their current customers more fixed-term deposit offers. This is done through promotional advertisements on the Internet or media pages, and this task is the responsibility of the shopping department. In order to build a relationship with them, offer them the best deals, and be appropriate for the client with the company's assurance to recover these deposits, many banks or telecommunications firms store the data of their customers. The Portuguese bank increases its sales by establishing a relationship with its customers. This study proposes creating a prediction model using machine learning algorithms, to see how the customer reacts to subscribe to those fixed-term deposits or offers made with the aid of their past record. This classification is binary, i.e., the prediction of whether or not a customer will embrace these offers. Four classifiers that include k-nearest neighbor (k-NN) algorithm, decision tree, naive Bayes, and support vector machines (SVM) were used, and the best result was obtained from the classifier decision tree with an accuracy of 91% and the other classifier SVM with an accuracy of 89%.
2018
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe concept of machine learning has been around for decades, but now it is becoming more and more popular not only in the business, but everywhere else as well. It is because of increased amount of data, cheaper data storage, more powerful and affordable computational processing. The complexity of business environment leads companies to use data-driven decision making to work more efficiently. The most common machine learning methods, like Logistic Regression, Decision Tree, Artificial Neural Network and Support Vector Machine, with their applications are reviewed in this work. Insurance industry has one of the most competitive business environment and as a result, the use of machine learning techniques is growing in this industry. In this work, above mentioned machine learning methods are used to build predi...
COMPARATIVE STUDY OF SUPERVISED LEARNING IN CUSTOMER RELATIONSHIP MANAGEMENT
Customers are valuable to an organization. The competitive market environment makes customer relationship management very noteworthy for the business prospects. Therefore, research on customer relationship management is attracting data mining researchers. Data mining can support customer expansion by matching products with customers and better pursuing of product promotion campaigns. In this study, classification algorithms, namely J48, SGD, Bayes Net and Naïve Bayes Updatable were experimented on customer data. The comparison of these classification algorithms based on different performance metrics is presented. It will help to select a best suitable algorithm. The performance of the classification models is measured using 10-fold cross validation. The WEKA environment was utilized for the experiments and assessments of these methods. The 80% data were correctly classified by all these methods. It reveals that data mining, classification methods can be adopted for the customer relationship management study.
Prediction of Student Decisions in Choosing the Type of Bank Using Support Vector Machine (SVM)
Emerging Information Science and Technology
A bank is an intermediate financial institution authorized to take deposits, lend money, and issue promissory notes or banknotes. In the present day, every adult must have at least one bank account. Additionally, bank services range from regular and hajj savings to large-scale loans. Students, one of the bank’s customers, usually utilize services confined to savings to preserve pocket money received from their parents and ordinary transactions like transfers and payments. Several factors, including the atmosphere, administrative fees, and the accessibility of ATMs and bank branch offices, impact students’ decisions about where to save money. It prevents the bank from predicting which services must be enhanced to encourage customers, particularly students, to select the bank. Therefore, prediction is required to ascertain the students’ choice of bank. This study employed data mining and the Support Vector Machine (LibSVM) algorithm. The quantity of data impacted the outcomes of the S...