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1
Gholami, Atena, Reza Sheikh, Neda Mizani, and Shib Sankar Sana. "ABC analysis of the customers using axiomatic design and incomplete rough set." RAIRO - Operations Research 52, no. 4-5 (October 2018): 1219–32. http://dx.doi.org/10.1051/ro/2018022.
Повний текст джерелаАнотація:
Customer’s recognition, classification, and selecting the target market are the most important success factors of a marketing system. ABC classification of the customers based on axiomatic design exposes the behavior of the customer in a logical way in each class. Quite often, missing data is a common occurrence and can have a significant effect on the decision- making problems. In this context, this proposed article determines the customer’s behavioral rule by incomplete rough set theory. Based on the proposed axiomatic design, the managers of a firm can map the rules on designed structures. This study demonstrates to identify the customers, determine their characteristics, and facilitate the development of a marketing strategy.
2
Moudani, Walid, Grace Zaarour, and Félix Mora-Camino. "Fuzzy Classification of Customer Insolvency in Mobile Telecommunication." International Journal of Decision Support System Technology 6, no. 3 (July 2014): 1–29. http://dx.doi.org/10.4018/ijdsst.2014070101.
Повний текст джерелаАнотація:
This paper proposes a predictive model to handle customer insolvency in advance for large mobile telecommunication companies for the purpose of minimizing their losses while preserving an overall satisfaction of the customers which may have important consequences on the quality and on the consume return of the operations. A new mathematical formulation taking into consideration a set of business rules and the satisfaction of the customers is proposed. However, the customer insolvency is defined to be a classification problem since our main purpose is to categorize the customer in one of the two classes: potentially insolvent or potentially solvent. Therefore, a model with precise business prediction using the knowledge discovery and Data Mining techniques on an enormous heterogeneous and noisy data is proposed. A fuzzy approach to evaluate and analyze the customer behavior leading to segment them into groups that provide better understanding of customers is developed. These groups with many other significant variables feed into a classification algorithm based on Rough fuzzy Sets technique to classify the customers. A real case study is considered here, followed by analysis and comparison of the results for the reason to select the best classification model that maximizes the accuracy for insolvent customers and minimizes the error rate in the misclassification of solvent customers.
3
Xu, Yong, Jian Liu, Baomei Ma, and Shuai Zhang. "Service Mechanism and Pricing Based on Fairness Preference of Customers in Queuing System." Journal of Systems Science and Information 6, no. 6 (December 10, 2018): 481–94. http://dx.doi.org/10.21078/jssi-2018-481-14.
Повний текст джерелаАнотація:
AbstractService providers often adopt the mechanism of customer classification due to the heterogeneity of customer waiting cost. However, the classification service may cause unfairness feeling of regular customers, then affect the revenue and social welfare. This paper provides the first exact analysis about the situation that service providers offer two classes of non-preemptive priority service when customer fairness perception is explicitly modeled. We model customer fairness perception as a negative utility on regular customers that’s proportional to the waiting time difference between the two queues. By analyzing a stylized M/M/1 queue in monopoly service system, we can derive important results some of which reaffirm existed research results. First, from the perspective of revenue maximization, service providers should also adopt the mechanism of customer classification and set up the two kinds of customers where they can see each other. Next, considering customer utility maximization, service providers should cancel the mechanism of customer classification, and keep one queue (regular customers) only. Then, from the perspective of social welfare maximization, service providers should also adopt the mechanism of customer classification but set up the two kinds of customers where they cannot feel each other. Finally, this paper concludes the optimal pricing based on customer classification in the above three different perspectives. This research shows important reference value and practical significance for service providers who adopt the mechanism of classification service.
4
Du, Laihong, Hua Chen, Yadong Fang, Xiaowei Liang, Yujie Zhang, Yidan Qiao, and Zhen Guo. "Research on the Method of Acquiring Customer Individual Demand Based on the Quantitative Kano Model." Computational Intelligence and Neuroscience 2022 (April 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/5052711.
Повний текст джерелаАнотація:
In order to realize accurate marketing by analyzing customer individual demand, a new quantitative Kano model method is put forward, and it is helpful to provide customized products for heterogeneous customer classification groups. By improving the traditional Kano model, the customer satisfaction and the importance degree of products are defined, and the quantitative Kano demand model is established. Customers are classified as the price preference group, the brand preference group, and the service priority group, and decision-making of product attribute quality improvement for customer classification is realized. Lastly, electric vehicles (EVs) are selected as a study case, and their various demands for different classifications of customers are discussed by questionnaire survey and calculation of satisfaction and the importance degree. Furthermore, different customer group demands are classified as attractive demands, expected demands, nondifferential demands, or essential demands, and the important product attribute acquisition process for various customers is discussed to improve enterprise market competitiveness.
5
Shen, Anyuan. "Recommendations as personalized marketing: insights from customer experiences." Journal of Services Marketing 28, no. 5 (August 5, 2014): 414–27. http://dx.doi.org/10.1108/jsm-04-2013-0083.
Повний текст джерелаАнотація:
Purpose – The purpose of this paper is an exploratory study of customers’ “lived” experiences of commercial recommendation services to better understand customer expectations for personalization with recommendation agents. Recommendation agents programmed to “learn” customer preferences and make personalized recommendations of products and services are considered a useful tool for targeting customers individually. Some leading service firms have developed proprietary recommender systems in the hope that personalized recommendations could engage customers, increase satisfaction and sharpen their competitive edge. However, personalized recommendations do not always deliver customer satisfaction. More often, they lead to dissatisfaction, annoyance or irritation. Design/methodology/approach – The critical incident technique is used to analyze customer satisfactory or dissatisfactory incidents collected from online group discussion participants and bloggers to develop a classification scheme. Findings – A classification scheme with 15 categories is developed, each illustrated with satisfactory incidents and dissatisfactory incidents, defined in terms of an underlying customer expectation, typical instances of satisfaction and dissatisfaction and, when possible, conditions under which customers are likely to have such an expectation. Three pairs of themes emerged from the classification scheme. Six tentative research propositions were introduced. Research limitations/implications – Findings from this exploratory research should be regarded as preliminary. Besides, content validity of the categories and generalizability of the findings should be subject to future research. Practical implications – Research findings have implications for identifying priorities in developing algorithms and for managing personalization more strategically. Originality/value – This research explores response to personalization from a customer’s perspective.
6
G. Inyang, Udoinyang, Okure O. Obot, Moses E. Ekpenyong, and Aliu M. Bolanle. "Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification." Modern Applied Science 11, no. 9 (August 30, 2017): 151. http://dx.doi.org/10.5539/mas.v11n9p151.
Повний текст джерелаАнотація:
Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.
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Palaniappan, Shamala, Aida Mustapha, Cik Feresa Mohd Foozy, and Rodziah Atan. "Customer Profiling using Classification Approach for Bank Telemarketing." JOIV : International Journal on Informatics Visualization 1, no. 4-2 (November 15, 2017): 214. http://dx.doi.org/10.30630/joiv.1.4-2.68.
Повний текст джерелаАнотація:
Telemarketing is a type of direct marketing where a salesperson contacts the customers to sell products or services over the phone. The database of prospective customers comes from direct marketing database. It is important for the company to predict the set of customers with highest probability to accept the sales or offer based on their personal characteristics or behavior during shopping. Recently, companies have started to resort to data mining approaches for customer profiling. This project focuses on helping banks to increase the accuracy of their customer profiling through classification as well as identifying a group of customers who have a high probability to subscribe to a long term deposit. In the experiments, three classification algorithms are used, which are Naïve Bayes, Random Forest, and Decision Tree. The experiments measured accuracy percentage, precision and recall rates and showed that classification is useful for predicting customer profiles and increasing telemarketing sales.
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Fu, Ze, Bo Zhang, Lingjun Ou, Kaiyang Sun, Xinyi Sun, and Ningyan Chen. "Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm." Wireless Communications and Mobile Computing 2021 (November 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/6585486.
Повний текст джерелаАнотація:
Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.
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Thammaboosadee, Sotarat, Benjathip Chinomi, and Ehab Mohamed. "A Two-Stage Customer Journey Analytical Model in Single House Business." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 2 (September 8, 2020): 202–12. http://dx.doi.org/10.37936/ecti-cit.2020142.240239.
Повний текст джерелаАнотація:
The single housing industry is currently experiencing a continuous expansion in demand for housing. Addressing the needs of different customer groups is the key to increasing the rate of sales conversion. The objective of this research is to propose a two-stage single house customer journey analytical model that consists of two stages. The first stage concerns the customer journey between registration and reservation process and the second one identifies the customer loyalty from reservation to transfer stage. The four classification data mining techniques have experimented. The experiments include the accuracy and F-Measure in comparison and also perform the statistical testing. The Artificial Neural Network was the most accurate model for both stages. This model analyzes the probability of the customer progressing through the stages to the conclusion of purchase by learning the customer’s characteristics and the factors involved in the customer's decision. The model displays the reservation and transfer result for customers who have achieved the respective reservation and transference steps according to their registration profile. Experiments showed that the proposed two-stage models could predict customer loyalty, thereby enhancing relationship management between customers and organizations. It also confers a competitive advantage within the industry.
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Noura, Abdaoui, Hadj Khalifa Ismahène, and Faiz Sami. "Process of Personalizing the Ubiquitous Advertisements." International Journal of Knowledge Society Research 8, no. 2 (April 2017): 13–35. http://dx.doi.org/10.4018/ijksr.2017040102.
Повний текст джерелаАнотація:
In this paper, we propose an approach combining behavioral and targeting techniques for a better reaction of the customer with a star product using a personalized ubiquitous advertisement. We use the clustering to study the customer's behavior and the association rules to estimate the probability of star product's purchases in the near future. In order to validate our approach, we develop a prototype to send a personalized advertisement to loyal customers and potential customers in ubiquitous environment. Each target receives the advertising according his classification and his degree of loyalty obtained by the behavioral analysis. Loyal customers are the first to receive the personalized advertising in the ubiquitous environment.
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Moudani, Walid, Grace Zaarour, and Félix Mora-Camino. "Fuzzy Prediction of Insolvent Customers in Mobile Telecommunication." International Journal of Strategic Information Technology and Applications 5, no. 3 (July 2014): 1–23. http://dx.doi.org/10.4018/ijsita.2014070101.
Повний текст джерелаАнотація:
This paper presents a predictive model to handle customer insolvency in advance for large mobile telecommunication companies for the purpose of minimizing their losses. However, another goal is of the highest interest for large mobile telecommunication companies is based on maintaining an overall satisfaction of the customers which may have important consequences on the quality and on the consume return of the operations. In this paper, a new mathematical formulation taking into consideration a set of business rules and the satisfaction of the customers is proposed. However, the customer insolvency is defined to be a classification problem since our main purpose is to categorize the customer in one of the two classes: potentially insolvent or potentially solvent. Therefore, a model with precise business prediction using the knowledge discovery and Data Mining techniques on an enormous heterogeneous and noisy data is proposed. Moreover, a fuzzy approach to evaluate and analyze the customer behavior leading to segment them into groups that provide better understanding of customers is developed. These groups with many other significant variables feed into a classification algorithm based on Rough Set technique to classify the customers. A real case study is considered here, followed by analysis and comparison of the results for the reason to select the best classification model that maximizes the accuracy for insolvent customers and minimizes the error rate in the misclassification of solvent customers.
12
Alizadeh, H., and B. Minaei Bidgoli. "Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty." Engineering, Technology & Applied Science Research 6, no. 6 (December 18, 2016): 1235–40. http://dx.doi.org/10.48084/etasr.741.
Повний текст джерелаАнотація:
The main aim of this study was introducing a comprehensive model of bank customers᾽ loyalty evaluation based on the assessment and comparison of different clustering methods᾽ performance. This study also pursues the following specific objectives: a) using different clustering methods and comparing them for customer classification, b) finding the effective variables in determining the customer loyalty, and c) using different collective classification methods to increase the modeling accuracy and comparing the results with the basic methods. Since loyal customers generate more profit, this study aims at introducing a two-step model for classification of customers and their loyalty. For this purpose, various methods of clustering such as K-medoids, X-means and K-means were used, the last of which outperformed the other two through comparing with Davis-Bouldin index. Customers were clustered by using K-means and members of these four clusters were analyzed and labeled. Then, a predictive model was run based on demographic variables of customers using various classification methods such as DT (Decision Tree), ANN (Artificial Neural Networks), NB (Naive Bayes), KNN (K-Nearest Neighbors) and SVM (Support Vector Machine), as well as their bagging and boosting to predict the class of loyal customers. The results showed that the bagging-ANN was the most accurate method in predicting loyal customers. This two-stage model can be used in banks and financial institutions with similar data to identify the type of future customers.
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Rahman, A., and M. N. A. Khan. "A Classification Based Model to Assess Customer Behavior in Banking Sector." Engineering, Technology & Applied Science Research 8, no. 3 (June 19, 2018): 2949–53. http://dx.doi.org/10.48084/etasr.1917.
Повний текст джерелаАнотація:
A customer relationship management system is used to manage company relationships with current and possible customers. Following a thorough review of contemporary literature, different data mining techniques employed in different types of business, corporate sectors and organizations are analyzed. A model that would be helpful to identify customers’ behavior in the banking sector is then proposed. Three classifiers, k-NN, decision tree and artificial neural networks are used to predict customer behavior and are assessed in order to determine which classifier performs better for predicting customer behavior in the banking sector.
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Yin, Chengxin, Yan Guo, Jianguo Yang, and Xiaoting Ren. "A new recommendation system on the basis of consumer initiative decision based on an associative classification approach." Industrial Management & Data Systems 118, no. 1 (February 5, 2018): 188–203. http://dx.doi.org/10.1108/imds-02-2017-0057.
Повний текст джерелаАнотація:
Purpose The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system. Design/methodology/approach By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop. Findings The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision. Originality/value Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.
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Lv, Kang. "Spatial Clustering Analysis of K-Means Algorithm in the Classification of Bank Card Customers." Applied Mechanics and Materials 687-691 (November 2014): 1274–77. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1274.
Повний текст джерелаАнотація:
K-means algorithm is a simple and efficient data mining clustering algorithm. For the current status of the bank card customer relationship management, based on data mining technology, design based on K-means clustering algorithm banking customer classification system. Data mining techniques can extract vast amounts of customer information data bank card implicit knowledge and spatial relationship model will represent the bank customers feature set of data objects automatically classified into each composed of clusters of similar objects, bank card customers in the banking system classification. This paper analyzes the existing spatial clustering methods summary and conclusion, based on the combined data bank card customers, according to the volatility of funds used to different customer groups, the use of K-means analysis to study characteristics of client groups, providing appropriate financial services.
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Shi, Baofeng, Jing Wang, Junyan Qi, and Yanqiu Cheng. "A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/945359.
Повний текст джерелаАнотація:
We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.
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Sinaga, Jeprianto, and Bosker Sinaga. "Data Mining Classification Of Filing Credit Customers Without Collateral With K-Nearest Neighbor Algorithm (Case study: PT. BPR Diori Double)." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 2 (January 1, 2020): 204–10. http://dx.doi.org/10.47709/cnapc.v2i2.401.
Повний текст джерелаАнотація:
Unsecured loans are the community's choice for lending to banks that provide Reviews These services. PT. RB Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that Affect the customer's classification so that credit can be approved, the which is then submitted to the commissioner for credit approval. But what if Reviews those who apply for credit on the same day in large amounts, of course this will the make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to Facilitate the work of the assessor team in the process of analyzing the attributes that Affect customer classification. To find out the classification of customers who apply for unsecured loans for using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral.
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Agarwal, Reeti. "Indian Customers’ Attitude Towards Bundling: A Basis for Classification and Targeting." Global Business Review 19, no. 2 (November 23, 2017): 510–31. http://dx.doi.org/10.1177/0972150917714116.
Повний текст джерелаАнотація:
The present study was undertaken to gain an insight into the Indian customers’ attitude towards bundling. Combination of household items and tourism services was found to be most likely to be purchased by customers in bundles. Price was found to be the most important factor in influencing the decision of the customer to purchase/not purchase the bundled offer. Using factor and cluster analysis, demographic characteristics of the customers most likely to respond positively to bundle offers have been identified. This information can be used by companies in framing their promotional and targeting strategies.
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FAIZA, NUR, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "METODE QUEST DAN CHAID PADA KLASIFIKASI KARAKTERISTIK NASABAH KREDIT." E-Jurnal Matematika 4, no. 4 (November 24, 2015): 163. http://dx.doi.org/10.24843/mtk.2015.v04.i04.p106.
Повний текст джерелаАнотація:
This aim of this research is to find out the classification results and to compare the magnitude of misclassification of QUEST and CHAID methods on the classification of customer of Adira Kredit Elektronik branch Denpasar. QUEST (Quick, Unbiased, Efficient Statistical Trees) and CHAID (Chi-squared Automatic Interaction Detection) are nonparametric methods that produce tree diagram which is easy to interpret. The QUEST and CHAID classification methods conclude that: 1) QUEST method produces three groups which predict customers into the current category, whereas CHAID method produces four groups which also predict customer into the current category; 2) both methods generate the biggest classification accuracy for customers that current category which share similar characteristics; 3) both methods also have the same degree of accuracy in classifying customer data Adira Kredit Elektronik branch Denpasar.
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Dağ, Özge Hüsniye Namlı. "Predicting the Success of Ensemble Algorithms in the Banking Sector." International Journal of Business Analytics 6, no. 4 (October 2019): 12–31. http://dx.doi.org/10.4018/ijban.2019100102.
Повний текст джерелаАнотація:
The banking sector, like other service sector, improves in accordance with the customer's needs. Therefore, to know the needs of customers and to predict customer behaviors are very important for competition in the banking sector. Data mining uncovers relationships and hidden patterns in large data sets. Classification algorithms, one of the applications of data mining, is used very effectively in decision making. In this study, the c4.5 algorithm, a decision trees algorithm widely used in classification problems, is used in an integrated way with the ensemble machine learning methods in order to increase the efficiency of the algorithms. Data obtained via direct marketing campaigns from Portugal Banks was used to classify whether customers have term deposit accounts or not. Artificial Neural Networks and Support Vector Machines as Traditional Artificial Intelligence Methods and Bagging-C4.5 and Boosted-C.45 as ensemble-decision tree hybrid methods were used in classification. Bagging-C4.5 as ensemble-decision tree algorithm achieved more powerful classification success than other used algorithms. The ensemble-decision tree hybrid methods give better results than artificial neural networks and support vector machines as traditional artificial intelligence methods for this study.
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Swathi, G., Sudha Rani Donepudi, and K. Ramash Kumar. "Personified Behavioural Demand Response Model for the Reduction of Peak Time Energy Consumption Coincidence of Domestic Sector with the Utility." WSEAS TRANSACTIONS ON POWER SYSTEMS 16 (December 31, 2021): 361–73. http://dx.doi.org/10.37394/232016.2021.16.36.
Повний текст джерелаАнотація:
Curtailment of discrete customer’s demand coincidence with utility demand during peak time ends up in good benefits to the utility at different levels as this coincidence is very expensive due to additional requirement of demand. Though few Demand Response(DR) programs are working towards this peak time energy coincidence reduction, they are not that successful due to either requirements of technological installations near customer premises or penalising the customer or lack of encouraging the customer to achieve the reduction. This work proposes a Personified Behavioural Demand Response (P-BDR) model especially for residential customers as they are good contributors of peak time demand. Rather than coaxing or compelling the customer, the proposed model relies on customer’s motivation regarding the peak time energy conservation, setting targets based on their monthly contribution to utility peak time demand and measuring their achievements through feedback models. P-BDR model comprises of Target/Goal setting model based on forecasted data and feedback model based on real time data of individual customer. This model is observed on synthetic smart meter data of 20 discrete domestic customers. For the better application of the model, customers are clustered into 4 categories using K-Means Machine learning algorithm. The model sets an individual target of 5%-15% energy consumption reduction during utility peak time based on the customer classification. The model achieves an overall consumption reduction of 14.9% during peak time with the proposed model.
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Ramadhanti, Alya Rysda, Rafa Syafiq Bastikarana, Andry Alamsyah, and Sri Widiyanesti. "Determining Customer Relationship Management Strategy With Customer Personality Analysis Using Ontology Model Approach." Jurnal Manajemen Indonesia 20, no. 2 (August 30, 2020): 83. http://dx.doi.org/10.25124/jmi.v20i2.3196.
Повний текст джерелаАнотація:
Indonesia is a market with attractive e-commerce growth in the past four years. The tight competition made the company try to find ways to maintain customer loyalty, including making improvements to the Customer Relationship Management (CRM) strategy. The way to establish a good CRM strategy is driven by a good approach from the company to customers. Through the process of understanding customer personality, major companies able to understand their customer behaviour, therefore, companies driven by their marketers can perform product marketing activities that are tailored to the tendency of the customer's personality. This research was conducted to determine the personality of the customer by utilizing data obtained from online reviews of several products using the ontology model approach. Personality is measured based on words and phrases given by customers through their online reviews with the help of the Big Five Personality theory in the classification process. The results of this study indicate that the measurement of personality using an ontology model reconstructed with the n-gram algorithm shows that customers on several products have different personalities. These personalities can be used as a foundation in determining and establishing CRM strategies in order to gain higher customer engagement.
Keywords—Customer Relationship Management; Online Review; Ontology Model; Personality Measurement
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Solainayagi, Palaiyah, and Ramalingam Ponnusamy. "To Improve Feature Extraction and Opinion Classification Issues in Customer Product Reviews Utilizing an Efficient Feature Extraction and Classification (EFEC) Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 587. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp587-595.
Повний текст джерелаАнотація:
<span lang="EN-US">Currently, customer's product review opinion plays an essential role in deciding the purchasing of the online product. A customer prefers to acquire the opinion of other customers by viewing their opinion during online products' reviews, blogs and social networking sites, etc. The majority of the product reviews including huge words. A few users provide the opinion; it is tough to analysis and understands the meaning of reviews. To improve user fulfillment and shopping experience, it has become a general practice for online sellers to allow their users to review or to communicate opinions of the products that they have sold. The major goal of the paper is to solve feature extraction problem and opinion classification problem from customers utilized product reviews which extract the feature words and opinion words from product reviews. To propose an Efficient Feature Extraction and Classification (EFEC) algorithm is implementing to extracts a feature from opinion words. The reviewer usually marks both positive and negative parts of the reviewed product, despite the fact that their general opinion on the product may be positive or negative. An EFEC algorithm is utilized to predict the number of positive and negative opinion in reviews. Based on Experimental evaluations, proposed algorithm improves accuracy 15.05%, precision 13.7%, recall 15.59% and F-measure 15.07% of the proposed system compared than existing methodologies</span>
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L.T, Priyanka, and Neethu Baby. "Classification approach based Customer Prediction Analysis for Loan Preferences of Customers." International Journal of Computer Applications 67, no. 8 (April 18, 2013): 27–31. http://dx.doi.org/10.5120/11416-6752.
Повний текст джерела
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ZHANG, JIE, JIE LU, and GUANGQUAN ZHANG. "AN INTELLIGENT CLASSIFICATION METHOD IN BANK CUSTOMER RELATIONSHIP MANAGEMENT." New Mathematics and Natural Computation 03, no. 01 (March 2007): 111–21. http://dx.doi.org/10.1142/s1793005707000665.
Повний текст джерелаАнотація:
Customer classification is one of the major tasks in customer relationship management. Customers often have both static characteristics and dynamic behavioral features. Using both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In the proposed classification method, customer dynamic data is clustered using a hybrid genetic algorithm. The result is then combined with customer static data to give reasonable customer segmentation supported by neural network technique. A bank dataset-based experiment shows that applying the proposed method can obviously improve the accuracy of customer classification comparing with the traditional methods where only static data is used.
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Wu, Shisong. "Design of Intelligent Customer Service Questioning and Answering a System for Power Business Scenario Based on AI Technology." Mathematical Problems in Engineering 2022 (January 27, 2022): 1–10. http://dx.doi.org/10.1155/2022/5279919.
Повний текст джерелаАнотація:
In order to solve the problems of large demand for power business and small number of customer service, an intelligent customer service questioning and answering a system for power business scenario based on AI technology is designed. The approach first uses the particle swarm optimization algorithm to automatically classify the question attributes and then uses the fuzzy c-means clustering algorithm to match the answers with the highest similarity to the questions and return to the customers. The system collects the questions raised by customers through the acquisition module, uploads the question work order to the knowledge base through the information assistance module, and stores the preprocessed questions to the knowledge base. After completing the problem attribute classification through the particle swarm clustering algorithm classification model in the batch analysis and calculation module, the question answers are matched through fuzzy c-means clustering. At the same time, the similarity of different keywords is calculated to find a series of related questions. After the obtained data are analyzed in real time through the self-service customer service module, the answer is extracted and fed back to the customer, and the question answer is presented to the customer in the system interface. The experimental results show that the designed system has low worst-case time complexity, which is up to 0.35 only. The reason is that the system in this paper can use priority information to deal with the problems raised by customers, which is different from the past work where dealing with customers request via priority information is not used. The system can give the corresponding answers according to the customer’s options. It has convenient operation, high integrated control ability, and good information management performance. Compared with the traditional approach which could waste a lot of resource and data, the proposed approach can reduce the differences between problem data, eliminate invalid data, and simplify the data classification process. The application of the system can effectively accelerate the information transmission efficiency of the power company and can be used for power exchange platform automation in the future.
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Karlia, Jefry Antonius, and Wawan Nurmansyah. "Application of C4.5 Algorithm for Late Payment Classification of Insurance Premiums." Tekinfo: Jurnal Ilmiah Teknik Industri dan Informasi 9, no. 2 (May 29, 2021): 100–113. http://dx.doi.org/10.31001/tekinfo.v9i2.1090.
Повний текст джерелаАнотація:
The problem that often arises in insurance companies is the number of customers who do not smoothly pay premiums. The procedure that applies to the insurance during the grace period is 30 days. The insured customer must follow the premium payment procedure, if the customer does not pay the premium, the insurance policy will be canceled, this is part of the company's loss. An insurance company has a lot of data and this data can be processed to produce information on how to find out potential customer delays from a pattern formed using the C4.5 method. This research was conducted by applying the C4.5 algorithm using insurance customer data. The results of this study are a classification system for late payment of insurance premiums that can classify insurance customer premium payment status as a consideration for accepting insurance customers. The system test results show that the system can classify the status of insurance customer premium payments with a classification accuracy of 88%.
Keywords: Algorithm C 4.5, Insurance, Classification, Premium
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Luk, C. C., K. L. Choy, and H. Y. Lam. "Design of an Intelligent Customer Identification Model in e- Commerce Logistics Industry." MATEC Web of Conferences 255 (2019): 04003. http://dx.doi.org/10.1051/matecconf/201925504003.
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The emergence of e-commerce in recent years has lead to revolutionary changes in the logistics industry, as e-commerce relies heavily on efficient logistics to deliver the online goods to customers in a short period of time. Compared with traditional logistics, e-commerce orders, with a high variety of goods but small in quantity, are generally received from large number of customers worldwide. With a huge customer base, it is challenging for logistics service providers (LSPs) to provide satisfactory time-critical logistics services to meet the diversified customer requirements. In order to differentiate its services from others e-commerce LSPs, it is important to identify potential target groups of customers, and their behaviour so as to attract their attention. In this paper, an intelligent customer identification model (ICIM) is designed to support data analysis for managing customer relationships in a systematic way. The ICIM integrates the k-means clustering algorithm and the C4.5 classification algorithm in order to be able to deal with both continuous and discrete attributes for extracting valuable hidden knowledge. This effectively supports the identification of actual customer needs, and the classification of new customers in the future with minimum time for developing customer relationship management (CRM) recommendations to customers, thus improving business performance. Through a pilot study in a freight forwarding company in Hong Kong, it provides a real world demonstration and validation of data mining for CRM in the emerging e-commerce logistics industry.
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Fan, Bo, and Yu Yu Li. "Study on an Inventory Model of Sales System with Multiple Customer Classification." Advanced Materials Research 204-210 (February 2011): 1924–28. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1924.
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Firms segment customers into different classes to which they offer the same product in order to fulfill their demands better. An inventory model of sales system is developed, where customers are classified based on the volume they purchase and penalties levied for delays. The global optimal solution is proved and obtained theoretically and numerically. It is shown that seller should improve the service level of certain class of customer, decrease the order quality, and shorten the length of order cycle when the shortage cost of this class is increased, and seller should keep service level, shorten the length of order cycle, and increase the order quality when the demand of any class of customer is increased.
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Kucharska. "Dynamic Vehicle Routing Problem—Predictive and Unexpected Customer Availability." Symmetry 11, no. 4 (April 15, 2019): 546. http://dx.doi.org/10.3390/sym11040546.
Повний текст джерелаАнотація:
The Dynamic Vehicle Routing Problem (DVRP) is one of the most important problems in the area of enterprise logistics. DVRP problems involve these dynamics: the appearance of customers, travel times, service times, or vehicle availability. One of the most often considered aspects of the DVRP is the availability of customers, in which a part or all of the customers are revealed dynamically during the design or execution of the routes. A classification of the DVRP problem due to various elements causing dynamism is proposed. The aim of the paper is to distinguish dynamic VRP, which takes into account the dynamic appearance of customers to serve during the design or execution of the routes. In particular, the difference between the predictive and unexpected aspects of the customer’s availability is considered. Above all, the variant of customer’s availability which is predicted according to an appropriate general rule is modeled using the algebraic-logical meta-model (ALMM). It is a methodology which enables making collective decisions in successive process stages, not separately for individual vehicles. The algebraic-logical model of the dynamic vehicle routing problem with predicted consumer availability is proposed. The paper shows the possibilities of applying the ALMM approach to dynamic problems both with predicted and unexpected customer availability.
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Jain, Ankur, Lalit Wangikar, Martin Ahrens, Ranjan Rao, Suddha Sattwa Kundu, and Sutirtha Ghosh. "Classification Of 3G Mobile Phone Customers." International Journal of Data Warehousing and Mining 3, no. 2 (April 2007): 22–31. http://dx.doi.org/10.4018/jdwm.2007040103.
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Ghnemat, Rawan, and Edward Jaser. "Classification of Mobile Customers Behavior and Usage Patterns using Self-Organizing Neural Networks." International Journal of Interactive Mobile Technologies (iJIM) 9, no. 4 (September 25, 2015): 4. http://dx.doi.org/10.3991/ijim.v9i4.4392.
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Mobile usage is witnessing a booming growth attributed to advances in smartphone technologies, the extremely high penetration rate and the availability of popular mobile applications. Telecommunication markets have been injecting huge investments to fulfil the sheer demand on wireless network and mobile services as a result. Such potentials highlights the importance of behavioral segmentation of mobile network users to target different sectors of customers with efficient marketing strategies and ensure customer retention in light of the intense competition. A major hurdle in applying this approach is the number of dimensions underlying customer preferences which makes it hard to visualize similarities among customers and formulate behavioral segments correctly and efficiently. In this paper, we use self-organizing maps, to detect different usage patterns of mobile users. The proposed system is tested using a large sample of customers’ data provided by major mobile operator in Jordan. The study detected different behavioural segments in this market and highlights the role of data users in modern mobile markets. In this context, we give detailed analysis of our results on user behavioral segmentation.
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Yang, Gong Xin. "The Research of Improved Apriori Mining Algorithm in Bank Customer Segmentation." Advanced Materials Research 760-762 (September 2013): 2244–49. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2244.
Повний текст джерелаАнотація:
The This paper studies bank customers segmentation problem. Improved Apriori mining algorithm is a kind of data mining technology which is an important method in bank customers segmentation. In practical application, the traditional algorithm has shortcomings of the initial values sensitive and easy to fall into local optimal value, which will lead to low accuracy rate of silver class customer classification. According to the shortcomings of traditional algorithm, this paper puts forward a bank customer segmentation method based on improved Apriori mining algorithm in order to improve the bank customer segmentation accuracy. Experimental results show that the algorithm can effectively overcome the traditional algorithms shortcomings of easy to fall into local optimal value, improve the customer classification accuracy, make mining results more reasonable, lay down different customer service strategies for different client base, improve effective reference opinions of bank decision makers, and bring more benefits for the bank.
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van Scheers, Louise. "The importance that customers place on service attributes of sale personal in the retail sector." Investment Management and Financial Innovations 13, no. 3 (September 23, 2016): 222–27. http://dx.doi.org/10.21511/imfi.13(3-1).2016.08.
Повний текст джерелаАнотація:
When examining retail patronage, customer satisfaction must also be considered. Secondary resources (American Marketing Association, 2007; Berman, 2011; Berry, 2008; Chang, 2006, p. 209; Helgesen &amp; Nesset, 2007, p. 129, Kong and Jogaratnam, 2007, p. 279) observed that customer satisfaction is the degree to which customer’s expectations agree with the actual performance of the product and/or service South African consumers situated in Gauteng consider a sales person’s product knowledge as the most important attribute when making purchasing decisions. American consumers, in contrast, consider sales person respect as the most important attribute when making purchasing decisions. The implications for marketers and sales managers are that marketers and sales managers must provide adequate training for their sales personnel in order for them to treat customers in such a way to obtain their loyalty. The quality of the products sold at the retailer does not form part of the trade off options that customers are presented with. Keywords: retail store customer, prices compared to competitors, salesperson product knowledge, salesperson responsiveness, South African retail consumers. JEL Classification: L81, M31
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Strhan, Rastislav. "Research of product design consumer perception." Studia Commercialia Bratislavensia 13, no. 45 (September 1, 2020): 242–53. http://dx.doi.org/10.2478/stcb-2020-0010.
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Abstract Professionals perceive product design as an essential factor in product competitiveness, especially in international markets. To achieve that, customers and buyers must accept the quality of the offered product design. Especially by mass-produced products are producers facing a lot of customers with limited knowledge about design requirements. Appropriate communication is necessary to persuade potential customers about design quality characteristics. Paper utilizes previously formed customer classification based on quality perception. It discusses the possibility of applying such classification for product design perception too. Different methods and tools to prove the relevance of the presented model are analyzed to form an optimal model for future research. The result of such research would be the optimization of the use of product design in communication strategy.
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Li, Rui. "Application of the Sino US Financial Technology in Banks." Tobacco Regulatory Science 7, no. 5 (September 30, 2021): 4366–74. http://dx.doi.org/10.18001/trs.7.5.2.2.
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Objectives: With the rise and development of Internet finance, the application of Sino US financial technology in the banking field is becoming more and more widely. Methods: In this study, for the data collection of bank customer deposits, data mining and decision tree analysis algorithms were used to classify bank customers. Results: The classification accuracy of the traditional algorithm was low, so the optimization algorithm Adaboost and the random forest improvement algorithm were proposed in this paper. The simulation effects of its application in data combination show that the classification effect of the optimization algorithm is obviously better than the traditional classification algorithm. Conclusion: The results of this study can help banks gain customers and reduce expenditures.
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Yangyudongnanxin, Guo. "A Relevant Customer Identification Algorithm Based on the Internet Financial Platform." Journal of Mathematics 2021 (November 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/9770471.
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In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.
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Steyn, Derik, and Pierre Mostert. "Contracts versus relationship intention as indicator of customer trust in and commitment to cell phone service providers." Management 27, no. 1 (June 22, 2022): 167–90. http://dx.doi.org/10.30924/mjcmi.27.1.10.
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Social exchange theory postulates long-term customer-company relationships are built on trust and commitment. Cell phone service providers seek to secure the trust and commitment of their customers through service contracts. Relationship intention (RI) is a more precise measure of customer trust and commitment. This paper compares the trust of cell phone service customers and their commitment to cell phone service providers based on customers’ contract status and RI classification. Data from 1,473 cell phone customers from South Africa (n = 589) and the Philippines (n = 884) were analyzed. The study shows for both the South African and Philippine samples that there is no relationship between respondents’ contract status and their trust in or commitment to cell phone service providers and that trust in or commitment to cell phone service providers is significantly higher among high relationship intention (HRI) customers than among low relationship intention (LRI) customers. RI is a stronger indicator of customers’ trust in and commitment to cell phone service providers than contracts in both countries. This makes HRI customers more receptive to relationship marketing strategies than customers with contracts or LRI customers, as HRI customers trust and commit to cell phone service providers significantly more.
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Jarocka, Marta, and Hao Wang. "Definition and classification criteria of logistics services for elderly." Engineering Management in Production and Services 10, no. 4 (December 1, 2018): 65–75. http://dx.doi.org/10.2478/emj-2018-0023.
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Abstract An ageing population is a natural and inevitable phenomenon that constitutes an opportunity for the development of the logistics services industry. This is related to the fact that a new demographic profile of the world is determined by a growing number of customers — seniors – with special needs that generate the demand for services such as carriage and home delivery of food and medicines. Therefore, considering the growing demand for logistics services intended for older adults, there is a justified need to develop theoretical knowledge in this area. The paper aims to define a logistics service dedicated to an elderly person as the ultimate recipient as well as to identify the classification criteria of such services. The first part of the article is based on a literature review and presents definitions of a service and a logistics service according to various researchers. It also identifies different classifications of logistics services. These theoretical aspects provided a basis for authors to propose the notion of a logistics service and a catalogue of criteria for systemising logistics services dedicated to older adults. Logistics services for the elderly may be grouped according to classification criteria applicable to what is widely understood as logistics services in source literature. The classification criteria are the type of service, the immateriality of service, the frequency of contacting the customer, the type of purchaser market, the degree of service customisation, the type of a relationship between the service enterprise and the customer, and the place of service provision. Nonetheless, due to the customer-oriented approach in logistics, the authors proposed the classification criteria of these services with regard to age, financial situation, needs, health, expectations, hobby, skills and problems of older adults. Such an approach to classification is determined by considerable inherent diversification of the discussed group of customers as well as a specialised catalogue of logistics services. The classification of logistics services may contribute to the improved design of such services.
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Hiziroglu, Abdulkadir. "Observing Customer Segment Stability Using Soft Computing Techniques and Markov Chains within Data Mining Framework." International Journal of Information Systems and Social Change 6, no. 1 (January 2015): 59–75. http://dx.doi.org/10.4018/ijissc.2015010104.
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This study proposes a model that utilizes soft computing and Markov Chains within a data mining framework to observe the stability of customer segments. The segmentation process in this study includes clustering of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the proposed model. Segmenting customers was done according to the purchasing behaviours of customers based on RFM (Recency, Frequency, Monetary) values. The model was applied to real-world data that were procured from a UK retail chain covering four periods of shopping transactions of around 300,000 customers. Internal validity was measured by two different clustering validity indices and a classification accuracy test. Some meaningful information associated with segment stability was extracted to provide practitioners a better understanding of segment stability over time and useful managerial implications.
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Yontar, Meltem, Özge Hüsniye Namli, and Seda Yanik. "Using machine learning techniques to develop prediction models for detecting unpaid credit card customers." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6073–87. http://dx.doi.org/10.3233/jifs-189080.
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Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.
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Taylor, Susan Elizabeth, Susan Balandin, Erin Wilson, and Kevin Murfitt. "Customer service communication with customers with disability." Journal of Consumer Marketing 36, no. 1 (January 14, 2019): 228–39. http://dx.doi.org/10.1108/jcm-10-2017-2400.
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PurposeThe purpose of this paper is to review published research on retail customer service communication with people with complex communication needs (CCN) and impacts on their social inclusion.Design/methodology/approachThe researchers searched electronic databases EBSCOHost and Web of Science and found no studies on retail customers with CCN. The search was expanded with the intention of exploring factors affecting people with disability as a group and to locate the experience of people with CCN within findings. Studies found were reviewed by the first author and two external reviewers.FindingsTwelve articles met the broadened inclusion criteria. Analysis using the International Classification of Functioning, Disability and Health (ICF) found the literature demonstrated some environmental and personal factors that are likely to construct disability in the retail environment for people with CCN. The authors proposed further research to further explore ICF factors not identified in research and to establish links with social inclusion.Research limitations/implicationsFurther research is needed to understand the role of retail customer service communication in the social inclusion of people with CCN.Social implicationsThe social inclusion of people with CCN will be assisted by findings on good practice customer service communication.Originality/valueShopping is rarely considered in social inclusion research. This review discovered an absence of research into the impact of retail customer communication on inclusion of customers with CCN and proposed a framework for further enquiry.
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Tanone, Radius, and Arnold B. Emmanuel. "Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang." AITI 17, no. 1 (August 31, 2020): 42–55. http://dx.doi.org/10.24246/aiti.v17i1.42-55.
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Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.
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Bafghi, E. P. "Clustering of Customers Based on Shopping Behavior and Employing Genetic Algorithms." Engineering, Technology & Applied Science Research 7, no. 1 (February 12, 2017): 1420–24. http://dx.doi.org/10.48084/etasr.752.
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Clustering of customers is a vital case in marketing and customer relationship management. In traditional marketing, a market seller is categorized based on general characteristics like clients’ statistical information and their lifestyle features. However, this method seems unable to cope with today’s challenges. In this paper, we present a method for the classification of customers based on variables such as shopping cases and financial information related to the customers’ interactions. One measure of similarity was defined as clustering and clustering quality function was further defined. Genetic algorithms been used to ensure the accuracy of clustering.
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Rama, Ali. "AN EXPLORATION OF CUSTOMERS’ SWITCHING BEHAVIOR IN ISLAMIC BANKING INDUSTRY." Journal of Islamic Monetary Economics and Finance 2, no. 2 (February 28, 2017): 251–86. http://dx.doi.org/10.21098/jimf.v2i2.653.
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The existence of the switching behavior among Islamic bank customers may affect to the survival of the Islamic banks of the country. Switching behavior is mostly as an outcome of the negative service experience that may be related to several factors. The purpose of the study is to provide an insight of the drivers that lead to a bank customer switching behavior from one Islamic bank to another bank. The study employed survey method through questionnaire instrument and distributed to Islamic banking customers in several areas of Banten Province, Indonesia. The result of statistical analysis shows that customer satisfaction, service quality, shariah compliance, prices and involuntary switching have their significant effect on customers’ switching behavior in the Islamic banks. However, service failure and advertisement are not statistically significant in driving bank switching. Therefore, the Islamic bank manager should shape their business model around customers’ needs and focuses operational improvements on customers’ most valued interactions. Keywords: Switching behavior, Customer satisfaction, Service quality, Shariah compliance, PricesJEL Classification: G14, G20, G21, M30, D10
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Rajaobelina, Lova, Isabelle Brun, and Élissar Toufaily. "A relational classification of online banking customers." International Journal of Bank Marketing 31, no. 3 (April 8, 2013): 187–205. http://dx.doi.org/10.1108/02652321311315294.
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Chicco, G., R. Napoli, F. Piglione, P. Postolache, M. Scutariu, and C. Toader. "Load Pattern-Based Classification of Electricity Customers." IEEE Transactions on Power Systems 19, no. 2 (May 2004): 1232–39. http://dx.doi.org/10.1109/tpwrs.2004.826810.
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Yu, J. X., Yuming Ou, Chengqi Zhang, and Shichao Zhang. "Identifying Interesting Customers through Web Log Classification." IEEE Intelligent Systems 20, no. 3 (May 2005): 55–59. http://dx.doi.org/10.1109/mis.2005.47.
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Režňáková, Mária, and Jaromír Jedlička. "Customers’ classification by the using mathematics methods." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 3 (2010): 219–24. http://dx.doi.org/10.11118/actaun201058030219.
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Companies, as well as financial institutions, deal with the same problem – verification of credibility of and enterprise. How can they identify enterprises with real threat of insolvency? One of the ways are various scoring models oriented on evaluation of the failure probability. This possibility is, however, very demanding on information about financial behaviour of a given subject. Our article discusses the utilisation of cluster analysis to identify similarities of companies and their distribution into groups. The aim of this contribution is to show what possibility are hidden in utilization of the cluster analysis and test by using of cophenetic correlation coefficient. The firms’ distribution in the class may be used to determine strategy for granting trade credits.
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Sebt, Mohammad Vahid, Elahe Komijani, and Shiva S. Ghasemi. "Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 15 (September 11, 2020): 157. http://dx.doi.org/10.3991/ijim.v14i15.16127.
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<p class="0abstract">Nowadays, the banking system is known as one of the inherent sectors of customer relationship management systems. Its main advantage is to redesign a more responsive organization to satisfy the customers. The banking system aims to improve the structure of organizations to provide a better customer service through a set of automated and integrated processes. The final goal is to collect and reprocess the personal information of customers. To handle this dilemma, a number of new techniques in data mining provide a powerful tool to explore customers’ information regarding a set of data and tools for customer relationship management. Accordingly, the customers’ classification and coordination of banking system are the main challenging issues of today's world. These reasons motivate the attempts of this study to apply a composition of neural network by considering the C4.5 decision tree and the k-closest neighbor method as a variant of core boosting methodology with maximal strategy. To validate the proposed solution approach, a case study of Ansar Bank in Iran is utilized. From the results, it is observed that the proposed method provides a competitive output with the rate of 95% for the customers’ classification. It also outperforms other existing methods with the rate of C4.5 decision tree, neural network, Naive Bayes and KNN with the rate of 1.04%. The main finding of this research is to propose an algorithm with the error rate of 1.9% and error squared of 0.72% as the best performance among other methods from the literature.<strong></strong></p>