Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics

Antecedents of ESG-Related Corporate Misconduct: Theoretical Considerations and Machine Learning Applications

The core objective of this cumulative dissertation is to generate new insights in the occurrence and prediction of unethical firm behavior disclosure. The first two papers investigate predictors and antecedents of (severe) unethical firm behavior disclosure. The third paper addresses frequently occurring methodological issues when applying machine learning approaches within marketing research. Hence, the three papers of this dissertation contribute to two recent topics within the field of marketing: First, marketing research has already focused intensively on the consequences of corporate misconduct and the accompanying media coverage. Meanwhile, the prediction and the process of occurrence of such threatening events have been examined only sporadically so far. Second, companies and researchers are increasingly implementing machine learning as a methodology to solve marketing-specific tasks. In this context, the users of machine learning methods often face methodological challenges, for which this dissertation reviews possible solutions. Specifically, in study 1, machine learning algorithms are used to predict the future occurrence of severe threatening news coverage of corporate misconduct. Study 2 identifies relationships between the specific competitive situation of a company within its industry and unethical firm behavior disclosure. Study 3 addresses machine learning-based issues for marketing researchers and presents possible solutions by reviewing the computer science literature

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management

Churn prediction using customers' implicit behavioral patterns and deep learning

The processes of market globalization are rapidly changing the competitive conditions of the business and financial sectors. With the emergence of new competitors and increasing investments in the banking services, an environment of closer customer relationships is the demand of today’s economics. In such a scenario, the concept of customer’s willingness to change the service provider – i.e. churn, has become a competitive domain for organizations to work on. In the banking sector, the task to retain the valuable customers has forced management to preemptively work on customers data and devise strategies to engage the customers and thereby reducing the churn rate. Valuable information can be extracted and implicit behavior patterns can be derived from the customers’ transaction and demographic data. Our prediction model, which is jointly using the time and location based sequence features has shown significant improvement in the customer churn prediction. Various supervised models had been developed in the past to predict churning customers; our model is using the features which are derived jointly from location and time stamped data. These sequenced based feature vectors are then used in the neural network for the churn prediction. In this study, we have found that time sequenced data used in a recurrent neural network based Long Short Term Memory (LSTM) model can predict with better precision and recall values when compared with baseline model. The feature vector output of our LSTM model combined with other demographic and computed behavioral features of customers gave better prediction results. We have also iv proposed and developed a model to find out whether connection between the customers can assist in the churn prediction using Graph convolutional networks (GCN); which incorporate customer network connections defined over three dimension

Selecting the best model for predicting a term deposit product take-up in banking

In this study, we use data mining techniques to build predictive models on data collected by a Portuguese bank through a term savings product campaign conducted between May 2008 and November 2010. This data is imbalanced, given an observed take-up rate of 11.27%. Ling et al. (1998) indicated that predictive models built on imbalanced data tend to yield low sensitivity and high specificity, an indication of low true positive and high true negative rates. Our study confirms this finding. We, therefore, use three sampling techniques, namely, under-sampling, oversampling and Synthetic Minority Over-sampling Technique, to balance the data, this results in three additional datasets to use for modelling. We build the following predictive models: random forest, multivariate adaptive regression splines, neural network and support vector machine on the datasets and we compare the models against each other for their ability to identify customers that are likely to take-up a term savings product. As part of the model building process, we investigate parameter permutations related to each modelling technique to tune the models, we find that this assists in building robust models. We assess our models for predictive performance through the use of the receiver operating characteristic curve, confusion matrix, GINI, kappa, sensitivity, specificity, and lift and gains charts. A multivariate adaptive regression splines model built on over-sampled data is found to be the best model for predicting term savings product takeup

Un-factorize non-food NPS on a food-based retailer

Dissertação de mestrado em Estatística para Ciência de DadosO Net Promoter Score (NPS) é uma métrica muito utilizada para medir o nível de lealdade dos consumidores. Neste sentido, esta dissertação pretende desenvolver um modelo de classificação que permita identificar a classe do NPS dos consumidores, ou seja, classificar o consumidor como Detrator, Passivo ou Promotor, assim como perceber os fatores que têm maior impacto nessa classificação. A informação recolhida permitirá à organização ter uma melhor percepção das áreas a melhorar de forma a elevar a satisfação do consumidor. Para tal, propõe-se uma abordagem de Data Mining para o problema de classificação multiclasse. A abordagem utiliza dados de um inquérito e dados transacionais do cartão de fidelização de um retalhista, que formam o conjunto de dados a partir dos quais se consegue obter informações sobre as pontuações do Net Promoter Score (NPS), o comportamento dos consumidores e informações das lojas. Inicialmente é feita uma análise exploratória dos dados extraídos. Uma vez que as classes são desbalanceadas, várias técnicas de reamostragem são aplicadas para equilibrar as mesmas. São aplicados dois algoritmos de classificação: Árvores de Decisão e Random Forests. Os resultados obtidos revelam um mau desempenho dos modelos. Uma análise de erro é feita ao último modelo, onde se conclui que este tem dificuldade em distinguir os Detratores e os Passivos, mas tem um bom desempenho a prever os Promotores. Numa ótica de negócio, esta metodologia pode ser utilizada para fazer uma distinção entre os Promotores e o resto dos consumidores, uma vez que os Promotores são a segmentação de clientes mais prováveis de beneficiar o mesmo a longo prazo, ajudando a promover a organização e atraíndo novos consumidores.More and more companies realise that understanding their customers can be a way to improve customer satisfaction and, consequently, customer loyalty, which in turn can result in an increase in sales. The NPS has been widely adopted by managers as a measure of customer loyalty and predictor of sales growth. In this regard, this dissertation aims to create a classification model focused not only in identi fying the customer’s NPS class, namely, classify the customer as Detractor, Passive or Promoter, but also in understanding which factors have the most impact on the customer’s classification. The goal in doing so is to collect relevant business insights as a way to identify areas that can help to improve customer satisfaction. We propose a Data Mining approach to the NPS multi-class classification problem. Our ap proach leverages survey data, as well as transactional data collected through a retailer’s loyalty card, building a data set from which we can extract information, such as NPS ratings, customer behaviour and store details. Initially, an exploratory analysis is done on the data. Several resam pling techniques are applied to the data set to handle class imbalance. Two different machine learning algorithms are applied: Decision Trees and Random Forests. The results did not show a good model’s performance. An error analysis was then performed in the later model, where it was concluded that the classifier has difficulty distinguishing the classes Detractors and Passives, but has a good performance when predicting the class Promoters. In a business sense, this methodology can be leveraged to distinguish the Promoters from the rest of the consumers, since the Promoters are more likely to provide good value in long term and can benefit the company by spreading the word for attracting new customers

Optimization of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis and Machine Learning Techniques

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution