Competitive Positioning in International Logistics: Identifying a System of Attributes Through Neural Networks and Decision Trees

Firms involved in international logistics must develop a system of service attributes that give them a way to be profitable and to satisfy customers’ needs at the same time. How customers trade-off these various attributes in forming satisfaction with competing international logistics providers has not been explored well in the literature. This study explores the ocean freight shipping sector to identify the system of attributes that maximizes customers’ satisfaction. Data were collected from shipping managers in Singapore using personal interviews to identify the chief concerns in choosing and evaluating ocean freight services. The data were then examined using neural networks and decision trees, among other approaches to identify the system of attributes that is connected with customer satisfaction. The results illustrate the power of these methods in understanding how industrial customers with global operations process attributes to derive satisfaction. Implications are discussed

Data Exploration as a Trigger for Customer Relationship Management

Today’s research shows a significant increase in the role of data exploration in the management of organizations. Despite the great importance of this problem, in the scientific literature on the subject, too little attention is paid to research presenting the benefits that organizations can derive from data mining methods and techniques in customer relationship management. This paper aims to identify the essence of customer relationship management systems and the most important methods and techniques of data mining (data, text, web and graph mining), as well as to examine the key benefits that Knowledge Intensive Business Services (KIBS) can derive from their use. The research covered seven selected organizations representing the KIBS sector. The collected extensive research material allowed to answer following questions: what methods do organizations use to collect information, what types of data are most often the subject of data analysis, what techniques are used for data mining, and what benefits organizations and their customers derive from data mining

Segmenting Publics

This research synthesis was commissioned by the National Co-ordinating Centre for Public Engagement (NCCPE) and the Economic and Social Research Council (ESRC) to examine audience segmentation methods and tools in the area of public engagement. It provides resources for assessing the ways in which segmentation tools might be used to enhance the various activities through which models of public engagement in higher education are implemented. Understanding the opinions, values, and motivations of members of the public is a crucial feature of successful engagement. Segmentation methods can offer potential resources to help understand the complex set of interests and attitudes that the public have towards higher education. Key findings: There exist a number of existing segmentations which address many of the areas of activity found in Universities and HEIs. These include segmentations which inform strategic planning of communications; segmentations which inform the design of collaborative engagement activities by museums, galleries, and libraries; and segmentations that are used to identify under-represented users and consumers. Segmentation is, on its own, only a tool, used in different ways in different contexts. The broader strategic rationale shaping the application and design of segmentation methods is a crucial factor in determining the utility of segmentation tools. Four issues emerged of particular importance: 1. Segmentation exercises are costly and technically complex. Undertaking segmentations therefore requires significant commitment of financial and professional resources by HEIs; the appropriate interpretation, analysis, and application of segmentation exercises also require high levels of professional capacity and expertise 2. Undertaking a segmentation exercise has implications for the internal organisational operations of HEIs, not only for how they engage with external publics and stakeholders 3. Segmentation tools are adopted to inform interventions of various sorts, and superficially to differentiate and sometime discriminate between how groups of people are addressed and engaged. 4. For HEIs, the ethical issues and reputational risks which have been identified in this Research Synthesis as endemic to the application of segmentation methods for public purposes are particularly relevant

Managing customer relationship within financial organisations

The paper points out the key market changes in the first decades of the twenty-first century and their implications on business philosophy, concepts, principles and techniques of relationship marketing from the point of making strategic marketing decisions within financial organizations. In this context points are made to the important role of information and communication technologies (ICT) in accomplishing executive and creative marketing activities, highlighting the analysis of the process of customer relationship management (CRM) in financial organizations and providing rational insight in CRM potential for improving business results, in order to identify useful tools in this complex area, and offer appropriate solutions, which confirms the benefits of its application in financial services

Profiling for profit : a report on target marketing and profiling practices in the credit industry

This report examines how many businesses make significant investments to purchase and develop customer relationship management systems. Given such investments, information about these systems is not widely available, but some publicly available information gives indication of the extent, and purpose, of the use. Recognising that lenders use customer information and highly sophisticated systems to target their marketing strategies, is the first step towards ensuring that these practices are taken into account in the development of consumer policy and law reform. This research was funded by the consumer advisory panel of the Australian Securities and Investment Commission (ASIC)

The credit risk evaluation models: an application of data mining techniques

In the banking sector, credit risk assessment is an important operation in ensuring that loans could be paid on time, and banks could maintain their credit performance effectively; despite restless business efforts allocated to credit scoring yearly, high percentage of loan defaulting remains a major issue. With the availability of tremendous banking data and advanced analytics tools, classification data mining algorithms can be applied to develop a platform of credit scoring and to resolve the loan defaulting problem. With the dataset of 5,960 observations representing information about characteristics of underlying-collateral loans, the paper sets out a data mining process to compare four classification algorithms, including logistic regression, decision tree, neural network, and XGboost in performance. Via the confusion matrix and Monte Carlo simulation benchmarks, the XGboost outperforms as the most accurate and profitable model, displaying a high consistency about the major factors which could be attributable for default possibilities of the credit scoring

Research trends in customer churn prediction: A data mining approach

This study aims to present a very recent literature review on customer churn prediction based on 40 relevant articles published between 2010 and June 2020. For searching the literature, the 40 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to six main dimensions: Reference; Areas of Research; Main Goal; Dataset; Techniques; outcomes. The research has proven that the most widely used data mining techniques are decision tree (DT), support vector machines (SVM) and Logistic Regression (LR). The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Therefore, the telecom company can effectively predict the churn of customers, and then avoid customer churn by taking measures such as reducing monthly fixed fees. The present literature review offers recent insights on customer churn prediction scientific literature, revealing research gaps, providing evidences on current trends and helping to understand how to develop accurate and efficient Marketing strategies. The most important finding is that artificial intelligence techniques are are obviously becoming more used in recent years for telecom customer churn prediction. Especially, artificial NN are outstandingly recognized as a competent prediction method. This is a relevant topic for journals related to other social sciences, such as Banking, and also telecom data make up an outstanding source for developing novel prediction modeling techniques. Thus, this study can lead to recommendations for future customer churn prediction improvement, in addition to providing an overview of current research trends.info:eu-repo/semantics/acceptedVersio