Bio: Euiho Suh is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Ubiquitous computing & Knowledge sharing. The author has an hindex of 21, co-authored 69 publications receiving 3034 citations.
Papers published on a yearly basis
TL;DR: The goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework using a keyword index and article title search.
Abstract: Nowadays, numerous journals and conferences have published articles related to context-aware systems, indicating many researchers' interest. Therefore, the goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework. This paper is based on a literature review of context-aware systems from 2000 to 2007 using a keyword index and article title search. The classification framework is developed based on the architecture of context-aware systems, which consists of the following five layers: concept and research layer, network layer, middleware layer, application layer and user infrastructure layer. The articles are categorized based on the classification framework. This paper allows researchers to extract several lessons learned that are important for the implementation of context-aware systems.
TL;DR: An LTV model considering past profit contribution, potential benefit, and defection probability of a customer is suggested and a framework for analyzing customer value and segmenting customers based on their value is covered.
Abstract: Since the early 1980s, the concept of relationship management in marketing area has gained its importance. Acquiring and retaining the most profitable customers are serious concerns of a company to perform more targeted marketing campaigns. For effective customer relationship management, it is important to gather information on customer value. Many researches have been performed to calculate customer value based on Customer lifetime value (LTV). It, however, has some limitations. It is difficult to consider the defection of customers. Prediction models have focused mainly on expected future cash flow derived from customers' past profit contribution. In this paper we suggest an LTV model considering past profit contribution, potential benefit, and defection probability of a customer. We also cover a framework for analyzing customer value and segmenting customers based on their value. Customer value is classified into three categories: current value, potential value, and customer loyalty. Customers are segmented according to three types of customer value. A case study on calculating customer value and segmenting customers of a wireless communication company will be illustrated.
TL;DR: In this article, the authors proposed a customer-oriented approach to evaluate the effectiveness of customer relationship management (CRM) activities in e-commerce, and measured the intangible attributes of these benefits such as value enhancement, effectiveness, innovation, and service improvement.
Abstract: Customer relationship management (CRM) has become one of the leading business strategies in the new millennium. CRM is a broad term for managing business interactions with customers. The effectiveness of CRM can be measured as a satisfaction level achieved by CRM activities. Although CRM has emerged as a major business strategy for e-commerce, little research has been conducted in evaluating the effectiveness of CRM. Because it is difficult to demonstrate tangible returns on the resources expanded to plan, develop, implement, and operate CRM, the aim of our research is to measure the intangible attributes of these benefits, such as value enhancement, effectiveness, innovation, and service improvement. In this paper, we propose a customer-oriented
TL;DR: A framework for analyzing customer value and segmenting customers based on their value is proposed and strategies building according to customer segment will be illustrated through a case study on a wireless telecommunication company.
Abstract: The more a marketing paradigm evolves, the more long-term relationship with customers gains its importance. CRM, a recent marketing paradigm, pursues long-term relationship with profitable customers. It can be a starting point of relationship management to understand and measure the true value of customers since marketing management as a whole is to be deployed toward the targeted customers and profitable customers, to foster customers' full profit potential. Corporate success depends on an organization's ability to build and maintain loyal and valued customer relationships. Therefore, it is essential to build refined strategies for customers based on their value. In this paper, we propose a framework for analyzing customer value and segmenting customers based on their value. After segmenting customers based on their value, strategies building according to customer segment will be illustrated through a case study on a wireless telecommunication company.
TL;DR: An agent-based framework for providing the personalized services using context history on context-aware computing is proposed and a prototype system is implemented to show the feasibility of the framework.
Abstract: Predicting the preferences of users and providing the personalized services or products based on their preferences are the important issues. However, the research considering users' preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose an agent-based framework for providing the personalized services using context history on context-aware computing. Based on the proposed framework, we implement a prototype system to show the feasibility of the framework. Previous researches require that the users input their preference manually, but this research provides the personalized services extracting the relationship between users' profile and services under the same context automatically.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.
TL;DR: A literature review of the applications of Analytic Hierarchy Process, which aims to provide a ready reference on AHP, and act as an informative summary kit for the researchers and practitioners for their future work.
Abstract: This article presents a literature review of the applications of Analytic Hierarchy Process (AHP). AHP is a multiple criteria decision-making tool that has been used in almost all the applications related with decision-making. Out of many different applications of AHP, this article covers a select few, which could be of wide interest to the researchers and practitioners. The article critically analyses some of the papers published in international journals of high repute, and gives a brief idea about many of the referred publications. Papers are categorized according to the identified themes, and on the basis of the areas of applications. The references have also been grouped region-wise and year-wise in order to track the growth of AHP applications. To help readers extract quick and meaningful information, the references are summarized in various tabular formats and charts. A total of 150 application papers are referred to in this paper, 27 of them are critically analyzed. It is hoped that this work will provide a ready reference on AHP, and act as an informative summary kit for the researchers and practitioners for their future work.
TL;DR: Findings of this paper indicate that the research area of customer retention received most research attention and classification and association models are the two commonly used models for data mining in CRM.
Abstract: Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.