Sang Chan Park

Intelligent profitable customers segmentation system based on business intelligence tools

For the success of CRM, it is important to target the most profitable customers of a company. Many CRM researches have been performed to calculate customer profitability and develop a comprehensive model of it. Most of them, however, had some limitations and accordingly the customer segmentation based on the customer profitability model is still underutilized. This paper aims at providing an easy, efficient and more practical alternative approach based on the customer satisfaction survey for the profitable customers segmentation. We present a multi-agent-based system, called the survey-based profitable customers segmentation system that executes the customer satisfaction survey and conducts the mining of customer satisfaction survey, socio-demographic and accounting database through the integrated uses of business intelligence tools such as DEA (Data Envelopment Analysis), Self-Organizing Map (SOM) neural network and C4.5 for the profitable customers segmentation. A case study on a Motor companys profitable customer segmentation is illustrated.

Application of data mining tools to hotel data mart on the Intranet for database marketing

Abstract Data mining, which is also referred to as knowledge discovery in databases, is the process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions. In this paper, we present the data mining process from data extraction to knowledge interpretation and data mining tasks, and corresponding algorithms. Before applying data mining techniques to a real-world application, we build a data mart on the enterprise Intranet. RFM (recency, frequency, and monetary) data extracted from the data mart are used extensively for our analysis. We then propose a new marketing strategy that fully utilizes the knowledge resulting from data mining.

Customer's time-variant purchase behavior and corresponding marketing strategies: an online retailer's case

The traditional customer relationship management (CRM) studies are mainly focused on CRM in a specific point of time. The static CRM and derived knowledge of customer behavior could help marketers to redirect marketing resources for profit gain at the given point in time. However, as time goes on, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date.In this paper, we propose a dynamic CRM model utilizing data mining and a monitoring agent system to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the retailer. Furthermore, we show that longitudinal CRM could be usefully applied to solve several managerial problems, which any retailer may face.

Web mining for distance education

Application of data mining techniques to the WWW (World Wide Web), referred to as Web mining, has been the focus of several research projects and papers. One of several possibilities can be its application to distance education. Taken as a whole, the emerging trends in distance education are facilitating its usability on the Internet. With the explosive growth of information sources available on the WWW, Web mining has become suitable for keeping pace with the trends in education, such as mass customization. In this paper, we define Web mining and present an overview of distance education. We describe the possibilities of application of Web mining to distance education, and, consequently, show that the use of Web mining for educational purposes is of great interest.

Case-based reasoning and neural network based expert system for personalization

Abstract We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains. We propose feature-weighted CBR with neural network, which uses value difference metric (VDM) as distance function for symbolic features. In our system, the feature weight set calculated from the trained neural network plays the core role in connecting both the learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. To validate our system, illustrative experimental results are presented. We use datasets from the UCI machine learning archive for experiments. Finally, we present an application with a personalized counseling system for cosmetic industry whose questionnaires have symbolic features. Feature-weighted CBR with neural network predicts the five elements, which show customers’ character and physical constitution, with relatively high accuracy and expert system for personalization recommends personalized make-up style, color, life style and products.

Design of intelligent data sampling methodology based on data mining

We present a new and better application of data mining techniques by designing an intelligent in-line measurement sampling method for process parameter monitoring in a wafer fabrication. The sampling method specifies the chip locations within the wafer to be measured, and the number of measured chip locations per wafer in order to represent a good sensitivity of 100% wafer coverage and defect detection. To more effectively detect all the abnormalities of process parameters, we extract the spatial defect features in the historical wafer bin map data and then cluster the chip locations having similar defect features through SOM neural network. We merge the homogeneous clusters through a statistical homogeneity test and then select the chip location having the best detection power of each of the existing bins through interactive explorative data analysis of SOM weight vectors. We illustrate the effectiveness of the proposed sampling method using actual fabrication data, and the results indicate that if the sampled chip locations are chosen rationally by optimal data mining techniques, that sampling can provide accurate detection of all defects.

Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning

Abstract Data envelopment analysis (DEA), a non-parametric productivity analysis, has become an accepted approach for assessing efficiency in a wide range of fields. Despite its extensive applications, some features of DEA remain unexploited. We aim to show that DEA can be used to evaluate the efficiency of the system integration (SI) projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning. In this methodology, we generate the rules for classifying new decision-making units (DMUs) into each tier and measure the degree of affecting the efficiencies of the DMUs. Finally, we determine the stepwise path for improving the efficiency of each inefficient DMU.

A new intelligent SOFM-based sampling plan for advanced process control

Abstract Sample measurement inspecting for a process parameter is a necessity in semiconductor manufacturing because of the prohibitive amount of time involved in 100% inspection while maintaining sensitivity to all types of defects and abnormality. In current industrial practice, sample measurement locations are chosen approximately evenly across the wafer, in order to have all regions of the wafer equally well represented, but they are not adequate if process-related defective chips are distributed with spatial pattern within the wafer. In this paper, we propose the methodology for generating effective measurement sampling plan for process parameter by applying the Self-Organizing Feature Map (SOFM) network, unsupervised learning neural network, to wafer bin map data within a certain time period. The sampling plan specifies which chips within the wafer need to be inspected, and how many chips within the wafer need to be inspected for a good sensitivity of 100% wafer coverage and defect detection. We finally illustrate the effectiveness of our proposed sampling plan using actual semiconductor fab data.

Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce

Abstract Recently, as the Internet has become more widely used, Electronic Commerce (EC) has emerged and has developed a high-level business environment. The customer-centric EC model is important for the success of EC and this study presents a new customer-centric EC model in make-to-order (MTO) semiconductor manufacturing environment. In this study we proposed the EC model providing the process transparency of process sampling method that can provide online semiconductor customers with the performance information of available process sampling methods which can be used at all manufacturing process steps for their own products in MTO manufacturing environment, and then the capability to select a desirable one among them based on their purchase situations on EC web site. In the proposed EC model the customer can select a process sampling method that is most suitable to him/her according to the customers purchase situation. In this model the use of intelligent decision support system called customized sampling decision support system (CSDSS) that can autonomously generate available customized sampling methods and provide the performance information of those methods to EC system is requisite. We implemented an Internet-based prototype of CSDSS which had an architecture based on intelligent agent technology and also the successful integration of data mining process for the generation of optimal sampling method into DSS framework by means of applying that technology.

Decision support system for service quality management using customer knowledge in public service organization

As the service quality has been reconsidered in the public sector as well as private enterprises, the need for public sectors to adopt principle and practices of private sectors is concerned with customer-focused approach. However, the different business culture of public service organizations makes it difficult to improve service quality. It is required to establish a structured framework that leads employees to make efforts to improve their service delivery processes and supports continuous improvement of service delivery processes based on the data about the process performance from the customer-perceived value-oriented viewpoint.In this paper, we propose a structured framework that identifies the key service processes, validates from customer perspectives and establishes the measurements to monitor based on the data about the process performance. It uses periodic customer satisfaction index (CSI) surveys (S.C. Park) for understanding customer-perceived values. The proposed framework consists of three phases; the questionnaire design, the key process (KP) identification from the integrated viewpoints of importance and contribution, and the key process indicator (KPI) derivation and management. For the application, we established a web-based decision support system for a public service organization for tourism in Korea.