Comparing alternative classifiers for database marketing: The case of imbalanced datasets

There are various algorithms used for binary classification where the cases are classified into one of two non-overlapping classes. The area under the receiver operating characteristic (ROC) curve is the most widely used metric to evaluate the performance of alternative binary classifiers. In this study, for the application domains where the high degree of imbalance is the main characteristic and the identification of the minority class is more important, we show that hit rate based measures are more correct to assess model performances and that they should be measured on out of time samples. We also try to identify the optimum composition of the training set. Logistic regression, neural network and CHAID algorithms are implemented for a real marketing problem of a bank and the performances are compared.     

The usage of best practices and procedures in the database community

Database management has an important role to play in the management of data assets which are at the heart of every organization. In a fast moving technological era, where data is rapidly expanding, understanding the current best practices and procedures is important for continuous improvement. This paper investigates how databases are actually administered and identifies what practices and procedures are utilized throughout the database lifecycle. The paper highlights the demographics of people who manage database systems and the diverse requirements of database systems given the wide range of software and hardware available. The results of this paper show the breadth of issues relevant to database management. The paper concludes by showing where existing practice and procedures are not optimal, and by highlighting the complexities in the field.     

一种安全的数据库体系结构

随着互联网的快速发展,网络化已经成为各个行业信息化发展的趋势,如企业的账务数据、贸易记录、工程数据等均需要利用大量的数据库资源。这使得数据库的安全问题成为信息时代所面临的挑战,它的安全问题也成为当务之急,如果不能很好地解决这个问题,必然会阻碍整个行业发展进程。该文结合实际应用,在用户登录、数据存储方面提出了一种新的安全的数据库体系结构,以达到有效保护数据库安全的目的。     

Applying knowledge engineering techniques to customer analysis in the service industry

Using the customer relationship management perspective to investigate customer behavior, this study differentiates between customers through customer segmentation, tracks customer shifts from segment to segment over time, discovers customer segment knowledge to build an individual transition path and a dominant transition path, and then predicts customer segment behavior patterns. By using real-world data, this study evaluates the accuracy of predictive models. The concluding remarks discuss future research in this area.     

The use of database techniques in production control- a practical example from the aerospace industry

Over the last fifteen years Short Brothers, the Northern Ireland Aerospace Company, has developed individual batch systems handling all areas of finance and production. This has led to the typical situation of many stand along batch systems and many diverse files. These systems have grown in size and complexity both in terms of the information they hold and in the sophistication of the processing carried out. With regard to the production applications, information first enters the system when a part number in allocated in the Drawing Office, and thereafter as it progresses through Production Engineering, Material Control, Tool Design, more and more reference data is added to the files. Delivery programmes are fed in for all products and progress is monitored against those programmes. Consequently there are large files of information, some of which is duplicated and most of which, because of the standard file management techniques used is difficult to access. To find a firm basis for forward development both of central integrated batch systems and on-line systems, Shorts chose to use an Integrated Data Management System (IDMS) software package supplied by International Computers Ltd. (ICL). The paper discusses the initial central database development and where it will fit in with future distributed systems and data.     

针对肌肉功能障碍患者的数据库研究与开发

建立标准数据库是临床研究病情变化的重要手段,建立适用于肌肉功能障碍患者的标准数据库模板更具有重要临床意义。本研究针对临床上所采集的具有肌肉功能障碍患者的肌力、肌电数据,通过ODBC连接方式,引入了统一建模语言(UML),使用VC++开发工具,设计和完成了Access数据库,以对获取到的大量的肌力、肌电数据进行管理和使用。数据库有效存储并管理肌力、肌电数据,同时能减少数据占有空间和数据冗余,它更好地满足了医院康复科的需求。     

Customer relationship management in the hairdressing industry: An application of data mining techniques

With the increase of living standards and the sustainable changing patterns of people’s lives, nowadays, hairdressing services have been widely used by people. This paper adopts data mining techniques by combining self-organizing maps (SOM) and K-means methods to apply in RFM (recency, frequency, and monetary) model for a hair salon in Taiwan to segment customers and develop marketing strategies. The data mining techniques help identify four types of customers in this case, including loyal customers, potential customers, new customers and lost customers and develop unique marketing strategies for the four types of customers.     

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.     

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.     

A new knowledge-based constrained clustering approach: Theory and application in direct marketing

Clustering has always been an exploratory but critical step in the knowledge discovery process. Often unsupervised, the clustering task received a huge interest when reinforced by different kinds of inputs provided by the user. This paper presents an approach giving the possibility to incorporate business knowledge in order to guide the clustering algorithm. A formalization of the fact that an intuitive a priori prioritization of the variables might exist, is presented in this paper and applied in a direct marketing context using recent data. By providing the analyst with a new approach offering different clustering perspectives, this paper proposes a straightforward way to apply constrained clustering with soft attribute-level constraints based on feature order preferences.     

The priority factor model for customer relationship management system success

As the market competition becomes keen, constructing a customer relationship management system is coming to the front for winning over new customers, developing service and products for customer satisfaction and retaining existing customers. However, decisions for CRM implementation have been hampered by inconsistency between information technology and marketing strategies, and the lack of conceptual bases necessary to develop the success measures. Using a structural equation analysis, this study explores the CRM system success model that consists of CRM initiatives: process fit, customer information quality, and system support; intrinsic success: efficiency and customer satisfaction; and extrinsic success: profitability. These constructs underlie much of the existing literature on information system success and customer satisfaction perspectives. We found the empirical support for CRM implementation decision-making from 253 respondents of 14 companies which have implemented the CRM system. These findings should be of great interest to both researchers and practitioners.     

Support for modeling relationships in object-oriented databases

Modeling real-world applications typically consists of two parts: the representations of entities of interest, and the representations of relationships among the entities. Object-oriented data models support the first task. This paper argues through examples that the second modeling task is not well supported in current object-oriented database systems. An extended object-oriented data model that facilitates both modeling tasks is developed. This extension provides support for both the representation of complex relationships among entities and their manipulations (queries, updates). A prototype implemented on the ONTOS database management system is briefly described.     

数据挖掘在证券公司CRM客户细分中的应用

将数据挖掘技术应用于证券公司客户关系管理中的客户细分,阐述了相关概念,并给出了一个应用实例。     

分散型系统中分布式数据库的建立和维护

本文叙述分散型系统的分布式数据库的建立和维护，说明了系统数据库的关系模式，讨论了数据库在内存中的组织形式，并叙述了它们在系统中的建立和维护方法。     

CAD智能工程数据库实现方法的研究与应用

在引入了STEP标准之后,各CAD系统用户之间信息共享和信息集成效率大大提高,这对CAD用户系统的开发设计、制造、销售都起到了极大的推动作用。论文主要针对引入STEP后,系统的实现方法作了较为深入的分析,同时提出了利用数据库实现方法较新的技术解决方案,并给出了具体的应用实例。     

Paving the way for CRM success: The mediating role of knowledge management and organizational commitment

Customer relationship management (CRM) has become one of the most influential technologies in the world, and companies are increasingly implementing it to create value. However, despite significant investment in CRM technology infrastructure, empirical research offers inconsistent support for its positive impact on performance. This study develops and tests a research model analyzing the process through which CRM technology infrastructure translates into organizational performance, drawing on the resource-based view (RBV) and the knowledge-based view (KBV) of the firm. Based on an international sample of 125 hotels, the results suggest that organizational commitment and knowledge management fully mediate this process.     

The effect of CRM use on internal sales management control: An alternative mechanism to realize CRM benefits

This case study investigates how effective internal sales management control is achieved through CRM use and why enhanced control is beneficial to firms, which are largely overlooked by the extant literature. The main theoretical lens is organizational control theories. Results show that CRM use strengthened formal control and informal control simultaneously. Formal control was significantly boosted by enhanced outcome measurability and process visibility of sales work. CRM use also facilitated informal control, by serving as the carrier of new institutional processes and sales philosophy, which bolstered clan control, and by functioning as tools for self-control.