Customer-related Data Research Articles

WEB FARMING WITH CLICKSTREAM

In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.

Statistical engine knock modelling and adaptive control

A new statistical concept of the knock control of a spark ignition automotive engine is proposed. The control aim is associated with the statistical hypothesis test which compares the threshold value with the average value of the maximum amplitude of the knock sensor signal at a certain frequency. Achievement of the control aim implies the desired separation between the average value of the maximum amplitude and the target value and hence the desired probability of knock occurrence. This new control concept allows the control algorithm parameters to be connected to the probability of knock occurrence and customer-related data. The regulation error is defined as the difference between the actual and the desired values of the statistic utilized. A control algorithm which is used for minimization of the regulation error realizes a simple count-up—count-down logic. A new adaptation algorithm for the knock detection threshold is developed. The confidence interval method is used as the basis for adaptation. A knock detection threshold is presented using a confidence interval with a certain significance level. The adaptation is performed for an aged engine so that the significance level is the same for the new and the aged engines despite the fact that the detection threshold values are different. This, in turn, guarantees the same knock detection performance for new and aged engines. A simple statistical model which includes generation of the amplitude signals, threshold value determination, and a knock sound model is developed for evaluation of the control concept. The statistical knock audibility concept is associated with the outlier detection method and is used in this paper for knock audibility judgement. A Volvo six-cylinder prototype engine equipped with cylinder pressure and block vibration sensors was used in the experiments. An external microphone was used for the knock sound measurements.

Strategic, Operational, and Analytical Customer Relationship Management

Customer Relationship Management (CRM) means different things to different people. For some, CRM is the term used to describe a set of IT applications that automate customer-facing processes in marketing, selling and service. For others, it is about an organizational desire to be more customer focused. Others associate CRM with the capture, analysis and exploitation of customer-related data. One distinction that has been made is between strategic, operational and analytical CRM. This paper sets out to understand, conceptualize and operationalize these terms. Our research generally supports the idea of a multi-dimensional conceptualization of CRM. We develop and present an instrument, consisting of a thirteen-item scale, which can be used to assess an organization's orientation towards one or more of these three forms of CRM.

A strategic customer relationship management system: a hybrid OLAP-neural approach

Competitiveness is a key word for business success. Manufacturers face the challenge of offering customers high quality products and shorter delivery times while remaining cost-effective. In order to stay ahead of competitors, many enterprises are implementing strategic CRM programs in order to respond quickly to customer demands. Thus, modern enterprises are tending to move from a traditional CRM to an approach integrating other enterprise operational systems. This enables enterprises to improve business intelligence, make better decisions and enhance customer relations. In this paper, the design of a Strategic Customer Relationship Management System (SCRMS) is described which collects, integrates and diagnoses various customer-related data from different operation systems in departments within an enterprise. The proposed system aims to establish a cost-effective strategic CRM solution for achieving total customer satisfaction. It integrates the data warehouse concept with two emerging technologies, Online Analytical Processing (OLAP) and Artificial Neural Networks (ANNs), to support in the customer relation strategy. The system was applied in Ka Shui Manufactory Company Limited to support their customer relationship planning. The result shows that significant improvements were made in customer service efficiency and cost reduction.

From listening to the customer to controlling with customer focus: facing the barriers in Nordic financial services

Increasing competition and demanding customers in the financial retail sector challenge the monitoring and control systems in banks and insurance companies, because traditional systems seem to be of little help in focusing on profitable customers. This article compares how Danish and Finnish financial companies are responding to the information-processing challenge of getting and using customer-related data. Results based on two nation-wide surveys among accounting managers indicate that companies are confronted with different barriers to customer-oriented management control, as Danish companies seem to have the more sophisticated market intelligence and control systems while the Finnish companies have the more focused customer-oriented strategies. Nevertheless, differences between companies seem more important than differences between countries. In both countries access to advanced information technology plays a basic but minor role in the development of the technology of listening and control, which in turn may lead to increased competitive advantages. Despite the differences, most companies are faced with major obstacles related to organisation and poor use of market research and it is argued that ‘supported empowerment’ is a viable road to improvement.

Managing the quality and completeness of customer data

Although companies have been collecting customer-related data for years, this was normally for administration rather than customer management. While larger companies have more recently collected customer data for database marketing (to recruit new customers, sell more to existing customers, support customer service operations and retain customers), returns are usually limited because most data are still held and used departmentally. The growth of contact centres, e-commerce and more complex value chains has raised additional issues of enterprise data management and exploitation, while demonstrating beyond doubt that available data are insufficient to support new customer management processes. This paper considers these issues and proposes tried and tested approaches for addressing these customer data management issues in a practical and achievable manner.