A web-based intelligent fault diagnosis system for customer service support

In traditional help desk service centres, service engineers provide a world-wide customer support service through the use of long-distance telephone calls. Such a mode of support is found to be inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. With the advent of the Internet technology, it is possible to deliver customer service support over the World Wide Web. This paper describes a Web-based intelligent fault diagnosis system, known as WebService, to support customer service over the Web. In the WebService system, a hybrid case-based reasoning (CBR) and artificial neural network (ANN) approach is adopted as the intelligent technique for machine fault diagnosis. Instead of using traditional CBR technique for indexing, retrieval and adaptation, the hybrid CBR–ANN approach integrates ANN with the CBR cycle to extract knowledge from service records of the customer service database and subsequently recall the appropriate service records using this knowledge during the retrieval phase.     

Development of technology for customer requirement-based reference design retrieval

Engineering design is a knowledge-intensive process, and includes conceptual design, detailed design, engineering analysis, assembly design, process design, and performance evaluation. Each task involves various aspects of knowledge and experience. Whether this knowledge and experience can be effectively shared is key to increasing product development capability and quality, and also to reducing the duration and cost of the development cycle. Therefore, offering engineering designers various query methods for retrieving engineering knowledge is one of the most important tasks in engineering knowledge management.The study develops a technology for customer requirement-based reference design retrieval to provide engineering designers with easy access to relevant design and associated knowledge. The tasks involved in this research include (i) designing a customer requirement-based reference design retrieval process, (ii) developing techniques related to the technology for customer requirement-based reference design retrieval, and (iii) implementing a customer requirement-based reference design retrieval mechanism. The retrieval process comprises the steps of customer requirement-based query, case searching and matching, and case ranking. The technology involves (1) a structured query model for customer requirement, (2) an index structure for historical design cases, (3) customer requirement-based case searching and matching mechanisms, (4) a customer requirement-based case ranking mechanism, and (5) a case-based representation of designed entities.     

基于95598大数据的电力客户满意度分析

客户满意度反映了客户期望值与客户体验的匹配程度,是评价客户服务质量的一个重要指标。在电力客户满意度研究中,最重要的是获取真实的客户满意度评价结果。本文利用电力客户服务所积累的真实业务数据,提出了基于分层结构的客户满意度得分判定方法,该方法具有严谨的三层结构设计,与实际数据紧联系,逻辑逐层递进,互为补充,从而实现了从模糊的满意度评价到直观的满意度得分的顺利转化。随后通过实际应用案例研究,将电力客户满意度评价与具体的业务过程相联系,提出研究技术路线,对抽取的业务数据进行满意度得分转化,并利用R语言挖掘客户满意度较低的深层原因。所提方法突出了电力客户服务测评的综合性,能客观地从用户体验的角度反映服务质量,具有现实应用价值。     

Conversational Case-Based Reasoning

Conversational case-based reasoning (CCBR) was the first widespread commercially successful form of case-based reasoning. Historically, commercial CCBR tools conducted constrained human-user dialogues and targeted customer support tasks. Due to their simple implementation of CBR technology, these tools were almost ignored by the research community (until recently), even though their use introduced many interesting applied research issues. We detail our progress on addressing three of these issues: simplifying case authoring, dialogue inferencing, and interactive planning. We describe evaluations of our approaches on these issues in the context of NaCoDAE and HICAP, our CCBR tools. In summary, we highlight important CCBR problems, evaluate approaches for solving them, and suggest alternatives to be considered for future research.     

Service improvement by business process management using customer complaints in financial service industry

In financial service industry, service improvement should be considered from process viewpoint and customer viewpoint because the value creation is ultimately linked with internal business processes on the back office and customers are involved as a co-producer of value. In this perspective, customer complaints through call centers are adequate to support the analysis for service improvement in financial service industry. In this study, we propose a web-based decision support system for business process management employing customer complaints, namely Voice of the Customer (VOC), and its handling data for service improvement. It involves VOC conversion for data enrichment and includes analysis of summarization, exception and comparison. The proposed system is evaluated on a major credit card company in South Korea.     

A multi-perspective knowledge-based system for customer service management

The e-business arena is a dynamic, complex and demanding environment. It is essential to make optimal reuse of knowledge of customer services across various functional units of the enterprise. On the other hand, it is also important to ensure that the customer service staff can access and be trained up with dynamically updated knowledge that meets the changing business environment of an enterprise in customer services. However, conventional way of customer service management (CSM) is inadequate to achieve the multi-perspective of an enterprise for achieving knowledge acquisition, knowledge diffusion, business automation and business performance measurement so as to drive the continuous improvement of the customer service quality. In this paper, a multi-perspective knowledge-based system (MPKBS) is proposed for CSM. The MPKBS incorporates various artificial intelligence technologies such as case-based reasoning (CBR) and adaptive time-series model which are used for decision analysis, performance measurement and monitoring. A prototype customer service portal has been built based on the MPKBS and implemented successfully in a consultancy business.     

Customer complaints monitoring with customer review data analytics: An integrated method of sentiment and statistical process control analyses

This study presents a data-driven method to monitor customer complaints for efficient service quality management. Recognising the value of customer reviews as a pool of 'voice of the customer', we propose an integrated method of sentiment and statistical process control analyses. The use of customer review data for statistical process control analysis extends the scope of research and application from the supplier perspective to customer-centric service quality management. The sentiment analysis enables systematic identification of a customer satisfaction score from customer review data while the statistical process control chart analysis allows early detection of significant customer complaints and prevents service failures. The integration of two analyses makes it possible to monitor customer complaints at acceptable time and cost. We applied and validated the proposed method using a mobile game service, offering a guideline for its implementation and customisation. The proposed method is expected to be used as an effective tool for customer complaints monitoring over time, which enables responsive and preventive quality management.     

What to do after a data breach? Examining apology and compensation as response strategies for health service providers

Innovative IT-enabled health services promise tremendous benefits for customers and service providers alike. Simultaneously, health services by nature process sensitive customer information, and data breaches have become an everyday phenomenon. The challenge that health service providers face is to find effective recovery strategies after data breaches to retain customer trust and loyalty. We theorize and investigate how two widely applied recovery actions (namely apology and compensation) affect customer reactions after a data breach in the specific context of fitness trackers. Drawing on expectation confirmation theory, we argue that the recovery actions derived from practice, apology, and compensation address the assimilation-contrast model’s tolerance range and, thus, always lead to satisfaction with the recovery strategy, which positively influences customers’ behavior. We employ an experimental investigation and collect data from fitness tracker users during a running event. In the end, we found substantial support for our research model. Health service providers should determine specific customer expectations and align their data breach recovery strategies accordingly.

     

A case-based reasoning system with the two-dimensional reduction technique for customer classification

Many studies have tried to optimize parameters of case-based reasoning (CBR) systems. Among them, selection of appropriate features to measure similarity between the input and stored cases more precisely, and selection of appropriate instances to eliminate noises which distort prediction have been popular. However, these approaches have been applied independently although their simultaneous optimization may improve the prediction performance synergetically. This study proposes a case-based reasoning system with the two-dimensional reduction technique. In this study, vertical and horizontal dimensions of the research data are reduced through our research model, the hybrid feature and instance selection process using genetic algorithms. We apply the proposed model to a case involving real-world customer classification which predicts customers’ buying behavior for a specific product using their demographic characteristics. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of the typical CBR system.     

The credit game on network supplier and customer based on big data

In this paper, we make B2C transaction as the research object, to analyze the transaction credit game on network supplier and customer based on big data. In the condition of considering the cost of B2C network supplier improving commodity credit, and the cost of B2C customer analyzing and managing network supplier commodity credit big data, constructing the credit game relationship model, analyzing the game process and the influence factors, and simulating the game process using System Dynamics theory model. The research only is a part of the research on B2C network supplier and customer transaction credit relationship, and is a part of the research on B2C network supplier and customer transaction process in big data environment, this paper is only to illustrate the variation that big data bring to B2C network supplier and customer transaction behavior. To the application and research of big data in network transaction credit management, this paper research result has a certain reference value.     

Developing a multi-layer reference design retrieval technology for knowledge management in engineering design

Engineering design is a knowledge-intensive process that encompasses conceptual design, detailed design, engineering analysis, assembly design, process design, and performance evaluation. Each of these tasks involves various areas of knowledge and experience. The sharing of such knowledge and experience is critical to increasing the capacity for developing products and to increasing their quality. It is also critical to reducing the duration and cost of the development cycle. Accordingly, offering engineering designers various methods for retrieving engineering knowledge is one of the most important tasks in managing engineering knowledge.This study develops a multi-layer reference design retrieval technology for engineering knowledge management to provide engineering designers with easy access to relevant design and related knowledge. The tasks performed in this research include (i) designing a multi-layer reference design retrieval process, (ii) developing techniques associated with multi-layer reference design retrieval technology, and (iii) implementing a multi-layer reference design retrieval mechanism. The retrieval process contains three main phases—‘customer requirement-based reference design retrieval’, ‘functional requirement-based reference design retrieval’ and ‘functional feature-based reference design retrieval’. This technology involves (1) customer requirement-based reference design retrieval, which involves a structured query model for customer requirements, a case-based representation of designed entities, a customer requirement-based index structure for historical design cases, and customer requirement-based case searching, matching and ranking mechanisms, (2) functional requirement-based reference design retrieval, which includes a structured query model for functional requirements, a functional requirement-based index structure for historical design cases, and functional requirement-based case searching, matching and ranking mechanisms, and (3) functional feature-based reference design retrieval, which is a binary code-based representation for functional features, an ART1 neural network for functional feature-based case clustering and functional feature-based case ranking.     

Developing a multi-layer reference design retrieval technology for knowledge management in engineering design

Engineering design is a knowledge-intensive process that encompasses conceptual design, detailed design, engineering analysis, assembly design, process design, and performance evaluation. Each of these tasks involves various areas of knowledge and experience. The sharing of such knowledge and experience is critical to increasing the capacity for developing products and to increasing their quality. It is also critical to reducing the duration and cost of the development cycle. Accordingly, offering engineering designers various methods for retrieving engineering knowledge is one of the most important tasks in managing engineering knowledge.

This study develops a multi-layer reference design retrieval technology for engineering knowledge management to provide engineering designers with easy access to relevant design and related knowledge. The tasks performed in this research include (i) designing a multi-layer reference design retrieval process, (ii) developing techniques associated with multi-layer reference design retrieval technology, and (iii) implementing a multi-layer reference design retrieval mechanism. The retrieval process contains three main phases—‘customer requirement-based reference design retrieval’, ‘functional requirement-based reference design retrieval’ and ‘functional feature-based reference design retrieval’. This technology involves (1) customer requirement-based reference design retrieval, which involves a structured query model for customer requirements, a case-based representation of designed entities, a customer requirement-based index structure for historical design cases, and customer requirement-based case searching, matching and ranking mechanisms, (2) functional requirement-based reference design retrieval, which includes a structured query model for functional requirements, a functional requirement-based index structure for historical design cases, and functional requirement-based case searching, matching and ranking mechanisms, and (3) functional feature-based reference design retrieval, which is a binary code-based representation for functional features, an ART1 neural network for functional feature-based case clustering and functional feature-based case ranking.     

电力客户标签体系的研究

针对当前电力体制改革的深入推进,以及电力客户移动化、智慧化和碎片化的能源消费需求,中心利用大数据技术构建了一套先进的电力客户标签体系,制定了按照自然人和组织两个主体的标签层级和划分维度,构建了全方位的电力客户标签体系框架,为精准描绘客户画像,提高对电力客户的全面认知奠定基础。形成了电力客户标签体系的管理机制和流程,确保体系运作高质高效。创建了电力客户标签体系的应用能力框架,并从精准营销、优质服务和风险防控三个方面列举了应用开发项目。电力客户标签体系的研究和实际应用不论在供电企业内部管理还是提高客户满意度上都取得了积极成效,成为供电企业创新发展的关键技术支撑。     

A view-based monitoring for usage control in web services

Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, and accepting a service by the customer. The criticality is more pronounced when the service provider handles the non-functional QoS attribute of privacy, i.e., privacy related to the customer’s personal data. In this regard, the customer needs some guarantee(s) from the service provider about confidentiality management, leading to overall quality characterization of the provided service. A service level agreement (SLA) is primarily intended to specify (in terms of clauses) the level of such non-functional QoS delivered to the customer. The aim is to provide customers with tools that show the fulfillment of QoS guarantees, through SLA monitoring process. In this paper, we address the problem of usage control of private data in service based applications ensuring end-to-end QoS capabilities. We propose a query containment based approach to support the monitoring of privacy-aware SLA compliance, that spells out a customer’s privacy rights, and shows how the customer’s private information must be handled by a Web service provider. We introduce the private data usage flow model upon which the monitoring is performed to observe the data usage flow, and capture the privacy vulnerabilities that may lead to non-compliance. The model is built on top of (i) properties and time-related privacy requirements to be monitored, and (ii) a set of identified privacy violations. As proof of concept, a privacy aware SLA monitoring system, which is an easy-to-use, and efficient tool for observing the dynamic private data usage flow is developed. Experiment results indicate the relevance and applicability of the proposed approach.     

From self-service to super-service: a resource mapping framework for co-creating value by shifting the boundary between provider and customer

Improved understanding of how interactions between service providers and customers create value, along with advances in technology, can expand and redefine the roles of both provider and customer in the service delivery process. The traditional boundary between provider and customer can shift toward self service, with the customer performing many of the tasks previously done by the provider, as in self-service check-in at the airport or self-serve salad bars in restaurants. Alternatively, the boundary can shift in the opposite direction—toward what we define as super service—with the provider performing many tasks previously done by the customer, such as a car rental company that delivers vehicles to customers or automatic bill paying services provided by banks. Why do providers shift this boundary in one direction or the other? Increased competition, changing customer attitudes, and new technologies all contribute to the development of new self-service and new super-service delivery configurations. But these new configurations are only effective when they can create additional value for both provider and customer. In this paper, we introduce a resource mapping framework for shifting the service boundary effectively. This framework allows us to examine the continuum of options from self service to super service from a service systems perspective to identify conditions under which shifting the provider-customer boundary creates value.     

The moderating role of customer–technology contact on attitude towards technology-based services

Previous studies in information systems research and service marketing treat customer behaviour towards technology-based services (TBS) homogeneously. However, recent studies recognize that users have different attitude towards different technologies even if these technologies used to support the same service. Drawing on literature from service marketing (i.e. customer contact theory), information systems (unified theory of technology acceptance), and organizational behaviour (task complexity theory), this study proposes a construct that classifies TBS according to the level of customer–technology interaction they require, namely the customer–technology contact (CTC). The moderating effect of this construct on the relationship between individual characteristics – that is technology readiness and attitude towards TBS – is examined through an empirical study. Technology-based retail services scenarios, with different levels of technology contact, are presented to supermarket shoppers (n=600). Results show that CTC, as a unique service attribute, moderates the effect of personality traits to customers’ attitude. The current study introduces this new service attribute that is applicable to ubiquitous computing services, application and design.     

智慧城市中QOE感知服务组合策略的研究

无线传感器的发展和物联网技术的应用,配有物联网的设备正以一种全新的“智能终端服务”的形式进入人类生活,要想在复杂冗余的物联网环境中寻得满足不同用户需求的服务,需要考虑服务组合的概念。为此,在对传统服务组合研究方法深入分析的基础上,提出了智慧城市的服务组合选择策略。首先根据物联网应用服务的多样性,通过改进蚁群算法获得用户对服务的偏好顺序,然后使用层次化QOE评价模型量化客户体验质量,进而选出最优服务组合提供给用户。根据提出的服务组合策略,给出服务选择具体的实施流程,通过仿真模拟表明基于改进蚁群算法的服务组合策略在服务选择问题上具有一定的推广价值。     

基于自适应特征权重聚类算法的用电问题分析

提升客服系统对于群体客户用电问题的分析与理解能力是改善电力行业客服质量的重要途径之一.本文基于数据挖掘中的聚类技术,以电力客服中心记录的客户用电问题为数据基础,建立客户服务数据分析聚类模型,进而提出了针对用电问题分析的改进的自适应特征权重K-Means聚类算法.实验验证了该方法可快速准确地实现客服数据的自动聚类,可挖掘出隐藏的客户用电问题关键信息,为改进用电力客服质量与潜在服务风险预测提供了技术支撑.     

Web-based intelligent helpdesk-support environment

With the advent of Internet technology, it is now feasible to provide effective and efficient helpdesk service over the global Internet to meet customers' requirements and satisfaction. In this research, we have designed and developed a Web-based intelligent helpdesk-support environment, WebHotLine, to support the customer service centre of a large multinational corporation in the electronics industry. The paper describes the basic architecture of the environment that supports the major functions of Web-based fault information retrieval, online multilingual translation capability, different operating modes of video-conferencing for enhanced support and direct intelligent fault diagnosis by customers or customer support engineers. As a result, WebHotLine helps to save cost in eliminating the expensive overseas telephone charges, reduction in machine down time and number of on-site visits by service engineers as in traditional helpdesk environment.