Customer benefits and value co-creation activities in corporate social networking services

Despite the growing importance of customers as value co-creators, little is known about their reasons for voluntarily participating in value co-creation activities. Based on expectancy-value theory, this paper empirically examines the specific benefits that customers anticipate from engaging in certain co-creation activities. Furthermore, it examines the manner in which interactions between customer benefits and joint activities/industry types influence customers’ intentions to continuously engage in value co-creation in the context of corporate social networking service (SNS)-based virtual customer environments (VCEs). Study data were collected from 327 corporate Facebook and Twitter VCE users through an online panel survey. Results of the main effect relationships reveal that all three customer benefits – cognitive, social integrative, and hedonic – significantly impacted customer intentions to continuously participate in value co-creation. Furthermore, the benefit–intention relationships differed depending upon the type of co-creation activities (e.g. ideation, design, testing, support, and marketing) and industry characteristics. Theoretical discussions about these results are provided, and implications for future research and practice are presented.     

Customer segmentation issues and strategies for an automobile dealership with two clustering techniques

Companies can use customer segmentation to group customers with similar characteristics together and identify the differences between groups to develop marketing strategies. This study investigates the problem of customer segmentation in relation to automotive customer relationship management and presents a real case study of an automobile dealer in Taiwan. Although several past studies have adopted different clustering techniques with which to group customer attributes, few have simultaneously considered customer transaction behaviour and customer satisfaction variables. In addition, most previous work has used only a single clustering method for customer segmentation, which results in unreliable results and leads to inadequate marketing decisions. Therefore, in this study, we consider two clustering techniques, k‐means and expectation maximization, and compare their results for correctness. The experimental results show that four customer groups are identified with both clustering methods: loyal, potential, VIP and churn customer groups. Based on the segmentation results, several customized marketing strategies aimed at each of the four customer groups are suggested to improve the quality of services for effective customer relationship management.     

电力客户信用评价系统的设计及实现

基于组合评价模型对电力系统客户信用进行综合评级,通过综合考虑电力系统信用相关因素,选取了电力系统大客户购电水平、信用状况、潜在价值、用电特性、可持续能力5个方面的相关参数,建立了电力大客户评价指标体系,进而建立了基于多种单一评价法的组合评价模型对电力系统客户信用进行综合评级分析的系统.实际应用表明该系统获得了较为满意的应用效果.     

iValue: A knowledge-based system for estimating customer prospect value

In the past, companies changed their focus from product-oriented within marketing to demand-oriented within quality improvement. Today, they emphasize customer service, customer loyalty, and customer profitability. The significance of customer-centric services has become critical and essential. However, certain research which investigates the effect of customer lifetime value focuses only on lifetime values of existing customers. This study devises a novel model to predict customers’ prospect value. In the proposed model, we utilize the concept of finance which stands in the current status and predicts future value based on historical data. The simulated results reveal that, in a long-term simulation, customer prospect value rises when reach rate increases. Decreasing reach rate and costs result in high customer prospect value; however, the value of customer prospect value decreases in a long-term simulation. The new model complements the existing customer lifetime value model from a different perspective and provides clues to customer value for future researches.     

K-means与朴素贝叶斯在商务智能中的应用

不同的客户给企业带来的效益并不相同,为了提高企业的客户关系管理水平,采用基于K-means的聚类的Naive Bayesian算法来预测客户价值,从而使企业可以针对不同的客户采用不同的营销策略,为企业决策提供依据。朴素贝叶斯分类模型是一种简单有效的分类方法,它理论基础好,分类精度高,由于朴素贝叶斯分类中的独立假设前提,使得在特征选择步骤能否准确有效的分类显得尤为重要。实验结果表明,该算法能在保证一定的准确率的同时,可以预测出更多的潜在高价值客户。     

A knowledge-based supplier intelligence retrieval system for outsource manufacturing

Knowledge management is to promote business success through a formal, structured initiative to improve the use of knowledge in an organization, in which an effective organizational memory information system plays an increasingly important role. Unlike the past, the performance of an enterprise now depends much on the performance and relationship of its customer–suppliers in the value chain. Good customer–supplier relationships are important for an organization to respond to dynamic and unpredictable changes. This paper describes a knowledge-based supplier selection and evaluation system, which is a case-based reasoning decision support system for outsourcing operations at Honeywell Consumer Products (Hong Kong) Limited in China. As a result, collaborative suppliers are identified quickly during the new product development process. By using the system, the cumulative performance of suppliers is constantly updated automatically according to past practice. This means that the knowledge of suppliers can be retained, categorized, retrieved and managed effectively.     

基于ANP和K-means聚类的客户价值分类模型及应用

建立两维度的客户价值评价模型,从客户当前价值和潜在价值两个方面评价客户价值。该模型首先运用网络层次分析法（ANP）对指标赋权,兼顾了指标间的相互影响,根据各指标权重和得分计算客户价值;然后使用K-means聚类算法对客户群进行细分,确定客户等级划分的标准,划分客户类别。最后以某市烟草公司零售终端客户价值分类为应用实例,运用定性和定量的方法建立多角度的客户价值评价指标体系,采用ANP确定指标权重,并根据客户价值评价结果使用K-means聚类进行客户分类,同时对每个类别的客户特征及其相应的营销策略进行分析,所提模型能够更全面、客观地对客户价值进行评价与分类     

基于全域市场数据感知的终端客户推荐

终端客户推荐系统是大型制造商终端营销的一种有效工具.如何在互联网+环境下通过采集全域市场数据,设计一个寻找最佳目标客户的推荐方法成为了一项挑战.为解决这一问题,本文提出一种基于全域市场数据感知的终端客户推荐方法（GMF）.即采用全域分析的思想对全国范围内的客户数据进行预处理,建立全方位,多角度的评估指标,得到目标客户价值.然后通过域子空间分解的方法,在域子空间中对数据进行分解分析,得到某一区域内的客户评价标准,将二者分析结果进行有效融合,通过计算耦合对象相似度,并筛选出最相似的TopN个数据作为最佳目标客户结果集.在大型制造商营销活动所生成的数据集上的实验结果表明：本文提出的推荐算法其性能明显优于当前主流的协同过滤算法.     

新时期医院HIS系统设计架构模式探讨

随着信息化产品在医疗服务、管理和运营中的应用,医院HIS系统得到了长足的发展,系统设计架构采用两层、三层等架构模式,更加适用于用户登录使用、海量数据管理。本文基于笔者多年的HIS系统运行管理实践,详细地分析了HIS系统采用的架构模式,探讨HIS系统架构未来发展趋势。     

改进的决策树算法在潜在客户获取中的应用

在企业营销活动中,对潜在客户进行有针对性的营销活动,可以节省很大的开支,增加企业利润,该文将引入boosting思想的改进的决策树算法用于挖掘预测潜在客户群,并提出了获取潜在客户的合理可行的数据挖掘流程,用以指导企业的营销决策。试验结果表明,该方法有着很好的理论价值和应用价值。     

A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network

With the increasing interest and emphasis on customer demands in new product development, customer requirements elicitation (CRE) and evaluation have secured a crucial position in the early stage of product conceptualization. As such, it is highly desired to have a systematic strategy for soliciting customer requirements effectively, and subsequently, analyzing customer orientations quantitatively. For this purpose, a customer-oriented approach has accordingly been proposed and investigated in this study. It concerns both breadth and depth perspectives of customer requirements acquisition as well as customer and marketing analysis. This prototype system comprises two interrelated components, namely, the CRE and the customer/marketing analysis (CMA) modules. The process starts from the voice of customers and ends with the identified opportunities from marketing analysis. In the prototype system, the laddering technique has been employed to enable CRE via the so-called customer attributes hierarchy (CAM). In addition, the adaptive resonance theory, particularly ART2, neural network has been applied as a toolkit for further customer and marketing analysis. A case study on wood golf club design is used to demonstrate and validate this approach.     

Product portfolio identification based on association rule mining

It has been well recognized that product portfolio planning has far-reaching impact on the company's business success in competition. In general, product portfolio planning involves two main stages, namely portfolio identification and portfolio evaluation and selection. The former aims to capture and understand customer needs effectively and accordingly to transform them into specifications of product offerings. The latter concerns how to determine an optimal configuration of these identified offerings with the objective of achieving best profit performance. Current research and industrial practice have mainly focused on the economic justification of a given product portfolio, whereas the portfolio identification issue has been received only limited attention. This article intends to develop explicit decision support to improve product portfolio identification by efficient knowledge discovery from past sales and product records. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with requirement analysis enacted among customers, marketing folks, and designers. An association rule mining system (ARMS) is proposed for effective product portfolio identification. Based on a scrutiny into the product definition process, the article studies the fundamental issues underlying product portfolio identification. The ARMS differentiates the customer needs from functional requirements involved in the respective customer and functional domains. Product portfolio identification entails the identification of functional requirement clusters in conjunction with the mappings from customer needs to these clusters. While clusters of functional requirements are identified based on fuzzy clustering analysis, the mapping mechanism between the customer and functional domains is incarnated in association rules. The ARMS architecture and implementation issues are discussed in detail. An application of the proposed methodology and system in a consumer electronics company to generate a vibration motor portfolio for mobile phones is also presented.     

Visualization method for customer targeting using customer map

An important issue in customer-oriented marketing area is target selection which enables the determination of potential customers. In this paper, we suggest a novel customer targeting method: customer map for a service industry. The customer map is the visualization method for customer targeting. It depicts value distribution across customer needs and customer characteristics. To develop the customer map, we integrate numerous customer data from various data sources, perform data analyses using data mining techniques, and finally visualize the information derived by the former analyses. The customer map helps decision makers to build customer-oriented strategy under the unified goal of customer targeting. It also affords to monitor and perceive real time state and the change of customer value distribution based on their information without preconception. We apply the customer map to a credit card company, build web-based prototype system for the customer map and acquire managerial implications from this study.     

A dynamic decision support system to predict the value of customer for new product development

In recent years, firms have focused on how to enter markets and meet customer requirements by improving product attributes and processes to boost their market share and profits. Consequently, market-driven product design and development has become a popular topic in the literature. However, past research neither covers all of the major influencing factors that together drive customers to make purchase decisions, nor connects these various influencing factors to customer purchasing behavior. Past studies further fail to take the time value of money and customer value into consideration. This study proposes a decision support system to (a) predict customer purchasing behavior given certain product, customer, and marketing influencing factors, and (b) estimate the net customer lifetime value from customer purchasing behavior toward a specific product. This will not only enable decision-makers to compare alternatives and select competitive products to launch on the market, but will also improve the understanding of customer behavior toward particular products for the formulation of effective marketing strategies that increase customer loyalty and generate greater profits in the long term. Decision-makers can also make use of the system to build up confidence in new product development in terms of idea generation and product improvement. The application of the proposed system is illustrated and confirmed to be sensible and convincing through a case study.     

客户标签在95598业务中的应用

基于电力客户标签体系在供电企业的研究成果,介绍了电力客户标签体系在95598业务中的若干典型应用:一是建立了基于“客户画像+满意度测评”的闭环管理模式,有力支撑了客户服务工作的常态化开展;二是构建了基于“话务引导”和“服务策略推送”的话务服务模式,实现了客户精准洞察,帮助95598客服人员迅速识别客户特征,缩短反应时间,针对性的为客户提供差异化、精细化服务。客户标签在供电企业的实际应用为以客户为中心的现代电力营销体系起到了中流砥柱的作用。     

电力营销信息化客户画像的应用研究

随着电气化水平提高和社会信息化普及,传统电力营销信息化开始受到冲击。多方面形势反映出传统电力营销信息化需转型,客户画像作为一种营销切入方法已被许多领域应用。本文从模型构建、数据来源、关键技术和应用场景研究分析。电力客户画像由客户标签、价值需求、服务策略构成,借由关键技术实现,并从电力用户和衍生领域进行应用探索。通过对电力营销信息化客户画像的应用研究探索,为客户洞察、电力服务和质量提升以及客户满意度提升奠定良好的基础。     

An examination of the determinants of customer loyalty in mobile commerce contexts

While the importance of customer loyalty has been recognized in marketing literature for at least three decades, the development and empirical validation of a customer loyalty model in a mobile commerce (m-commerce) context had not been addressed. The purpose of our study was to develop and validate such a customer loyalty model. Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 users of m-commerce systems in Taiwan. Structural modeling techniques were then applied to analyze the data. The results indicated that customer loyalty was affected by perceived value, trust, habit, and customer satisfaction, with customer satisfaction playing a crucial intervening role in the relationship of perceived value and trust to loyalty. Based on the findings, its implications and limitations are discussed.     

客户价值评价体系在高尔夫CRM 中的研究

客户价值评价体系是CRM（CustomerRelationshipManagement,即客户关系管理）系统的核心,通过对高尔夫行业客户价值评价体系的分析,建立了高尔夫球场预定服务业客户价值评价体系模型,介绍了应用层次分析法计算该模型的客户价值．根据不同的客户价值把客户分为四种类型,建议针对不同类型的客户采用不同的营销、销售以及服务策略．最后介绍了该模型在CRM系统中的应用．     

Integrated, computer-aided order fulfillment

The process for producing bank checks is well established, and semi automated systems for doing so have been in place for several years. However, these legacy systems are now having difficulty meeting new customer demands. To support greater product variability and responsiveness to new customer requirements, our company (Deluxe Corp.) decided to reexamine its current process with an eye to increased automation. The resulting Post Press System replaced highly labor-intensive, inflexible check order processing and fulfillment with an approach that integrates flexible manufacturing with real time, object database concepts. Several months after initial deployment, the benefits of this integration are significant. Our company can process new orders more quickly, with higher accuracy, and at lower cost. The system also allows rapid order cancellation. Further, the new plastic wrap provides a tamper-evident package, a security feature of paramount importance to our clients. In addition, the PPS's modularity facilitates its rapid deployment in a new factory setting. Deluxe's investment in the PPS's design and deployment has been repaid in cost and time savings. The new architecture has laid the foundation for further manufacturing process automation-beyond order fulfillment and packaging. It provides an interface to new manufacturing technologies and helps Deluxe and its clients implement innovative marketing techniques     

Segmentation of telecom customers based on customer value by decision tree model

The more the telecom services marketing paradigm evolves, the more important it becomes to retain high value customers. Traditional customer segmentation methods based on experience or ARPU (Average Revenue per User) consider neither customers’ future revenue nor the cost of servicing customers of different types. Therefore, it is very difficult to effectively identify high-value customers. In this paper, we propose a novel customer segmentation method based on customer lifecycle, which includes five decision models, i.e. current value, historic value, prediction of long-term value, credit and loyalty. Due to the difficulty of quantitative computation of long-term value, credit and loyalty, a decision tree method is used to extract important parameters related to long-term value, credit and loyalty. Then a judgments matrix formulated on the basis of characteristics of data and the experience of business experts is presented. Finally a simple and practical customer value evaluation system is built. This model is applied to telecom operators in a province in China and good accuracy is achieved.