Product development with data mining techniques: A case on design of digital camera

Many enterprises have been devoting a significant portion of their budget to product development in order to distinguish their products from those of their competitors and to make them better fit the needs and wants of customers. Hence, businesses should develop product designing that could satisfy the customers’ requirements since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C5.0 algorithms are methodologies of association rules and decision trees for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing.     

数据挖掘技术在寿险客户购买行为分析中的应用

随着中国加入WTO,我国寿险市场的竞争日益白热化.我国寿险行业经过近二十年的发展,取得长足进步,积累了大量客户数据.本文的目的就是希望在这些公司所积累的寿险客户对产品的购买记录上,通过数据挖掘的方法,发现客户对险种类型选择的模式.在数据挖掘中,通过对客户的数据进行抽取、清洗和预处理,生成数据挖掘库,并使用SPSS Clementine数据挖掘工具,利用C5.0算法建立决策树模型,并对不同的模型进行了分析和对比,以发现客户在寿险产品选择上的一些模式.     

基于兴义供电局2017年客户诉求数据的分析和探讨

为了促进电网企业在电力体制改革纵深推进过程中探索出一条“赢得市场、赢得客户”的双赢之路,本文以贵州电网区域内面临“两网并存”竞争压力的兴义供电局为例,通过对兴义供电局2017年所有客户诉求数据进行提取、筛选、挖掘,分析,将竞争区域内的客户需求和客户抱怨逐条分析,并精准定位到兴义供电局下属分县局具体区域、时间段、业务类型、处理内容,通过数据对比和筛查,查找出客户问题的痛点和要点,针对性提出合理化建议和解决方案,促进兴义供电局供电服务能力提升和经营决策改善。目前,客户诉求数据分析已作为大数据分析的一项在电网企业中陆续开展,其所包括的丰富内容和数据挖掘方法还有待进一步深入研究。兴义供电局已将2017年客户诉求分析数据作为服务提升指引,纳入客户满意度提升细项制定相关举措,通过更有针对性的改善,提升两网并存局面下的供电服务竞争力。     

Mining shopping behavior in the Taiwan luxury products market

The rapid growth of Taiwan’s economy has been accompanied by the country’s developing market for luxury products. To successfully establish the new market demand chain for the luxury industry in Taiwan, it is essential to understand customer preferences. Thus, this study uses an association rules approach and clustering analysis for data mining to mine knowledge among luxury product-buying customers in Taiwan. The results of knowledge extraction from data mining, illustrated as knowledge patterns, rules and knowledge maps, are used to make recommendations for future developments in the luxury products industry.     

浅析IT软件产品需求收集与挖掘

需求收集和挖掘真正的体现了需求的市场和用户驱动.用户访谈,问卷调查,头脑风暴,竞品分析等都是需求收集和挖掘的方法.有效的需求收集和需求挖掘需要我们搞清楚用户真正的需求和问题背后的深层次问题,这样才有可能使得我们的产品解决方案真正的满足客户的需要.     

数据挖掘技术在中小型制造企业CRM中的应用

客户关系管理(CRM)是一种全新的管理理念,是提高企业竞争力的一种手段,而数据挖掘技术为CRM的实施提供了良好的支持。简单介绍了CRM系统的基本理论知识,分析了数据挖掘技术在中小型制造企业CRM系统中的主要应用,详细论述中小型制造企业运用数据挖掘技术,提高企业信息管理水平和企业竞争力,使企业建立以客户为中心的经营模式。结合中小型制造企业的特点,详细分析当前制造企业管理的困境,提出了中小型制造业具体在CRM中实施的步骤和方法。     

High-end IT Security: No Longer a Distant Pipe Dream for SMEs

Many of Europe’s small to medium enterprises (SMEs) have been left behind in the E-commerce race. They still have a long way to go to catch up with larger companies already responding to customer demand for online cashless payment schemes, sales automation programs, supply chain integration applications, secure database access for remote employees, secure messaging and Web access, and digital document management. Already struggling to grasp market share and provide value to customers which differentiates them, provision of an E-commerce channel to market is an additional pressure.     

Enterprise systems and demand chain management: a cross-sectional field study

Increased product variety, compressed cycle times, supply chain network based competition and the uncertainty in business environment are forcing organizations to shift their emphasis towards demand chain management. Though enterprise systems and supply chain management software solutions are well in place in most of the large enterprises in Australia, their ability to support demand chain management approach is not yet known. This paper investigates the adoption of demand chain management in Australian organizations using cross-sectional qualitative field study. It found that the adoption is limited despite the organizational capabilities and sound digital platforms. Standardization, integration, visibility and control of processes and information, enabled by enterprise systems, are helping the organizations in improving their ability to sense and shape market demand and customer needs, and build agility into their decision making processes. Attempts to synchronise operating and finance cycles and collecting market intelligence and sharing of that across the supply chain are some of the initiatives that have started showing benefits. Adopting the demand chain management initiatives such as establishment of market intelligence mechanism, building agility into decision making processes, sophisticated usage of optimization tools embedded in software solutions, and process frameworks such as Supply Chain Operations Reference are helping organizations to achieve both agility and control simultaneously.     

A web-based system for analyzing the voices of call center customers in the service industry

We developed a Web-based system for analyzing the voices of call center customers of a life insurance company, so that it would help decision makers understand customer needs better and it would help them make consistent decisions regarding customer support. It used conventional statistical and data mining techniques to identify customer voice patterns. To demonstrate results, we gathered actual customer complaints from the service operation of a target company. Using this data, the system pinpointed problematic areas where complaints happened (one-dimensional analysis), the relationship among problems (two-dimensional analysis), and the root cause of problems (Failure Mode and Effects Analysis).     

Mining customer knowledge for electronic catalog marketing

Direct marketing is the use of the telephone and non-personal media to communicate product and organizational information to customers, who then can purchase products via mail, telephone, or the Internet. In contrast, catalog marketing is a type of marketing in which an organization provides a catalog from which customers make selections and place orders by mail or telephone. However, most catalogs for retailing firms are presented to customers in the format of paper catalogs without strategic segmentation design and implementation. In this regard, electronic catalog design and marketing could be a method to integrate the Internet and catalog marketing using market segmentation in order to enhance the effectiveness of direct marketing and sales management in retailing. This paper uses data mining based on association rules from relational database design and implementation for mining customer knowledge. As result, marketing knowledge patterns and rules are extracted for the electronic catalog marketing and sales management of a retailing mall in Taiwan.     

基于观点挖掘的笔记本电脑评论分析系统

主要以商业领域的需求和应用为背景,构建一个智能化的笔记本电脑评论分析系统.该系统对国内大型购物网站上非结构化、自由式的笔记本电脑评论文本进行情感倾向识别和产品特征归纳,实现了利用数据挖掘和商务智能的手段分析网络消费者对特定产品的反馈,帮助企业管理人员了解特定产品的市场需求、制定商业决策.实验结果证明该系统能够较准确的得出分类结果并归纳出产品特征.     

考虑产品替代的供应链协调问题研究

替代品大大增加了企业间的竞争,使企业难以达到所期望的盈利水平。对供应链进行合理有效的管理可最大限度地促进企业适应当今多变的市场环境。人们对于产品的需求不仅仅取决于产品本身的价格,也取决于互补产品的价格。对此,构建一个以产品替代的供应链协调问题为研究目标,以两种供应链管理模式(集中供应链(CSC)和分散供应链(DSC))为依托,通过建立混合整数规划模型并对相关参数进行验证,得出集中供应链模式更能灵活应对当今市场。     

基于关系图提取算法的烟草销售客户关系管理的应用研究

展望客户关系管理,数据挖掘技术在其中起到了至关重要的作用,它可以挖掘出千万数据中的有用信息,企业才能对客户进行各类价值分类,然后预测客户的行为,从而做出正确有效的决策。在CRM应用中,基于数据挖掘技术的基础上,结合CRM系统的不足,为避免或减少客户管理系统因管理不到位而导致客户的流失,造成企业的损失,提出了客户关系图的提取算法,很好地分析了数据挖掘技术在CRM中的应用。     

A fit-gap analysis of e-business curricula and job demand in Taiwan and the US

During the past decade, many enterprises have been re-directing their resources into critical business areas to keep up with economic and market changes. They have adopted electronic business (EB) systems, such as supply chain management, enterprise resource planning (ERP), and enterprise application integration (EAI), to ensure seamless integration with internal processes as well as suppliers and customers. This phenomenon indicates that EB systems are playing an important role in attaining the effectiveness of enterprise operations. However, there is a constant complaint from recruiters that EB professionals with the right skills are hard to come by. To gauge the rapid changes in EB education and industry demand, EB curricula from top universities and job announcements from popular career Web sites in both the US and Taiwan are collected. We analyze the data and compare the results between the two countries to recommend remedial actions for narrowing the gap between industry and academia.     

产品创新中的人性化设计与个性化设计

文章通过论述产品创新中人性化设计的发展与定位,个性化设计由人性化设计开始及个性化设计可行性以及实现途径,说明创新设计的过程不仅仅是设计师借助技术和发挥想象力的过程,还是设计师与使用者不断沟通表达消费者需求的过程。设计师要充分考虑产品的使用群体的审美心理因素,满足用户的物质与精神需求,并在此前提下将个性和风格融入进去,为产品创新设计起到指导作用,从而提高产品吸引力和企业核心竞争力。     

数据整合与数据挖掘技术在医疗保险信息系统的研究与应用

通过分析医疗保险管理信息化深入发展的需求,从技术的角度提出医疗保险信息系统数据整合及数据挖掘的总体解决方案,并对医疗保险信息系统的数据仓库的设计、数据整合的方案以及数据挖掘的技术和应用进行概要的分析和论述,并用关联规则挖掘算法实证研究医保信息挖掘的可能性与必要性。利用编码、解码技术和SQL的聚集函数,实现基于SQL的FP-Growth算法,从而突破机器内存对数据挖掘的处理效率,实现对海量数据挖掘的高效挖掘。     

基于K-均值聚类算法的大客户用电行为分析

为掌握大客户的用电习惯和用电特征、满足不断变化的客户用电需求、逐步提升大客户领域供电服务水平,本文采用K-均值聚类算法、二八定律、归一化算法等统计分析方法,运用SPSS等数学分析工具,从行业特点、客户价值、用电需求、成长性等方面刻画用电客户群体特征,挖掘大客户用电数据中的隐藏数据和潜在价值,得出五类典型的大客户用电负荷...     

Constructing credit auditing and control & management model with data mining technique

The 2008 financial tsunami, hitting the globe across all types of industries, causing tides of bankruptcies and severe unemployment, had its epicenter at American subprime in the housing market. In fact, the US subprime storm was just a premonition, while the root cause of the financial tsunami lied in the oversupply of structured credit products. Credit card business, one of the structured credit products, which under an intensively competitive environment, have been released by many banks with high spread, high return, and easy-to-apply appeals to carter to consumers needs. In order to allure the customers, some banks even go to the extent as simplify the credit rating, which in turn has increased credit risk, causing high non-performing ratio, increased debt collection cost, and growing bad debt counts. Accordingly, credit risk auditing plays a vital role in the successful management of credit card business. In response to such needs, the present study aims to conduct analysis and investigation on the current status of the industry with CRISP-DM model. First, customers’ demographic data and payment-related statistics were analyzed to identify feature variables, which were then sorted out as demographic data, debt data, payment rating etc. Next, by utilizing artificial neural network of data mining technique, the study tries to predict customer’s regular pattern of consumption, payment and/or default and bad debt, and to develop a set of credit granting principle by employing the decision tree technique. Since data mining classification model has a greater power in discriminating credit card granting, it can thus be used to construct accurate credit variable rules and predictive model, to further improve credit checking effect and credit risk control. Using the credit auditing data of a certain bank as a case study, the study intends to verify that the model constructed by the researcher can effectively identify the potential key factors of its credit card granting rule, to minimize the cost loss of Model I and Model II credit business, and eventually enhance the stability and profitability of the bank’s credit card business.     

数据挖掘技术在CRM中的应用

CRM（客户关系管理）是一种旨在改善企业与客户之间关系的新型管理机制。数据挖掘技术应用于CRM中，能够加强和改善客户关系管理，从而为企业带来更多的利润。该文对数据挖掘技术在CRM中的应用内容和过程进行了研究。     

基于数据挖掘的电力需求侧管理综述

电力需求侧管理突破了传统的电力管理模式,改变了依靠单纯地扩大供应能力以满足日益增长的电力需要的方式,在更高层次上处理供应侧和需求侧的关系,特别强调用户参与负荷管理的积极性,做到和用户共同实施用电管理。针对这一特点,通过分析时下最热门的数据挖掘技术的现状,并结合数据挖掘技术分别从电力需求侧管理的需求侧响应,有序用电和能效管理三个方面对基于数据挖掘的电力需求侧管理进行了综述。结论得出,基于数据挖掘的电力需求侧管理是未来电力需求侧管理的主要发展方向,研究如何将数据挖掘技术应用到用户需求侧管理中,不仅可以推动电力需求侧管理的进一步发展,还可以加快电力行业的快速转型,为能源互联网的快速建设提供技术支持。