Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

A hybrid Kansei engineering design expert system based on grey system theory and support vector regression

Nowadays customers choose products strictly in terms of their specific demands. How to quickly and accurately catch customers’ feelings and transform them into design elements and vice versa becomes an important issue. This study explores the bi-directional relationship between customers’ demands or needs and product forms by using a novel integral approach. High-price machine tools are used as our demonstration target. This integral approach adopts the “grey system theory (GST)”, and the state-of-the-art machine learning based modeling formalism “support vector regression (SVR)” in the “Kansei engineering (KE)” process. The GST is used to effectively determine the influence weighting of form parameters on product images and the SVR is used to precisely establish the mapping relationship between product form elements and product images. Furthermore, for practical concerns, a user-friendly design hybrid design expert system was developed based on the proposed novel integral schemes.     

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.     

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.     

User preference mining techniques for personalized applications

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing.     

Doctor Faustus in the twenty-first century

In the medieval legend, Doctor Faustus strikes a dark deal with the devil; he obtains vast powers for a limited time in exchange for a priceless possession, his eternal soul. The cautionary tale, perhaps more than ever, provides a provocative lens for examining humankind’s condition, notably its indefatigable faith in knowledge and technology and its predilection toward misusing both. A variety of important questions are raised in this meditation including What is the nature of knowledge today and how does it differ from knowledge in prior times? What is its relation to technology and power? What paths are we heading along and which alternative ones are being avoided? Not insignificantly, we also raise the issue of civic ignorance, including that which is intentionally cultivated and that which is simply a lack of knowledge. We also consider the identity of Doctor Faustus in the twenty-first century and in a more material world like ours, what is the soul that he would lose in the bargain, and what damage might be done to Faustus and to innocent bystanders. Finally since people don’t always live up to the terms of agreements they make, what, if anything, could Faustus do to wriggle out of the bargain, to avoid the loss of his all-important soul. Our response is not to disavow knowledge (as the implicit “lesson” of the original myth might suggest) but to shift to another approach to knowledge that is more collective and more responsive to actual needs of our era. This approach which we call civic intelligence is considered as a way to avoid the possible catastrophes that the Faustian bargain we’ve seemingly struck is likely to bring.     

Product portfolio identification with data mining based on multi-objective GA

In the initial stage of product design, it is essential to define product specifications according to various market niches. An important issue in this process is to provide designers with sufficient design knowledge to find out what customers really want. This paper proposes a data mining method to facilitate this task. The method focuses on mining association rules that reflect the mapping relationship between customer needs and product specifications. Four objectives, support, confidence, interestingness and comprehensibility, are used for evaluating the extracted rules. To solve such a multi-objective problem, a Pareto-based GA is utilized to perform the rule extraction. Through computational experiments on an electrical bicycle case, it is shown that our approach is capable of extracting useful and interesting knowledge from a design database.     

Automated self-service modeling: predictive analytics as a service

Research into service provision and innovation is becoming progressively more important as automated service-provision via the web matures as a technology. We describe a web-based targeting platform that uses advanced dynamic model building techniques to conduct intelligent reporting and modeling. The impact of the automated targeting services is realized through a knowledge base that drives the development of predictive model(s). The knowledge base is comprised of a rules engine that guides and evaluates the development of an automated model-building process. The template defines the model classifier (e.g., logistic regression, multinomial logit, ordinary least squares, etc.) in concert with rules for data filling and transformations. Additionally, the template also defines which variables to test (“include” rules) and which variables to retain (“keep” rules). The “final” model emerges from the iterative steps undertaken by the rules engine, and is utilized to target, or rank, the best prospects. This automated modeling approach is designed to cost-effectively assist businesses in their targeting activities—independent of the firm’s size and targeting needs. We describe how the service has been utilized to provide “targeting services” for a small to medium business direct marketing campaign, and for direct sales-force targeting in a larger firm. Empirical results suggest that the automated modeling approach provides superior “service” in terms of cost and timing compared to more traditional manual service provision.     

What Are We Talking About When We Talk About Technology Pivots? – A Delphi Study

Technology pivots were designed to help digital startups make adjustments to the technology underpinning their products and services. While academia and the media make liberal use of the term “technology pivot,” they rarely align themselves to Ries’ foundational conceptualization. Recent research suggests that a more granulated conceptualization of technology pivots is required. To scientifically derive a comprehensive conceptualization, we conduct a Delphi study with a panel of 38 experts drawn from academia and practice to explore their understanding of “technology pivots.” Our study thus makes an important contribution to advance the seminal work by Ries on technology pivots.     

An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis

This paper proposes an anticipation model of potential customers’ purchasing behavior. This model is inferred from past purchasing behavior of loyal customers and the web server log files of loyal and potential customers by means of clustering analysis and association rules analysis. Clustering analysis collects key characteristics of loyal customers’ personal information; these are used to locate other potential customers. Association rules analysis extracts knowledge of loyal customers’ purchasing behavior, which is used to detect potential customers’ near-future interest in a star product. Despite using offline analysis to filter out potential customers based on loyal customers’ personal information and generate rules of loyal customers’ click streams based on loyal customers’ web log data, an online analysis which observes potential customers’ web logs and compares it with loyal customers’ click stream rules can more readily target potential customers who may be interested in the star products in the near future.     

数据挖掘技术在数码产品销售商家的应用

该文介绍了数据挖掘技术在数码产品销售商家中的应用,洪娄底诚信科技公司为例,首先让人对数据挖掘技术建立一个正确的观念．消除对于数据挖掘技术的误区,为后面数据挖掘技术应用打下基础;接着提供数据挖掘技术中类聚分析将客户有效分类,运用关联规则挖掘技术找出业务间的关联性．从而进行货柜商品摆设,针对不同客户进行有效、个性化的营销。     

基于形状文法的产品形态创新设计研究与实践

为了更好地继承本族群及参考产品的优势设计特征,增强产品形态设计推敲阶段的客观性和科学性,更好满足用户需求,提出基于形状文法的产品形态创新设计方法和模型。此法结合目标产品形态特征,分别从产品族群与可参考产品中提取能够反映用户需求的形态要素,然后应用形状文化规则进行演变推理,以实现产品设计方案自适应进化。并在此基础上,详细探讨了产品研究与形态分析、形态推演、方案生成等关键技术,最后结合具体的某款电动踏板车形态设计过程对该方法进行了说明和验证。     

以用户为中心的音乐播放器设计分析

随着个人计算机的普及,音乐播放器渐渐成为人们使用频率很高的一种软件。开电脑后打开播放器听音乐成了一种很自然的行为。怎样的播放器设计才是能赢得用户青睐的,从设计的角度需要注意到哪些设计要点?在这里,作者将从界面设计者的角度,通过对千千静听这一较为成功软件进行案例分析,对以用户为中心的音乐播放软件界面的设计作一些分析讨论。     

A Clustered-Based Categorization of Millennials in their Technology Behavior

There is an increasing interest for millennials; however, to date, millennials’ segmentations regarding their technology behavior are scarce. This study addresses the following questions: “Are there segments within this generation group regarding their digital technology behavior?”. And if so: “Are there variances in the way that millennial segments use digital technology?”; and further: “What are the main differences among millennial segments regarding their technology behavior?”.

Data from a sample of 707 millennials were analyzed through principal components and cluster analysis. Then, millennials’ segments were profiled using a multivariate analysis of variance (MANOVA) analysis. Findings revealed that not every millennial has the same technology use and behavior. Moreover, there are five clustered-based segments of millennials regarding their technology behavior: technology devotees, technology spectators, circumspects, technology adverse users, and productivity enhancers. This study contributes with a detailed perspective of how different millennial segments use digital technology.     

How online self-customization creates identification: Antecedents and consequences of consumer-customized product identification and the role of product involvement

Online self-customization (OSC) enables customers to design a product tailored to their preferences and needs via the online platform. This study mainly argues that a successful OSC experience goes beyond simply increasing a consumer’s preference fit; it provides an opportunity to develop a meaningful relationship with customers by allowing them to embed their sense of self into the customized products and thus identify themselves with the products. Consumer-customized product identification (C-C identification) was proposed as central to our understanding of why and under what conditions OSC processes enable consumers articulate their identities. This study is theoretically based on internal motivations from social identity theory and identification literature to develop a model. The model was tested using a scenario-based survey with respondents collected from Amazon’s Mechanical Turk. Structural equation modeling analyses showed that value congruence and distinctiveness of the customized products positively influenced C-C identification, which in turn positively influenced attitudinal responses. The results further showed that the relative impact of two antecedents on C-C identification varies with a consumer’s product involvement. A similar pattern of results was obtained in two product categories. Theoretical and managerial implications for OSC marketers are also discussed.     

A knowledge support approach for the preliminary design of platform-based products in Engineering-To-Order manufacturing

A product platform is a design approach for meeting the demand for customizable products. Traditional knowledge-based technologies or systems lack flexibility in supporting both configuration and parameter design of platform-based products. In many cases, customers’ requirements and knowledge models both contain incomplete information, and there are complex relations among various solutions, functions and solution parameters in Engineering-To-Order (ETO) products. A knowledge model for the preliminary design of ETO products is presented in this paper, and linkages are established between configuration design knowledge and parameter design procedures. The basis of the knowledge model is the Extended Function-Solutions (EFS) tree, from which design case trees, design modules, constraint checking rules, and module interface templates derive. A corresponding knowledge retrieval and reuse strategy is also presented. It uses an improved fuzzy information axiom to search for the optimal configuration with incomplete information. The parameter design process model of new products then can be generated based on the optimal configuration. The case study demonstrates the knowledge modeling, retrieval and reuse for the preliminary design of open-type crank presses. Moreover, the effectiveness of the methodology is discussed by analyzing the verification approach and the satisfaction of customers’ requirements.     

Design for six sigma through robust optimization

The current push in industry is focused on ensuring not only that a product performs as desired but also that the product consistently performs as desired. To ensure consistency in product performance, “quality” is measured, improved, and controlled. Most quality initiatives have originated and been implemented in the product manufacturing stages. More recently, however, it has been observed that much of a product’s performance and quality is determined by early design decisions, by the design choices made early in the product design cycle. Consequently, quality pushes have made their way into the design cycle, and “design for quality” is the primary objective. How is this objective measured and met? The most recent quality philosophy, also originating in a manufacturing setting, is six sigma. The concepts of six sigma quality can be defined in an engineering design context through relation to the concepts of design reliability and robustness – probabilistic design approaches. Within this context, design quality is measured with respect to probability of constraint satisfaction and sensitivity of performance objectives, both of which can be related to a design “sigma level”. In this paper, we define six sigma in an engineering design context and present an implementation of design for six sigma – a robust optimization formulation that incorporates approaches from structural reliability and robust design with the concepts and philosophy of six sigma. This formulation is demonstrated using a complex automotive application: vehicle side impact crash simulation. Results presented illustrate the tradeoff between performance and quality when optimizing for six sigma reliability and robustness.     

Advanced computational reasoning based on the NKRL conceptual model

After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of “rules” – e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWL – this paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL’s methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich non-fictional ‘narrative’ data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between “plain/static” and “structured/dynamic” knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of “functional roles” – associated with the possibility of making use of n-ary structures – allows us to build up highly ‘expressive’ rules whose “atoms” can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, “functional roles” are primitive symbols interpreted as “relations” – like “subject”, “object”, “source”, “beneficiary”, etc. – that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the “semantic roles” that are equated to ordinary concepts like “student”, to be inserted into the “non-sortal” (no direct instances) branch of a traditional ontology.     

Development,analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization

Consumer-oriented companies are getting increasingly more sensitive about customer's perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customer's satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.     

Scalable prediction of non-functional properties in software product lines: Footprint and memory consumption

ContextA software product line is a family of related software products, typically created from a set of common assets. Users select features to derive a product that fulfills their needs. Users often expect a product to have specific non-functional properties, such as a small footprint or a bounded response time. Because a product line may have an exponential number of products with respect to its features, it is usually not feasible to generate and measure non-functional properties for each possible product.ObjectiveOur overall goal is to derive optimal products with respect to non-functional requirements by showing customers which features must be selected.MethodWe propose an approach to predict a product’s non-functional properties based on the product’s feature selection. We aggregate the influence of each selected feature on a non-functional property to predict a product’s properties. We generate and measure a small set of products and, by comparing measurements, we approximate each feature’s influence on the non-functional property in question. As a research method, we conducted controlled experiments and evaluated prediction accuracy for the non-functional properties footprint and main-memory consumption. But, in principle, our approach is applicable for all quantifiable non-functional properties.ResultsWith nine software product lines, we demonstrate that our approach predicts the footprint with an average accuracy of 94%, and an accuracy of over 99% on average if feature interactions are known. In a further series of experiments, we predicted main memory consumption of six customizable programs and achieved an accuracy of 89% on average.ConclusionOur experiments suggest that, with only few measurements, it is possible to accurately predict non-functional properties of products of a product line. Furthermore, we show how already little domain knowledge can improve predictions and discuss trade-offs between accuracy and required number of measurements. With this technique, we provide a basis for many reasoning and product-derivation approaches.