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.     

基于客户满意度的第四方物流多属性指派决策机制

基于第四方物流客户的多样化需求,建立了客户满意度属性体系及量化标准;通过对物流客户和第三方物流进行需求和供给的多属性分析,给出了属性需求和供给值矩阵;通过研究属性需求和供给值之间的关系,建立了基于客户满意度的第四方物流多属性指派决策方案;利用Microsoft Excel中的规划求解对一个实例计算,并通过对比分析验证了这一多属性指派决策方案的可行性、实用性和有效性。     

Composition and synthesis with a formal interactor model

This paper discusses the formal specification of interactors, which are primitive abstractions of user interface software, and focuses on the formal aspects of their composition. The composition of interactors is discussed formally in the framework of the Abstraction-Display-Controller (ADC) interactor model. The ADC model has been defined as a LOTOS specification template tailored for specifying user interface software. LOTOS behaviour expressions combining instances of this template specify the composition of interactors to model complex user interfaces. Synthesis is defined as a transformation of these behaviour expressions which supports the generic structure of the ADC model while preserving the meaning of the specified behaviour. Further, the notion of abstract views of interactors is introduced. It is shown how abstract views are themselves primitives for specifying complex interface architectures.     

再制造系统消费者类型分布对定价策略与战略决策的影响机制研究

EOL产品的回收、再制造及其商业化是发展循环经济、解决资源与环境问题的重要途径。消费者的类型分布影响生产者的定价策略,定价策略又影响再制造品的市场结构与战略决策。论文导入了消费者的类型划分与分布,定量构建了消费者类型划分对生产者定价策略的影响,给出了分界点;评估了定价策略对生产者战略决策的影响机制,确定了阈值分布,绘制了决策路线图。     

A service recovery method to enhance customer satisfaction with a case study of motion sport game

Service recovery is a concept that involves regaining customer satisfaction and loyalty. Although existing studies have addressed its importance, few service recovery methodologies have been developed. Thus, there is a need to develop a systematic framework that can first detect customer status and then provide appropriate recovery recommendations to ensure customer satisfaction. This study develops such a framework in relation to an individual health maintenance practice. While regular moderate exercise is known to promote good health and help prevent disease, the lack of exercise remains a crucial health issue. This study presents a logistic regression model that uses both physiological data (heart rate and blood pressure) and psychological data (ratings of perceived exertion) to detect the effectiveness of an individual's exercise and to provide sports‐related service recovery suggestions as needed. To implement and assess this service recovery model, the use of a motion gaming system is proposed.     

考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化

针对目前不合理的废旧产品回收以及物流活动产生的碳排放污染，提出了一种考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化模型。首先，结合实际物流网络的动态性假设客户需求量和回收率的不确定性特征，以最小运营成本和最小环境影响为目标建立选址-路径优化模型；其次，对多目标进化算法进行改进，提出了考虑客户聚类结果的两级物流设施选址-路径问题求解算法；最后，对该优化算法进行算法性能分析，并以重庆市某企业为例进行了模型和算法验证。结果表明，所建立的模型和算法能有效降低决策难度并提高物流系统的运作效率，所求出的优化方案能减少物流运作成本和降低物流运输过程对环境的影响。     

Use of a lightweight formal method to model the static aspects of state machines

In this paper the author uses Alloy as a modeling language to model the elements that form a state machine and the rules that govern how they can be connected. In particular, the model proposed in this paper focuses in the static aspects of state machines. To analyze and detect design errors in the model, the Alloy analyzer was used. With the use of this tool, design errors can be detected very quickly. This tool can also generate instances of the model without making a line of code. The paper presents two models based on the formal approach: a graphical model and a textual model. The graphical model is used as an overview of the system while the textual model is used to establish further constraints on the graphical model.     

Learning to predict life and death from Go game records

This article presents a new learning system for predicting life and death in the game of Go. It is called Gone. The system uses a multi-layer perceptron classifier which is trained on learning examples extracted from game records. Blocks of stones are represented by a large amount of features which enable a rather precise prediction of life and death. On average, Gone correctly predicts life and death for 88% of all the blocks that are relevant for scoring. Towards the end of a game the performance increases up to 99%. A straightforward extension for full-board evaluation is discussed. Experiments indicate that the predictor is an important component for building a strong full-board evaluation function.     

Prioritisation of public investments in social infrastructures using multicriteria value analysis and decision conferencing: a case study

Not‐for‐profit private organisations that provide social services to children, the elderly and the disabled apply for financial support to develop or to renew their social infrastructures, through the Portuguese Institute for Social Welfare. In the context of scarce financial resources, the Institute decision‐makers felt the need to adopt an improved “rationality” in resource allocation, in order to increase transparency and to ensure that the collective best use is made of a limited budget. This paper describes the socio‐technical process followed in building a multicriteria value model, under a decision conferencing framework in which participation and interaction among decision‐actors were key features in the development of the three main phases of problem structuring, evaluation and prioritisation.     

On the use of formal methods to model and verify neuronal archetypes

Having a formal model of neural networks can greatly help in understanding and verifying their properties, behavior, and response to external factors such as disease and medicine. In this paper, we adopt a formal model to represent neurons, some neuronal graphs, and their composition. Some specific neuronal graphs are known for having biologically relevant structures and behaviors and we call them archetypes. These archetypes are supposed to be the basis of typical instances of neuronal information processing. In this paper we study six fundamental archetypes (simple series, series with multiple outputs, parallel composition, negative loop, inhibition of a behavior, and contralateral inhibition), and we consider two ways to couple two archetypes: (i) connecting the output(s) of the first archetype to the input(s) of the second archetype and (ii) nesting the first archetype within the second one. We report and compare two key approaches to the formal modeling and verification of the proposed neuronal archetypes and some selected couplings. The first approach exploits the synchronous programming language Lustre to encode archetypes and their couplings, and to express properties concerning their dynamic behavior. These properties are verified thanks to the use of model checkers. The second approach relies on a theorem prover, the Coq Proof Assistant, to prove dynamic properties of neurons and archetypes.     

Optimal maintenance and replacement via a semi-Markov decision model

Optimal bang-coast maintenance policies for a machine, subject to failure, are considered. The approach utilizes a semi-Markov model for the system. A simplified model for modifying the probability of machine failure with maintenance is employed. A numerical example is presented to illustrate the procedure and results.     

Verifiable shuffles: a formal model and a Paillier-based three-round construction with provable security

A shuffle takes a list of ciphertexts and outputs a permuted list of re-encryptions of the input ciphertexts. Mix-nets, a popular method for anonymous routing, can be constructed from a sequence of shuffles and decryption. We propose a formal model for security of verifiable shuffles and a new verifiable shuffle system based on the Paillier encryption scheme, and prove its security in the proposed dmodel. The model is general and can be extended to provide provable security for verifiable shuffle decryption.This paper is the extended version of the paper [37] presented at ACNS ‘04.     

Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company’s case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.     

Developing a personal value analysis method of social media to support customer segmentation and business model innovation

Companies need to find out about the personal values of customers and identify customer segments before developing effective business models (BMs) or marketing strategies. Therefore, understanding the personal values of customers is critical in BM design. Traditional personal value evaluation methods are laborious and time‐consuming. In recent years, social media have become an important platform for people to share ideas and express views, making the presence of a huge amount of opinions on platforms that can cater for the demand of data for personal value analysis of customers. This study took Facebook and Instagram as the targets and developed a novel personal value forecasting method to help enterprises obtain the various personal values of customer segments automatically at a lower cost. This study adopted Schwartz's value theory as the value model and proposed a consistency model and a relativity model for weighted calculations, so as to determine the feature of a value tag. Finally, the feature selection algorithm and classification algorithm were used for judging values. In the evaluation phase of this study, 61 participants were recruited to test the proposed method. The proposed method could assist enterprises in better understanding personal value information.     

基于形式概念分析与统计理论的本体构建模型*

为了解决目前本体建立技术严重依赖领域专家的参与、不够客观的现状,提出将形式概念分析与统计理论相结合的本体构建模型。通过利用统计算法对术语在文件中出现概率进行统计,根据设定的阈值对术语进行抽取,同时对术语出现的文件进行记录,进而将以上所得的知识组织成具有层次关系的概念格,最后将概念格映射为领域本体。实践证明,利用该模型建立的领域本体具有较好的客观性,同时能极大提高本体搜索的效率。     

基于数据挖掘的客户细分模型研究及应用

为企业更深入了解消费者的行为和偏好,帮助企业制定决策和发展客户关系,结合现有的客户细分方法,提出一种多指标客户细分模型。从宏观和微观角度,对传统指标进行优化,构建RFMPA多指标客户体系;采用熵值法客观赋权;采用因子分析降维;采用改进的K-means算法完成客户细分。利用大型连锁超市客户消费数据进行实证研究,对比数据实验结果表明,该模型能够更好解决客户细分问题,提高企业客户关系管理和决策质量。     

Depth and image recovery using a MRF model

This paper deals with the problem of depth recovery and image restoration from sparse and noisy image data. The image is modeled as a Markov random field and a new energy function is developed to effectively detect discontinuities in highly sparse and noisy images. The model provides an alternative to the use of a line process. Interpolation over missing data sites is first done using local characteristics to obtain initial estimates and then simulated annealing is used to compute the maximum a posteriori (MAP) estimate. A threshold on energy reduction per iteration is used to speed up simulated annealing by avoiding computation that contributes little to the energy minimization. Moreover, a minor modification of the posterior energy function gives improved results for random as well as structured sparsing problems. Results of simulations carried out on real range and intensity images along with details of the simulations are presented     

Markov transition model to dementia with death as a competing event

This study evaluates the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. A multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data; transitions among states depend on four covariates age, education, prior state (intact cognition, or M.C.I., or G.I.) and the presence/absence of an apolipoprotein E-4 allele (APOE4). A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the survival from death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Simulation studies determine the sensitivity of the maximum likelihood estimates to the violations of the Weibull and Cox PH model assumptions. Results are illustrated with an application to the Nun Study, a longitudinal cohort of 672 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.