Bayesian networks of customer satisfaction survey data

A Bayesian network (BN) is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, BNs are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies among the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable, or a hypothesis. They are not restricted to represent random variables, which form the “Bayesian” aspect of a BN. Efficient algorithms exist that perform inference and learning in BNs. BNs that model sequences of variables are called dynamic BNs. In this context, [A. Harel, R. Kenett, and F. Ruggeri, Modeling web usability diagnostics on the basis of usage statistics, in Statistical Methods in eCommerce Research, W. Jank and G. Shmueli, eds., Wiley, 2008] provide a comparison between Markov Chains and BNs in the analysis of web usability from e-commerce data. A comparison of regression models, structural equation models, and BNs is presented in Anderson et al. [R.D. Anderson, R.D. Mackoy, V.B. Thompson, and G. Harrell, A bayesian network estimation of the service–profit Chain for transport service satisfaction, Decision Sciences 35(4), (2004), pp. 665–689]. In this article we apply BNs to the analysis of customer satisfaction surveys and demonstrate the potential of the approach. In particular, BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.     

商场购物环境、顾客感知价值与企业竞争优势关系研究

基于对415名消费者的随机抽样调研数据,从理论和实证两方面研究了商场购物环境、顾客感知价值及零售企业竞争优势间的相互关系。研究表明,购物环境以顾客信任和购物体验价值为中介变量,正向影响企业竞争优势的获得,且购物环境中的氛围因素、社会因素正向影响顾客体验价值;顾客信任受氛围因素、社会因素、设计因素和体验价值等显著正向影响;体验价值和顾客信任显著正向影响企业竞争优势的获得。     

供电服务质量与客户满意关系的实证研究

利用采集于江苏电力的770位用户数据对供电服务质量与用户满意的关系进行了实证研究。研究表明供电服务质量对用户满意影响显著，但不同顾客的影响程度存在差异，研究结果对电力企业管理具有重要意义。     

Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network

Bayesian networks (BNs) are probabilistic expert systems which have emerged over the last few decades as a powerful data mining technique. Also, BNs have become especially popular in biomedical applications where they have been used for diagnosing diseases and studying complex cellular networks, among many other applications. In this study, we built a BN in a fully automated way in order to analyse data regarding injuries due to the inhalation, ingestion and aspiration of foreign bodies (FBs) in children. Then, a sensitivity analysis was carried out to characterize the uncertainty associated with the model. While other studies focused on characteristics such as shape, consistency and dimensions of the FBs which caused injuries, we propose an integrated environment which makes the relationships among the factors underlying the problem clear. The advantage of this approach is that it gives a picture of the influence of critical factors on the injury severity and allows for the comparison of the effect of different FB characteristics (volume, FB type, shape and consistency) and children's features (age and gender) on the risk of experiencing a hospitalization. The rates it consents to calculate provide a more rational basis for promoting care-givers’ education of the most influential risk factors regarding the adverse outcomes.     

Inference in hybrid Bayesian networks using dynamic discretization

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.     

基于新三维客户细分模型的线上会员客户价值研究

从客户的当前价值、潜在价值、忠诚度价值三个方面出发,建立适用于线上企业的会员客户价值评价指标体系。同时,在传统RFM模型的基础上构建了新三维的客户细分模型,并根据改进的客户价值细分维度进一步细分客户,利用主成分分析法计算每个客户群的价值得分。最终选取某网站的会员客户数据进行算例分析,通过识别不同客户群体、价值和状态,为企业实施精准营销提供更加细致的客户细分依据。     

基于SEM的分散农户农药残留认知实证研究

基于结构方程模型对江苏省3个地区6县（区）241个分散农户进行调研,分析影响分散农户农药残留认知的主要因素。结果表明：除家庭特征对分散农户农药残留认知的影响难以测度外,地区、性别、年龄、受教育年限、农药知识培训、对粮食安全性的认识等均不同程度地影响分散农户的农药残留认知。     

基于数据挖掘的客户资产测量研究

由于单个客户消费行为具有极大不确定性导致预测客户资产比较困难,提出一种以数据挖掘技术为基础,在客户分类的基础上测算客户资产的动态方法,该方法使客户资产的测量更加准确并具有可操作性。同时通过实证分析证实该方法可以为企业实现精确营销提供有力支持。     

互联网接入服务客户满意度实证研究——以陕西省为例

针对陕西省网站用户流失率超过80％反映出的陕西省互联网接入服务客户满意度指数很低的现实,构建互联网接入服务客户满意度模型,在对陕西省223家网站用户抽样问卷调查的基础上,运用结构方程模型,实证分析互联网接人服务客户满意度及其影响因素的关系,认定接人资费、客户沟通、硬件设施、服务质量及客户关怀对网站用户满意度都有显著影响,提出提升满意度的几点对策.     

消费者对可追溯食品的购买行为及影响因素分析——基于大连市和哈尔滨市的实地调研

基于大连市和哈尔滨市实地调查的700份消费者问卷数据,依据消费者行为理论并选用二元Logit模型分析了消费者的可追溯食品购买行为及其影响因素。研究发现:消费者对可追溯食品的认知水平整体偏低,只有约三分之一的人听说过可追溯食品,其中有近一半的人购买过可追溯食品;学历、食品追溯意识、可追溯食品认知水平、个人月平均收入等变量显著影响消费者的可追溯食品购买行为;最后根据研究结论提出相关对策建议。     

两种数据挖掘技术在预测顾客满意度中的对比研究

分类发现是数据挖掘的重要内容,贝叶斯分类和决策树在数据挖掘中应用相当广泛,它们是生成分类器的两种有效方法。文章分别用两种方法对顾客满意度进行分类及预测,并将两种方法进行比较分析,认为用决策树分类法来预测顾客满意度具有简洁、高效等特点。     

顾客体验价值影响因素研究——以星巴克咖啡为例

随着社会经济的发展,消费水平和层次也相应提高。消费者需要的不仅仅是产品或服务,更需要通过对产品或服务的消费来获得充满感性的享受和难以忘怀的愉悦。以顾客体验价值理论为基础,以星巴克咖啡为实例,利用因子分析、相关分析和多元回归分析了影响顾客体验价值的因素,结果表明:影响顾客体验价值的因素有情境因素,也有顾客因素,而顾客的体验价值既包括心理体验价值,也包括感官体验价值。     

关于顾客满意度调查的若干思考

本文按照ISO9000:2000(GB/T19000-2000)讨论了顾客和顾客满意度的几个基本概念,介绍了一些顾客满意度调查的知识和国外顾客满意度调查的实施,论述了国内外顾客满意度调查的重要作用。指出中国应该重视顾客满意度调查,正确地进行顾客满意度调查,利用顾客满意度调查的结果来提高企业和国家的竞争力。     

A comparison of count data models with an application to daily cigarette consumption of young persons

The objective of this study is providing a comparative assessment for researchers to deal with the challenges of analyzing count data and examining the factors associated with daily cigarette consumption among the young people in Turkey. We fitted Poisson (P), negative binomial (NB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), Poisson hurdle (PH) and negative binomial hurdle (NBH) regressions to cigarette consumption count data by using the 2014 Turkey Health Survey. Our results showed that the ZINB and NBH models should be preferred. We also found that, gender, employment and tobacco use at home are more effective factors for smokers and nonsmokers in the 15–24 age group in Turkey.     

顾客满意度模型的样本量研究

本文在对顾客满意度模型及其估计方法PLS（Partial Least Square）进行简单讨论的基础上,详细研究了顾客满意度模型PLS估计方法需要的样本量,并针对中国顾客满意度研究的实际案例数据,给出了顾客满意度模型的样本量要求的建议,对顾客满意度实践有指导意义。     

在线购物背景下的顾客满意过程

为辨识电子商务时代顾客满意的机制,从整体在线购物体验的视角,开发了一个三阶段的顾客满意过程模型。研究表明：在线购物背景下,满意是顾客历经各个购买阶段的体验的结果;每个阶段的满意度既取决于那个阶段的购物体验,也取决于前面阶段的满意程度;前一阶段的满意将影响后面阶段的满意,它们联合起来形成了整体满意。不同类别的在线顾客之间在顾客满意程度上存在着显著差异。     

Investigating the European perception of food using moments obtained from non-symmetrical correspondence analysis

The perception of food in Europe has been a topic of research for many years due to its importance in better understanding the role of food in helping to define the culture of a country. It is also important from a marketing perspective for identifying how consumers relate to food. Recently, this topic was discussed by Guerrero et al. (2010) who used a graphical statistical technique called correspondence analysis to identify the association between the countries that participated in the study and words that were linked with “Traditional” food. This paper explores the use of non-symmetrical correspondence analysis and provides an interpretation of the configuration of points in the graphical display in terms of its first four moments. In particular, we will focus on the skewness and kurtosis of such a configuration. Such measure's provide further detail on the nature of the association between the countries studied and the words linked with “Traditional” food.     

Using unlabelled data to update classification rules with applications in food authenticity studies

Summary.  An authentic food is one that is what it purports to be. Food processors and consumers need to be assured that, when they pay for a specific product or ingredient, they are receiving exactly what they pay for. Classification methods are an important tool in food authenticity studies where they are used to assign food samples of unknown type to known types. A classification method is developed where the classification rule is estimated by using both the labelled and the unlabelled data, in contrast with many classical methods which use only the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering. A missing data formulation of the mixture model is used and the models are fitted by using the EM and classification EM algorithms. The methods are applied to the analysis of spectra of food-stuffs recorded over the visible and near infra-red wavelength range in food authenticity studies. A comparison of the performance of model-based discriminant analysis and the method of classification proposed is given. The classification method proposed is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis.     

A statistical social network model for consumption data in trophic food webs

We adapt existing statistical modeling techniques for social networks to study consumption data observed in trophic food webs. These data describe the feeding volume (non-negative) among organisms grouped into nodes, called trophic species, that form the food web. Model complexity arises due to the extensive amount of zeros in the data, as each node in the web is predator/prey to only a small number of other trophic species. Many of the zeros are regarded as structural (non-random) in the context of feeding behavior. The presence of basal prey and top predator nodes (those who never consume and those who are never consumed, with probability 1) creates additional complexity to the statistical modeling. We develop a special statistical social network model to account for such network features. The model is applied to two empirical food webs; focus is on the web for which the population size of seals is of concern to various commercial fisheries.