Good and bad market research: A critical review of Net Promoter Score

Net Promoter Score, touted as the “single customer metric you need” and calculated from customers' answer to one simple question about their loyalty, has been in use since 2003 and adopted in a wide variety of settings. However, it has not lived up to its claimed benefits. This article evaluates the NPS approach in terms of its positive and negative results. This article is for people interested in NPS, still considering implementing NPS in their company, or interested in its technical underpinnings. It points out the benefits and shortcomings and explains why, and it describes what can be done to achieve the outcomes NPS theory claimed it would produce, but has not. The article is written in two parts for quite distinct audiences: firstly, for executives and managers who need customer data and information to make marketing decisions; and secondly, for market researchers, statisticians, and business analysts who are responsible for capturing and providing reliable, understandable, and meaningful customer data to the executives and managers who need the information. Consequently, the two sections are written in two different styles. The first section takes the form of a summary for managers and executives of our findings and recommendations in language aimed at business leaders; the second section provides a detailed analysis and critical review of NPS for market researchers, statisticians, and business analysts. Both sections present a better solution than NPS for understanding what customers value, delivering the best value to customers, winning market share, and creating truly loyal customers.     

The throughput performance of a prioritized LIFO service discipline

The LIFO service discipline maximizes throughput for certain queuing systems with delay-dependent customer behavior. To study the effects of priorities in such a system, we obtain delay distributions for each customer class of a K-class, single server system with nonpreemptive priorities and LIFO within each queue.     

Departures inGR^{X{n}} /G_n /\infty

Departure processes in infinite server queues with non-Poisson arrivals have not received much attention in the past. In this paper, we try to fill this gap by considering the continuous time departure process in a general infinite server system with a Markov renewal batch arrival process ofM different types of customers. By a conditional approach, analytical results are obtained for the generating functions and binomial moments of the departure process. Special cases are discussed, showing that while Poisson arrival processes generate Poisson departures, departure processes are much more complicated with non-Poisson arrivals.This research has been supported in part by the Natural Science and Engineering Research Council of Canada (Grant A5639).     

移动电话客户流失数据挖掘

本文首先回顾了顾客流失的相关文献,然后利用统计分析方法和数据挖掘技术分析了移动电话号码与移动电话型号对客户流失的影响,对分析结果进行了解释,并给出一些营销建议。     

研究生录取问题的优化模型与评述

针对2004年首届全国部分高校研究生数学建模竞赛的D题“研究生录取”问题的评卷情况,概括地介绍了这个问题的背景、评卷要点、答卷中存在的问题.并且给出了这个问题的一种有效的解决方法.     

基于精英单亲遗传算法的冷链物流VRP模型优化研究

针对冷链物流配送的特殊性,探索冷链物流车辆路径问题(VRP)优化方案.首先,在保证货物不超载的条件下,建立基于时间和品质因素的顾客满意度约束的多配送中心VRP(MDVRP)模型;其次,采用重心分区法和改进的精英单亲遗传算法,求解顾客在配送中心的分配,确定配送车辆数以及顾客服务次序;最后,用Matlab工具编程对模型进行求解分析.结果表明构建基于满意度的冷链物流MDVRP模型更适合冷链物流配送最优路径选择,并且改进单亲遗传算法能够有效求解这类问题.     

基于最大期望满意度与背景风险的模糊投资组合模型研究

模糊投资组合是不确定性理论研究的重要领域.然而,由于人的理性局限性,投资者在决策的过程中,可能不是追求理性的效用最大化,而是追求心理满意度的最大化;在金融市场中,投资者不仅面临市场风险,也需要承担由自身因素产生的背景风险.因此,提出了一个考虑背景风险等因素的最大期望满意度模型,该模型的目标是最大化投资组合收益与最小收益证券的差值;最后,以上海证券交易所180指数的十支证券构成的的投资组合为例,分析了此模型在分散投资风险与增加满意度方面的有效性.     

援疆效果满意度的公因子分析:综合评价和政策取向——以南疆三地州数据为例

以居民对援疆政策满意度为出发点,前学者的研究基础上确定援疆政策效果的满意度的衡量指标,选取6个大方向31个指标来测量援疆效果满意度.通过南疆三地州进行实地问卷调查获得数据为样本,采用因子分析方法确定二级指标及三级指标的权重,进行对援疆政策效果满意度综合评价.研究结论是南疆居民对援疆效果的满意度总得分接近70分,说明总体满意度较高,其中满意度最高的为收入及补贴,排在第一位,排在末尾的就是教育领域,排在第二位的是住房建设,而基层服务的满意度仅仅高于教育领域,排在第五位.排在第三位的就是医疗,第四位才是居民出行为核心内容的基础设施建设.从而为援疆政策效果满意度优化,解决存在的问题,全面改善援疆政策效果提出政策建议.     

A residual-based message passing algorithm for constraint satisfaction problems

Message passing algorithms, whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages, provide a powerful toolkit in tackling hard computational tasks in optimization, inference, and learning problems. In the context of constraint satisfaction problems (CSPs), when a control parameter (such as constraint density) is tuned, multiple threshold phenomena emerge, signaling fundamental structural transitions in their solution space. Finding solutions around these transition points is exceedingly challenging for algorithm design, where message passing algorithms suffer from a large message fluctuation far from convergence. Here we introduce a residual-based updating step into message passing algorithms, in which messages with large variation between consecutive steps are given high priority in the updating process. For the specific example of model RB (revised B), a typical prototype of random CSPs with growing domains, we show that our algorithm improves the convergence of message updating and increases the success probability in finding solutions around the satisfiability threshold with a low computational cost. Our approach to message passing algorithms should be of value for exploring their power in developing algorithms to find ground-state solutions and understand the detailed structure of solution space of hard optimization problems.     

关系感知在银行业顾客忠诚形成中的中介机制研究

以银行业为背景,以顾客关系感知中的满意和信任为中介变量,建立企业形象、转换成本和服务质量三个典型营销要素影响顾客忠诚意向和忠诚行为的概念模型,并进行了实证检验.研究发现:企业形象、服务质量和转换成本会显著影响顾客忠诚行为,其中服务质量影响最大;顾客满意在企业形象和服务质量对顾客忠诚行为的影响中有不完全中介作用;信任在服务质量对顾客忠诚行为的影响中也有不完全中介作用.此外,顾客忠诚意向是营销要素影响顾客忠诚行为的重要中介变量.