调查你的客户满意度

客户是上帝。了解到客户的需要，企业就找到了改进的渠道。调查如同巡诊，早发现问题也许能为企业免去无妄之灾。如何了解客户的真实意见和需要，文中的几种调查方法企业不妨参考一下。     

第三方物流企业关键客户的多级可拓综合评价

为了有效地评价第三方物流企业的关键客户，在具体分析第三方物流企业的基础上建立了关键客户多级综合评价参考体系，给出了形式化的关键客户多级综合评价物元模型。提出了一种定性与定量结合的关键客户多级可拓综合评价方法，通过关联函数、合格度和优度计算，客观地给出了综合评价结果。对实际数据进行处理的结果表明，该方法能够有效地保证关键客户综合评价的质量和效率。     

呼叫中心进入客户2.0时代

在当前的经济和社会环境下，客户满意度已经成为企业可持续发展的核心竞争力。自主客户过去，企业关注的是产品的质量和成本，但是随着客户获得企业产品信息的渠道增多，能力增强，他们在交易过程中掌握了主动权和控制权。而且客户要求在企业提供产品的同时，也能够提供与产品配套的个性化服务。也就是说，未来的客户具有非常强的自主性，他们掌握着很多的资料，他们是专家型的客户，他们被称为“客户2．0”。     

知识竞争的利器

尽管ＣＲＭ目前还没有十分统一的定义，但总的说来，ＣＲＮ是一种旨在改善企业与客户之间关系的新型管理机制，它实施于企业的市场营销、销售、服务与技术支持等与客户有关的领域，ＣＲＭ的目标是一方面通过提供更快速和周到的优质服务吸引和保持更多的客户，另一方面通过...     

获取客户信息的渠道和方法

CRM(Customer Relationship Management，客户关系管理系统)是一种能反映企业和客户间复杂关系的管理系统，不仅能够记录、追踪客户资料，以及客户和企业间的业务行为，而且能分析这些信息，为企业提供决策依据。目前，CRM的运用已成为众多商业企业追求的目标。     

客户／服务器计算与同处理的比较

本文对客户／服务器计算的特点和协同处理的特点进行了对比，指出了各自的优缺点，在此基础上，给出了适合客户／服务器计算或协同处理的几种环境，使信息系统管理者在确定他们企业的应用和硬件环境时，能选择一个更适合本身应用特点的技术。     

客户／服务器计算与协同处理的比较

本文对客户／服务器计算的特点和协同处理的特点进行了对比，指出了各自的优缺点．在此基础上，给出了适合客户／服务器计算或协同处理的几种环境，使信息系统管理者在确定他们企业的应用和硬件环境时，能选择一个更适合本身应用特点的技术．     

电信企业客户价值模型的构建及对营销的启示

如今我们进八客户导向经济时代，客户在市场竞争中逐步占了上风，在电信市场竞争愈演愈烈的今天，电信企业要健康长远地发展．留住和争取客户成这当务之急。据测算，留住忠诚客户增长5％，大概就会给公司带来25-50％的利润增长，客户是电信企业     

超市业态运用CRM的现状

客户关系管理（CRM）是为增进赢利、收入和客户满意度而设计的,是企业范围的一种商业战略。从技术上讲,CRM是一种以信息技术为手段、有效提高企业收益、客户满意度的具体软件和实现方法。从管理上讲CRM是指企业通过富有意义的交流沟通,理解并影响客户行为,最终实现提高客户获得、客户保留、客户忠诚和客户创利的目的。     

三大心态决定忠诚度

企业为吸引和留住最忠诚、最能提供利润的客户,必须首先了解忠诚度的真正推动力及核心要素——客户心态。不同的客户心态产生不同的客户行为,而客户行为正是需要企业加以了解和努力培养的。根据客户体验以及当前领先的学术研究,埃森哲开发了一种忠诚模型,以便对     

Improving Personalization Solutions through Optimal Segmentation of Customer Bases

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable. It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization.     

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.     

再多一次接触:顾客反馈机制对服务失败后顾客评价的影响

It is difficult to control service quality for any service firm and service failure is inevitable due to human or non-human factors. As a result, service recovery stirred the interest of researchers and practitioners. However, service failure may not be identified by firms because a majority of dissatisfied customers will not complain to the service provider. Thus, this research proposes a proactive customer feedback mechanism comprised of proactive solicitation of customer feedback, customers providing feedback and follow-up communication and examine their impact of customer evaluations in the context of service failure. This research tries to establish that through adding a continuous series of satisfied service encounters or "moment of truth" to service failure encounters, the customer evaluations (e.g. satisfaction, trust, purchase intent and positive WOM) toward the service will be significantly improved. Our research employed a 2×2×2 between subjects experimental design. Three independent variables were manipulated as presence and absence level, that is, firms either proactively solicitted customer feedback or employed no solicitation of feedback, Customers either provided feedback to the service firm or refused to provide feedback, and service firm either conducted follow-up communication or no follow-up communication. Our research adoptted a scenario in the context of patronizing a three-star hotel on a business trip. In total, 221 business administration graduates from a famous university in Shanghai participated in the experiment. MANOVA was used to test the hypotheses. The results indicated that proactive solicitation significantly influenced trust (p<0.01). Besides satisfaction, the other dependent variables such as trust, purchase intent and positive WOM significantly differred between with and without follow-up communication groups (p<0.05). Third, a significant difference was detected regarding satisfaction, trust, purchase intent and positive WOM between customer providing feedback groups and no customer feedback groups (p<0.05). Moreover, significant interactive effects were found of customers provided feedback and follow-up communication on trust and purchase intent. Trust and purchase intent reached highest when a follow-up communication occurred after customers provided feedback. However, no interactive effects were found of proactive solicitation of customer feedback and customer providing feedback. This research suggests that service providers should create new encounters with customers when they are confronted with service failure, and in this study, through proactive customer feedback mechanism. That is, first, the service provider should encourage customers to provide feedback, which can improve customer evaluations after service failure and after customers providing feedback. At the same time, the firm should employ follow-up communication with the customer and inform them about how the firm responded to their complaints, suggestions and comments. It is worth to note that companies just need to have follow-up communication with those customers who have provided feedback. More important, as information technology and internet prevails, it is very likely sending email and short messages after a service failure to maintain customer relationships could significantly enhance customer evaluations of the service provider. Of course, how consumers respond to such measures online is an promising research area and needs further investigation.     

基于混合数据聚类算法的异质顾客群体识别

顾客作为产品满意度测度过程中评价决策的主体,对其进行分类研究,识别不同顾客群体异质评价特征具有重要意义。顾客评价特征存在多元性和冲突性,根本原因是顾客作为决策者的异质性,而顾客的异质性来源于顾客本身属性,包含分类型属性和数值型属性。提出了一种基于惩罚竞争机制的混合属性顾客分类方法,根据数值型和分类型属性值的分布规律,给出了混合数据初始聚类中心的确定方法;建立了统一相似性度量模型,并引入惩罚竞争机制,实现了聚类过程中的基本迭代和自动优化聚类数。以某产品异质顾客分类问题为例验证了所提方法的可行性,继而通过“Heart Disease”标准数据集将所提算法与K-means和K-prototypes两种经典聚类算法进行对比,验证了该方法的有效性。     

An integrated data mining and behavioral scoring model for analyzing bank customers

Analyzing bank databases for customer behavior management is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. This study proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A self-organizing map neural network was used to identify groups of customers based on repayment behavior and recency, frequency, monetary behavioral scoring predicators. It also classified bank customers into three major profitable groups of customers. The resulting groups of customers were then profiled by customer's feature attributes determined using an Apriori association rule inducer. This study demonstrates that identifying customers by a behavioral scoring model is helpful characteristics of customer and facilitates marketing strategy development.     

基于AP聚类与随机森林的客户流失预测研究

利率市场化、大数据迅速发展,银行业均表现出明显的“二八定律”现象,20%的优质客户占据了银行的大部分资产。那么,如何防止银行客户流失,尤其是优质客户的流失,已经成为银行越来越关注的问题。因此,建立优质客户流失预警模型就显得尤为重要。以某商业银行为例,重新对客户流失进行定义,重点关注银行优质客户的流失预警,首先使用AP聚类算法进行属性选择,然后使用随机森林方法建立客户流失预警模型,预测零售优质客户未来3个月流失的可能性。为了验证该方法的有效性,首先在UCI数据集上进行验证,得到了较好的效果,然后使用该方法构建银行业优质客户流失预测模型,实验结果表明该模型的实际预测效果相较于一般的决策树方法,具有更高的准确性。     

电子商务客户流失的DBN预测模型研究

在电子商务迅速发展,企业快速抢占市场的背景下,客户成为企业竞争的核心因素。现有相关研究多致力于采用全数据输入模式解析客户流失现象,不同类型客户造成的差异性还有待进一步探讨。鉴于传统RFM模型不能精确解释电子商务客户流失原因,该研究将客户分为活跃与非活跃两个集群,提出一种优化的RFM理论模型与深度信念网络实证模型对电子商务客户流失进行预测。结果表明,不同类型客户流失因素的影响强度不同。对活跃用户而言,客户购买总金额是影响客户流失的主要因素;对非活跃用户而言,客户进入店铺的时间越长越可能留住客户。通过剖析非活跃用户不流失和活跃用户流失的原因,可帮助企业制定有效的客户管理策略,以最大程度地吸引潜在客户及保留现有客户,获取最多的市场利益。     

数据挖掘技术在客户关系管理（CRM）中的应用及具体实现策略

人们普遍认识到,各种规模的企业需要学习效仿那些小型的、以服务为导向的商业模式,总是能做好建立一对一的客户关系。客户关系管理是广泛出现在很多书籍和会议上的议题。从铅跟踪软件到广告系列管理软件,再到呼叫中心软件。如今,在销售中一切都作为顾客关系管理工具。通过提高公司的能力和我们的顾客形成学习关系,数据挖掘可以在提升顾客关系管理。每一个行业中,高瞻远瞩的公司正在试图走向一对一的理解每一个客户,运用那种了解以方便客户做生意,而不是一个竞争者。同样,这些公司正在学习看终身价值的每一个客户,这样他们就能知道有哪些值得投资金钱和精力来坚持,哪些让下降。从大市场分割到个体消费者,这一关注的转变需要公司的改变,更重要的莫过于市场营销,销售和顾客的支持。     

A PSO-based intelligent service dispatching mechanism for customer expectation management

In the era of experience economy, service providers have to provide customers with high quality service experience in order to attract more customers and achieve higher customer satisfaction. Managing customer expectation is a critical approach for service providers to consider. Although customer expectation has been discussed across different research disciplines, to our knowledge, there is still no systematical and feasible way to apply customer expectation management into real environments. This study attempts to establish an intelligent service dispatching mechanism by using particle swarm optimization for customer expectation management. This mechanism can help service providers design and deliver satisfactory service experience to customers. In order to evaluate the effectiveness and robustness of this mechanism, this study employs micro- and macro-simulation experiments to confer and analyze its performance. The simulation results show service providers can gain benefit and raise customer satisfaction by managing customer expectation during service experience delivery. Meanwhile, customers can also receive memorable experiences and have positive responses to service providers and other customers. Consequently, a high performance ecosystem within service providers and customers can be formed.     

基于.NET框架结构的烟草工业客户服务系统（CSS）的设计

中国加入WTO以后,烟草工业企业面对市场的压力越来越大,推进服务营销尤为重要。然而,在Internet时代客户比以前有了更多的选择,而且只需轻轻点击鼠标就可以贴近你或离你而去。因此,对于企业来说仅仅满足客户的需求已远远不够,更重要的是如何能让服务给客户留下深刻的印象。CSS作为一种前景广阔的企业解决方案,也越来越多地显示出其在以客户服务为中心的时代无可替代的重要地位。