聚类回归分析(CLR)在市场细分研究中的应用

本文从市场细分方法的角度,打破了传统的根据样本在心理感知或偏好等多个变量的距离进行细分的思路,采用一种新的聚类回归分析方法基于变量间的因果关系对顾客进行细分,不仅可以把顾客有效地划分成具有不同特点的群体,而且可以根据不同要素的因果关系确定不同群体中的关键影响要素.本文以电热水器行业顾客满意度研究为例,根据不同要素对满意度影响的差异进行市场细分.分析表明,采用这种方法,企业可以在经营管理过程中通过差异化策略获得竞争优势.     

顾客忠诚计划作用机制研究

顾客忠诚计划作为一种重要的关系营销手段,已经越来越多的受到企业界和学术界的重视.基于以往关于忠诚计划的研究,从企业利益到顾客利益视角,提出基于顾客利益的忠诚计划作用机制,并进一步探讨关系价值对顾客忠诚的影响.结果显示关系价值对顾客忠诚的影响则主要是通过计划满意和计划忠诚两个中介变量间接实现的.     

基于结构方程模型的市场细分方法应用研究

结构方程模型是当前研究顾客满意和顾客忠诚的主流方法之一,但基于结构方程对顾客满意和顾客忠诚进行市场细分方法的研究却十分缺乏.基于结构方程模型进行市场细分算法的应用研究,并对得出的细分结果进行了解释.     

面向顾客资产的三维顾客细分模型及其应用

简单依照顾客利润贡献对顾客细分未能充分考虑顾客的无形贡献,会低估顾客对企业的价值.从面向顾客资产的视角进行顾客细分,可以兼顾到顾客当前的利润贡献和顾客的潜在有形和无形贡献,从而提出基于顾客当前显性贡献、潜在显性贡献和潜在隐性贡献三维顾客细分模型及度量方法,并结合实证阐述顾客细分方法的实现过程.     

基于MaxDiff和潜在类别分析的移动支付客户细分研究

在竞争激烈的移动支付市场中,对客户进行细分尤为重要。从客户的心理、态度等角度出发,首先采用MaxDiff调查方法测量受访者对移动支付产品的偏好,从而得到MaxDiff数据,再利用潜在类别分析对客户进行分类得到每个细分群体在各个对象上的偏好得分,最后从人口统计学和行为特征方面对细分群体进行验证分析。研究结果显示:客户被分成3个细分群体,分别是"安全和场景"类、"优惠和便捷"类以及"功能和速度"类,它们在移动支付的偏好上有显著差异。这样的细分结果可为移动支付服务商实施精准营销提供决策参考。     

基于改进RFM模型对民航客户的细分研究

近年来航空公司将客户分成不同的群体为了给客户提供差异化服务和有针对性的营销.现有传统的客户细分RFM模型由于存在缺乏科学的指标建立,已无法准确和完整的描述实际情况中客户的细分结果,根据民航客户价值的特点,在传统客户细分的RFM模型上进行改进,创建LRFMC模型,对某航空公司客户采用数据挖掘K-means算法进行聚类分析...     

顾客满意心理对企业利润的贡献率研究

已往考虑顾客满意的结果绝大数都是考虑顾客满意对顾客忠诚的影响.这里我们将顾客满意结果的应用进行拓展,借助随机前沿面模型来构建顾客满意对企业利润的影响模型,进一步探讨顾客满意对企业利润的贡献率问题.文中论证了该方法的可行性及计算步骤,我们的工作对企业决策者将提供更合理可靠的决策依据.     

关系营销中的心理逆反和顾客忠诚研究

关系营销是企业提升顾客忠诚度,维持与顾客长期稳定关系的重要手段.研究引入心理逆反理论探讨了关系营销的负面作用,在文献综述和理论推导的基础上,以商业银行高端客户为样本,分析关系营销策略对顾客状态逆反与顾客忠诚的影响.实证结果表明非物质层面的关系营销策略正向影响状态逆反,而物质奖励可以降低状态逆反;关系营销策略正向影响顾客忠诚,而顾客状态逆反负向影响顾客忠诚,降低了关系营销的效率;顾客的营销契合度对联系沟通与状态逆反关系有着正向的调节作用.结论从一定意义上解释了关系营销效率变化的问题,同时也为商业银行营销行为提供了参考依据.     

基于logit回归模型的顾客资产驱动因素与忠诚意向关系实证研究

国内关于顾客资产驱动因素模型的实证研究较少.将顾客资产三大驱动因素(价值资产、品牌资产和关系资产)与忠诚意向联系起来,用我国银行业688个数据进行了Logit回归建模,研究顾客资产驱动因素分别与忠诚意向之间的关系、驱动因素两两之间的关系以及顾客资产驱动因素组合对忠诚意向产生的协同效应.研究结论表明,首先,价值资产、品牌资产、关系资产均对顾客忠诚意向产生显著正影响;其次,品牌资产对关系资产和价值资产均产生显著正影响,但价值资产对关系资产的直接影响不显著,这可能是因为品牌资产在价值资产和关系资产之间的中介作用;最后,同时投资价值资产和品牌资产或同时投资关系资产和品牌资产反而会使顾客忠诚意向降低.研究结论能够为银行管理者优化营销经费配置提供理论支持.     

基于海量交易数据的保健药品客户流失规律及预测研究

保健药品企业如何有效预判客户流失率、如何精准识别不同产品在客户吸引力和流失方面的差别等是目前急需解决的问题.基于企业海量交易数据,从客户层面构建顾客流失率预测模型,并从产品层面对交易记录进行分析.研究得出以下结论:顾客购买时间间隔服从正态分布;在保健药品购买中女性所需的产品最受欢迎,其在购买自身所需药品时也可能会为其他家庭成员购买保健品;研究还同时发现具备很多功效的保健品并未受到顾客的欢迎,相反其流失率最高.     

回报计划对重复购买行为模式的影响研究

客户回报计划已成为一种重要的关系营销手段。本文在讨论回报计划如何对稳定市场结构下的重复购买行为产生影响的基础上,通过建立NBD-DM随机模型,提供了一种研究消费者重复购买行为的模型方法,并利用一组护肤品品类销售的固定样本组数据(panel data)对该方法进行了实证分析。结果表明NBD-DM模型是研究消费者重复购买行为的有效模型方法,并且证实回报计划在改变客户重复购买行为上的有效性,其是企业建立长期客户关系的有效手段。最后讨论了结论对战略及营销管理实践的意义。     

带有回报计划的动态客户关系管理模型及实验应用分析

在客户最大化效用及公司最大化CLV的动态环境下。对所提的带有回报计划的动态客户关系管理模型用于某超市的客户数据库中，发现模型的结果对这类客户是适用的。并给出了不同的客户状态空间对应的有效营销组合策略。结果表明：合适的回报计划可以促进客户的购买、提高公司的利润及缓解价格竞争。回报极限应该比客户的平均购买水平偏高，回报率应该与回报极限的改变方向一致。计划的时间范围应定在一年左右比较合适。对于累积购买水平较高的客户一般不邮寄商品信息。在回报计划的初期与末期不用打折。中期对那些购买次数很少的客户可以实行相应的降价策略。     

Customer choice prediction based on transfer learning

Choice behaviour prediction is valuable for developing suitable customer segmentation and finding target customers in marketing management. Constructing good choice models for choice behaviour prediction usually requires a sufficient amount of customer data. However, there is only a small amount of data in many marketing applications due to resource constraints. In this paper, we focus on choice behaviour prediction with a small sample size by introducing the idea of transfer learning and present a method that is applicable to choice prediction. The new model called transfer bagging extracts information from similar customers from different areas to improve the performance of the choice model for customers of interest. We illustrate an application of the new model for customer mode choice analysis in the long-distance communication market and compare it with other benchmark methods without information transfer. The results show that the new model can provide significant improvements in choice prediction.     

Optimizing referral reward programs under impression management considerations

We examine referral reward programs (RRP) that are intended for a service firm to encourage its current customers (inductors) to entice their friends (inductees) to purchase the firm’s service. By considering the interplay among the firm, the inductor, and the inductee, we solve a “nested” Stackelberg game so as to determine the optimal RRP in equilibrium. We determine the conditions under which it is optimal for the firm to reward the inductor only, reward the inductee only, or reward both. Also, our results suggest that RRP dominates direct marketing when the firm’s current market penetration or the inductor’s referral effectiveness is sufficiently high. We then extend our model to incorporate certain key impression management factors: the inductor’s intrinsic reward of making a positive impression by being seen as helping a friend, the inductor’s concerns about creating a negative impression when making an incentivized referral, and the inductee’s impression of the inductor’s credibility when an incentive is involved. In the presence of these impression management factors, we show that the firm should reward the inductee more and the inductor less. Under certain conditions, it is optimal for the firm to reward neither the inductor nor the inductee so that the optimal RRP relies purely on unincentivized word of mouth.     

证券客户生命周期模式分析及实证研究

随着证券经纪业务市场的变化,国内券商的经营模式逐渐发生变化,客户逐渐成为券商各项工作的重点和中心。本文通过对证券客户真实交易数据和财务数据的实证分析,研究了证券客户生命周期模式,提出了证券行业客户生命周期模式的特点和阶段,并指出应根据客户所处的具体阶段来制定有针对性的营销目标,从而帮助券商有效提高客户关系水平,最大化客户利润。     

Expulsion and scheduling control for multiclass queues with heterogeneous servers

We consider an M/M/2 system with nonidentical servers and multiple classes of customers. Each customer class has its own reward rate and holding cost. We may assign priorities so that high priority customers may preempt lower priority customers on the servers. We give two models for which the optimal admission and scheduling policy for maximizing expected discounted profit is determined by a threshold structure on the number of customers of each type in the system. Surprisingly, the optimal thresholds do not depend on the specific numerical values of the reward rates and holding costs, making them relatively easy to determine in practice. Our results also hold when there is a finite buffer and when customers have independent random deadlines for service completion.     

A survey of personalized treatment models for pricing strategies in insurance

We consider a model for price calculations based on three components: a fair premium; price loadings reflecting general expenses and solvency requirements; and profit. The first two components are typically evaluated on a yearly basis, while the third is viewed from a longer perspective. When considering the value of customers over a period of several years, and examining policy renewals and cross-selling in relation to price adjustments, many insurers may prefer to reduce their short-term benefits so as to focus on their most profitable customers and the long-term value. We show how models of personalized treatment learning can be used to select the policy holders that should be targeted in a company’s marketing strategies. An empirical application of the causal conditional inference tree method illustrates how best to implement a personalized cross-sell marketing campaign in this framework.     

Customer equilibrium and optimal pricing in an M/G/1 queue with heterogeneous rewards and waiting cost rates

We consider an unobservable M/G/1 queue in which customers are allowed to join or balk upon arrival. The service provider charges the same admission fee to all joining customers. All joining customers receive a reward from completion of service and incur a waiting cost. The reward and waiting cost rate are random, however the customers know their own values upon arrival. We characterize the customer’s equilibrium strategy and the optimal prices associated with profit and social welfare maximization.     

Optimal admission control in queues with abandonments

We consider a multi-sever Markovian queueing system with abandonments where admitted customers pay a reward either at the time of arrival or service completion. There is a cost associated with abandonments and a holding cost associated with customers in the system. We prove that the policy that maximizes the long-run average reward is of threshold type and completely characterize the optimal thresholds. We conclude with a comparison of various characteristics of the two variants of the model.     

Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods

The segmentation of customers on multiple bases is a pervasive problem in marketing research. For example, segmentation service providers partition customers using a variety of demographic and psychographic characteristics, as well as an array of consumption attributes such as brand loyalty, switching behavior, and product/service satisfaction. Unfortunately, the partitions obtained from multiple bases are often not in good agreement with one another, making effective segmentation a difficult managerial task. Therefore, the construction of segments using multiple independent bases often results in a need to establish a partition that represents an amalgamation or consensus of the individual partitions. In this paper, we compare three methods for finding a consensus partition. The first two methods are deterministic, do not use a statistical model in the development of the consensus partition, and are representative of methods used in commercial settings, whereas the third method is based on finite mixture modeling. In a large-scale simulation experiment the finite mixture model yielded better average recovery of holdout (validation) partitions than its non-model-based competitors. This result calls for important changes in the current practice of segmentation service providers that group customers for a variety of managerial goals related to the design and marketing of products and services.