基于BP算法的客户关系管理有效性评价研究

根据客户关系管理（CRM）的基本原理，提出了客户关系管理的过程模型，基于该模型建立了CRM有效性的评价指标体系，针对BP算法的自适应性、自学习性、自组织性特点，提出基于BP算法的客户关系管理有效性评价方法，最后运用数字实例验证了该方法的有效性和可行性。     

客户关系动态优化模型与实证研究

提出了客户关系与营销活动的动态交互模型,以长期收益最大化为目标,优化企业的营销活动。模型假设客户关系可离散为几个层级状态,并设客户关系所处状态受营销活动的影响而动态的变化,服从马尔可夫决策过程。客户关系状态所处层级不可直接观测,但其与客户购买水平有概率相关关系。提出模型参数估计的最大似然估计方法。以国内某企业的客户关系管理数据为例,说明了模型变量的定义方法,通过客户交互历史数据估计模型参数,并对客户管理策略进行优化。结果表明,最优策略管理下期望提升客户价值61％～82％。     

应用Markov链方法分析销售策略

本简单地分析了销售的过程，利用认知心理得到的销售状态指标和Markov链在Pfeifer工作的基础上建立的客户关系模型，对几种不同的销售策略进行了比较。分析结果表明，在与客户关系不好的情况下．采用先改善与客户的关系、后实施销售活动效果比直接销售好些。     

客户价值研究及其对客户关系管理绩效的影响

客户价值研究是客户关系管理研究中的重要内容，本基于客户视角对客户价值进行分析，提出客户价值应包含五个关键维度：功能价值、社会价值、情感价值、知识价值和感知牺牲。同时建立了客户价值对基于客户行为的客户关系管理(CRM)绩效的影响理论模型并提出相关假设，然后通过实证分析验证该假设，最后讨论了实证结果及其对客户关系管理实践的指导意义。     

基于收入管理的CRM策略优化研究

在以客户关系为竞争导向的服务经济时代,有效的收入管理必须深入的结合客户关系管理策略.利用赋值马尔科夫过程方案决策的基本理论构建起了基于收入管理的CRM策略优化方案决策的随机模型,并对该模型方法进行了实证应用.结果表明通过应用该模型方法进行CRM策略的优化选择后,企业在加强客户关系承诺的同时能有效的提升其收入管理水平.讨论了研究结论对企业管理实践的意义.     

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。     

客户发展关系的Markov链一般模型

根据客户发展关系的Markov链的转移概率矩阵，建立了客户发展关系的一般模型，它包含许多特殊模型。前人研究的一些模型正是该一般模型的特例。一般模型的建立不但刻画了各种客户发展关系，而且为企业对客户发展进行定量分析和管理奠定了基础。     

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。     

人口投入产出模型

本文分别用人口转换关系和人口结构系数两种体系建立了动态人口投入产出模型,求出了模型的解和动态逆,并进一步研究了人口投入产出模型的应用.     

基于信任-承诺互动的渠道关系定性模拟研究

本文采用定性仿真结合规划论的方法对渠道关系发展的动态特征进行了研究。根据渠道关系的信任-承诺互动模型,设计了关系价值的定性目标函数,建立渠道关系的定性仿真模型。将目标规划的思想融入到QSIM算法之中,设计了新的状态转换规则和过滤规则,建立渠道关系互动过程的模拟步骤。最后分析模拟结果,表明所设计的模拟方法是合理的,可以用于渠道关系动态过程的描述。     

基于企业网络舆情的客户满意度分析及管理方法

社交网络平台的迅速发展,促使网络舆情成为企业获取商业情报、扩大竞争优势的重要信息来源。本文针对网络舆情环境下的企业客户关系管理问题展开研究。通过构建企业客户推动式信息反馈模型,描述了企业客户、网络用户与企业网络舆情间的联系,并依据信息反馈模型,提出变尺度聚类算法。该算法将传统聚类方法的求解过程由单一尺度分析扩展到多尺度分析,克服了实际数据聚类应用过程中的聚类结果特征不显著问题。本文选取新浪微博作为数据源,利用企业网络舆情数据集和企业客户数据集进行数据分析实验。实验结果表明,企业可以通过获取与其主营业务相关的网络舆情信息,实现客户满意度预测;同时,变尺度聚类算法结果能够为企业进一步制定销售战略和销售战术提供决策支持。     

竞争环境下考虑顾客惰性行为的易逝品动态定价

消费者购买决策的行为倾向对企业的收益管理有着不可忽视的影响。本文在竞争企业动态定价问题中, 考虑了顾客惰性行为——即顾客购买决策的非理性拖延倾向——的影响。证明了库存充足情况下, 多企业多期博弈各销售期都存在唯一的纯策略纳什均衡。数值模拟表明, 顾客惰性对企业的最优价格和企业最大期望收益均造成负面影响, 且对销售低质量产品的企业的负面影响更大。此外, 顾客惰性宽度对各企业最优价格和最大期望收益的影响边际效应递增, 而顾客惰性深度产生的影响边际效应递减。     

基于收益管理和仿真的第三方仓储资源分配

针对不确定市场需求条件下第三方仓储资源的能力规划与分配问题,构建随机数学规划模型,理论分析证明了最优资源分配量的存在性,并指出最优资源分配量是单位资源成本的递减函数、单位资源收益和单位损失成本的递增函数。鉴于解析求解的复杂性,基于收益管理思想,结合离散事件仿真技术和响应曲面法,提出一种新的分析求解框架:收益管理用于细分顾客、构建资源分配策略,仿真模型刻画系统随机特性并评估系统绩效指标,响应曲面法则优化分配策略并探寻绩效改进方向。案例研究和仿真实验结果显示,根据顾客类别分配仓储能力的策略优于传统的先到先服务策略,收益管理、响应曲面法与仿真的综合集成,能够提高系统收益,从而使本文所提方法体系得到了有效验证。     

Phase-type distribution of customer relationship with Markovian response and marketing expenditure decision on the customer lifetime value

In recent years, more and more companies have adopted relationship marketing (RM). At the core of RM is the development and maintenance of long-term relationships with valuable customers. In RM, the customer lifetime value (CLV) is the discounted profit streams of a customer across the entire customer life cycle. The CLV plays a key role in customer acquisition and retention decisions. In this paper, we present a general mathematical framework for RM, and introduce a Markov chain model which is appropriate in modeling RM because of its flexibility and probabilistic nature. We also develop the life distribution of the customer relationship, which is a phase-type distribution since it is the distribution of the first arrival stopping state. And we obtain the expectation of the CLV, which is an important statistic for good decision-making. Finally, we illustrate how to find the optimal remarketing policy numerically. The framework developed for RM systems in this paper should be seen as a practical approach to RM where one can directly apply the results of phase-type distribution and expectation of CLV to marketing decisions.     

Single vehicle routing problems with a predefined customer sequence, compartmentalized load and stochastic demands

We consider the problem of finding the optimal routing of a single vehicle that delivers K different products to N customers according to a particular customer order. The demands of the customers for each product are assumed to be random variables with known distributions. Each product type is stored in its dedicated compartment in the vehicle. Using a suitable dynamic programming algorithm we find the policy that satisfies the demands of the customers with the minimum total expected cost. We also prove that this policy has a specific threshold-type structure. Furthermore, we investigate a corresponding infinite-time horizon problem in which the service of the customers does not stop when the last customer has been serviced but it continues indefinitely with the same customer order. It is assumed that the demands of the customers at different tours have the same distributions. It is shown that the discounted-cost optimal policy and the average-cost optimal policy have the same threshold-type structure as the optimal policy in the original problem. The theoretical results are illustrated by numerical examples.     

Finite and infinite-horizon single vehicle routing problems with a predefined customer sequence and pickup and delivery

We consider the problem of finding the optimal routing of a single vehicle that starts its route from a depot and picks up from and delivers K different products to N customers that are served according to a predefined customer sequence. The vehicle is allowed during its route to return to the depot to unload returned products and restock with new products. The items of all products are of the same size. For each customer the demands for the products that are delivered by the vehicle and the quantity of the products that is returned to the vehicle are discrete random variables with known joint distribution. Under a suitable cost structure, it is shown that the optimal policy that serves all customers has a specific threshold-type structure. We also study a corresponding infinite-time horizon problem in which the service of the customers is not completed when the last customer has been serviced but it continues indefinitely with the same customer order. For each customer, the joint distribution of the quantities that are delivered and the quantity that is picked up is the same at each cycle. The discounted-cost optimal policy and the average-cost optimal policy have the same structure as the optimal policy in the finite-horizon problem. Numerical results are given that illustrate the structural results.     

Time-dependent and independent control rules for coordinated production and pricing under demand uncertainty and finite planning horizons

We address the effect of uncertainty on a manufacturer’s dynamic production and pricing decisions over a finite planning horizon. The demand for products, which depends on their price, is characterized by two stochastic processes: potential demand and customer price sensitivity. An optimal policy for coordinating production and pricing is a time-dependent feedback rule with respect to the state of the manufacturer’s inventories. We show that when the volatility of customer sensitivity to the product price is negligible, the optimal policy can be obtained analytically. Moreover, our simulations demonstrate that the volatility of stochastic customer price sensitivity does not have a strong effect on the manufacturer’s expected profit. Therefore, the solution derived for the case of customer price sensitivity with zero volatility can serve as a good approximation heuristic for the optimal policy if the true volatility of customer price sensitivity is within 40 % of its mean and the volatility of potential demand is within 25 % of its mean. Moreover, under these conditions, a simplified, time-independent control rule deteriorates expected profits by only 1.5 %.