Assessing the predictive effectiveness of the variety seeking and reinforcement models

Prediction of customer choice behaviour has been a big challenge for marketing researchers. They have adopted various models to represent customers purchase patterns. Some researchers considered simple zero–order models. Others proposed higher–order models to represent explicitly customers tendency to seek [variety] or [reinforcement] as they make repetitive choices. Nevertheless, the question [Which model has the highest probability of representing some future data?] still prevails. The objective of this paper is to address this question. We assess the predictive effectiveness of the well–known customer choice models. In particular, we compare the predictive ability of the [dynamic attribute satiation] (DAS) model due to McAlister (Journal of Consumer Research, 91, pp. 141–150, 1982) with that of the well–known stochastic variety seeking and reinforcement behaviour models. We found that the stochastic [beta binomial] model has the best predictive effectiveness on both simulated and real purchase data. Using simulations, we also assessed the effectiveness of the stochastic models in representing various complex choice processes generated by the DAS. The beta binomial model mimicked the DAS processes the best. In this research we also propose, for the first time, a stochastic choice rule for the DAS model.     

Analyzing consumers’ shopping behavior using RFID data and pattern mining

The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer??s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.     

基于迁移学习的客户信用评估模型研究

客户信用评估是银行等金融企业日常经营活动中的重要组成部分。一般违约样本在客户总体中只占少数,而能按时还款客户样本占多数,这就是客户信用评估中常见的类别不平衡问题。目前,用于客户信用评估的方法尚不能有效解决少数类样本稀缺带来的类别不平衡。本研究引入迁移学习技术整合系统内外部信息,以解决少数类样本稀缺带来的类别不平衡问题。为了提高对来自系统外部少数类样本信息的使用效率,构建了一种新的迁移学习模型:以基于集成技术的迁移装袋模型为基础,使用两阶段抽样和数据分组处理技术分别对其基模型生成和集成策略进行改进。运用重庆某商业银行信用卡客户数据进行的实证研究结果表明:与目前客户信用评估的常用方法相比,新模型能更好地处理绝对稀缺条件下类别不平衡对客户信用评估的影响,特别对占少数的违约客户有更好的预测精度。     

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。     

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。     

Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model

The general aim of this study is to provide a guide to the future marketing decisions of a firm, using a model to predict customer lifetime values. The proposed framework aims to eliminate the limitations and drawbacks of the majority of models encountered in the literature through a simple and industry-specific model with easily measurable and objective indicators. In addition, this model predicts the potential value of the current customers rather than measuring the current value, which has generally been used in the majority of previous studies. This study contributes to the literature by helping to make future marketing decisions via Markov decision processes for a company that offers several types of products. Another contribution is that the states for Markov decision processes are also generated using the predicted customer lifetime values where the prediction is realized by a regression-based model. Finally, a real world application of the proposed model is provided in the banking sector to show the empirical validity of the model. Therefore, we believe that the proposed framework and the developed model can guide both practitioners and researchers.     

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。     

Likelihood-ratio changepoint features for consumer-behaviour models

Some predictive models for customer value management might benefit from information about certain changes in individual-consumer behaviour. We take changepoint methods as the first step in producing a model-input feature for this purpose. An unusual feature in the application of changepoint methods to consumer data is there are as many streams of data as there are customers. This property is used to help decide whether an individual has changed their behaviour by ordering likelihood-ratio statistics from the changepoint models. Following a review of changepoint methods, the approach is demonstrated on cash machine transactions. Models for the amount, location and time of transaction are used and accounts exhibiting large evidence of change are examined in detail. For the data set used the approach performs sensibly. The worth of likelihood-ratio statistics to rank evidence for change is considered more generally through some of the literature.     

Customer lifetime value: stochastic optimization approach

Since the early 1980s, the concept of relationship marketing has been becoming important in general marketing, especially in the area of direct and interactive marketing. The core of relationship marketing is the maintenance of long-term relationships with the customers. However, the relationship marketing is costly and therefore, the determination of the customer lifetime value (CLV) is an important element in making strategic decisions in both advertising and promotion. In this paper, we propose a stochastic dynamic programming model with a Markov chain for the optimization of CLV. Both cases of infinite horizon and finite horizon are discussed. The model is then applied to practical data of a computer service company.     

Customer knowledge management

Customer knowledge has received little attention in the knowledge management literature. The authors of this exploratory study argue that practices in marketing and customer relationship management have not been able to capture knowledge from customers that comes from social interactions with firm employees. The authors propose a three-step model by which companies can obtain this knowledge. The model's theoretical base comes from the information retrieval and socialization concepts of the knowledge management literature. The paper identifies cultural changes required to make this process successful.     

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。     

Predicting time-to-churn of prepaid mobile telephone customers using social network analysis

Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.     

Product line optimization in the presence of preferences for compromise alternatives

Recent advances in customer choice analysis demonstrated the strong impact of compromise alternatives on the behaviour of decision-makers in a wide range of decision situations. Compromise alternatives are characterized by an intermediate performance on some of the relevant attributes. For instance, price compromises are well known in the sense that customers tend to buy neither the cheapest, nor the most expensive alternative, but the mid-priced one. However, thus far, the literature on product line optimization has not considered such context effects.In this paper, we propose a model-based approach for optimal product line selection which incorporates customers’ preferences for compromise alternatives. We consider customer choice in a realistic, sophisticated fashion by applying an established utility model that integrates compromise variables into a multinomial logit model. We formulate the resulting optimization problem as a mixed-integer linear program. The challenging feature for modelling – making the formulation substantially more complicated than existing ones without compromises – are the endogenous effects of selected products on other alternatives’ utilities that need to be adequately captured via compromise variables. Based on data we collected by a stated choice experiment in a retail setting, we perform a computational study and demonstrate the superiority of our product line selection approach compared to a reference model that does not take compromises into account. Even under uncertainty of the estimated utility parameters, profit gains of, on average, 23% can be achieved in our experimental setting.     

New insights into churn prediction in the telecommunication sector: A profit driven data mining approach

Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical measures.In the second part an extensive benchmarking experiment is conducted, evaluating various classification techniques applied on eleven real-life data sets from telecom operators worldwide by using both the profit centric and statistically based performance measures. The experimental results show that a small number of variables suffices to predict churn with high accuracy, and that oversampling generally does not improve the performance significantly. Finally, a large group of classifiers is found to yield comparable performance.     

Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products?? first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products?? sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests.     

An improved customer lifetime value model based on Markov chain

Firms are increasingly looking to provide a satisfactory prediction of customer lifetime value (CLV), a determining metric to target future profitable customers and to optimize marketing resources. One of the major challenges associated with the measurement of CLV is the choice of the appropriate model for predicting customer value because of the large number of models proposed in the literature. Earlier models to forecast CLV are relatively unsuccessful, whereas simple models often provide results which are equivalent or even better than sophisticated ones. To predict CLV, Rust et al. (2011) proposed a framework model that performs better than simple managerial heuristic models, but its implementation excludes cases where customer's profit is negative and does not handle lost‐for‐good situations. In this paper, we propose a modified model that handles both negative and positive profits based on Markov chain model (MCM), hence offering a greater flexibility by covering always‐a‐share and lost‐for‐good situations. The proposed model is compared with the Pareto/Negative Binomial Distribution (Pareto/NBD), the Beta Geometric/Negative Binomial Distribution (BG/NBD), the MCM, and the Rust et al. (2011) models. Based on customer credit card transactions provided by the North African retail bank, an empirical study shows that the proposed model has better forecasting performance than competing models. Copyright © 2014 John Wiley & Sons, Ltd.     

Scheduling periodic customer visits for a traveling salesperson

The problem considered in this paper deals with determining daily routes for a traveling salesperson who provides customers in Upper Austria with product range information of a large, global food wholesaler. Each customer has to be visited at least once a year, with some customers requiring up to one visit per month. Further, some customers may not be visited each day of the week. Our decision support system uses a commercial GIS software to extract customer data for input into the optimization procedure and to visualize the results obtained by the algorithm. The optimization approach is based on the variable neighborhood search algorithm which assigns customers to days and determines routes for the salesperson for each day with the primary objective to minimize the total travel time of the salesperson. Another objective studied is to minimize the number of days needed by the salesperson to visit all customers in a given month. Further we analyze the effects of changes in the business environment like increases in the amount or flexibility of the salesperson’s working time and variations in the possible days for customer visits. Finally, we enrich the objective function by considering periodicity requirements for customer visits. Specifically, we penalize irregular schedules, where the time between two successive customer visits varies.     

基于RFM模型方法的忠诚计划会员顾客价值识别研究

尽管顾客忠诚计划已作为一种关系营销手段在企业中得到了广泛的应用,但却很少有研究关注如何利用忠诚计划收集的顾客交易数据对会员顾客进行价值识别和细分.基于RFM模型及聚类分析方法,提供了一种对忠诚计划会员顾客进行价值识别与细分的方法,并利用一家购物中心的会员顾客交易数据对该方法进行了实证分析.结果表明该方法不仅从统计意义上可以区分开具有差异的会员顾客群体,而且从管理意义上也可找出不同顾客群体间的差异特征,从而为企业针对差异化的顾客实现服务及产品的定制化和差异化提供了理论基础和方法.     

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,得到航空公司旅客分类的结果.依据细分结果对不同旅客提供差异化的服务和有针对性的营销策略,改善民航企业的服务质量,提高民航企业在市场中的竞争力.     

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.