Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

基于粗糙集的RFM分析对银行贷款客户分类的研究

长期以来,信用风险是金融行业,特别是银行业的最主要的风险形式。贷款是商业银行的主要资产业务,因此其经营风险与生俱来,商业银行要保持稳健经营,必须加强贷款的风险控制管理,建立健全包括银行贷款风险管理在内的金融系统。本文旨在运用基于粗糙集的数据挖掘技术,将市场营销中的RFM客户细分的方法运用于贷款客户信用度的分类中去,为银行贷款的风险控制管理提供决策支持。     

基于RFM模型的烟草客户聚类分析研究

随着客户细分理论的不断丰富和完善,构建合理的客户细分指标体系,借助聚类分析的方法,来实现对企业客户的细分。本文针对当前烟草公司在客户细分方面工作的不足,结合烟草行业及零售户的特征通过聚类分析实现烟草行业零售户的细分,帮助烟草公司实现零售户的合理分类,优化客户资源与卷烟货源的配置。     

How many,how often,and how new? A multivariate profiling of mobile app users

Despite enormous demand for and explosive growth of mobile phone apps in recent years, few studies have been conducted to arrive at a multi-faceted valuation of app users. Our paper addresses this important gap in literature. Drawing on Household Production Theory and Hedonic and Utilitarian Consumption Theory, we investigate how mobile app users behave in the dimensions of possession quantity, usage Frequency, and acquisition Recency. We propose a multivariate model to examine these behaviors jointly and calibrate it using data from a survey of app users. We take a Bayesian MCMC computational approach for model calibration. The results are consistent with our theoretically derived expectations. The significance of the findings is discussed.     

基于SOM和PSO聚类组合算法的客户细分研究

文章在对RFM指标体系进行分析的基础上,应用自组织特征映射（SOM）神经网络和粒子群优化（PSO）的聚类组合算法,通过客户关系的特征衡量分析客户的内在价值和忠诚度,对客户数据进行了科学、客观、深层次的挖掘分析,为企业有针对性的制定营销策略提供了依据。     

Making customer relationship management work: evidence from the banking industry in Taiwan

Soon after becoming a WTO member, Taiwan found the internationalisation and liberalisation in the financial industry ushered its domestic banks into a new era. In response to this global trend, all its banks strove to rely on customer relationship management (CRM) to enhance customer value (CV). This study aims to probe further into the connection between CV and CRM. A series of examinations revealed that (1) both functional and social value impact customer behaviour directly and positively; (2) customer satisfaction positively and directly affects customer loyalty; (3) a positive and direct relationship exists between customer loyalty and customer behaviour; and (4) the positive and significant relationship between CV and customer behaviour can be developed through mediators such as customer satisfaction and customer loyalty. Consequently, banks should offer their customers different services, products, and marketing channels to meet their diversified needs to cultivate a win-win environment of CRM for both parties.     

Data accuracy's impact on segmentation performance: Benchmarking RFM analysis,logistic regression,and decision trees

Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for direct marketing: RFM (recency, frequency, and monetary value) analysis, logistic regression, and decision trees. For two real-life direct marketing data sets analyzed, the results demonstrate that (1) under optimal data accuracy, decision trees are preferred over RFM analysis and logistic regression; (2) the introduction of data accuracy problems deteriorates the performance of all three segmentation techniques; and (3) as data becomes less accurate, decision trees retain superior to logistic regression and RFM analysis. Overall, this study recommends the use of decision trees in the context of customer segmentation for direct marketing, even under the suspicion of data accuracy problems.     

基于模糊神经网络的客户分类方法研究

随着电子商务的深层次发展,客户关系管理(CRM)已经引起了各行各业的关注。有效地进行客户分类,是实现客户关系管理目标的前提和保障。文章将传统的RFM(近度Recency、频度Frequency、值度Monetary)指标细化作为客户分类标准,从而不但衡量客户价值等静态因素,还分析客户行为等动态因素。运用模糊理论和BP神经网络建立客户分类模型,得到了较好的结果。     

RFM-based repurchase behavior for customer classification and segmentation

Customer behavior modeling and classification are well-studied areas for applications in retail. Past studies implemented the purchase behavior modeling based on the physical behavior of a subject. In this research, we apply the recency, frequency, and monetary (RFM) model and data modeling techniques to detect behavior patterns for a customer. Each transaction attributed to a customer is part of one's behavior, and an instance of the feature vector, it is modeled on a set of transactions to constitute repurchase behavior. The proposed scheme is validated by simulating a publicly accessible real-world data set with a need-tailored multi-layer perceptron (MLP) and also support vector machine (SVM) and decision tree classification (DTC) methods. The experiments yield a high customer classification rate of more than 97% for the different numbers of the customers. Empirical analysis shows that eight transactions are sufficient to classify a customer with high accuracy.     

A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting

ABSTRACT

This practitioner note proposes a new approach considering two-stage clustering and LRFMP model (Length, Recency, Frequency, Monetary and Periodicity) simultaneously for customer segmentation and behavior analysis and applies it among the Iranian Fintech companies. In this practitioner note, the K-means clustering algorithm and LRFMP model are combined in the customer segmentation process. After initial clustering, for a better understanding of valuable customers, additional clustering is implemented in segments that needed further investigation. This approach contributes to a better interpretation of different customer segments. Customer segments, consisting of 23524 business customers are analysed based on their characteristics and appropriate strategies are recommended accordingly. The first stage clustering result shows that customers are best segmented into four groups. The first and fourth segments are clustered again and the final 11 groups of customers are determined. This note provides a systematic and practical approach for researchers and practitioners for segmentation, interpretation, and targeting of customers especially in the B2B setting and the Fintech industry and helps managers to make effective marketing strategies and enhance customer relationship and marketing intelligence.