Joint optimization of customer segmentation and marketing policy to maximize long-term profitability

With the advent of one-to-one marketing media, e.g. targeted direct mail or internet marketing, the opportunities to develop targeted marketing campaigns are enhanced in such a way that it is now both organizationally and economically feasible to profitably support a substantially larger number of marketing segments. However, the problem of what segments to distinguish, and what actions to take towards the different segments increases substantially in such an environment. A systematic analytic procedure optimizing both steps would be very welcome.In this study, we present a joint optimization approach addressing two issues: (1) the segmentation of customers into homogeneous groups of customers, (2) determining the optimal policy (i.e., what action to take from a set of available actions) towards each segment. We implement this joint optimization framework in a direct-mail setting for a charitable organization. Many previous studies in this area highlighted the importance of the following variables: R(ecency), F(requency), and M(onetary value). We use these variables to segment customers. In a second step, we determine which marketing policy is optimal using markov decision processes, following similar previous applications. The attractiveness of this stochastic dynamic programming procedure is based on the long-run maximization of expected average profit. Our contribution lies in the combination of both steps into one optimization framework to obtain an optimal allocation of marketing expenditures. Moreover, we control segment stability and policy performance by a bootstrap procedure. Our framework is illustrated by a real-life application. The results show that the proposed model outperforms a CHAID segmentation

Credit Ranking of Bank Customers (An Integrated Model of RFM, FAHP and K-means)

In this paper, with the aim to rank customers in terms of credit, three patterns namely Hsieh (RFM), FAHP, and K-means were integrated. The main effective factors on ranking customers including transactions, repayment and RFM (Recency, Frequency and Monetary) variables were defined. For classifying the legal customers of Export Development Bank of Iran in terms of credit, 5 variables were extracted from the bank’s database and normalized accordingly. The weight of each variable was calculated through interviewing bank experts using FAHP. Using the values of the variables and K-means algorithm, the optimal clusters of customers were determined. Finally, bank customers were ranked in 5 credit clusters and the value of each cluster was estimated. According to the findings, recency, repayment behavior, transaction, frequency, and monetary variables had maximum effects on customers’ ranks, respectively. Therefore, 54% of the customers fell in the third cluster (with cluster value of 0.95) and the fifth cluster (with cluster value of 0.76) composed of good and very good customers. Credit risk of the two clusters (especially the third one) was at least. 32% of the customers positioned in the second cluster (with cluster value of 0.59) including the average customers in terms of credit. 14% of the customers fell in the fourth and first clusters with cluster values of 0.42 and 0.26 including highest risky customers

An investigation on relationship between CRM and organizational learning through knowledge management: A case study of Tehran travel agency

Customer relationship management (CRM) plays essential role on the success of many business units. CRM integrates necessary data from internal and external sources to assist managers and employees for business development. This paper attempts to analyze relationship between CRM, organizational learning, and knowledge management. Research population includes travel agencies in Tehran, Iran and their manager are considered for the purpose of this study. This research has four variables 1- Successful implementation of KM, 2- Organizational learning, 3- customer orientation, and 4- information share with customers. The preliminary results of this survey indicate that any development of CRM will significantly contribute relative efficiency of this travel agency. The results also indicate that there is a meaningful relationship among components of CRM including organizational learning, and knowledge management in this travel agency

Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

Modelling Customer Relationships as Hidden Markov Chains

Models in behavioural relationship marketing suggest that relations between the customer and the company change over time as a result of the continuous encounter. Some theoretical models have been put forward concerning relationship marketing, both from the standpoints of consumer behaviour and empirical modelling. In addition to these, this study proposes the hidden Markov model (HMM) as a potential tool for assessing customer relationships. Specifically, the HMM is submitted via the framework of a Markov chain model to classify customers relationship dynamics of a telecommunication service company by using an experimental data set. We develop and estimate an HMM to relate the unobservable relationship states to the observed buying behaviour of the customers giving an appropriate classification of the customers into the relationship states. By merely accounting for the functional and unobserved heterogeneity with a two-state hidden Markov model and taking estimation into account via an optimal estimation method, the empirical results not only demonstrate the value of the proposed model in assessing the dynamics of a customer relationship over time but also gives the optimal marketing-mixed strategies in different customer state

Applying Kohonen Vector Quantization Networks for Profiling Customers of Mobile Telecommunication Services

Customer clustering is used to understand customers’ preferences and behaviors by examining the differences and similarities between customers. Kohonen vector quantization clustering technology is used in this research and is compared with Kmeans clustering. The data set consists of customer records obtained from a mobile telecommunications service provider. The customers are clustered using various attributes that are broadly grouped under usage, revenue, handset, and service. The clustering results are examined to see the relationships between different types of attributes. The analysis leads to the discovery of several interesting facts about customers that may be of use to mobile service providers

Direct Mailing Decisions for a Dutch Fundraiser

Direct marketing firms want to transfer their message as efficientlyas possible in order to obtain a profitable long-term relationshipwith individual customers. Much attention has been paid to addressselection of existing customers and on identifying new profitableprospects. Less attention has been paid to the optimal frequency ofthe contacts with customers. We provide a decision support system thathelps the direct mailer to determine mailing frequency for activecustomers. The system observes the mailing pattern of these customersin terms of the well known R(ecency), F(requency) and M(onetary)variables. The underlying model is based on an optimization model forthe frequency of direct mailings. The system provides the directmailer with tools to define preferred response behavior and advisesthe direct mailer on the mailing strategy that will steer thecustomers towards this preferred response behavior.decision support system;direct marketing;Markov decision process