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

Measuring customer loyalty using an extended RFM and clustering technique

Today, the ability to identify the profitable customers, creating a long-term loyalty in them and expanding the existing relationships are considered as the key and competitive factors for a customer-oriented organization. The prerequisite for having such competitive factors is the presence of a very powerful customer relationship management (CRM). The accurate evaluation of customers’ profitability is considered as one of the fundamental reasons that lead to a successful customer relationship management. RFM is a method that scrutinizes three properties, namely recency, frequency and monetary for each customer and scores customers based on these properties. In this paper, a method is introduced that obtains the behavioral traits of customers using the extended RFM approach and having the information related to the customers of an organization; it then classifies the customers using the K-means algorithm and finally scores the customers in terms of their loyalty in each cluster. In the suggested approach, first the customers’ records will be clustered and then the RFM model items will be specified through selecting the effective properties on the customers’ loyalty rate using the multipurpose genetic algorithm. Next, they will be scored in each cluster based on the effect that they have on the loyalty rate. The influence rate each property has on loyalty is calculated using the Spearman’s correlation coefficient

Linking Customer Retention to Intelligent Technology: An Optimization Approach

Marketing managers in the telecommunication sectors are confronted with considerable complexity. They have to make decisions about the optimum combination of products or offerings, customer groups and the means of interacting with potential customers. Further, in saturated markets such as mobile telephony, it is increasingly important to retain customers potentially to churn. On the optimal campaign planning, this research describes how the customer survey was conducted for those potentially churning customers based on which an optimal campaign planning was followed. This research engages with the subjects of customer retention from the perspective of a major mobile operator in Taiwan. Customers’ preferences with C&C (campaign offer and communication channel) were predicted and input for further analysis for target selection optimization. These models was proved novel in an organizational prototype project suggesting that the use of the hybrid of data mining and optimization approaches can be effective for target selection

Editorial

CIT’s year 2013 last issue brings papers from the Journal’s regular section. It consists of six contributions spanning a wide scope, from distributed platforms, security, image processing, supply chain management and up to profitability evaluation of air transport