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

3rd Workshop in Symbolic Data Analysis: book of abstracts

This workshop is the third regular meeting of researchers interested in Symbolic Data Analysis. The main aim of the event is to favor the meeting of people and the exchange of ideas from different fields - Mathematics, Statistics, Computer Science, Engineering, Economics, among others - that contribute to Symbolic Data Analysis

Comparing spatial features of urban housing markets:

Various location specific attributes contribute to the spatial dynamics of housing markets. This effect may partly be of a qualitative and discontinuous nature, which causes market segmentation into submarkets. The question however is, whether the most relevant partitioning criteria is directly related to the transaction price of to other, socioeconomic, demographic and physical features of the location. Two neural network techniques are used for analysing statistical house price data from Amsterdam and Helsinki. The analytic hierarchy process is used as a supporting technique. With these techniques it is possible to analyse various dimensions of housing submarket formation. The findings show that, while the price and demand factors have increased in importance, supply factors still prevail as key criteria in both cases. The outcome also indicates that the housing market structure of Amsterdam is more fragmented than that of Helsinki, and that the main discriminating housing market features, and the ways they have changed in time, are somewhat different

Customer Segmentation: An application to dental medicine patients

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceCustomer segmentation allows to divide a company’s customers into multiple market segments, enabling the development of customized marketing actions based on each segment’s characteristics. This work describes the application of a customer segmentation approach to the patients of a Portuguese dental company. The approach taken to select the feature subset for the final model was mostly based on the LRFM (length, recency, frequency, and monetary) model, and the monetary variable was split into multiple variables according to the treatment category where the amount was spent. K-Means and Self-organizing maps were used to cluster the company’s patients using these variables, and the results returned by both algorithms are compared. The final solution was obtained with K-Means, and 7 clusters of patients were identified. An overview of the 7 clusters is provided, and possible marketing actions are suggested based on their main characteristics. The results allowed the company to understand how its turnover was distributed across segments, and to develop an initiative to contact the patients belonging to a segment where most of them did not have an appointment in one of the company’s clinics for a long time