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

Profiling for profit : a report on target marketing and profiling practices in the credit industry

This report examines how many businesses make significant investments to purchase and develop customer relationship management systems. Given such investments, information about these systems is not widely available, but some publicly available information gives indication of the extent, and purpose, of the use. Recognising that lenders use customer information and highly sophisticated systems to target their marketing strategies, is the first step towards ensuring that these practices are taken into account in the development of consumer policy and law reform. This research was funded by the consumer advisory panel of the Australian Securities and Investment Commission (ASIC)

Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Business clients´segmentation based on activity : a banking approach

Internship report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in knowledge Management and Business IntelligenceClustering algorithms are frequently used by companies to segment their customers in order to develop accurate marketing strategies. The K-means is one of the most popular algorithms, despite its drawbacks in terms of seeds’ generation. In this study, several clustering algorithms were tested but in the end the K-means initialized with random seeds was used to segment the data due to its better performance. This B2B segmentation resulted in four clusters based on the activity patterns of each business client, The Loyals, The Minglers, The Challengers and The Believers. Each one of these clusters shows a different type of relationship with the bank, being the bank the first choice for The Loyals and for the Believers but not for the others

BIG DATA IN MARKETING & RETAILING

Data is increasingly being created, stored, analyzed, and applied. Big Data is becoming an everyday phrase that appears in popular media and people’s daily conversations. This paper provides a framework to define Big Data from technical and business perspectives, to present its enormous value in different fields, to share its applications in marketing and retailing, market segmentation, targeting and positioning as well in developing marketing mix. We also provide some real life industry examples, to shed light on the challenges in harnessing the potential of Big Data, and to discuss its future. Big Data will separate the winners from the losers in the business field in the future. The leading companies in the Big Data field, such as Google, Amazon, and Wal-Mart, will continue to build their competitive advantage, both in marketing and other areas, by acting on the insights developed from Big Data analysis

Strategic understanding of Malaysian online customers’ service quality preference through demographic customer profiling and e-product bundling

In order to stay competitive in the arena of e-commerce, conventional e-marketing research have provided solutions to online businesses and marketing practitioners by understanding online purchasing behavior and thereby proposing various determinants influencing online purchasing behavior. Little research has been done in order to assist marketing practitioners to identify the precise online customer segmentation, making market targeting and positioning and use of effective marketing campaigns complex. Hence, this study aims to identify the appropriate online customer segmentation (product bundles) based on three determinants of online purchasing behavior, i.e. e-service quality, demographic profiles and types of product purchased. 680 useful data was collected from existing online shoppers and data mining technique was employed to identify the product bundles and decision trees were used for customer profiling. Findings have identified Tickets, Clothing and Travel product bundles as the basis of segmentation. Result from this study will assist online marketing practitioners to be conscious of online customers needs and astutely create marketing campaigns aiming at their targeted online customers segment