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Koyi Anusha
Amrita Vishwa Vidyapeetham University, Mysuru campus
Computer Science & IT Electrical & Electronics Engineering

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Segmentation of Retail Mobile Market Using HMS Algorithm Koyi Anusha; Yashaswini C; Manishankar S
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (293.402 KB) | DOI: 10.11591/ijece.v6i4.pp1818-1827

Abstract

In the modern world of marketing, analyzing the trends in market is a key point towards to scope of improvement of any company. Considering the analysis of a retail market where market trends change very frequently based on customer needs and interest is highly challenging. Market segmentation is one of the approaches included in analysis of market trends which gives a diverse view of the market.  The research here concentrates, especially on a case study based on fast moving consumable goods market and identifying market change patterns by applying a novel data mining approach. Data mining includes a wide variety of techniques and algorithm which can be effectively used in the process of market analysis. The research work carried out coins a new algorithm which combines various association rules and techniques, the HMS (Hybrid market segmentation) algorithm with some specialized criteria is used to support the market segmentation. The primary data needed for the analysis and operation are collected through a questionnaire based survey conducted on people from various demographic regions as well as various age groups. Used a quota based sampling approach for the research, The data mining approach here helps to study the large dataset collected and also to extract the useful information required to model the system. The system here is a learning system which improves the market segmentation functionality as data set improves, The paper implements a hybrid data mining approach which effectively segments the retail mobile market in to various customer and product groups and also provides a prediction and suggestion system for company as well as customer. 
Co-Authors Manishankar S Yashaswini C