Fuzzy rule based cluster analysis to segment consumers’ preferences to eco and non-eco friendly products

Sanjukta Ghosh, Doan Van Thang, Suresh Chandra Satapathy, Sachi Nandan Mohanty

Research output: Contribution to journalArticlepeer-review

Abstract

Environment protection and basic health improvement of all social communities is now considered as one of the key parameters for the development. It has become a responsibility for both industry and academia to optimize the usage of finite natural resources and preserve them. Efficient promotion and strategic marketing of Eco Friendly products can contribute to this development. It is important to consider any market as a heterogeneous mix, which requires well-organized and intelligent split or segmentation. A survey was conducted in Kolkata, metropolitan city in India, through a structured questionnaire to measure Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to 18 product categories identified by Central Pollution Control Board for Eco Mark Scheme, 2002. Two hundred and twenty three data inputs from the respondents were analysed for this study. Here in this study a fuzzy rule based clustering technique was performed to segregate customers into two sections considering three parameters like Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to Eco friendly product, which acts as an input variable. The rule base has linguistic variables like Significantly High, Little High, Medium, Little Low and Significantly Low and output as “Eco friendly” or “Non-ecofriendly” consumers. A set of 5×5×5= 125 rules were developed for output determination. They were designed manually and the method is applied for detection of a set of good rules. Thirteen such good rules were identified through Fuzzy Reasoning Tool, which can lead to better Decision Making and facilitate the marketers to develop strategy and take up effective marketing decisions.
Original languageEnglish
Pages (from-to)343-351
Number of pages9
JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
Volume24
Issue number4
Publication statusPublished - 18-01-2021

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