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A Data Mining Approach to Modeling Customer Preference: A Case Study of Intel Corporation

Abstract Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents a decision support framework that provides a holistic view on customer preference by following a two-phase procedure. Phase-1 uses cluster analysis to create product profiles based on which customer profiles are derived. Phase-2 then delves deep into each of the customer profiles and investigates causality behind their preference using Bayesian networks. This thesis illustrates the working of the framework using the case of Intel Corporation, world’s largest semiconductor manufacturing company.
Created Date 2017
Contributor Ram, Sudarshan Venkat (Author) / Kempf, Karl G (Advisor) / Wu, Teresa (Advisor) / Ju, Feng (Committee member) / Arizona State University (Publisher)
Subject Engineering / Statistics / Bayesian Networks / Cluster Analysis / Customer Preference / Data mining / Machine Learning
Type Masters Thesis
Extent 89 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Industrial Engineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis