Description
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.
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Details
Title
- A Data Mining Approach to Modeling Customer Preference: A Case Study of Intel Corporation
Contributors
- Ram, Sudarshan Venkat (Author)
- Kempf, Karl G. (Thesis advisor)
- Wu, Teresa (Thesis advisor)
- Ju, Feng (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2017
Subjects
Resource Type
Collections this item is in
Note
- Masters Thesis Industrial Engineering 2017