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. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2017
| Identifer | oai:union.ndltd.org:asu.edu/item:46323 |
| Date | January 2017 |
| Contributors | Ram, Sudarshan Venkat (Author), Kempf, Karl G (Advisor), Wu, Teresa (Advisor), Ju, Feng (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 89 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
Page generated in 0.0537 seconds