Maria Camila Ortegon Herrera
IMPROVEMENT OF CUSTOMER SATISFACTION ANALYSIS IN FIAT CHRYSLER AUTOMOBILES.
Rel. Domenico Augusto Francesco Maisano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2019
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Abstract: |
This thesis aims to present a proposal to improve the analysis of customer satisfaction in Fiat Chrysler Automobiles company, specifically it was developed in the Marketing vehicle importers for Light Commercial Vehicles area. Since customer satisfaction has become a key factor for the growth and success of a company in the last few years, FCA is concerned about having a reliable analysis of not only their position in their market but also going beyond that to understand how customers perceive them and the reasons behind their satisfaction or dissatisfaction with their products/services. The analysis of customers perception is a broad subject that takes into account several dimensions as customer satisfaction, dissatisfaction, loyalty, quality of the products/services, etc. The characteristics being measured to assess customers’ perceptions change from one sector to another, specifically this thesis was developed in the automotive sector. Some guidelines for the creation of the questionnaires will be presented and statistical tools such as, descriptive statistics, correlation test and regression analysis will be described, which will help to analyze the collected information. This document describes the theoretical basis including marketing, customer satisfaction and statistical analysis theory, as well as internal guidelines used in FCA to evaluate customer experience. Based on this information two questionnaires were developed for the assessment of sales and after sales customer experience. Additionally, this theory and guidelines were crucial in the establishment of the procedure to verify the quality of the questionnaires and data collected. Finally, the steps toward the implementation of the proposal will be presented, including also the guidelines to extract the information from the raw data. |
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Relators: | Domenico Augusto Francesco Maisano |
Academic year: | 2018/19 |
Publication type: | Electronic |
Number of Pages: | 80 |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management) |
Classe di laurea: | New organization > Master science > LM-31 - MANAGEMENT ENGINEERING |
Aziende collaboratrici: | FCA ITALY SPA |
URI: | http://webthesis.biblio.polito.it/id/eprint/10568 |
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