TY  - UNPB
N1  - Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/).
A1  - Li, Xingyi
PB  - UCL (University College London)
AV  - public
TI  - Unstructured Data in Digital Marketing and Supply Chain Management
Y1  - 2024/09/28/
M1  - Doctoral
UR  - https://discovery.ucl.ac.uk/id/eprint/10196692/
ID  - discovery10196692
EP  - 167
N2  - The thesis focuses on the utilization of unstructured data in digital marketing and supply chain management. The first chapter, I explore the effect of expert opinions on consumer experience via the lens of consumer reviews in the restaurant industry, where the expert opinions are conveyed by Michelin stars. We apply two synthetic-control-based methods to estimate the effect of Michelin star changes on the sentiment and content of consumer reviews. We find that decreases in Michelin stars improve consumer review ratings, suggesting that the expectation effect of expert opinions is stronger than the reputation effect. In the second chapter, I move to explore how businesses adapt and respond to expert opinions. To do this, we analyze restaurants historical menus to explore how the restaurants responded
to Michelin star awards. We find that one of the reasons why restaurants with decreases in Michelin stars received higher star ratings after the star decrease is that they streamlined their menu structure and thereby improved the service quality. In the final chapter, we conduct a spend analysis of a procurement practice for manufacturers. We propose the three-component classification model to automate spend analysis and replicate the experts know-how. Using the spend data from Cranswick plc, a major food producer in the UK, we demonstrate improved accuracy of our methodology and superior performance compared to benchmark models.
ER  -