Shock-ready fashion: how data and machine learning anticipate and guide responses to external shocks in the fashion industry
Marras, Jacopo (A.A. 2024/2025) Shock-ready fashion: how data and machine learning anticipate and guide responses to external shocks in the fashion industry. Tesi di Laurea in Advanced marketing management, Luiss Guido Carli, relatore Marco Francesco Mazzù, pp. 101. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Framing the problem & course alignment. Scope, definitions & shock taxonomy. Conceptual positioning within the BVTJ. Literature review & theoretical framework. Consumer response to constraints. Marketing levers are under shocks. Data, OSINT & measurement. ZEGNA case: context, public data & methods. ZEGNA during the pandemic. Data construction & governance. Identification strategy and regime-based design. Evidence: regime diagnostics and proxy outcomes. Demand and attention under shock regimes. Access, feasibility and macro frictions. Cross-market heterogeneity and stability. From diagnostics to decision logic. Nowcasting and regime detection. Decision logic under constraints.
References
Bibliografia: pp. 98-100.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Management, English language (LM-77) |
| Chair: | Advanced marketing management |
| Thesis Supervisor: | Mazzù, Marco Francesco |
| Thesis Co-Supervisor: | Laura, Luigi |
| Academic Year: | 2024/2025 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 16 Sep 2026 10:57 |
| Last Modified: | 16 Sep 2026 10:57 |
| URI: | https://tesi.luiss.it/id/eprint/46815 |
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