An analytical trajectories method for understanding shoppers' buying patterns in intelligent retail environment for a business analysis purpose
Nanni, Lorenzo (A.A. 2022/2023) An analytical trajectories method for understanding shoppers' buying patterns in intelligent retail environment for a business analysis purpose. Tesi di Laurea in Customer intelligence e logiche di analisi dei big data, Luiss Guido Carli, relatore Emanuele Frontoni, pp. 84. [Master's Degree Thesis]
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Abstract/Index
State of the art. Literature review. Dataset description. Research methodology. Practical development of the analysis. Clustering. Unsupervised learning. Focus on clustering method: k-means and spectral clustering. Script mode. Analysis of code passages. Output. Output survey methodology. Business analysis: improving business processes through clustering. Silhouette score: compare clustering algorithms. INtelligent retail environments and GDPR. Methodologies for compliance in surveying. The new role of the physical retail space.
References
Bibliografia: pp. 81-84.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Marketing (LM-77) |
| Chair: | Customer intelligence e logiche di analisi dei big data |
| Thesis Supervisor: | Frontoni, Emanuele |
| Thesis Co-Supervisor: | Romeo, Luca |
| Academic Year: | 2022/2023 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 13 Dec 2023 15:54 |
| Last Modified: | 13 Dec 2023 15:54 |
| URI: | https://tesi.luiss.it/id/eprint/37334 |
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