Clustering-Based Analysis of Clickstream Data for Customer Journey Characterization
This thesis explores the use of clickstream data to characterize customer journeys through clustering-based analysis. The study focuses on a dataset from a single product line, while aiming to develop a generalizable framework applicable to other products and contexts. After preprocessing and normal...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/452753 |
| Acceso en línea: | https://hdl.handle.net/2117/452753 |
| Access Level: | acceso abierto |
| Palabra clave: | Cluster analysis Big data Consumer behavior Clickstream data Customer journeys Clustering analysis Anàlisi de conglomerats Dades massives Consumidors--Conducta Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
| Sumario: | This thesis explores the use of clickstream data to characterize customer journeys through clustering-based analysis. The study focuses on a dataset from a single product line, while aiming to develop a generalizable framework applicable to other products and contexts. After preprocessing and normalizing raw activity data, exploratory analysis and feature engineering were conducted to represent user sessions. Unsupervised learning methods, primarily K-Means clustering, were applied to group sessions and reveal behavioural patterns. The resulting clusters highlight distinct modes of engagement, from exploratory browsing to purchase-oriented interactions, and are compared against existing journey frameworks without enforcing strict alignment. Despite limitations in data granularity, the analysis demonstrates the potential of clustering to uncover customer behaviour dynamics. Future work should enrich datasets with multi-product interactions, campaign data, and user feedback, while extending the framework with supervised models for real-time journey stage prediction and personalized recommendations. |
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