Paolantoni, Riccardo (A.A. 2024/2025) Predictive lead scoring: interpretable machine learning approaches for business-oriented decision-making. Tesi di Laurea in Big data and smart data analytics, Luiss Guido Carli, relatore Irene Finocchi, pp. 57. [Master's Degree Thesis]

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Abstract/Index

Lead scoring models: theoretical background. The shift to customer-centricity. Customer relationship management (CRM). Customer acquisition. Lead management. Lead scoring and lead scoring modelling. The explainability gap in predictive lead scoring. Literature review. Predictive lead scoring models. Explainability methods. Empirical analysis. Data & business settings. Preprocessing. Modelling. Evaluation metrics. Hyperparameter tuning. Explainability techniques. Results: predictive performance. Model interpretation & explainability.

References

Bibliografia: pp. 50-55.

Thesis Type:	Master's Degree Thesis
Institution:	Luiss Guido Carli
Degree Program:	Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91)
Chair:	Big data and smart data analytics
Thesis Supervisor:	Finocchi, Irene
Thesis Co-Supervisor:	Martino, Alessio
Academic Year:	2024/2025
Session:	Autumn
Deposited by:	Alessandro Perfetti
Date Deposited:	18 Mar 2026 16:11
Last Modified:	18 Mar 2026 16:11
URI:	https://tesi.luiss.it/id/eprint/45173

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