- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
Leveraging open and synthetic data to simulate customer behavior and infer households in a telecom scenario
- Claudia Sartirana
- Giulio Gatti
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.64
This study presents a methodology for integrating real-world mobile cell data with synthetically generated population and behavioral datasets to construct a novel resource for prototyping and evaluating algorithmic solutions in the telecommunications domain. Focusing on the city center of Pavia, Italy, we simulate realistic scenarios based on demographic distributions and modeled user behaviors. The approach involves mapping cell coverage across the city center, generating synthetic individuals, and simulating their interactions and cell registrations based on assumptions about population density and urban geography. Two case studies are then conducted on the simulated datasets. The first enhances customer profiling by combining interaction patterns, cell registration data, and demographic attributes to infer latent family relationships and identify household groupings. The second links these insights with customer complaint records, enabling the detection of hidden service issues. Specifically, we identify a network fault affecting a localized area where multiple associated customers reside.
- Keywords:
- Corruption,
- Indicators,
- Well-being,
- Social Capital,
- Artificial intelligence,
- Machine Learning,
University of Milan La Statale, Italy - ORCID: 0000-0002-5837-0480
SAS Institute, Italy
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Informazioni sul capitolo
Titolo del capitolo
Leveraging open and synthetic data to simulate customer behavior and infer households in a telecom scenario
Autori
Claudia Sartirana, Giulio Gatti
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.64
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series