Leveraging fine-grained transaction data ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Leveraging fine-grained transaction data for customer life event predictions
Author(s) :
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
de Bock, Koen [Auteur]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
de Bock, Koen [Auteur]
Journal title :
Decision Support Systems
Pages :
113232
Publisher :
Elsevier
Publication date :
2020-03
ISSN :
0167-9236
English keyword(s) :
Life event prediction
Predictive modeling
Pseudo-social networks
Customer relationship management (CRM)
Big data
Data science
Predictive modeling
Pseudo-social networks
Customer relationship management (CRM)
Big data
Data science
HAL domain(s) :
Sciences de l'Homme et Société/Gestion et management
English abstract : [en]
This real-world study with a large European financial services provider combines aggregated customer data including customer demographics, behavior and contact with the firm, with fine-grained transaction data to predict ...
Show more >This real-world study with a large European financial services provider combines aggregated customer data including customer demographics, behavior and contact with the firm, with fine-grained transaction data to predict four different customer life events: moving, birth of a child, new relationship, and end of a relationship. The fine-grained transaction data—approximately 60 million debit transactions involving around 132,000 customers to >1.5 million different counterparties over a one-year period—reveal a pseudo-social network that supports the derivation of behavioral similarity measures. To advance decision support systems literature, this study validates the proposed customer life event prediction model in a real-world setting in the financial services industry; compares models that rely on aggregated data, fine-grained transaction data, and their combination; and extends existing methods to incorporate fine-grained data that preserve recency, frequency, and monetary value information of the transactions. The results show that the proposed model predicts life events significantly better than random guessing, especially with the combination of fine-grained transaction and aggregated data. Incorporating recency, frequency, and monetary value information of fine-grained transaction data also significantly improves performance compared with models based on binary logs. Fine-grained transaction data accounts for the largest part of the total variable importance, for all but one of the life events.Show less >
Show more >This real-world study with a large European financial services provider combines aggregated customer data including customer demographics, behavior and contact with the firm, with fine-grained transaction data to predict four different customer life events: moving, birth of a child, new relationship, and end of a relationship. The fine-grained transaction data—approximately 60 million debit transactions involving around 132,000 customers to >1.5 million different counterparties over a one-year period—reveal a pseudo-social network that supports the derivation of behavioral similarity measures. To advance decision support systems literature, this study validates the proposed customer life event prediction model in a real-world setting in the financial services industry; compares models that rely on aggregated data, fine-grained transaction data, and their combination; and extends existing methods to incorporate fine-grained data that preserve recency, frequency, and monetary value information of the transactions. The results show that the proposed model predicts life events significantly better than random guessing, especially with the combination of fine-grained transaction and aggregated data. Incorporating recency, frequency, and monetary value information of fine-grained transaction data also significantly improves performance compared with models based on binary logs. Fine-grained transaction data accounts for the largest part of the total variable importance, for all but one of the life events.Show less >
Language :
Anglais
Popular science :
Non
Collections :
Source :
Files
- https://hal.archives-ouvertes.fr/hal-02507998/document
- Open access
- Access the document
- https://hal.archives-ouvertes.fr/hal-02507998/document
- Open access
- Access the document
- document
- Open access
- Access the document
- S0167923619302611.pdf
- Open access
- Access the document