Author
Listed:
- Arno de Caigny
(LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
- Kristof Coussement
(LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
- Steven Hoornaert
(LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
- Matthijs Meire
(LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
Abstract
This paper investigates how firms can leverage innovative data sources and Artificial Intelligence (AI) for life event prediction to better manage the relationship with their customers. In this study, we leverage deep learning to explore the added value of incorporating textual customer-generated data in life event prediction models. Furthermore, we propose a new framework to calculate the profit of life event based-marketing campaigns. We empirically validate our research questions on a real-world dataset including 94,161 email messages of 21,898 customers in the financial services industry. First, we show that life events have a significant impact on both product possession and customer value. Second, we demonstrate that textual data significantly boosts the predictive performance of life event prediction models. Third, our framework to calculate profit for life event-based marketing campaigns shows that running such campaigns can lead to a substantial return on investment but requires a performant life event prediction model.
Suggested Citation
Arno de Caigny & Kristof Coussement & Steven Hoornaert & Matthijs Meire, 2025.
"Life event-based marketing using AI,"
Post-Print
hal-05114860, HAL.
Handle:
RePEc:hal:journl:hal-05114860
DOI: 10.1016/j.jbusres.2025.115349
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