Life event-based marketing using AI

Arno de Caigny (), Kristof Coussement (), Steven Hoornaert and Matthijs Meire
Additional contact information
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

Post-Print from HAL

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.

Date: 2025-04
References: Add references at CitEc
Citations:

Published in Journal of Business Research, 2025, 193, pp.115349. ⟨10.1016/j.jbusres.2025.115349⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05114860

DOI: 10.1016/j.jbusres.2025.115349

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().