Paper Type
Complete
Abstract
This study introduces a novel Deep Marketing Mix Model (DMMM) leveraging machine learning (ML) and deep learning (DL) to overcome the limitations of traditional methods in handling granular data. By employing ML/DL with derivative-free optimization, DMMM enables accurate daily budget allocation across media channels. Comparative analyses show that DMMM achieves superior performance in prediction accuracy, budget allocation efficiency, and computational speed compared to competing models. This research makes a significant contribution to marketing analytics in academia and industry.
Paper Number
2000
Recommended Citation
Kim, Kitae; Lee, Minhyung; and Park, Sunghyuk, "DMMM: Deep Marketing Mix Model for Optimal Budget Allocation" (2025). AMCIS 2025 Proceedings. 5.
https://aisel.aisnet.org/amcis2025/sig_odis/sig_odis/5
DMMM: Deep Marketing Mix Model for Optimal Budget Allocation
This study introduces a novel Deep Marketing Mix Model (DMMM) leveraging machine learning (ML) and deep learning (DL) to overcome the limitations of traditional methods in handling granular data. By employing ML/DL with derivative-free optimization, DMMM enables accurate daily budget allocation across media channels. Comparative analyses show that DMMM achieves superior performance in prediction accuracy, budget allocation efficiency, and computational speed compared to competing models. This research makes a significant contribution to marketing analytics in academia and industry.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIGODIS