Exploring multimodal learning applications in marketing: A critical perspective

Author:

César Inês1,Pereira Ivo1234,Rodrigues Fátima12,Miguéis Vera2,Nicola Susana12,Madureira Ana12

Affiliation:

1. ISRC – Interdisciplinary Studies Research Center, ISEP, Porto, Portugal

2. INESC TEC, FEUP, University of Porto, Rua Dr. Roberto Frias, Porto, Portugal

3. Universidade Fernando Pessoa, Porto, Portugal

4. E-goi, Matosinhos, Portugal

Abstract

This review discusses the integration of intelligent technologies into customer interactions in organizations and highlights the benefits of using artificial intelligence systems based on a multimodal approach. Multimodal learning in marketing is explored, focusing on understanding trends and preferences by analyzing behavior patterns expressed in different modalities. The study suggests that research in multimodality is scarce but reveals that it is as a promising field for overcoming decision-making complexity and developing innovative marketing strategies. The article introduces a methodology for accurately representing multimodal elements and discusses the theoretical foundations and practical impact of multimodal learning. It also examines the use of embeddings, fusion techniques, and explores model performance evaluation. The review acknowledges the limitations of current multimodal approaches in marketing and encourages more guidelines for future research. Overall, this work emphasizes the importance of integrating intelligent technology in marketing to personalize customer experiences and improve decision-making processes.

Publisher

IOS Press

Reference80 articles.

1. I. César, I. Pereira, F. Rodrigues, V. Miguéis, S. Nicola and A. Madureira, Multimodal Learning Applications in Digital Marketing, in: 23rd International Conference on Hybrid Intelligent Systems (2023).

2. D. Casey, A. Jump, G. Rigon, A. Zimmermann, M. Xiang, R. Cozza, E. Brethenoux, J. Skowron and S. Sicular, Emerging Tech Impact Radar: Conversational Artificial Intelligence, Gartner, Inc. (2022).

3. A. Jump, D. Casey and A. Lee, Emerging Technologies: Tech Innovators in Advanced Virtual Assistants, Gartner, Inc. (2023).

4. I. César, I. Pereira, A. Madureira, D. Coelho, M.Â. Rebelo and D.A. de Oliveira, Analysing and Modeling Customer Success in Digital Marketing, in: International Conference on Innovations in Bio-Inspired Computing and Applications, Cham: Springer Nature Switzerland (2022), 404–413.

5. M. Paulo, V.L. Miguéis and I. Pereira, Leveraging email marketing: Using the subject line to anticipate the open rate, Expert Systems with Applications 207 (2022).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3