{"title":"Image recommendation for social media marketing in maternity and baby care product industry – a machine learning approach","authors":"Kung-Jeng Wang, Jeh-An Wang","doi":"10.1108/apjml-04-2024-0463","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.</p><!--/ Abstract__block -->","PeriodicalId":47866,"journal":{"name":"Asia Pacific Journal of Marketing and Logistics","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asia Pacific Journal of Marketing and Logistics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/apjml-04-2024-0463","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.
Design/methodology/approach
This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.
Findings
The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.
Originality/value
The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.
期刊介绍:
The Asia Pacific Journal of Marketing and Logistics (APJML) provides a unique focus on marketing and logistics in the Asia Pacific region. It publishes research which focus on marketing and logistics problems, new procedures and practical approaches, systematic and critical reviews of changes in marketing and logistics and cross-national and cross-cultural comparisons of theory into practice. APJML is to publish articles including empirical research, conceptual papers, in-depth literature review and testing of alternative methodologies and theories that have significant contributions to the knowledge of marketing and logistics in the Asia Pacific region. The journal strives to bridge the gap between academia and practice, hence it also publishes viewpoints from practitioners, case studies and research notes of emerging trends. Book reviews of cutting edge topics are also welcome. Readers will benefit from reports on the latest findings, new initiatives and cutting edge methodologies. Readers outside the region will have a greater understanding of the cultural orientation of business in the Asia Pacific and will be kept up to date with new insights of upcoming trends. The journal recognizes the dynamic impact of Asian Pacific marketing and logistics to the international arena. An in-depth understanding of the latest trends and developments in Asia Pacific region is imperative for firms and organizations to arm themselves with competitive advantages in the 21st century. APJML includes, but is not restricted to: -Marketing strategy -Relationship marketing -Cross-cultural issues -Consumer markets and buying behaviour -Managing marketing channels -Logistics specialists -Branding issues in Asia Pacific markets -Segmentation -Marketing theory -New product development -Marketing research -Integrated marketing communications -Legal and public policy -Cross national and cross cultural studies