{"title":"Beyond text: Marketing strategy in a world turned upside down","authors":"Xin (Shane) Wang, Neil Bendle, Yinjie Pan","doi":"10.1007/s11747-023-01000-x","DOIUrl":null,"url":null,"abstract":"<p>Analyzing unstructured text, e.g., online reviews and social media, has already made a major impact, yet a vast array of publicly available, unstructured non-text data houses latent insight into consumers and markets. This article focuses on three specific types of such data: image, video, and audio. Many researchers see the potential in analyzing these data sources, going beyond text, but remain unsure about how to gain insights. We review prior research, give practical methodological advice, highlight relevant marketing questions, and suggest avenues for future exploration. Critically, we spotlight the machine learning capabilities of major platforms like AWS, GCP, and Azure, and how they are equipped to handle such data. By evaluating the performance of these platforms in tasks relevant to marketing managers, we aim to guide researchers in optimizing their methodological choices. Our study has significant managerial implications by identifying actionable procedures where abundant data beyond text could be utilized.</p>","PeriodicalId":17194,"journal":{"name":"Journal of the Academy of Marketing Science","volume":"5 1","pages":""},"PeriodicalIF":9.5000,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Academy of Marketing Science","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s11747-023-01000-x","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Analyzing unstructured text, e.g., online reviews and social media, has already made a major impact, yet a vast array of publicly available, unstructured non-text data houses latent insight into consumers and markets. This article focuses on three specific types of such data: image, video, and audio. Many researchers see the potential in analyzing these data sources, going beyond text, but remain unsure about how to gain insights. We review prior research, give practical methodological advice, highlight relevant marketing questions, and suggest avenues for future exploration. Critically, we spotlight the machine learning capabilities of major platforms like AWS, GCP, and Azure, and how they are equipped to handle such data. By evaluating the performance of these platforms in tasks relevant to marketing managers, we aim to guide researchers in optimizing their methodological choices. Our study has significant managerial implications by identifying actionable procedures where abundant data beyond text could be utilized.
期刊介绍:
JAMS, also known as The Journal of the Academy of Marketing Science, plays a crucial role in bridging the gap between scholarly research and practical application in the realm of marketing. Its primary objective is to study and enhance marketing practices by publishing research-driven articles.
When manuscripts are submitted to JAMS for publication, they are evaluated based on their potential to contribute to the advancement of marketing science and practice.