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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Social Media Analytics and Its Applications in Marketing focuses on the intersection of three closely related domains – marketing, social media, and social media analytics (SMA). In other words, this monograph concerns marketing-based decisions related to social media and social media analytics (SMA) or social media and social media analytics from the perspective of the marketing discipline. In SMA, the authors emphasize the roles of data and tools in tackling marketing problems. In marketing applications within the context of SMA, they discuss not only current problems, but also trends and future directions. The basic approach of this monograph can help both marketing practitioners and academics better understand how to use marketing analytics to identify the common patterns and trends produced by consumers and firms. The monograph is organized as follows. First, it describes the general state of social media and SMA offering both practical and theoretical perspectives. A four-step SMA framework called AAVF (Acquisition-Analysis-Value-Feedback) is proposed based on examination of some existing frameworks and processes for SMA. Second, it examines social media analytics (SMA) particularly within marketing as a collection of tools and methods used to unearth the practical values of consumer and brand data. Third, it shows how common methods for SMA have been utilized for common marketing problems. Some common SMA methods encompass the analysis of big data, text analytics, visual analytics, artificial intelligence, machine learning, and deep learning. Finally, trends and future research topics for SMA are discussed, particularly within marketing.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Social Media Analytics and Its Applications in Marketing focuses on the intersection of three closely related domains – marketing, social media, and social media analytics (SMA). In other words, this monograph concerns marketing-based decisions related to social media and social media analytics (SMA) or social media and social media analytics from the perspective of the marketing discipline. In SMA, the authors emphasize the roles of data and tools in tackling marketing problems. In marketing applications within the context of SMA, they discuss not only current problems, but also trends and future directions. The basic approach of this monograph can help both marketing practitioners and academics better understand how to use marketing analytics to identify the common patterns and trends produced by consumers and firms. The monograph is organized as follows. First, it describes the general state of social media and SMA offering both practical and theoretical perspectives. A four-step SMA framework called AAVF (Acquisition-Analysis-Value-Feedback) is proposed based on examination of some existing frameworks and processes for SMA. Second, it examines social media analytics (SMA) particularly within marketing as a collection of tools and methods used to unearth the practical values of consumer and brand data. Third, it shows how common methods for SMA have been utilized for common marketing problems. Some common SMA methods encompass the analysis of big data, text analytics, visual analytics, artificial intelligence, machine learning, and deep learning. Finally, trends and future research topics for SMA are discussed, particularly within marketing.