CAIT: A Predictive Tool for Supporting the Book Market Operation Using Social Networks

A new predictive support tool for the publishing industry is presented in this note. It consists of a combined model of Artificial Intelligence techniques (CAIT) that seeks the most optimal prediction of the number of book copies, finding out which is the best segmentation of the book market, using...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 1; p. 366
Main Authors Martín Sujo, Jessie, Golobardes i Ribé, Elisabet, Vilasís Cardona, Xavier
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2022
Subjects
Artificial intelligence
book copies
Brand loyalty
Clustering
forecasting
Knowledge acquisition
Learning algorithms
Machine learning
Market segments
Publishing industry
Sales
Segmentation
Social network analysis
Social networks
Social organization
Statistical methods
Success
Time series
Spain
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Summary:A new predictive support tool for the publishing industry is presented in this note. It consists of a combined model of Artificial Intelligence techniques (CAIT) that seeks the most optimal prediction of the number of book copies, finding out which is the best segmentation of the book market, using data from the networks social and the web. Predicted sales appear to be more accurate, applying machine learning techniques such as clustering (in this specific case, KMeans) rather than using current publishing industry expert’s segmentation. This identification has important implications for the publishing sector since the forecast will adjust more to the behavior of the stakeholders than to the skills or knowledge acquired by the experts, which is a certain way that may not be sufficient and/or variable throughout the period.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12010366