Applications of Big Data Analysis for Sustainable Growth of Firms
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (26 March 2023) | Viewed by 8209
Special Issue Editors
Interests: recommender systems; big data analytics; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: personalized service; big data analysis; social network analysis; business analytics
Special Issue Information
Dear Colleagues,
Consumer expectations are changing due to changes in population distribution and the diversity of customers’ experiences, and consumption trends are rapidly changing as well. In addition, purchasing channels are becoming more diverse and complex. Such changes in the business environment require firms to constantly innovate themselves. One promising innovation is the application of big data analysis. Some firms are maximizing customer satisfaction and the operational efficiency of internal work through the discovery of new megatrends, customer preference analysis, and consumption pattern analysis previously unrecognized by using big data accumulated inside and outside the firm. For example, Amazon provides recommended products predicted through big data analysis as banners while customers shop on Amazon.
According to an IDC (International Data Corporation) report, digital data is expected to accumulate about 175 zettabytes by 2025. Especially, it is also estimated that 80% of all data will be unstructured data such as media, imaging, audio, sensor data, e-mail messages, and word processing documents. Thus, big data analysis is a very complex process that requires the analysis of structured, semi-structured, and unstructured data to discover insightful information including hidden patterns, unknown correlations, market trends, customer preferences, and so on in different sizes from terabytes to zettabytes. There are various techniques such as machine learning, random forest classifiers, predictive modeling, cluster analysis, text mining, deep learning, Kalman filtering, and ensemble analysis for analyzing such big data. Therefore, the purpose of this Special Issue is to presents ways in which big data analysis can be applied to customer profiling, forecasting, clustering, classification, etc. for the sustainable growth of firms.
More specifically, the topics of interest include, but are not limited to:
- Improving predictive models using big data analytics;
- Deep learning from big data;
- Machine learning from big data;
- Recommender systems and big data;
- Customer relationship management and big data;
- Real-world applications of big data in commerce.
Thank you for your contributions
Prof. Dr. Jae Kyeong Kim
Dr. Il Young Choi
Guest Editors
Manuscript Submission Information
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Keywords
- big data analysis in commerce
- machine learning in commerce
- deep learning in commerce
- recommender systems in commerce
- predictive modeling in commerce
- customer relationship management using big data analysis
