Analysis on the Application of Data Science in Business Analytics

DOI

10.2991/aebmr.k.220306.046How to use a DOI?

Keywords

Machine learning; Business analytics; Data science; Segmentation; Customer value; Measures

Abstract

Business analytics is a new term, which can be seen as an expanded filed of data science. The mathematical formulas, statistical models, and programming skills in data science help companies to utilize big data and collect useful information from customers. However, for data science, it just collects numerical values from different resources, and uses programming tools to analyze data in the model to get numerical results. To convert numbers into useful information, people need to use business knowledge to interpret numerical results. With clustering models in data science, customers can be segmented into different groups based on their backgrounds including basic information which helps the company learn characteristics of each customer groups. Once machine learning is incorporated into business intelligence, the algorithms can use historical data as input to predict the trend of market and their customers’ behaviors as well, helping company improve productivity, quality, and customer service and so on. Data science gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of companies’ business in the future. It is more like a tool for business to help companies address problems in operations, provide better services for customers, and make their business long-lasting and profitable.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Yiran Wang
PY  - 2022
DA  - 2022/03/17
TI  - Analysis on the Application of Data Science in Business Analytics
BT  - Proceedings of the 7th International Conference on Economy, Management, Law and Education (EMLE 2021)
PB  - Atlantis Press
SP  - 319
EP  - 323
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220306.046
DO  - 10.2991/aebmr.k.220306.046
ID  - Wang2022
ER  -