Proceedings of the Fifth International Conference on Economic and Business Management (FEBM 2020)

Research of the Marketing Tactics for the Enterprise Adaptive to the Market Conditions Under the Big Data

Authors
Zhihong Li
Corresponding Author
Zhihong Li
Available Online 14 December 2020.
DOI
10.2991/aebmr.k.201211.114How to use a DOI?
Keywords
Big data, enterprises, marketing tactics, adaptation
Abstract

The traditional marketing model is facing more and more challenges under the big data environment. How to tailor it to adapt to the marketing conditions under the big data for the enterprise is an issue needed to be solved as soon as possible. To solve this problem, the paper first disentangles some main traditional marketing models and their features, then expounds the existent challenges faced under the Big Data in China. Finally, put forward the correspondent marketing tactics adaptive to the market conditions under the big data.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the Fifth International Conference on Economic and Business Management (FEBM 2020)
Series
Advances in Economics, Business and Management Research
Publication Date
14 December 2020
ISBN
10.2991/aebmr.k.201211.114
ISSN
2352-5428
DOI
10.2991/aebmr.k.201211.114How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhihong Li
PY  - 2020
DA  - 2020/12/14
TI  - Research of the Marketing Tactics for the Enterprise Adaptive to the Market Conditions Under the Big Data
BT  - Proceedings of the Fifth International Conference on Economic and Business Management (FEBM 2020)
PB  - Atlantis Press
SP  - 664
EP  - 667
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.201211.114
DO  - 10.2991/aebmr.k.201211.114
ID  - Li2020
ER  -