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Innovative Marketing Framework for Enterprises Using Edge-Enabled Data Analysis
Author(s) -
Guangming Wang,
Yifeng Wang
Publication year - 2021
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2021/6699420
Subject(s) - computer science , social media , enhanced data rates for gsm evolution , task (project management) , particle swarm optimization , resource (disambiguation) , digital marketing , knowledge management , telecommunications , algorithm , world wide web , computer network , management , economics
An effective marketing strategy has become a challenging task with the development of the social economy for the reform and innovation of many domestic and foreign enterprises. ,e idea of edge computing is also gaining prominence and is broadly recognized. Edge-enabled solutions provide computing, analysis, storage, and control nearer to the edge of the network. ,e existing literature highlights the concepts of social media marketing and evaluates the advantages of social media for communication between individuals and companies. However, there is a lack of an effective marketing strategy for enterprises. ,is article proposes an innovative marketing framework for enterprises using data analysis and wireless networks. ,e proposed framework is composed of two different modules. Initially, a corporate innovation and marketing framework module is established through edge computing to carry out a wireless transmission of relevant data and information at the edges of the network. Later, the enterprise marketing resource optimization module is constructed based on the particle swarm algorithm.,e proposed framework performs efficient calculation and storage using a resource optimization module. ,e feasibility and effectiveness of the proposed framework are verified using the analysis of the actual case test results. ,is proposed framework can effectively improve the innovation efficiency level of existing enterprises and the optimization of marketing management resources.

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