物联网与数字营销的未来

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Science and Analytics Pub Date : 2023-05-07 DOI:10.59615/jda.2.1.24

David Uver

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引用次数: 0

摘要

在当今的数字世界中，随处可见物联网(IoT)的痕迹。商业将随着这项技术的使用而改善，因为设备将能够收集大量的用户信息。如果这些信息使用得当，商业活动就可以扩大活动范围，降低成本，为用户提供功能更多、更好的设备。这些设备只允许某些人访问，因为它们包含大量的信息。然而，人们对这些设备有很多担忧。尽管科技日新月异，但与之相关的风险也在增加。随着营销走向数字化的趋势，我们正在见证数字化在各行各业的传播。但由于物联网对每个问题和挑战都有解决方案，因此它也有新的解决方案。在这篇文章中，我试图解释物联网在数字营销中的作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Internet of things and the future of digital marketing

In the digital world these days, everywhere you look, traces of the Internet of Things (IoT) can be seen. Businesses will improve with the use of this technology because devices will be able to collect a huge amount of user information. . If this information is used appropriately, commercial activities are able to expand their activities, reduce costs and provide users with devices with more and better features. These devices are only allowed to be accessed by certain people because they contain a large amount of information. However, there are many concerns about these devices. Even though the technology is advancing day by day, the risks related to it also increase. With the trend of marketing toward digitalization, we are witnessing its spread in various businesses. But since the Internet of Things has a solution for every issue and challenge, it also has new and fresh solutions for this issue. In this article, an attempt has been made to explain the role of the Internet of Things in digital marketing.

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics