客户关系管理(CRM)软件的物联网:机遇与利益

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Science and Analytics Pub Date : 2023-05-06 DOI:10.59615/jda.2.1.17
Sevidon Wang, Mahmonir Bayanati
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引用次数: 0

摘要

客户关系管理软件对不同的商业和经济企业具有许多优势。今天,互联网已成为信息交换、商务、通信的主要工具,成为日常生活中不可或缺的一部分。十多年来,互联网技术见证了交互方式的根本性变化。有了新技术,物体也可以连接到互联网和人类;智能系统和数据正在相互作用和交换。物联网将提高客户关系的成熟度,并将对客户关系管理产生深远的影响。这是因为物联网的庞大数据中包含了客户的信息,而文本输入可以大大提高管理与客户关系的能力。因此,未来的客户关系管理将由认知计算、大数据分析和在组织中创造深度知识来提供。本文研究了基于物联网的CRM系统的重要特征、机遇和优势。
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Internet of Things for Customer Relationship Management (CRM) Software: Opportunities and Benefits
Customer relationship management software has many advantages for different businesses and economic enterprises. Today, the Internet has become the main tool for information exchange, business, communication, and an integral part of daily life. Internet technologies have witnessed fundamental changes in the way of interaction over a decade. With new technology, objects are also connected to the Internet and with humans; Intelligent systems and data are interacting and exchanging. The Internet of Things will increase the maturity level of customer relationships and will have a profound impact on customer relationship management. This is because the huge data of the Internet of Things contains information about the customer, and textual inputs can greatly increase the capabilities of managing relationships with customers. Therefore, the future of customer relationship management will be provided by cognitive computing, big data analysis, and creating deep knowledge in organizations. In this paper, the vital features and opportunities, and benefits of CRM systems based on the Internet of Things have been examined.
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来源期刊
CiteScore
6.40
自引率
8.30%
发文量
72
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
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