Disruptive technologies such as the internet of things, big data analytics, blockchain, and artificial intelligence have changed the ways businesses operate. Of all the disruptive technologies, artificial intelligence (AI) is the latest technological disruptor and holds immense marketing transformation potential. Practitioners worldwide are trying to figure out the best fit AI solutions for their marketing functions. However, a systematic literature review can highlight the importance of artificial intelligence (AI) in marketing and chart future research directions. The present study aims to offer a comprehensive review of AI in marketing using bibliometric, conceptual and intellectual network analysis of extant literature published between 1982 and 2020. A comprehensive review of one thousand five hundred and eighty papers helped to identify the scientific actors' performance like most relevant authors and most relevant sources. Furthermore, co-citation and co-occurrence analysis offered the conceptual and intellectual network. Data clustering using the Louvain algorithm helped identify research sub-themes and future research directions to expand AI in marketing.

Artificial intelligence in marketing: Systematic review and future research direction

The evolving role of artificial intelligence in marketing: A review and research agenda

In this article, we introduce Wireless 2.0, the future generation of wireless communication networks, in which the radio environment becomes controllable and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces (RMSs) and artificial intelligence (AI). In particular, we emphasize AI-based computational methods and commence with an overview of the concept of intelligent radio environments (IREs) based on RMSs. Then, we elaborate on data management aspects, the requirements of supervised learning by examples, and the paradigm of reinforcement learning to learn by acting. Finally, we highlight numerous open challenges and research directions.

Wireless 2.0: Toward an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence

This article discusses technology and opportunities available to embrace artificial intelligence (AI) in the design of autonomous wireless systems. A vision is presented for knowledge-driven wireless operation by means of AI disciplines, such as sensing, reasoning, knowledge management, and active learning. The aim is to provide readers with the motivation and a general big data-independent AI methodology for autonomous agents in the context of self-organization in real time by unifying knowledge management with sensing, reasoning, and active learning. Differences between training-based methods for matching problems and training-free methods for environment-specific problems are highlighted. Finally, we conceptually introduce the functions of an autonomous agent with knowledge management.

Autonomous Wireless Systems with Artificial Intelligence: A Knowledge Management Perspective

Disruptive technologies like the internet of things, big data analytics, blockchain, and artificial intelligence have transformed how businesses operate. Artificial intelligence (AI) is the most recent technological disruptor and has enormous marketing transformation potential. Practitioners all over the world are attempting to determine which AI solutions are best suited to their marketing needs. A systematic literature review, on the other hand, can highlight the importance of artificial intelligence (AI) in marketing and point the way for future research. The goal of this study is to provide a comprehensive review of AI in marketing by analysing extant literature published between 1982 and 2020 using bibliometric, conceptual, and intellectual network analysis. The performance of the scientific actors, such as the most relevant authors and sources, was identified through a comprehensive review of 1,584 papers. Furthermore, the conceptual and intellectual network was revealed through co-citation and co-occurrence analysis. The Louvain algorithm was used to cluster data and identify research sub-themes and future research directions in order to expand AI in marketing.

pdf/systematic-analysis-and-future-research-directions-in-gj5hnjwfhh.pdf

Systematic Analysis and Future Research Directions in Artificial Intelligence for Marketing

With the advancements of next-generation programmable networks, traditional rule-based decision- making may not be able to adapt effectively to changing network and customer requirements and provide an optimal customer experience. CEM components and implementation challenges with respect to operator, network, and business requirements must be understood to meet required demands. This article gives an overview of CEM components and their design challenges. We elaborate on data analytics and artificial intelligence driven CEM and their functional differences. This overview provides a path toward an autonomous CEM framework in next-generation networks and sets the groundwork for future enhancements.

pdf/artificial-intelligence-paradigm-for-customer-experience-ncofhsn8yg.pdf

Artificial Intelligence Paradigm for Customer Experience Management in Next-Generation Networks: Challenges and Perspectives

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mark Wagner

Author Tools

Chat about Author

Papers

Artificial Intelligence Paradigm for Customer Experience Management in Next-Generation Networks: Challenges and Perspectives