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

Gacanin, Haris; Wagner, Mark

doi:10.1109/mnet.2019.1800015

Cited by 53 publications

(27 citation statements)

References 13 publications

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“…Cognitive technologies utilization, which is composed of cognitive automation [22,23], cognitive insight [24,25], cognitive sensing (Chen et al, 2018), and cognitive engagement [21] will assist rms to develop deeper understandings of their market and competitors [28,29]. Cognitive technology utilization is therefore an important factor in the rm's ability to perform cognitive analysis.…”

Section: Research Modelmentioning

confidence: 99%

The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competencies

Saheb

Amini

2021

Preprint

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Purpose: The goal of digital marketing is to enhance strategic decision making to discover competitive and consumer behavior, and to provide early warnings of risks and opportunities. In the age of digitalization, digital marketing may be influenced with the use of cognitive technologies and also open sources of big data. The aim of this paper is to understand the impact of artificial intelligence analytics on digital marketing and the role of open big data and AI analytics competencies in this relationship.Design: A structural equation modeling of the 227 questionnaire was carried out. Findings: Our analysis shows that open big data has a significant impact on digital marketing, and AI analytical competencies moderate the impact of open big data on digital marketing. Value: Theoretically, this study extends the literature on knowledge-based view and AI analytics in digital marketing. Practically, this study broadens understandings regarding the perquisites of digital marketing through the use of cognitive technologies and open big data

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Section: Research Modelmentioning

confidence: 99%

The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competencies

Saheb

Amini

2021

Preprint

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“…Several papers have been published by different authors and technology related websites pertaining to Artificial Intelligence in marketing and some papers were focused on Customer Experience such as the paper by Gacanin and Wagner (2019) [7], which talks about the CEM components and the challenges with respect to the operator and the business requirement. The paper provides an overview of the path towards autonomous CEM framework and sets groundwork for future enhancements.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Tools for Enhancing Customer Experience

2019

ijrte

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Customers are the key to any business and the major challenge for any established business is retaining an existing customer and acquiring a new customer. One of the many ways to reduce the churn rate and increase customer retention is to improve the customer experience. As businesses are growing, their customer base is also increasing. Each and every customer is different and needs different kind of motivators to engage with the business and hence we need to understand each and every customer uniquely. Artificial Intelligence tools can blend the gap between the business and the client, creating enormous information that can prompt further comprehension of the client’s preferences. Understanding these artificial intelligence tools and how these tools can assist organizations with retaining clients and help them give better involvement to their clients is significant. However, in academic research this significant research area stays under-focussed. Hence this study tries to address this gap by proposing a conceptual model for understanding how the Artificial Intelligence tools are can help in enhancing customer experience. The narrative literature review approach has been adopted for conceptualization of the model. The study provides implications to practitioners for designing and developing AI tools such that they enhance customer experience, to managers for designing the information technology strategy of their companies, to academicians as it helps explore new technologies in the marketing domain and to the society as it will help improve customer experience thereby leading to customer satisfaction.

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“…Recent developments in artificial intelligence (AI) has shifted the interest towards the development of "autonomous and connected intelligence" [6], [7]. Thereby, learning techniques will take a central stage in the sixth generation (6G) communication systems to design autonomous networks with the stringent application requirements of next generation usercentric communications [8]. However, as often observed [9], the usage of machine learning techniques (i.e.…”

Section: Introductionmentioning

confidence: 99%

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

2020

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The fifth generation (5G) of wireless communications has led to many advancements in technologies such as large and distributed antenna arrays, ultra-dense networks, software based networks and network virtualization. However, the need for a higher level of automation to establish hyper-low latency and hyper-high reliability for beyond 5G applications requires extensive research on machine learning with applications in wireless communications. Thereby, learning techniques will take a central stage in the sixth generation of wireless communications to cope up with the stringent application requirements. This paper studies the practical limitations of these learning methods in the context of resource management in nonstationary radio environment. Based on the practical limitations we carefully design and propose supervised, unsupervised, and reinforcement learning models to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment, we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. These models need an environment-specific design to reach the accuracy of an optimal algorithm. Such an approach is practically not realistic due to the high resource requirement needed for frequent retraining. INDEX TERMS Machine learning, low-latency application, learning model ageing, mobile communications, 6G.

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

Cited by 53 publications

References 13 publications

The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competencies

The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competencies

Artificial Intelligence Tools for Enhancing Customer Experience

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

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