Artificial Intelligence in Customer-Facing Industries

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 36227

Special Issue Editors


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Guest Editor
Universität Duisburg-Essen, Campus Essen, Fakultät für Wirtschaftswissenschaften, Universitätsstr. 9D-45141 Essen, Germany
Interests: big data; retail; artificial intelligence; machine learning; information systems

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Guest Editor
Department of Information, Universität Duisburg-Essen, Essen, Germany
Interests: enterprise systems; IS architectures; digitization of institutions; information modeling; scientific theory problems of business informatics

Special Issue Information

Dear Colleagues,

Following public media, professional journals, and the scientific discussion, Artificial Intelligence (AI) is the omnipresent topic of today and seems to become integrated into every part of everyday life sooner than later. Yet, most companies do not even use digital interaction with their customers. In reality, recent studies discovered that 91 percent of business leaders still expect their businesses to see obstacles to the realization of AI*.

However, AI has already changed how customers interact with brands, products, and services. Consider the increase in chatbots and virtual assistants, both of which improve the customization and overall experience of companies (today mainly driven by the innovation of the big tech companies). Three major buzzwords are leading the discussion when it comes to the application of AI for customers: advanced analytics, conversational AI, and robotics.

Advanced Analytics

To deliver a positive customer experience, organizations must maintain a holistic view and ensure that every interaction is consistent across all interaction points in real time. Consolidating data from all customer contact points is an integrated way to map customer behavior patterns, which should be analyzed as quickly and accurately as possible. Based on the integrated data, an interface to the customer, sometimes referred to “Analytical Face”, can be established to understand current customer actions, anticipate future customer needs, and initiate corresponding actions.

Conversational AI

Chatbots have found an enormous interest in recent years. Personal language assistants like Siri or Alexa accompany many people’s everyday life. Artificial intelligence is increasingly changing customer service: Where a consultant used to take care of the customer's wishes, with AI, this experience can be shifted to a personal and automated manner.

Robotics

AI also allows automating task and shifting human work towards machines. Some retailers are already using humanoid robots in their stores. The robot serves the customer as a contact person for simple questions and information or can fetch goods from the warehouse.

This Special Issue on “Artificial Intelligence in Customer-Facing Industries” calls for manuscripts proposing methods of Artificial Intelligence (AI), machine learning (ML), and deep learning (DL), new approaches, and applications that are facing direct customer interaction in several industries (from retail/trade, tourism to entertainment).

Potential topics include but are not limited to the following:

  • Human–computer interaction;
  • Natural language processing;
  • Conversational AI;
  • Customer-facing robotics;
  • Application of AI, ML, and DL in transportation/logistics to the end-user;
  • Application of AI, ML, and DL in marketing;
  • Application of AI, ML, and DL in sales;
  • Application of AI, ML, and DL in customer services;
  • AI/ML/DL for optimization and personalization;
  • Advanced analytics;
  • Customer-facing applications, methods, and tools enabled by AI/ML/DL.

Dr. Felix Weber
Prof. Dr. Reinhard Schütte
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

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13 pages, 329 KiB  
Article
Adversarial Learning for Product Recommendation
by Joel R. Bock and Akhilesh Maewal
AI 2020, 1(3), 376-388; https://doi.org/10.3390/ai1030025 - 01 Sep 2020
Cited by 5 | Viewed by 3847
Abstract
Product recommendation can be considered as a problem in data fusion—estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from [...] Read more.
Product recommendation can be considered as a problem in data fusion—estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323% to 1.763%. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers. Full article
(This article belongs to the Special Issue Artificial Intelligence in Customer-Facing Industries)
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Review

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12 pages, 746 KiB  
Review
Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation
by Wiebke Reim, Josef Åström and Oliver Eriksson
AI 2020, 1(2), 180-191; https://doi.org/10.3390/ai1020011 - 03 May 2020
Cited by 54 | Viewed by 31184
Abstract
Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many [...] Read more.
Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many firms are investing heavily to capitalize on the technology through business model innovation. However, managers are left with little support from academia when aiming to implement AI in their firm’s operations, which leads to an increased risk of project failure and unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used as a catalyst for business model innovation. Due to the increasing range and variety of the available published material, a literature review has been performed to gather current knowledge within AI business model innovation. The results are presented in a roadmap to guide the implementation of AI to firm’s operations. Our presented findings suggest four steps when implementing AI: (1) understand AI and organizational capabilities needed for digital transformation; (2) understand current BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed to implement AI; and (4) reach organizational acceptance and develop internal competencies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Customer-Facing Industries)
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