Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.8
3.1
Journals
Informatics
Special Issues
Big Data Analytics, AI and Machine Learning in Marketing
share announcement

Big Data Analytics, AI and Machine Learning in Marketing

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Big Data Mining and Analytics".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 49521

Special Issue Editor

Dr. Devon S. Johnson

E-Mail Website
Guest Editor
Associate Professor of Marketing, The Feliciano School of Business, Montclair State University, New York, NJ 07043, USA
Interests: big data analytics; AI and marketing strategy; services marketing; trust; skepticism; pain of paying; algorithm bias

Special Issue Information

Dear Colleagues,

The ubiquity of customer data resulting from purchases being made increasingly via digital channels such as websites, digital applications, and mobile phones and advances in the ability to capture data associated with virtually all transactions is revolutionizing marketing and sales. Marketers are using a variety of technologies and statistical techniques including artificial intelligence (AI) and machine learning to gleam insights from big data and make real-time decisions. These developments are improving efficiency in how firms acquire customers and deliver customized products. Meanwhile, consumer exposure to powerful personal technologies such as social media and mobile applications is changing customer shopping behavior and decision-making.

This Special Issue of the Journal of Informatics aims to improve understanding of the unfolding role of big data analytics (BDA), artificial intelligence (AI), and machine learning in marketing strategy and customer decision-making. Well-prepared papers approved for publication may be eligible for discounts at the Editorial Office’s discretion. We welcome submissions on data-driven marketing related to the following topics:

  • New models and applications of predictive modelling, AI, and Machine learning to marketing issues;
  • AI and data-driven decision-making implications for brand and product management;
  • How big data analytics is changing personal selling and salesforce management;
  • Customer data privacy and customer value personalization complementarity and trade-offs;
  • Customer trust and skepticism of data-driven personalization and AI;
  • Big data and AI in retailing: logistic economies, predictive personalization and customer value;
  • BDA and AI implementation impact on the marketing organization and decision making;
  • Big data, AI, and machine learning implications for service interactions, service failure and recovery;
  • The role of AI and machine learning in pricing, promotional offers, and adverting;
  • Ethics and social justice issues is AI and machine learning;
  • Algorithm and data bias: unearthing embedded prejudices and social injustices against customers.

Dr. Devon S. Johnson
Guest Editor

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. Informatics 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 1800 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 (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

29 pages, 4140 KiB  
Article
Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper
by Dimitris C. Gkikas, Prokopis K. Theodoridis and Grigorios N. Beligiannis
Informatics 2022, 9(2), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics9020045 - 31 May 2022
Cited by 8 | Viewed by 3296
Abstract
An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine [...] Read more.
An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users’ needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers’ behaviour by using a decision-making model, which analyses the consumer’s choices and helps the decision-makers to understand their potential clients’ needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making. Full article
(This article belongs to the Special Issue Big Data Analytics, AI and Machine Learning in Marketing)
►▼ Show Figures

Figure 1

12 pages, 784 KiB  
Article
The Triadic Relationship of Sense-Making, Analytics, and Institutional Influences
by Imad Bani-Hani, Soumitra Chowdhury and Arianit Kurti
Informatics 2022, 9(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics9010003 - 28 Dec 2021
Cited by 1 | Viewed by 2892
Abstract
The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in [...] Read more.
The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context. Full article
(This article belongs to the Special Issue Big Data Analytics, AI and Machine Learning in Marketing)
►▼ Show Figures

Figure 1

19 pages, 870 KiB  
Article
Implementing Big Data Analytics in Marketing Departments: Mixing Organic and Administered Approaches to Increase Data-Driven Decision Making
by Devon S. Johnson, Debika Sihi and Laurent Muzellec
Informatics 2021, 8(4), 66; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040066 - 26 Sep 2021
Cited by 4 | Viewed by 4936
Abstract
This study examines the experience of marketing departments to become fully data-driven decision-making organizations. We evaluate an organic approach of departmental sensemaking and an administered approach by which top management increase the influence of analytics skilled employees. Data collection commenced with 15 depth [...] Read more.
This study examines the experience of marketing departments to become fully data-driven decision-making organizations. We evaluate an organic approach of departmental sensemaking and an administered approach by which top management increase the influence of analytics skilled employees. Data collection commenced with 15 depth interviews of marketing and analytics professionals in the US and Europe involved in the implementation of big data analytics (BDA) and was followed by a survey data of 298 marketing and analytics middle management professionals at United States based firms. The survey data supports the logic that BDA sensemaking is initiated by top management and is comprised of four primary activities: external knowledge acquisition, improving digitized data quality, big data analytics experimentation and big data analytics information dissemination. Top management drives progress toward data-driven decision-making by facilitating sensemaking and by increasing the influence of BDA skilled employees. This study suggests that while a shift toward enterprise analytics increases the quality of resource available to the marketing department, this approach could stymie the quality of marketing insights gained from BDA. This study presents a model of how to improve the quality of marketing insights and improve data-driven decision-making. Full article
(This article belongs to the Special Issue Big Data Analytics, AI and Machine Learning in Marketing)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 2376 KiB  
Review
Information Technology Adoption on Digital Marketing: A Literature Review
by Fátima Figueiredo, Maria José Angélico Gonçalves and Sandrina Teixeira
Informatics 2021, 8(4), 74; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040074 - 31 Oct 2021
Cited by 14 | Viewed by 9813
Abstract
Data generation is currently expanding at an astonishing pace, and the function of marketing is becoming increasingly sophisticated and customized. Companies seek to understand their internal corporate environment and externalities and to exponentially enhance their marketing power. This study aims to understand the [...] Read more.
Data generation is currently expanding at an astonishing pace, and the function of marketing is becoming increasingly sophisticated and customized. Companies seek to understand their internal corporate environment and externalities and to exponentially enhance their marketing power. This study aims to understand the influence of Big data analysis on digital marketing. The methodologies used to approach this issue were: (a) a systematic literature review based on articles dated between 2014 and 2020; and (b) a bibliometric analysis of articles dated between 2000 and 2020 using the software VOSviewer. The literature review allowed us to conclude that in the next decades, the business world in general, and marketing in particular, will define more oriented strategies based on a more profound knowledge of consumer behavior. Artificial intelligence agents driven by machine learning methods, technology, and Big data will be a conditioning factor in defining these strategies. Full article
(This article belongs to the Special Issue Big Data Analytics, AI and Machine Learning in Marketing)
►▼ Show Figures

Figure 1

34 pages, 2798 KiB  
Review
Fashion Recommendation Systems, Models and Methods: A Review
by Samit Chakraborty, Md. Saiful Hoque, Naimur Rahman Jeem, Manik Chandra Biswas, Deepayan Bardhan and Edgar Lobaton
Informatics 2021, 8(3), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8030049 - 26 Jul 2021
Cited by 27 | Viewed by 26498
Abstract
In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information [...] Read more.
In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems. Full article
(This article belongs to the Special Issue Big Data Analytics, AI and Machine Learning in Marketing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop