Machine Learning and Artificial Intelligence use in Marketing – A General Taxonomy
Authors: Eneh, Kingsley Monday, Chinagolum Ituma, Emeka Agwu, John Ndubuisi Ngene
Journal: International Journal of Information Science and Engineering (IJISE), ISSN 1694-4496
Citation: IJISE 6(3), 2022-12-09.
DOI: 10.5281/zenodo.7416889
PDF: Download full-text PDF
Type: Original Research
Abstract
Artificial intelligence and machine learning are crucial resources that can uncover enormous value that might otherwise go unused. In addition to ensuring that marketing budgets are used as effectively as possible and producing results that support business objectives, marketers can access great insight through AI and ML that leads to effective decisions. Many businesses can improve their understanding of consumers' needs, their ability to forecast future demand, their ability to provide better customer service, etc. Following a thorough PRISMA review of the academic and business literature in this field, we present in this paper a general taxonomy of machine learning and artificial intelligence applications in marketing. We identified different application areas, including segmentation and targeting, customer churn, customer lifetime value, recommendation engines, the marketing mix module, and customer attribution. This study shows that both supervised and unsupervised learning algorithms are frequently employed by marketers.
Keywords
Machine Learning, Artificial Intelligence, Marketing Authorship 1 Eneh, Kingsley Monday, 2 Chinagolum Ituma, 3 Emeka Agwu, & 4 John Ndubuisi Ngene DOI:
Full Text
Artificial intelligence and machine learning are crucial resources that can uncover enormous value that might otherwise go unused. In addition to ensuring that marketing budgets are used as effectively as possible and producing results that support business objectives, marketers can access great insight through AI and ML that leads to effective decisions. Many businesses can improve their understanding of consumers' needs, their ability to forecast future demand, their ability to provide better customer service, etc. Following a thorough PRISMA review of the academic and business literature in this field, we present in this paper a general taxonomy of machine learning and artificial intelligence applications in marketing. We identified different application areas, including segmentation and targeting, customer churn, customer lifetime value, recommendation engines, the marketing mix module, and customer attribution. This study shows that both supervised and unsupervised learning algorithms are frequently employed by marketers.