Towards the Adoption of Machine Learning-Based Analytical Tools in Digital Marketing

• Main Authors: Andrej Miklosik, Martin Kuchta, Nina Evans, Stefan Zak
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Big data; data-driven analytical tools; digital marketing; machine learning (ML); marketing agencies; marketing analysis
• Online Access: https://ieeexplore.ieee.org/document/8746184/

Exponential technological expansion creates opportunities for competitive advantage by applying new data-oriented approaches to digital marketing practices. Machine learning (ML) can predict future developments and support decision-making by extracting insights from large amounts of generated data. This functionality greatly impacts and streamlines the strategic decision-making process of organizations. The research gap analysis revealed that a little is known about marketers' attitude toward, and knowledge about, ML tools and their adoption and utilization to support strategic and operational management. The research presented here focuses on the selection and adoption of the ML-driven analytical tools by three distinct groups: marketing agencies, media companies, and advertisers. Qualitative and quantitative research was conducted on a sample of these organizations operating in Slovakia. The findings highlight: 1) the important role of intelligent analytical tools in the creation and deployment of marketing strategies; 2) the lack of knowledge about emerging technologies, such as ML and artificial intelligence (AI); 3) the potential application of the ML tools in marketing, and; 4) the low level of adoption and utilization of the ML-driven analytical tools in marketing management. A framework consisting of enablers and a process map was developed to help organizations identify the opportunities and successfully execute projects that are oriented toward the deployment and adoption of the analytical ML tools in digital marketing.