Abstract
This article explores the comparative effectiveness of traditional attribution models versus advanced, real-time Multi-Touch Attribution (MTA) models in optimizing marketing return on investment (ROI). Traditional models, such as first-click, last-click, linear, and time-decay, are simple to implement but often fail to capture the complexity of modern customer journeys, leading to inaccurate attributions and inefficient budget allocations. In contrast, advanced MTA models use machine learning algorithms and real-time data to dynamically assign credit across multiple touchpoints, providing a more precise understanding of each interaction's role in driving conversions. The article discusses the limitations of traditional models in multi-channel and omnichannel marketing environments, highlighting how real-time MTA models overcome these challenges by leveraging cross-device tracking, personalization, and predictive capabilities. It further addresses the technical and organizational challenges of implementing advanced MTA models, including data integration, skill requirements, and compliance with privacy regulations like GDPR and CCPA. Emerging technologies, such as AI, IoT, and blockchain, are also examined for their potential to enhance the transparency, security, and accuracy of MTA models. The article concludes that while advanced MTA models offer significant improvements in ROI optimization, they also come with increased complexity and cost. Future research is recommended to focus on improving model transparency, addressing ethical challenges, and balancing hyper-personalization with data privacy. This study provides insights for marketers and data scientists on leveraging advanced attribution models to enhance marketing performance.
Citations
IRE Journals:
Nigel Nkomo , Munashe Naphtali Mupa
"Marketing Return on Investment: A Comparative Study of Traditional and Modern Models" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 453-473
IEEE:
Nigel Nkomo , Munashe Naphtali Mupa
"Marketing Return on Investment: A Comparative Study of Traditional and Modern Models" Iconic Research And Engineering Journals, 8(5)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12