Article
Version 1
This version is not peer-reviewed
Cross-Channel Attribution Modeling in the Age of Privacy Regulations
Version 1
: Received: 31 July 2024 / Approved: 1 August 2024 / Online: 1 August 2024 (14:00:59 CEST)
How to cite: Bell, C.; Olukemi, A.; Gracias, A. Cross-Channel Attribution Modeling in the Age of Privacy Regulations. Preprints 2024, 2024080087. https://doi.org/10.20944/preprints202408.0087.v1 Bell, C.; Olukemi, A.; Gracias, A. Cross-Channel Attribution Modeling in the Age of Privacy Regulations. Preprints 2024, 2024080087. https://doi.org/10.20944/preprints202408.0087.v1
Abstract
In the evolving landscape of digital marketing, cross-channel attribution modeling plays a crucial role in understanding and optimizing the customer journey across various touchpoints. As consumers interact with brands through multiple channels—such as social media, email, search engines, and display ads—accurately attributing conversions to the right channels is vital for optimizing marketing strategies and budgets. However, the advent of stringent privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), has significantly impacted the data collection and analysis processes in digital marketing. This paper explores the challenges and opportunities presented by cross-channel attribution modeling in the age of heightened privacy awareness. It examines how traditional data-driven attribution models, which often rely on tracking individual user behavior, are being adapted or replaced in response to privacy concerns and regulatory requirements. The paper discusses the emergence of privacy-preserving techniques, such as aggregated data analysis, differential privacy, and the use of anonymized data, which aim to balance the need for accurate attribution with the protection of consumer privacy. Furthermore, the paper highlights the role of first-party data and the growing importance of consent management in the collection and utilization of consumer information. It also investigates how marketers are leveraging advancements in artificial intelligence and machine learning to enhance attribution models in a privacy-conscious world. The study concludes by offering best practices for businesses seeking to navigate the complexities of cross-channel attribution in the context of evolving privacy regulations, emphasizing the need for transparency, compliance, and ethical data handling. This research provides valuable insights for marketers, data scientists, and policymakers on how to effectively manage the interplay between accurate attribution and privacy, ensuring that marketing efforts remain effective while respecting consumer rights.
Keywords
Cross-Channel
Attribution Modeling
Age of Privacy Regulations
Attribution Modeling
Age of Privacy Regulations
Subject
Business, Economics and Management, Marketing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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