Year: 2025
Type: article
Abstract: This article examines the critical challenges of attribution systems in marketing and finance, identifying how these models often fail to deliver accurate insights despite their potential to improve return on investment. By analyzing common pitfalls, including data inaccuracies, model oversimplification, poor system integration, and inadequate handling of complex user behaviors, the article reveals how flawed attribution leads to misallocation of resources and suboptimal business outcomes. Case studies of failed implementations across both sectors demonstrate the substantial impact of attribution failures on marketing effectiveness and financial performance. The article then provides actionable recommendations for creating more reliable attribution models, focusing on advanced data validation methods, enhanced model complexity, improved transparency, and real-time analytics integration. These strategies help organizations develop attribution systems that can adapt to changing market conditions and deliver more accurate insights to support better decision-making processes in increasingly complex digital environments.
Author: Ashish Mohan
Institution: University of Connecticut
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4410925181