Keywords
artificial intelligence
customer lifetime value
brand equity
attribution modeling
marketing measurement
How to Cite
Abstract
The pervasive "short-termism trap" in marketing measurement pressures Chief Marketing Officers (CMOs) to prioritize immediate conversions while systematically undervaluing the enduring financial contributions of brand assets and customer relationships. This study addresses the persistent disconnect between marketing activities and enterprise valuation by proposing and empirically validating an integrated Artificial Intelligence (AI) framework. Utilizing a longitudinal dataset of over five million customer journeys spanning three years, the framework employs multi-task neural networks and structural equation modeling to unify short-term attribution signals with long-term marketing asset value and relationship dynamics. The findings demonstrate that the model achieves 89% accuracy in predicting two-year Customer Lifetime Value (CLV), significantly outperforming traditional measurement approaches. Crucially, the analysis reveals that approximately 40% of the ultimate customer value is attributable to marketing-driven improvements in latent brand constructs rather than immediate transactional responses. By providing a unified architecture that aligns tactical execution with strategic financial outcomes, this study advances marketing accountability and enables practitioners to optimize the Total Marketing Return on Investment (ROI). The results offer a scientific foundation for sustainable growth and provide a robust mechanism for brand valuation and budget reallocation in increasingly privacy-constrained environments.
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Copyright (c) 2026 Simon Suwanzy Dzreke, Semefa Elikplim Dzreke