Evolving Attribution Models in Digital Advertising: Balancing Clicks,Views and Customer Journeys

Abstract

As digital advertising continues to grow, accurately measuring the impact of marketing efforts across multiple channels has become a complex but essential task. Traditional attribution models like last-touch fail to capture the diverse ways consumers interact with ads before converting. Multi-touch attribution (MTA) models offer a solution by distributing credit across various interactions, reflecting the true influence of each touchpoint. This paper provides a comprehensive review of popular attribution models, including linear, time-decay, U-shaped, and custom models, assessing their strengths and limitations.Additionally, it highlights the importance of integrating both click-through and view-through metrics to understand customer engagement more fully. By exploring these models, this paper aims to guide marketers in selecting the most effective attribution strategies to optimize ad spend, improve campaign performance, and gain a holistic view of customer journeys.