Bias within Rule based Attribution Models for Evaluating ROI of Digital Advertising Spend
Author(s):
Varun Chivukula
Rule-based attribution models, such as first-touch, last-touch, and linear attribution, are widely used in digital marketing to assign credit for conversions across the customer journey. While these models are simple and easy to implement, they often fail to capture the complexity of multichannel marketing and the causal relationships between touchpoints. When not calibrated using randomized control trials (RCTs), rule-based models introduce systematic biases, leading to distorted performance metrics, inefficient budget allocation, and suboptimal decision-making.
This paper delves deeply into the biases inherent in uncalibrated rule-based attribution, illustrating their effects with detailed simulated examples. It explores the overestimation of specific touchpoints, the underestimation of incremental impacts, and the inability to capture synergies between channels. The discussion emphasizes how RCTs address these challenges by isolating causal relationships and recalibrating attribution models to reflect true campaign performance. The paper also provides actionable recommendations for marketers seeking to improve the accuracy and reliability of their attribution frameworks.
