Research: Evaluating Data-Driven Versus Rule-Based Multi-Touch Attribution Models in Di...

Research question: How do data-driven attribution models compare to rule-based models in accurately measuring channel contributions within multi-touch digital marketing campaigns?

Multi-touch attribution (MTA) is a critical aspect of modern digital marketing, allowing organizations to assign credit to various touchpoints that contribute to a consumer’s conversion journey. Traditionally, rule-based models such as last-click and linear attribution have been widely used due to their simplicity and transparency.

However, the emergence of data-driven attribution (DDA) models—which leverage statistical and machine learning methods—promises more nuanced insights by analyzing actual user paths and estimating marginal contributions. The literature to date provides limited direct, empirical comparisons of these approaches in terms of accuracy and actionable value for marketing optimization.

This research will conduct a comparative analysis using real or simulated multi-channel marketing datasets. It will implement both rule-based and data-driven MTA models (e.g., Markov chains, Shapley value) and evaluate their effectiveness using metrics such as attribution accuracy, model interpretability, and impact on channel investment decisions. The expected contribution is a robust, data-backed assessment of when and why organizations should adopt more complex data-driven models over traditional rule-based methods.

The findings will inform both practitioners and academics, enhancing marketing analytics best practices and supporting more effective go-to-market strategies under increasing digital complexity.