Why data-driven marketing attribution models don't work as promised

Marketing attribution models, including sophisticated data-driven approaches like Markov Chains and Shapley Value, aim to identify the most effective channels for marketing spend by analyzing user journeys and assigning credit for conversions. Despite their mathematical rigor and promises of granular insights, these models often fall short due to challenges such as incomplete data, the difficulty of distinguishing correlation from causation, oversimplified user journeys, and external factors that are not captured in user-level data. As a result, they can provide misleading guidance on where to allocate budgets. While these models can offer some directional insights, they require careful use and should be supplemented with controlled experiments and market mix analyses to provide a more accurate picture of marketing effectiveness. Understanding these limitations is crucial for marketers seeking to derive true value from attribution efforts.