The AI Black Box Problem
Why Your Marketing Attribution Is About to Get Worse
More than 80 percent of respondents say their organizations aren't seeing a tangible impact on enterprise-level EBIT from their use of gen AI, while only 27% say that employees review all content created by gen AI before it is used. This disconnect between AI investment and results is nowhere more apparent than in marketing attribution.
The Interpretability Crisis Deepens
Machine Learning (ML) and Artificial Intelligence (AI) are particularly useful in a market where tracking customer journeys becomes complex due to multiple devices usage and privacy regulations. But this utility comes at a cost: understanding. As AI attribution models become more sophisticated, they become more opaque.
Only 31% of marketing professionals are extremely confident in the accuracy of their marketing attributions. Without accurate marketing attribution, marketing professionals risk misallocating resources and budgets. The irony is that AI was supposed to solve attribution problems, but it's creating new ones by making the decision-making process unknowable.
The Death of Traditional Attribution Models
Research by Forrester suggests that 56% of consumers use their mobile device to research products and Marketing Week found that buyers also use an average of almost 6 touch-points on the buying journey. Traditional attribution models can't handle this complexity, but AI models that can handle it operate as black boxes.
Traditional attribution models typically rely on a simplistic approach, assigning all credit to the last touchpoint or splitting credit equally among all touchpoints. This doesn't reflect the true impact of each interaction, leading to inaccurate insights and suboptimal marketing strategies.
Building Trust in Unknowable Systems
Risk considerations could include algorithmic bias, the challenges of adoption and the shock of markedly different model results. Reward consideration could include improved productivity, cost-efficiency, speed, less reliance on historical data and more focus on the future.
The solution isn't simpler AI—it's better testing infrastructure. Brands must invest heavily in incrementality testing and synthetic control groups to validate AI performance without needing to understand the underlying decision-making process. SegmentStream uses ML and AI, analyzing various data, including ad clicks and website user behavior, correlating this information with sales data in specific regions to understand the real impact of marketing channels.
By 2027, successful marketing organizations will operate on faith in their AI systems, validated through rigorous testing rather than interpretable models. It's a fundamental shift from understanding why something works to proving that it works.