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NNN: Next-Generation Neural Networks for Marketing Mix Modeling
We present NNN, a Transformer-based neural network approach to Marketing Mix Modeling (MMM) designed to address key limitations of traditional methods. Unlike conventional MMMs which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, enables NNN to model complex interactions, capture long-term effects, and potentially improve sales attribution accuracy. We show that L1 regularization permits the use of such expressive models in typical data-constrained settings. Evaluating NNN on simulated and real-world data demonstrates its efficacy, particularly through considerable improvement in predictive power. Beyond attribution, NNN provides valuable, complementary insights through model probing, such as evaluating keyword or creative effectiveness, enhancing model interpretability.
View PDFHTML (experimental) Abstract:We present NNN, a Transformer-based neural network approach to Marketing Mix Modeling (MMM) designed to address key limitations of traditional methods. Unlike conventional MMMs which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, enables NNN to model complex interactions, capture long-term effects, and potentially improve sales attribution accuracy.
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