NNN: Next-Generation Neural Networks for Marketing Measurement
Abstract
We present NNN, an experimental Transformer-based neural network approach to marketing measurement. Unlike Marketing Mix Models (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, potentially enables NNN to model complex interactions, capture long-term effects, and 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. In addition to marketing measurement, the NNN framework can provide valuable, complementary insights through model probing, such as evaluating keyword or creative effectiveness.
Keywords
Cite
@article{arxiv.2504.06212,
title = {NNN: Next-Generation Neural Networks for Marketing Measurement},
author = {Thomas Mulc and Mike Anderson and Paul Cubre and Huikun Zhang and Ivy Liu and Saket Kumar},
journal= {arXiv preprint arXiv:2504.06212},
year = {2025}
}
Comments
The title was updated to reflect broader scope. We clarified that our method is not an MMM and emphasized its experimental nature. R2 values in Tables 1 and 2 were corrected after fixing a bug that inflated NNN scores. Notation in the unrolling section now uses 0-indexing. We noted NNN has not been tested with many channels, added an acknowledgment, and improved grammar