NNN: Next-Generation Neural Networks for Marketing Measurement
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910985523036160 |
|---|---|
| author | Mulc, Thomas Anderson, Mike Cubre, Paul Zhang, Huikun Liu, Ivy Kumar, Saket |
| author_facet | Mulc, Thomas Anderson, Mike Cubre, Paul Zhang, Huikun Liu, Ivy Kumar, Saket |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06212 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NNN: Next-Generation Neural Networks for Marketing Measurement Mulc, Thomas Anderson, Mike Cubre, Paul Zhang, Huikun Liu, Ivy Kumar, Saket Machine Learning Applications 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. |
| title | NNN: Next-Generation Neural Networks for Marketing Measurement |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2504.06212 |