A Synthetic Benchmark Dataset with Endogenous Marketing Spend for Validating Marketing Mix Models
Abstract
Marketing Mix Models (MMMs) estimate the incremental sales effect of advertising from observational time series, yet they are rarely validated against ground truth, because ground truth is unobservable in real data. Synthetic data closes that gap in principle, but existing generators produce marketing spend exogenously - omitting the central difficulty of the estimation problem, since real budgets are planned around promotional calendars, seasons, and recent performance. This paper presents a parameterized generator, and a fixed reference instance, of a synthetic weekly retail dataset (156 weeks, three media channels) in which spend arises from four documented coordination mechanisms - quarterly budget feedback, anticipatory spending ahead of a promotional calendar, scheduled TV bursts, and algorithmic performance chasing - on a demand baseline with seasonal, quality, price, and unobserved sentiment components. Spend translates into incremental sales through two transformations, carryover and diminishing returns, instantiated here as geometric adstock and logistic saturation with known parameters; the true causal decomposition of every week's sales is recorded alongside the error-contaminated variables a practitioner would observe. Every mechanism is a parameter that can be varied or switched off, and a companion procedure simulates go-dark geo-experiments with exact treatment effects. The seeded generator and reference instance are publicly released with notebooks that reproduce every number in this paper.
Related
- A Conditional-Distribution Framework for Validating Synthetic Multivariate Data
- A reproducible simulation dataset and benchmark for parametric competing-risks inference under censoring and latent dependence.
- Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data
- Reproducible benchmarking of four machine-learning classifiers on a large synthetic heart-disease competition dataset
- Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
BibTeX
@misc{heusch2026synthetic,
title = {{A Synthetic Benchmark Dataset with Endogenous Marketing Spend for Validating Marketing Mix Models}},
author = {Niklas Heusch},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.21130},
eprint = {2608.21130},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.21130},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Synthetic Benchmark Dataset with Endogenous Marketing Spend for Validating Marketing Mix Models
AU - Niklas Heusch
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2608.21130
UR - https://arxiv.org/abs/2608.21130
AB - Marketing Mix Models (MMMs) estimate the incremental sales effect of advertising from observational time series, yet they are rarely validated against ground truth, because ground truth is unobservable in real data. Synthetic data closes that gap in principle, but existing generators produce marketing spend exogenously - omitting the central difficulty of the estimation problem, since real budgets are planned around promotional calendars, seasons, and recent performance. This paper presents a parameterized generator, and a fixed reference instance, of a synthetic weekly retail dataset (156 weeks, three media channels) in which spend arises from four documented coordination mechanisms - quarterly budget feedback, anticipatory spending ahead of a promotional calendar, scheduled TV bursts, and algorithmic performance chasing - on a demand baseline with seasonal, quality, price, and unobserved sentiment components. Spend translates into incremental sales through two transformations, carryover and diminishing returns, instantiated here as geometric adstock and logistic saturation with known parameters; the true causal decomposition of every week's sales is recorded alongside the error-contaminated variables a practitioner would observe. Every mechanism is a parameter that can be varied or switched off, and a companion procedure simulates go-dark geo-experiments with exact treatment effects. The seeded generator and reference instance are publicly released with notebooks that reproduce every number in this paper.
ER -