用于验证营销组合模型的包含内生营销支出的合成基准数据集

中文摘要 AI 辅助

营销组合模型（MMMs）从观测时间序列中估算广告的增量销售效应，但它们很少用真实值进行验证，因为真实值在实际数据中不可观测。合成数据原则上可缩小这一差距，但现有生成器生成的营销支出是外生的，忽略了估算问题的核心难点——实际预算是围绕促销日历、季节和近期业绩规划的。本文提出一种参数化生成器和固定参考实例，生成合成零售数据集（共156周，包含3个媒体渠道），其中支出源于四种已记录的协调机制：季度预算反馈、促销日历前的预期支出、计划性电视投放时段、算法业绩追踪；需求基线包含季节性、质量、价格及未观测的情绪成分。支出通过两种转换转化为增量销售：留存效应（carryover）和边际收益递减效应，在此实例中具体化为具有已知参数的几何广告留存（geometric adstock）和逻辑饱和（logistic saturation）；每周销售的真实因果分解与从业者将观测到的受误差污染变量一同被记录。每种机制都是可调整或关闭的参数，配套程序可模拟具有精确处理效应的“停止投放”地理实验。该带种子的生成器和参考实例随可复现本文所有数值的笔记本一同公开。

英文摘要

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.

发表机构

• Zalando（赞达乐）

机构由 AI 辅助整理，请以论文原文为准。