Multi-Region Marketing Mix Modelling (MMM) Dataset for Several eCommerce Brands

This dataset contains anonymised, multi-region marketing and purchase data for close to 100 eCommerce brands to support the advancement and benchmarking of Marketing Mix Modelling techniques. This dataset is suitable for modelling customer acquisition costs (CAC) and return on advertising spend (ROAS) for online-only purchases and digital marketing channels (currently limited to Google, Meta, and Tiktok).<br><br>Please review the data dictionary provided for further details. Depending on the level of interest and feedback, there is scope to expand this dataset to more brands and marketing channels in later versions, so please use proper citation!本数据集涵盖近百家电子商务品牌的匿名多区域营销与购买数据，旨在为营销组合模型（Marketing Mix Modelling）技术的迭代优化与效果基准测试提供支撑。 本数据集可用于针对纯线上购买场景及数字营销渠道（目前仅限谷歌（Google）、Meta（Meta）与TikTok（Tiktok））的客户获取成本（Customer Acquisition Cost，CAC）与广告支出回报率（Return on Advertising Spend，ROAS）建模。 如需了解更多细节，请查阅所附的数据字典。根据受众关注度与反馈情况，后续版本具备扩展至更多品牌与营销渠道的空间，敬请规范引用。

搜集汇总

背景与挑战

背景概述

该数据集包含近100个电子商务品牌的匿名、多区域营销和购买数据，旨在支持营销组合建模技术的进步和基准测试。它特别适用于在线购买和数字营销渠道（如Google、Meta、TikTok）的客户获取成本和广告支出回报率建模，为研究人员和从业者提供了实用的分析资源。