Marketing mix modeling (MMM) is a widely used method to assess the effectiveness of marketing campaigns and optimize marketing strategies. Bayesian MMM is an advanced approach that allows for the incorporation of prior information, uncertainty quantification, and probabilistic predictions (1). In this paper, we describe the process of building a Bayesian MMM model for the online insurance company Lemonade. We first collected data on Lemonade's marketing activities, such as online advertising, social media, and brand marketing, as well as performance data. We then used a Bayesian framework to estimate the contribution of each marketing channel on total performance, while accounting for various factors such as seasonality, market trends, and macroeconomic indicators. To validate the model, we compared its predictions with the actual performance data from A/B-testing and sliding window holdout data (2). The results showed that the predicted contribution of each marketing channel is aligned with A/B test performance and is actionable. Furthermore, we conducted several scenario analyses using convex optimization to test the sensitivity of the model to different assumptions and to evaluate the impact of changes in the marketing mix on sales. The insights gained from the model allowed Lemonade to adjust their marketing strategy and allocate their budget more effectively. Our case study demonstrates the benefits of using Bayesian MMM for marketing attribution and optimization in a data-driven company like Lemonade. The approach is flexible, interpretable, and can provide valuable insights for decision-making.

翻译：营销组合建模（MMM）是一种广泛用于评估营销活动效果和优化营销策略的方法。贝叶斯营销组合建模是一种先进方法，能够整合先验信息、量化不确定性并提供概率预测（1）。本文描述了为在线保险公司Lemonade构建贝叶斯营销组合模型的过程。我们首先收集了Lemonade的营销活动数据，包括在线广告、社交媒体和品牌营销等，以及业绩数据。随后采用贝叶斯框架评估各营销渠道对总体业绩的贡献度，同时考虑了季节性、市场趋势和宏观经济指标等多种因素。为验证模型，我们将其预测结果与A/B测试的实际业绩数据及滑动窗口保留数据进行对比（2）。结果表明，各营销渠道的预测贡献度与A/B测试表现一致且具备可操作性。此外，我们通过凸优化进行了多场景分析，测试模型对不同假设的敏感性，并评估营销组合变化对销售额的影响。该模型获得的洞察使Lemonade能够调整营销策略并更有效地分配预算。本案例研究证明了贝叶斯营销组合建模在Lemonade这类数据驱动型企业中用于营销归因和优化的优势。该方法具有灵活性、可解释性，能为决策制定提供有价值的见解。