用可解释机器学习精准选客户配冷柜，提升饮料公司资产回报率。
Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets
- 基于多层级增长目标构建预测模型，结合SHAP分析特征重要性。
- 最佳模型在三个增长阈值下验证集AUC达0.857至0.898。
- 相比传统方式，能更准筛选高潜力客户，降低无效投放成本。
在快速消费品（FMCG）行业，决定物理资产如商用饮料冷柜的部署位置会直接影响收入增长与执行效率。尽管客户流失预测和需求预测在B2B领域已有广泛研究，但利用机器学习指导资产配置仍相对未被探索。本文提出一个框架，用于预测哪些饮料客户在获得冷柜后最有可能实现显著的销量增长。基于一家中美洲知名酿造与饮料企业2022年1月至2024年7月间3,119家传统渠道客户的私有数据，追踪安装前后12个月的销售记录，设定10%、30%和50%的年同比销量增长阈值。比较XGBoost、LightGBM、CatBoost等模型结合SHAP进行可解释性分析的效果，结果显示最优模型在验证集上三类阈值的AUC分别为0.857、0.877和0.898。模拟表明，该方法通过更精准选择预期增长客户，提高投资回报率，并减少未增长客户分配带来的成本浪费，为业务管理提供有力建议。
原文摘要 · Abstract (English)
In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and demand forecasting have been widely studied in B2B contexts, the use of machine learning to guide asset allocation remains relatively unexplored. This paper presents a framework focused on predicting which beverage clients are most likely to deliver strong returns in volume after receiving a cooler. Using a private dataset from a well-known Central American brewing and beverage company of 3,119 B2B traditional trade channel clients that received a cooler from 2022-01 to 2024-07, and tracking 12 months of sales transactions before and after cooler installation, three growth thresholds were defined: 10%, 30% and 50% growth in sales volume year over year. The analysis compares results of machine learning models such as XGBoost, LightGBM, and CatBoost combined with SHAP for interpretable feature analysis in order to have insights into improving business operations related to cooler allocation; the results show that the best model has AUC scores of 0.857, 0.877, and 0.898 across the thresholds on the validation set. Simulations suggest that this approach can improve ROI because it better selects potential clients to grow at the expected level and increases cost savings by not assigning clients that will not grow, compared to traditional volume-based approaches with substantial business management recommendations
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