用协同模型分解客户行为，提升营收预测精度。
Forecasting Revenue with its Customer-Base Drivers: When and Why Coordination Helps
- 构建多任务变压器模型，联合预测客户获取、复购等关键指标。
- 整体销售误差比基准低30%，74.3%的企业表现更优。
- 客户行为联动强时协同效果更明显，适合做精细化营收规划。
营收预测影响获客预算、需求计划和基于客户的估值，但传统聚合预测无法揭示增长源于客户获取、重复购买、客单价提升还是相互抵消的变动。本文基于25个行业966家公司的周度交易面板数据，提出客户基础多任务变压器（CBMT）模型，该模型学习共享结构、保留独立的原始预测，并将组合结果与下游营收对齐。CBMT的平均总销售额误差比最强的现有客户基础基准低30%。其表现也优于直接预测总销售额的Transformer模型（误差低2.65%），但差异不显著（p=.222）；在74.3%的企业中优于单任务独立预测。在24项基准-结果对比中，有23项其原始预测MAE更低，剩余一项无统计差异。客户原始指标联动越强，协同预测收益越高。情景分析支持共享表示和营收对齐带来增益，但仅为诊断性而非因果性证据。当客户行为波动剧烈时，所有模型精度下降，CBMT优势缩小。校准期路由规则未提升平均准确率，优于始终使用CBMT。结果表明，协同客户预测有助于营收规划，但在高波动环境下需更谨慎。
原文摘要 · Abstract (English)
Revenue forecasts guide acquisition budgets, demand planning, and customer-based valuations, yet an aggregate forecast does not show whether change reflects acquisition, repeat purchasing, spending per order, or offsetting movements. Using weekly transaction panels for 966 companies in 25 industries, the authors develop the Customer-Based Multi-task Transformer (CBMT), which learns shared structure, retains separate primitive forecasts, and aligns their combination with downstream revenue. CBMT's mean total-sales error is 30% below the strongest representative established customer-base benchmark. It is also 2.65% below a Transformer that forecasts total sales directly, although the paired difference is not statistically significant (p=.222), and it beats separately estimated single-task forecasts for 74.3% of firms. CBMT's source MAE is lower in 23 of 24 benchmark-by-outcome comparisons, with the remaining difference not statistically distinguishable from zero. Firms whose primitives co-move more strongly are more likely to benefit from joint forecasting; selected-family scenario-3 comparisons are consistent with gains from shared representation and revenue alignment but remain diagnostic rather than causal. Accuracy deteriorates for all models when customer-base dynamics are highly volatile, and CBMT's advantage narrows there. Calibration-period routing rules do not improve average accuracy over always deploying CBMT. The results show how coordinated customer-base forecasts support revenue planning and when they warrant greater caution.
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