Swiggy 用350+特征和多任务MLP预测客户终身价值
原标题：Swiggy Uses 350+ Features and Multi-Task MLP to Predict Customer Lifetime Value
AI 摘要
Swiggy 开发了内部预测客户终身价值（pLTV）模型，用于在客户首次下单前估算其长期价值，以优化广告竞价。该模型使用超过350个特征和简单的多层感知机架构，通过引入辅助任务将参数减少63%，同时提高了准确性。在生产中，Swiggy 将该信号用于 Google 的 tROAS 竞价，并进行了与第三方平台的 A/B 测试，结果显示其模型在留存率和总订单价值上表现更优。未来计划转向概率性 pLTV 预测。
正文节选
Swiggy has developed an in-house predicted lifetime value (pLTV) model to estimate the long-term value of new customers across its food delivery and Instamart quick commerce businesses. The model is designed to generate a useful signal before a customer's first order, allowing Swiggy to use predicted value in advertising bid optimization rather than relying on short-term conversion metrics. Swiggy pLTV model architecture (Source: Swiggy Blog Post) The prediction problem is complicated by sparse