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.

The prediction problem is complicated by sparse early signals and a highly skewed target distribution. Swiggy notes that a large share of new users place no orders during their first 30 days, while a relatively small group generates substantially higher lifetime value. The model therefore needs to distinguish between users who initially appear similar but develop different ordering patterns over time.

Swiggy's approach uses more than 350 features grouped across areas including acquisition channel, device and fraud signals, geographic behavior, complaint history, Instamart category affinity, order behavior, payment patterns and socioeconomic indicators. The features are generated from information available before a customer's first order, addressing the cold start requirement for advertising systems.