Swiggy Uses 350+ Features and Multi-Task MLP to Predict Customer Lifetime Value
350+ features. 63% fewer parameters. Swiggy's lifetime-value model shows how real companies are deploying ML at scale—not in the lab, but in acquisition.
Why it matters
A food-delivery giant's practical deployment of multi-task learning to predict customer value and optimize ad spend reveals how practitioners are moving beyond single-task models to compress complexity while improving performance. This is the applied-ML story: not a new model or capability, but how existing techniques solve real business problems.
The key facts
12 to know350+ pre-order features engineered for pLTV prediction
Multi-task MLP architecture with order count as auxiliary task
63% reduction in model parameters while improving predictive performance
pLTV signal integrated with Google Target ROAS for customer acquisition optimization
Deployed across both Food and Instamart product lines
Swiggy (food-delivery/e-commerce at scale)
350+ pre-order features in the pLTV model
Multi-task MLP architecture (order count as auxiliary task)
63% reduction in model parameters while improving performance
Deployed with Google Target ROAS bidding for customer acquisition
Applied across Food and Instamart verticals
Published Sep 2026 on InfoQ (engineering-focused outlet)
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Swiggy developed an in house predicted lifetime value model using more than 350 pre order features and a multi task MLP for Food and Instamart. Adding order count as an auxiliary task reduced model parameters by 63% while improving predictive performance. The pLTV signal is used with Google Target…