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Swiggy Uses 350+ Features and Multi-Task MLP to Predict Customer Lifetime Value

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 ROAS bidding to optimize customer acquisition. By Leela Kumili

Swiggy Uses 350+ Features and Multi-Task MLP to Predict Customer Lifetime Value

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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 ROAS bidding to optimize customer acquisition. By Leela Kumili

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