•1 min read•from InfoQ
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 KumiliWant to read more?
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Tagged with
#Swiggy
#Customer Lifetime Value
#pLTV
#MLP
#Multi-Task Learning
#Predictive Modeling
#Food Delivery
#Instamart
#Order Count
#Auxiliary Task
#Model Parameters
#Predictive Performance
#Google Target ROAS
#Customer Acquisition
#Bidding Optimization
#Pre-order Features
#Machine Learning
#Data Science
#Feature Engineering
#ROAS