Customer Life Time Value (CLTV) Prediction Using Embeddings
Ben Chamberlain (Imperial College London);Angelo Cardoso (ASOS);Bryan Liu (ASOS);Marc Deisenroth (Imperial College London);Roberto Paglieri (ASOS)
We describe the Customer Life Time Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. We describe our system, which adopts this approach, and our ongoing efforts to further improve it. Recently, domains including language, vision and speech have shown dramatic advances by replacing hand-crafted features with features that are learned automatically from data. We show that learning feature representations is a promising extension to the state of the art in CLTV modeling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.