  We describe the Customer LifeTime 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. Recently,
domains including language, vision and speech have shown dramatic advances by
replacing handcrafted features with features that are learned automatically
from data. We detail the system deployed at ASOS and show that learning feature
representations is a promising extension to the state of the art in CLTV
modelling. 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.
