BibTex format

author = {Chamberlain, B and Liu, CHB and Cardoso, A and Pagliari, R and Deisenroth, MP},
publisher = {ACM},
title = {Customer life time value prediction using embeddings},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - We describe the Customer Life Time Value (CLTV) prediction sys-tem deployed at, a global online fashion retailer. CLTVprediction is an important problem in e-commerce where an accu-rate estimate of future value allows retailers to effectively allocatemarketing spend, identify and nurture high value customers andmitigate exposure to losses.The system at ASOS provides dailyestimates of the future value of every customer and is one of thecornerstones of the personalised shopping experience. The state ofthe art in this domain uses large numbers of handcrafted featuresand ensemble regressors to forecast value, predict churn and evalu-ate customer loyalty. We describe our system, which adopts thisapproach, and our ongoing e‚orts to further improve it. Recently,domains including language, vision and speech have shown dra-matic advances by replacing hand-crafted features with featuresthat are learned automatically from data. We show that learningfeature representations is a promising extension to the state of theart in CLTV modeling. We propose a novel way to generate embed-dings of customers which addresses the issue of the ever changingproduct catalogue and obtain a signi€cant improvement over anexhaustive set of handcrafted features.
AU - Chamberlain,B
AU - Liu,CHB
AU - Cardoso,A
AU - Pagliari,R
AU - Deisenroth,MP
PY - 2017///
TI - Customer life time value prediction using embeddings
UR -
ER -