@inproceedings{Chamberlain:2017:10.1145/3097983.3098123,
author = {Chamberlain, BP and Cardoso, A and Liu, CHB and Pagliari, R and Deisenroth, MP},
doi = {10.1145/3097983.3098123},
pages = {1753--1762},
publisher = {ACM},
title = {Customer lifetime value pediction using embeddings},
url = {http://dx.doi.org/10.1145/3097983.3098123},
year = {2017}
}

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TY  - CPAPER
AB  - 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.
AU  - Chamberlain,BP
AU  - Cardoso,A
AU  - Liu,CHB
AU  - Pagliari,R
AU  - Deisenroth,MP
DO  - 10.1145/3097983.3098123
EP  - 1762
PB  - ACM
PY  - 2017///
SP  - 1753
TI  - Customer lifetime value pediction using embeddings
UR  - http://dx.doi.org/10.1145/3097983.3098123
UR  - http://arxiv.org/abs/1703.02596v3
UR  - http://hdl.handle.net/10044/1/77478
ER  - 

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