Customer Lifetime Value Prediction Using Embeddings
File(s)
Accepted version
Author(s)
Chamberlain, BP
Cardoso, A
Liu, CHB
Pagliari, R
Deisenroth, MP
Type
Conference Paper
Abstract
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.
Date Issued
2017-08-13
Date Acceptance
2017-05-01
Publisher
ACM
Start Page
1753
End Page
1762
Journal / Book Title
KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Is Replaced By
10044/1/77478
Copyright Statement
© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, http://doi.acm.org/10.1145/3097983.3098123
Identifier
http://dx.doi.org/10.1145/3097983.3098123
http://arxiv.org/abs/1703.02596v3
Source
International Conference on Knowledge Discovery and Data Mining
Subjects
cs.LG
cs.CY
cs.IR
cs.NE
stat.ML
Notes
10 pages, 11 figures
Publication Status
Published
Start Date
2017-08-13
Finish Date
2017-08-17
Coverage Spatial
Halifax, NS, Canada