Ben Chamberlain,

CLTV prediction is an important problem in e-commerce. An accurate estimate of CLTV allows retailers to correctly allocate marketing spend, identify and nurture high value customers, minimise exposure to unprofitable customers and attribute value to indirect marketing such as content production. We describe how ASOS combines automatic feature learning through deep neural models with hand-crafted features to produce CLTV estimates that outperform either paradigm used in isolation.

Ben Chamberlain is a senior data scientist at ASOS.com where he leads the Customer Understanding team. He holds a Royal Commission for the Exhibition of 1851 Industrial Fellowship, which funds his PhD studies in statistical machine learning at Imperial College London. Ben has previously worked as a data scientist in the social media, defence and security industries. He is a graduate of the University of Oxford.

This presentation took place at RE•WORK Deep Learning in Retail & Advertising Summit in London June 2017. View more videos from RE•WORK Summits here: http://videos.re-work.co/

RE•WORK events bring together breakthrough technology, cutting-edge science and entrepreneurship to re-work the future, finding solutions to challenges in business and society.

More about us: https://re-work.co/events
More on this event: https://re-work.co/events/deep-learni...