Fashion e-commerce industries experience a lot of product returns (or exchange) from customers. Most of these are attributed to incorrect size (or fitment). The talk will focus on this problem and present a solution to reduce such returns. Specifically, we present a data science driven approach to profile our customers based on their past purchases and returns and use that to recommend the right size product or flag a potential return.

Outline

Refer to the attached presentation deck.
Motivation
Problem definition
Our approach
Evaluation
Conclusion

Requirements

N/A

Speaker bio

Ashish Kulkarni works as a Principal data scientist at Jabong Labs. He’s currently also a Ph.D. candidate in the Computer Science department at IIT Bombay. His research interest is in the area of interactive machine learning and its application to information extraction and retrieval. Practical applications like information extraction, retrieval, machine translation, to name a few, might benefit from autonomous machine learning models, aided by user preferences. Interactive machine learning opens up promising avenues while posing research challenges in designing appropriate tools and algorithms. Studying and addressing these challenges forms the primary focus of his research. He has published in reputed conferences including IJCAI, PAKDD, K-CAP and others. Ashish has over eight years of prior industry experience.

Slides

https://goo.gl/kA6JkW