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E-Commerce businesses are rapidly growing, especially fashion products related brands. COVID-19 has been an accelerating factor in the growth, as consumers are trying to minimise the touch/physical interactions while purchasing (as well returning) the products. With the increase in sales there has been phenomenal increase in returns volume also. For fashion e-commerce brands returns comprises of 20-35% of total sold units. Though the returns are free (or involve minimal costs) for customers but there is a substantial cost for the fashion brands in processing those returns. Also, optimising returns processing means reducing the lead time in bringing returns products back to customers for sale. Work done also involves customer profiling based on sales and returns data.
- Developing e-commerce returns volume prediction model using machine learning methods.
- Developing interactive live dashboard on returns and sales data.
- Merchandising analytics: insights for fashion product manufacturer on their product sales and returns.
- Consumer analytics: studying consumer’s return and sales behaviour, and then profiling consumers based on it.
Nitin Jain is currently working as a KTP Associate at University of Leeds on a project with a leading 3rd party logistics provider in the UK to optimise e-commerce returns process using predictive modelling. He is developing an e-commerce returns solution through descriptive and predictive analytics. His prior work experience includes working at IT consulting MNC, Infosys and working with development sectors organisations in India on sustainable development projects using data science. He has been part of data science projects for clients based in USA, UK, and India, dealing in sectors like supply chain and logistics, development, retail, healthcare, financial services, risk management, cryptocurrency, and commodity trading.