Predicting Online Returns

Predicting Online Returns

Kumari Smriti
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-3056-5.ch010
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Abstract

The E-commerce industry is growing year on year in double digits. But customers today are not only buying more through their computers, they are also returning more. The volume of these returns is such that it just can't be ignored. E-tailers today are following many practices to handle these returns but the ‘predictability' factor is still missing from their approaches. This paper tries to fulfill that void. The framework suggested in this paper will help the E-tailers to predict the probability of a particular item being returned by a particular shopper. The idea is that if the E-tailer will know the probability of return during any transaction he/she would certainly be better equipped to handle the situation.
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Background

It has been just two decades since the first secure online retail transaction (by NetMarket or Internet Shopping Network) occurred in 1994, and the online shopping industry has exploded. However, the debate between which one is better and preferred by consumers hasn’t been put to an end. Online shopping has certainly seen a growing trend, but it doesn’t seem like it will replace regular offline shopping anytime soon. So what are the criteria under which a user decides the Place of his/her purchase, whether it is going to be online or at a physical store? These are convenience, variety, immediacy, quality, the experience, discounts and offers, personalized attention and recommendation from sales staff, and of course pricing, to mention a few (Abbasi, et. al. 2010).

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