The Analytics of Product Return Episodes in Retailing

Abstract

Often, the return of a product is merely one of a series of transactions that a consumer undertakes in search of a good. Recognizing this, whereas the extant literature views each product return transaction in isolation, we analyze returns as part of a search process: Upon returning a product, customers may immediately purchase an alternative one, which they may later replace with another product, and so on, until they either ultimately keep their last purchase (Keep outcome) or not (No-keep outcome). We call such a sequence of transactions a product return episode. Using data from a consumer electronics retailer, we show that episodic metrics of return behavior better explain managerially relevant measures, such as net customer spending, and offer a richer understanding of returns than commonly used transactional metrics. Our findings suggest that although higher price and larger store assortment both tend to increase the return probability, they also increase the probability of keeping a product at the end of an episode, which may point to profit-improving opportunities for retailers.