Return Rate Prediction

Sammanfattning: Product quality is a major concern for all companies. Predicting the lifetime return rate allows for identification of products with atypically high return rates. Such products might otherwise not have been detected before it is too late to take any action to reduce the return rate. Furthermore, predicting a product’s return rate allows for estimation of the company’s expected cost of handling returns. This thesis explores multiple methods for predicting a product’s lifetime return rate. One of the methods investigated utilizes a mixture cure model for the time to return distribution and includes a parameter for the probability of return. Different time to return distributions were used in the model; the negative binomial distribution and the Weibull distribution. The parameters of the model were estimated using variations of the Expectation Maximization algorithm. A simple way of estimating the lifetime return rate is by simply dividing the number of observed returns with the number of observed sales, called the aggregated return rate. All methods tested outperformed the aggregated return rate and the most accurate method proved to be the cure mixture model using a negative binomial distribution.