Improve Coolblue's direct demand model for substitutable products

Master thesis (2022)

Authors

J.A.M.L. Clercx Electrical Engineering, Mathematics and Computer Science

Contributors

Alexis Derumigny Statistics - EEMCS (supervisor 1)
Jaimy Van Dijk (supervisor 1)
A.J. Cabo Statistics - EEMCS (supervisor 2)
G.F. Nane Applied Probability - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Josephine Clercx
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Published Date

22-08-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis aims to improve Coolblue's direct demand estimation model for substitutable products. Their current model consists of three sub-models which all provide their direct demand estimations. For every product, the direct demand is taken from one of the sub-models based on their performance in estimating the sales. The sub-models are the mean, linear and expectation-maximisation (EM) model. The linear model gives the most accurate expectations, whereas the mean model scores the lowest. Therefore, we have improved the mean model's estimations by creating a new estimator. Furthermore, we have investigated if an out-of-stock (OOS) period influences the sales and, therefore, the direct demand estimations. From this investigation, we conclude that for a part of the products, the sales are affected by OOS periods. However, these results are dependent on how they are investigated. Moreover, the OOS periods' influences are as likely to be positive as negative on the sales. Therefore it is challenging to react to these influences.