Understanding the Choice of Collection & Delivery Point by the E- Consumer via a Machine Learning Model: Moroccan Case Study

Abstract

Collection and delivery points (CDP) are evolving rapidly, along with consumption and distribution trends. Understanding the e- consumer's choice of such points is essential for parcel transport companies, as it has a strong impact on their continuity in this competitive environment. the results of the statistical study that we did on our sample show that supermarkets, service stations, and post offices are the most used CDP by Moroccan consumers, especially: full-time employees aged between 18 and 40 and well educated. In addition, this paper focuses on machine learning algorithms such as: Decision Tree, Naive Bayes, K-Nearest Neighbors and Random Forest to create a CDP Classifier. The findings indicate that our model (Optimized Random Forest) outperforms the baseline models in terms of precision, recall, F1 measure and accuracy. In terms of accuracy, our model which achieved 87% outperforms the decision tree model by 9%, the Naive Bayes model by 13%, the KNN model by 6% and the original Random Forest model by 2%. This article has many implications, in terms of theory, it helps researchers to have a clear vision on the application of machine learning in the field of pick-up point delivery, since there are no previous studies applying this technique. On the other hand, it allows parcel transport companies to better understand the behavior of e-consumers using collection and delivery points, before choosing the optimal form of such points. For example, supermarkets are potentially suitable places to set up a CDP, as they are densely located in the city and easily accessible, especially in residential areas.