Predicting Revenue with Price Indices for Baskets of Spare Parts using Machine Learning

Sammanfattning: Companies in the spare part industry can implement a variety of different pricing techniques, which have traditionally been done through personnel know-how and industry conventions. One such technique is the use of price indices to track sales performance. This thesis investigates if machine learning or time series analysis can predict revenue using price and price indices in a data-driven manner which can potentially validate current pricing strategies or serve as a basis for sales teams pricing decisions. Price indices used were the Fisher Index and the Törnqvist Index. The data came from a spare parts supplier and consisted of daily transactions. Two target variables were tested: revenue as a continuous and categorical variable. The continuous target variable represented revenue the following day, while the categorical variable represented either an increase or decrease the following day. Models tested were OLS, XGBoost, ARIMAX and LSTM for the continuous case and Logistic Regression and XGBoost in the categorical case on several different feature sets. In the continuous case, ARIMAX outperformed the other models, but the best model was produced by the feature set not containing any indices. In the categorical case on a feature set containing price indices, XGBoost yielded an accuracy of 68% in classifying revenue increases or decreases. This study suggest that price indices contain some information about whether a revenue movement is going to happen, but not the magnitude of it.