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A simulation based analysis of price elasticity of demand
Kubišta, Michal ; Stráský, Josef (advisor) ; Červinka, Michal (referee)
i Abstract In this work, we describe a novel methodology to analyse the price elasticity of demand. This method combines an artificial neural network that serves as the model of the behaviour of the customers and a subsequent simulation based on this model. We present the validation of our approach using a real-world dataset obtained from an e-commerce retailer and demonstrate its advantages, notably the ability to estimate the elasticity in distinct price points and the inclusion of the complete pricing situations (not only product's own price). JEL Classification C45, C44, C15, D12 Keywords price elasticity of demand, artificial neural net- work, agent-based model Title A simulation based analysis of price elasticity of demand Author's e-mail Supervisor's e-mail
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Analysis of Weather Effect on Sales in the Czech FMCG Market
Kubišta, Michal ; Krištoufek, Ladislav (advisor) ; Brož, Václav (referee)
In this work, we aim to study the effect of weather conditions on the sales of the FMCG market. For this purpose, we have collected an extensive dataset consisting of weekly category sales of over 1000 stores in the Czech Republic for years 2015 to 2017, coupled with various meteorological variables for over 80 different weather stations. We introduce a novel approach to analysis, using tree-based machine learning algorithms. These flexible non-parametric methods can estimate complex relationships as well as performing an automatic variable selection. Both of those attributes are critical in our work, as the final dataset consists of over 130 variables. The central point of this thesis is to either conclude there is only a negligible relationship or to provide a model with robust performance and explainable results. We manage to show a significant sales reactions based on changing weather conditions for three top-selling categories, producing a model that significantly outperforms both benchmarks, lasso regression and tree-based model trained on non- meteorological variables only. Ultimately we present two conclusions, firstly that linear regression, a commonly used methodology in similar studies, is not a suitable approch for modeling the weather effects and secondly that the weather variables...
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