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Publicação
Predictive Modelling of Merchandising Sales in a Football Club
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| 1.62 MB | Adobe PDF |
Orientador(es)
Resumo(s)
This project develops a weekly merchandising sales forecasting model for a professional
football club with the goals of maximizing order quantity from suppliers, avoiding stockouts,
and reducing overstock. Based on the CRISP-DM process, historical sales, performance of the
club, and weather information were collected, cleaned, and analysed. The extracted variables
were used to depict product launches and promotions,so that the last dataset could be weekly
aggregated and employed to train and compare the various forecasting models: SARIMAX,
XGBoost, LightGBM, and Random Forest. XGBoost performed better than the other models,
exhibiting the following performance, RMSE of 84.221, MAE of 46010.88 and an adjusted R²
of 0.846, being superior in detecting non-linear relationships and intricate patterns in the
data. This study demonstrates how machine learning methodology can become a major value
driver of operational efficiency, enabling inventory management and creation of more
strategic marketing campaigns, in addition to maximizing fan experience through access to
most desirable products.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
Palavras-chave
Machine Learning Forecasting Demand Time series ARIMA XGBoost Random Forest LightGBM SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption SDG 17 - Partnerships for the goals