| Summary: | Loyalty programs are now considered industry standards in the hotel sector. Such programs aim to encourage repeat purchases, attract new customers, reward loyal ones, increase retention rates and market share, and collect customer information. Nonetheless, simple participation in a loyalty program does not imply active loyalty. This in-company project seeks to identify Hotel Group X's active loyal customers and provide the company with insights into who these guests are today and who may become one in the future, allowing them to design appropriate marketing strategies. The CRISP-DM methodology was employed in this study, and its data mining goals were to uncover the most important predictors of reward redemptions, which translate into active loyalty. Two predictive models were used in this study – C&RT and Logistic Regression. According to the C&RT model, reservations made on the company's website are the best predictor of reward redemptions, followed by stays in the Algarve region and city hotels. The Logistic Regression model suggests that there is a significant predictive power for the corporate customers, followed by all the direct booking channels. Our results can help enhance the practical direction for hotel managers who deal with vast volumes of data that can be further integrated into the model built in this study to generate novel insights on consumers.
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