Predicting Customer Churn for Avast Mobile Using Historical Data
Autor(ka) práce:
Santos Portillo, Hector Jonathan
Typ práce:
Diploma thesis
Vedoucí práce:
Zimmermann, Pavel
Oponenti práce:
Nedvěd, Vojtěch
Jazyk práce:
English
Abstrakt:
This thesis develops a propensity modeling framework to predict next-day churn among Avast Mobile Security users. Using behavioral, and notification data from the previous 7 days, and historical purchase data. The study trains two machine learning models, Logistic Regression and XGBoost, to generate churn propensity scores. Rather than making binary predictions, the models rank users by their likelihood to churn, enabling Avast to prioritize retention actions on the top 1% most at-risk users.
Klíčová slova:
avast; customer churn; propensity
Název práce:
Predicting Customer Churn for Avast Mobile Using Historical Data
Autor(ka) práce:
Santos Portillo, Hector Jonathan
Typ práce:
Diplomová práce
Vedoucí práce:
Zimmermann, Pavel
Oponenti práce:
Nedvěd, Vojtěch
Jazyk práce:
English
Abstrakt:
This thesis develops a propensity modeling framework to predict next-day churn among Avast Mobile Security users. Using behavioral, and notification data from the previous 7 days, and historical purchase data. The study trains two machine learning models, Logistic Regression and XGBoost, to generate churn propensity scores. Rather than making binary predictions, the models rank users by their likelihood to churn, enabling Avast to prioritize retention actions on the top 1% most at-risk users.