Data collection: Gathering a comprehensive dataset that includes all potential predictors of churn, such as customer interactions and service downtime.

Model evaluation: Using metrics like accuracy and ROC-AUC to compare the performance of different models on a validation set.

Data preprocessing: Applying techniques like normalization and encoding to prepare the data for analysis, ensuring that the dataset is clean and formatted correctly.

Model selection: Choosing algorithms known for their interpretability, such as decision trees or logistic regression, over more complex models like deep neural networks.

Feature engineering: Creating new features that might better capture the reasons behind customer churn, such as the frequency of complaints or changes in service usage patterns.