How to Implement K-Means Clustering in Email Marketing?
1. Data Collection: Gather all relevant data about your subscribers. 2. Preprocessing: Normalize the data to ensure that each attribute contributes equally to the distance calculations. 3. Choosing 'k': Use methods like the Elbow or Silhouette Method to determine the optimal number of clusters. 4. Running the Algorithm: Use a machine learning library like scikit-learn in Python to run the K-Means Clustering algorithm. 5. Segmenting the List: Assign each subscriber to a cluster and create targeted email campaigns for each cluster.