More computationally expensive than K-Means

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Can overfit if the number of components is too high.
Assumes a Gaussian distribution for each component, which might not always hold true.
The Customer Segmentation Workflow: From Data to Actionable Insights
Implementing clustering techniques for customer segmentation involves a systematic approach:

Data Collection and Preparation: Gather relevant country email list customer data from various sources (CRM, website analytics, transactional data, social media). Clean, transform, and handle missing values. Feature engineering (creating new variables from existing ones) can be crucial here.
Feature Selection: Choose the variables that are most relevant and impactful for defining customer segments. Avoid including noisy or redundant features.
Choosing the Right Clustering Algorithm: Select the algorithm that best suits your data characteristics, business objectives, and desired level of interpretability. Consider the size of your dataset, the expected cluster shapes, and whether you need probabilistic assignments.
Determining the Optimal Number of Clusters: This is often the trickiest part. Techniques like the Elbow Method (for K-Means), Silhouette Score, and Gap Statistic can help. For hierarchical clustering, the dendrogram helps in visualizing potential cut-off points.
Running the Clustering Algorithm: Execute the chosen algorithm on your prepared data.
Interpreting and Profiling Segments: Analyze the characteristics of each cluster. What defines Segment A vs. Segment B? Create detailed profiles for each segment, including demographics, behaviors, preferences, and pain points. Give each segment a descriptive name (e.g., "The Savvy Shopper," "The Engaged Explorer").
Validation and Refinement: Assess the quality of your clusters. Are they distinct and meaningful? Do they make business sense? You might need to adjust parameters, try different algorithms, or refine your features.
Actionable Strategy Development: Translate your segmented insights into concrete business strategies. Develop tailored marketing campaigns, product recommendations, customer service protocols, and pricing strategies for each segment.
Monitoring and Iteration: Customer behavior evolves. Continuously monitor your segments, track their performance, and re-run your clustering analysis periodically to ensure your segmentation remains relevant and effective.