Customer Segmentation Application

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Ideal for: Datasets with irregularly shaped clusters, identifying outliers (noisy customer data), and when clusters have varying densities.

Customer Segmentation Application:

Identifying High-Value Customer Pockets: Discover dense concentrations of highly engaged or profitable customers.
Fraud Detection: Pinpoint unusual customer behaviors that deviate significantly from typical patterns.
Geographic Segmentation: Cluster customers based on their physical proximity in urban or regional areas, especially when customer density varies.
Strengths:


Sensitive to the choice of parameters (epsilon and minPts).
Struggles with clusters of varying densities.
Not ideal for very high-dimensional data.
Gaussian Mixture Models (GMM): Probabilistic Segmentation
How it works: GMM assumes that the data points are country email list generated from a mixture of several Gaussian distributions (normal distributions). Instead of hard assignments, it assigns each data point a probability of belonging to each cluster.

Ideal for: When you believe your customer segments overlap or have fuzzy boundaries, and when you want to understand the probability of a customer belonging to a particular segment.


Customer Persona Refinement: Understand the "blend" of characteristics that define each customer segment, acknowledging that customers might exhibit traits from multiple groups.
Churn Probability Prediction: Estimate the likelihood of a customer churning based on their proximity to a "churn risk" segment.
Targeted Outreach with Confidence Scores: Use the probability scores to prioritize customers for specific campaigns.
Strengths:

Provides probabilistic cluster assignments (soft clustering).
Can capture more complex cluster shapes than K-Means.