How can you use unsupervised learning to improve customer segmentation?

2 How can you use clustering for customer segmentation?

Clustering is a powerful technique for customer segmentation because it can reveal hidden segments that you might not have thought of or that are not obvious from the data. For example, you can use clustering to segment your customers based on their purchase history, browsing behavior, demographics, feedback, or any other relevant data. Some of the most common clustering algorithms are k-means, hierarchical clustering, and density-based clustering. Each algorithm has its own advantages and disadvantages, depending on the type, size, and shape of your data.

3 How can you use dimensionality reduction for customer segmentation?

Dimensionality reduction can also be useful for customer segmentation because it can help you simplify your data and visualize it better. By reducing the number of features or dimensions in your data, you can eliminate noise, redundancy, and irrelevant information, and focus on the most important aspects of your customers. For example, you can use dimensionality reduction to identify the key factors that influence your customers' satisfaction, loyalty, or churn. Some of the most common dimensionality reduction algorithms are principal component analysis (PCA), singular value decomposition (SVD), and t-distributed stochastic neighbor embedding (t-SNE).

4 How can you evaluate your customer segments?

When it comes to unsupervised learning, there is no clear measure of how well it performs or how valid the customer segments are. To evaluate and compare customer segments with business goals and expectations, you can use internal validation metrics such as silhouette score, Davies-Bouldin index, or Calinski-Harabasz index to measure intra-cluster and inter-cluster distances. Additionally, external validation metrics such as adjusted Rand index, normalized mutual information, or Fowlkes-Mallows index can be used to measure consistency with external criteria or labels. Lastly, business validation metrics such as conversion rate, retention rate, lifetime value, or customer satisfaction can be used to measure effectiveness for marketing, sales, or service objectives and strategies.