Quick Answers

K-means is faster and works well when you know roughly how many client groups you're looking for. Hierarchical clustering builds a tree of relationships, which is useful when you want to explore different segmentation levels without deciding upfront how many clusters you need. For client segmentation, hierarchical clustering often reveals surprising relationship patterns that K-means might miss.

Initial analysis usually takes 2-4 weeks from data delivery. We'll clean your data, run multiple clustering approaches, validate the results against your business context, and deliver a detailed segmentation report with actionable insights. If you need refinements or want to explore additional segmentation angles, that's typically another 1-2 weeks per iteration.

Absolutely. Unsupervised learning finds natural groupings in your fund data based on actual holdings, performance metrics, and volatility patterns—not on category labels you've assigned. This often uncovers funds that behave similarly despite different classifications, or reveals that some traditional categories contain surprisingly diverse performance profiles.

We can work with data in most common formats—CSV, Excel, or database exports. Ideally, bring us your client characteristics (assets under management, investment style, account type), fund holdings, and performance metrics. We'll handle data cleaning, scaling, and missing value treatment as part of the analysis.

We identify them explicitly. Outliers are often the most interesting—they might be high-value clients with unique needs, or funds that don't fit traditional patterns. Our analysis flags these and explains why they're distinct, which helps you understand edge cases and special circumstances in your portfolio.

We validate clustering using multiple approaches: statistical metrics (silhouette score, Davies-Bouldin index), business logic checks against your domain knowledge, and stability testing across different subsets of your data. We also explain the characteristics of each cluster in plain language so you can judge whether the patterns make business sense.