Fabrikam’s marketing team wants to discover natural customer segments based on behavior (pages visited, products viewed, recency, frequency, and…

Discovering customer segments without any pre-existing labels is a classic unsupervised-learning problem; clustering algorithms (for example, k-means) are the canonical technique recommended by Microsoft for this exact marketing-analytics scenario.

Why Incorrect Option is Wrong: If the answer had been “False”, it would imply that a supervised or other family of models is required, which contradicts Microsoft guidance that clustering is the appropriate approach for unlabeled segmentation problems.

1. Microsoft Learn – “Explore clustering to segment data” (Machine learning crash course), Section: Customer segmentation with k-means (last updated 2023-11-15).

2. Azure Architecture Center – “Customer segmentation using machine learning on Azure”, Diagram > Step 3: Apply k-means clustering (Rev. 2023-08-14).

3. Bishop, C. M. (2006). Pattern Recognition and Machine Learning, §9.1 “Mixture models and k-means” (Springer).