The core task is to segment customers into tiers using a dataset that is explicitly described as "unlabeled." Unsupervised learning is the machine learning paradigm designed specifically for this type of problem. It aims to find hidden patterns, structures, or groupings within data that has no predefined labels or target outcomes. The process of classifying customers into tiers based on their inherent similarities is a classic example of clustering, which is a primary technique within unsupervised learning. This approach allows the company to discover natural customer segments directly from the data itself.

1. Ng

A. (2023). CS229 Machine Learning Course Notes. Stanford University. In Part V

"Unsupervised Learning

" it is stated: "In the unsupervised learning problem

we are given a dataset and we are told to find some structure in the data. A canonical example of unsupervised learning is clustering

in which we are given a set of unlabeled data and we want to group the data into 'cohesive' clusters." (p. 1).

2. Amazon Web Services. (n.d.). What is Unsupervised Learning?. AWS Machine Learning Documentation. In the section "Unsupervised learning use cases

" it specifies: "A common application of unsupervised learning is customer segmentation. You can use it to group customers based on their purchasing behavior

demographics

or other characteristics... to tailor marketing campaigns."

3. Hastie

T.

Tibshirani

R.

& Friedman

J. (2009). The Elements of Statistical Learning: Data Mining

Inference

and Prediction (2nd ed.). Springer. In Chapter 14

"Unsupervised Learning

" the text introduces the topic: "In the unsupervised learning problem we are given a set of observations... but are not told anything about them... The goal is to discover interesting things about the measurements... to group them into clusters." (Section 14.3

p. 485).