Collaborate with the Marketing team to manually define customer segments based on their expertise. Then, use the Data Labeling Service to label each customer in BigQuery accordingly and analyze label distributions with Data Studio for insights.

Use Dataprep to preprocess and clean the data in BigQuery, then manually analyze column-wise similarities to identify potential segments without employing machine learning.

Employ the Data Labeling Service for labeling each customer record in BigQuery based on preliminary analysis, then use AutoML Tables to train a supervised model. Evaluate the model's metrics to infer underlying data patterns and segments.

Develop a k-means clustering model via BigQuery ML, leveraging its capability to automatically determine the optimal number of clusters based on the data, thus identifying distinct customer segments and their common purchasing behaviors efficiently.

Combine both supervised and unsupervised learning approaches by first using AutoML Tables for initial segmentation based on labeled data, then applying k-means clustering in BigQuery ML to refine the segments further.