Which clustering technique would you use to understand sales patterns among different customer groups?

Hierarchical clustering is an appropriate technique when the goal is to understand sales patterns among different customer groups. This method is useful for exploratory data analysis, particularly when you want to identify natural groupings in your data without prior knowledge of the number of clusters.

By using hierarchical clustering, you can create a tree-like structure (dendrogram) that shows how different customer segments are related based on their sales patterns. This hierarchical approach allows you to examine relationships at various levels of granularity, which can yield insights into the similarities and differences among various customer groups.

Employing this technique, you can identify clusters of customers with similar purchasing behaviors, which can inform targeted marketing strategies, inventory management, and other business decisions. The visual representation of clusters can also aid in communicating findings to stakeholders.

The other options do not align with the goal of clustering for understanding customer sales patterns. Principal component analysis is primarily a dimensionality reduction technique rather than a clustering method, while logistic regression and Naive Bayes are classification algorithms that predict categorical outcomes rather than uncovering underlying group structures in data.