In the context of business intelligence, how can the nearest-neighbor algorithm be effectively utilized for model evaluation, particularly in assessing customer segmentation?

The nearest‑neighbor algorithm can be used to test how well a customer segmentation model groups similar customers by looking at each customer’s nearest peers in the feature space and checking whether they belong to the same segment. By computing the proportion of nearest neighbors that share the same segment label, you get a direct measure of intra‑segment cohesion versus inter‑segment separation. This evaluation step is performed after the segmentation model has assigned customers to clusters, and it reveals whether the model’s boundaries align with natural customer similarities. For example, if a customer in segment A has most of its nearest neighbors also in segment A, the model is likely capturing a coherent group; if many neighbors belong to different segments, the model may need refinement. Thus, nearest‑neighbor evaluation provides a simple, interpretable metric for validating and improving customer segmentation in business intelligence.