Your retail analytics team is trying to group customers into clear segments based on buying behavior, purchasing channels, and store proximity. The leadership team wants each customer assigned to exactly one group—similar to the customer profiles shown below—so that marketing and pricing strategies can be targeted without any overlap.

Which clustering method best fits this requirement?

Question

Your retail analytics team is trying to group customers into clear segments based on buying behavior, purchasing channels, and store proximity. The leadership team wants each customer assigned to exactly one group—similar to the customer profiles shown below—so that marketing and pricing strategies can be targeted without any overlap.

Which clustering method best fits this requirement?

BlackTom AI · Accepted Answer

The goal here is to assign each customer to exactly one group, with clear, non-overlapping segments based on buying behavior, purchasing channels, and store proximity.
Option 1: K-Means. This method partitions data into k clusters by minimizing within-cluster variance and assigns each data point (customer) to a single cluster, producing hard, exclusive group memberships. This directly satisfies the requirement of non-overlapping segments.
Option 2: Spectral. Spectral clustering uses graph theory and eigenvectors to separate data, which can produce complex, non-convex shapes and often relies on subsequent labeling. While it can yield hard labels, it is typically used for more intricate cluster structures and may be less interpretable for one-customer-one-cluster needs.
Option 3: DBSCAN. DBSCAN forms clusters based on density and can leave noise points unclustered. It doesn’t guarantee that every customer ends up in a cluster (some may be labeled as noise), which violates the requirement of assigning every customer to exactly one group.
Option 4: GMM (Gaussian Mixture Model). GMM uses probabilistic, soft assignments where each customer has a probability of belonging to each cluster. This inherently allows overlap between clusters and does not force a hard, exclusive assignment unless you impose a hard decision rule, which is not the default behavior.
In summary, for strict, non-overlapping segmentation where each customer must belong to exactly one group, a hard clustering method like K-Means aligns best with the stated requirement, whereas the other methods either permit overlap, leave points unassigned, or rely on probabilistic memberships.

IS 4487-006 Fall 2025 Final Exam December 10 from 10:30 to 12:30

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