AI Customer Segmentation
Behaviour-based customer segmentation reveals who the buyers really are — not who marketing wishes they were. OctOpus profiles the customer table, picks the right clustering family (KMeans for clean spheroids, DBSCAN for irregular shapes, gaussian mixture for soft membership), validates with silhouette and gap statistic, and ships labelled clusters back to the CRM.
What the agent does
Feature engineering from transactions: RFM (recency, frequency, monetary), inter-purchase intervals, category preference vectors, engagement decay. Dimensionality reduction (PCA or UMAP) when the feature space is wide. Cluster validation across k = 2..15. Segment naming via centroid interpretation and feature-importance per cluster.
Beyond RFM
Modern segmentation uses behavioural embeddings — sequence-of-events vectors, web-click trails, support-ticket topics. OctOpus handles all of these with the same agentic loop. Drop a long CSV with one row per customer event; the agent groups customers by behaviour, not just transactional totals.
Deploy the labels
Once OctOpus picks the winning model, every customer in the table gets a segment label and a soft-membership probability. Export to CRM, marketing platform, or feature store. Re-segment monthly — the workspace handles incremental refresh.
Key capabilities
- Auto-picks clustering family based on data shape.
- Validates clusters with silhouette + gap statistic.
- Interprets centroids — each segment gets a feature-importance card.
- Exports labelled segments back to CRM / warehouse.
- Behavioural sequence embeddings, not just RFM.