Sukun Sales Segmentation
Spatial segmentation of potential sales areas using Hierarchical Agglomerative Clustering to identify regional sales characteristics and support data-driven marketing strategies.
Research Background & Problem Framing
Territory sales distribution for Sukun consumer goods often encounters uneven market penetration across different administrative sub-districts (kelurahan). Without quantitative spatial clustering, marketing resource allocation remains heuristic and sub-optimal.
This study applies Hierarchical Agglomerative Clustering (HAC) with Euclidean distance metrics to segment 17 kelurahan in Kota Bekasi, deriving distinct demographic and transaction volume profiles.
Dataset Specifications
Methodology & Pipeline Architecture
Comparative Model Linkage Evaluation
(Scroll horizontally to inspect metrics →)| Linkage Method | Silhouette Score (Higher is Better) | Davies-Bouldin Index (Lower is Better) | Cophenetic Correlation (CCC) | Verdict |
|---|---|---|---|---|
| Average Linkage (Selected) | 0.6281 | 0.5347 | 0.9292 | Optimal Quality |
| Complete Linkage | 0.5812 | 0.6420 | 0.8415 | Sub-optimal |
| Ward Linkage | 0.5940 | 0.6110 | 0.8650 | Moderate |
| Single Linkage | 0.4320 | 0.8920 | 0.7120 | Chaining Issue |
Spatial Segmentation Visualizer
Kota Bekasi - Spatial Cluster Visualizer
17 Kelurahan across 4 Agglomerative Linkage Segments
Cluster 0: Very High Potential
High volume, rapid turnover sales territory with premium consumer density.
public/data/bekasi_kelurahan.geojson and enable the react-leaflet polygon renderer in this component.Strategic Business Insights & Optimization
Maintain high inventory buffer in Pekayon Jaya and Jaka Setia distribution hubs to prevent stockouts during peak retail cycles.
Deploy targeted merchant incentives and introductory retail discounts to convert high-potential emerging territories into high-frequency zones.