Market Segmentation: A Study Case of the FnB Industry in Kota Bogor

❓ Story Behind It: Business owners and decision makers often struggle to understand where demand is saturated, underserved, or high-value at the area level. Most location decisions are biased which rely on intuition, limited surveys, or generic assumptions, which can lead to poor site selection and inefficient market entry. 💡 Offered Solution: This project applies unsupervised learning and spatial analysis to segment Kota Bogor’s FnB market using real-world dataset (Google Maps) that give signals such as category distribution, popularity, and willingness to pay. The goal is not to predict demand, but to reveal patterns that support better strategic decisions. 🚀 How it Works: 1. Aggregates FnB business and area-level data across Bogor 2. Uses clustering techniques to identify distinct market segments 3. Visualizes insights through maps and cluster profiles for easy interpretation 🛠️ Tech Stack: - Data: Google Maps - Tools: Python, Pandas, Scikit-learn - Visualization: folium - AI-Assisted Development: OpenAI (Codex 5.1) - Skill: geospatial analysis, advanced feature engineering 🤔 What to Expect: The analysis highlights which areas are over-saturated vs under-served, which zones show higher willingness to pay, and how FnB categories concentrate spatially. These insights can support location strategy, market entry decisions, and prioritization for both small businesses and larger brands. ✨ Why This Matters: This project reflects my data mindset: using analytics not to replace human judgment, but to reduce guesswork and make local business decisions more transparent and evidence-based. Market segmentation becomes far more powerful when it is applied spatially. Understanding where patterns happen is often more valuable than knowing what performs well in isolation. repo: https://github.com/umarfadhil/fnb-market-segementation-in-bogor google colab: https://colab.research.google.com/drive/11zou2ePxNV9YLa1rpHFuTrnhRqMHS8h5#scrollTo=TLUyoh_FQNGa