Determining The Ideal Location For New Branches In Multifinance Companies Using Google Maps API With Clustering Method

Selecting the ideal location for new branches is crucial for growth in the multifinance industry, significantly impacting customer interest, sales performance, and profitability. This study employs clustering techniques to analyze large datasets, identify patterns, and predict optimal branch locations based on historical customer data and relevant factors. Utilizing the Google Maps API, the study conducted precise location analysis and visualized customer distribution, including distances between customers and branches. Analyzing personal customer data from 2018-2023 in Bogor and Tangerang, Indonesia, geographic coordinates were obtained for detailed distance and competitor analysis. The Elbow Method identified four optimal clusters using K-medoids clustering, revealing distinct characteristics such as customer location, competition levels, and product preferences. Principal Component Analysis (PCA) visualized these clusters, aiding strategic branch location decisions. This method enhances strategic planning and competitive positioning by accurately identifying market opportunities, enabling multifinance companies to strategically plan branch expansions and drive growth and profitability.