Leveraging Geospatial Analytics and Machine Learning for Precision Business Expansion: A Micro-Market Framework

Abstract

The process of strategic growth of business activities is a risky but significant task that is usually undermined by the use of macro-level indicators in the market, which do not reflect the local peculiarities. The following paper will resolve this problem by proposing an all-inclusive model of micro-market analytics, which will support precision-targeted business growth based on data. The model suggested combines the multi-source information multi-demographic, geospatial, transactional, and psychographic data in order to divide large urban locations into micro-markets. With the implementation of machine learning, namely a gradient boosting model, the system produces a so-called Market Potential Score, which, in turn, is an indicator of the probability of success of each grainy location. The validity of the methodology is tested using a hypothetical case study of retail growth in a Tier-2 Indian city, which shows that the approach can highlight high potential and low-risk opportunities that are not visible in the traditional analysis. The framework leads to a visualization dashboard, which gives the stakeholders a tool to make strategic decisions. This is a very effective way of ensuring that expansion strategies are highly accurate, financial risks are reduced and the growth of the business is sustained in competitive conditions.