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Strategic Customer Segmentation through Machine Learning for Retail Optimization
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 56 |
| Year of Publication: 2025 |
| Authors: Sathish Kumar Velayudam |
10.5120/ijca2025925997
|
Sathish Kumar Velayudam . Strategic Customer Segmentation through Machine Learning for Retail Optimization. International Journal of Computer Applications. 187, 56 ( Nov 2025), 71-79. DOI=10.5120/ijca2025925997
Abstract
The present retail sector is characterized by high levels of competition and shifting consumer behaviors; therefore, the demand for decisions to be fact-based as a means of survival in the market and success has become essential. The subject of this research paper is an end-to-end solution for taking raw customer data and turning it into actionable business intelligence. The general purpose is to illustrate a segmented customer process of behavior forecasting and customer segmentation enabling the retailers to develop segmented marketing programs, enhance customer experience, and also optimize the inventory. The research employs a simulated data set of 446 customer cases with attributes such as demographic indicators, revenues per year, and a firm-specific measure of spending. Customer segmentation through data preprocessing, exploratory data analysis (EDA), and application of unsupervised machine learning algorithms, i.e., K-Means clustering, for dividing customers into distinctly differentiating customer segments. Predictive modeling is also being applied to forecast customer spend behavior. All the analytics pipelines were executed with Python as the programming language using libraries such as Pandas for data manipulation, Scikit-learn for executing machine learning algorithms, and Matplotlib/Seaborn for visualization. The findings create five customer personas that are significantly different from each other and display distinct buying behavior. The research reaffirms the sheer worth of granular customer analytics as a prescription for retailers to capitalize on data assets to deliver strategic value and build long-term customer loyalty.
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