To Study Customer Analytics using K-Means Clustering for Business Growth at Sarth Solar

Prajakta M. Choudhari1                                                               Prof. Kanifnath S. Satav2

Student, MBA Department                                                           Professor, MBA Department

Dhole Patil College of Engineering, Pune                                       Dhole Patil College of Engineering, Pune

Abstract:Customer understanding has become a key factor for business growth in the renewable energy sector as competition continues to increase and customers gain more awareness about solar energy solutions. Unlike earlier markets where adoption was limited, today’s customers evaluate solar services based on cost savings, energy efficiency, and long-term benefits. This behaviour directly impacts business performance and increases the need for companies to adopt data-driven strategies. In this context, understanding customer patterns and preferences is essential for designing effective marketing and growth strategies. In this study, we attempt to analyze customer behavior for solar services using a dataset of 25 customers from Sarth Solar that includes attributes such as energy consumption, installation type, and purchase value. The approach combines data preprocessing, exploratory data analysis, and the application of machine learning techniques to identify meaningful customer segments. The K-Means clustering algorithm was implemented to group customers based on similarity in their characteristics. Since the dataset is small, careful normalization and feature selection were applied to ensure accurate clustering results. The results show that customers can be segmented into distinct groups such as high-value customers, medium consumption users, and low consumption users. Factors such as energy usage, investment capacity, and installation type were found to play a significant role in customer segmentation. These insights can help solar companies take proactive steps toward improving customer targeting and business growth. Overall, the study highlights the role of data-driven decision-making in enhancing customer analytics and supporting business growth in the solar energy sector.Key Words: Customer Analytics, K-Means Clustering, Solar Energy, Customer Segmentation, Machine Learning