Machine Learning for Revenue Maximization in Small Businesses: Applications in Customer Interaction, Operational Optimization, and Financial Planning
Bohdan Vihurskyi
Business Data Technologies, Amazon.com Services LLC Sunnyvale, USA
Abstract
The integration of machine learning (ML) in small businesses has become a critical strategy for maximizing revenue in highly competitive markets. This research explores and dicusses the diverse applications of ML across three key domains: customer interaction and personalization, operational optimization, and financial and strategic planning. In customer interaction, ML clustering techniques such as k-means clustering and hierarchical clustering enable detailed customer segmentation and personalized marketing strategies, while predictive analytics and natural language processing enhance customer retention and sentiment analysis. Operational optimization benefits from ML models like ARIMA and LSTM for accurate demand forecasting and inventory management, as well as reinforcement learning and genetic algorithms for supply chain optimization. Additionally, ML-powered robotic process automation (RPA) streamlines repetitive tasks, improving efficiency. In financial and strategic planning, dynamic pricing models, credit risk assessment, fraud detection, and financial forecasting using advanced ML techniques ensure robust financial performance and strategic decision-making. This comprehensive application of ML not only enhances revenue generation but also provides a sustainable competitive advantage, enabling small businesses to thrive in complex market environments. This study also shows the algorithms and workflows of implementing of the ML techniques in small business scenarios.
