Enhancing Customer Identification and Marketing Strategies for SMEs Through Machine Learning: A Study of Predictive Models and Data-Driven Insights

This research analyzes the effectiveness of certain Machine Learning (ML) models in helping small and medium-sized businesses (SMEs) to increase their customer identification and maximize marketing initiatives. This study attempted to show how ML, by using data-driven insights, may greatly increase the efficacy and precision of consumer targets. Datasets from various online platforms were subjected to three (3) ML models, including K-Means Clustering, Random Forests, and Logistic Regression. The study is primarily experimental and follows a quantitative approach. This method involves training and testing different machine-learning models on existing datasets to evaluate their performance. The experimental research approach allows for controlled testing of machine learning algorithms such as K-Means Clustering, Random Forest, and Logistic Regression on datasets that simulate real-time SME challenges, like customer segmentation and sales forecasting. The researchers used an accessible dataset of prospective clients from Kaggle, with demographic and activity data from customers using social media and E-commerce customer behavior. The results revealed that ML models outperform traditional statistical methods in predicting customer behavior and enable SMEs to make informed decisions based on real-time data analysis. It also highlights how machine learning has transformed SME operations and how companies may use analytics to gain a competitive advantage despite resource constraints. Incorporating machine learning into client acquisition procedures is beneficial and necessary for SMEs to succeed in the current market and develop steadily as the digital environment changes. The researchers recommended the proposed process of integrating the ML models in developing a platform for SMEs that is compliant with data privacy regulations.