Leveraging Machine Learning for Customer Segmentation and Personalized Client Targeting in E-commerce Systems

Background: In the past few decades, firms have realized the growing significance of customer- oriented marketing. Customer-specific targeting is becoming feasible with the introduction of one-customer methods, particularly in e-commerce, rendering conventional widespread advertising in this field more out of date. With this kind of approach, it becomes imperative to have a fundamental comprehension of each customer's motives and interests. In this paper we have worked on two crucial elements of successful marketing strategy that is Segmentation and Marketing Campaigns. Segmentation is one technique that is commonly employed for marketing purposes and has been improving over the last few years. Market segmentation involves dividing a broad target market into subsets of consumers who have common needs and priorities. Methods: In this research, K-Means clustering algorithm is used for performing the segmentation. The predictive performance of machine learning models (Random Forest, Logistic Regression and XG Boost) is determined. The dataset belongs to the Commcreative company that is a marketing and advertising company. RFM (Recency, Frequency, Monetary Value) marketing analysis technique is used to identify and segment customers based on their purchasing behavior. The purpose of segmentation is to enable a marketer to design and implement targeted, effective marketing strategies. The process of customer segmentation allows an audience to be related into groups of people who share characteristics associated with the advertisement, including gender, age, interests, and spending patterns. This allows businesses to target particular groups with offers, goods, or services that are specifically designed to appeal to them. The program learns using a dataset that originates from the retail trade and includes characteristics such as an average amount of monthly customers and purchasers. Furthermore, the temporal patterns and the adaptability of the applied algorithms to various dimensionalities of the data sets are analyzed. The methodology used in this research involves a four-phase process, and it comprises information (data) gathering, client representation, customer analysis through segmentation, and customer targeting based on the collection of this study. A thorough rundown of the techniques is offered and that is applied in the context of client visualization and examination via segmentation process. Future enhancements may include the integration of new data sources, such as IoT devices and social media interactions, that will provide a richer, more comprehensive view of customers. In this paper, we also discuss the Marketing Campaign Process. Once the market is segmented, the next step is to create targeted Marketing Campaigns. A well-planned marketing campaign typically involves goal setting, audience research, strategy development, execution and monitoring. An effective campaign aligns with the specific needs and preferences of the target segments, ensuring that the message resonates and prompts desired actions. In this research, we have used machine learning for the Marketing Campaign Process. Results: The predictive performance of machine learning models (Random Forest, Logistic Regression and XG Boost) is determined on a dataset of 2206 customers for marketing factors prediction from Tarai food manufacturing company. XGB Classifier (Extreme Gradient Boosting) shows a balanced performance with a high ROC AUC (0.906777) where ROC stands for Receiver Operating Characteristics and ROC AUC stands for Area Under the ROC Curve, which slightly surpasses Logistic Regression in distinguishing between the classes. However, according to the precision metric, Random Forest Classifier has the highest precision (0.728477), meaning it has the lowest false positive rate among the models tested. Logistic Regression offers a better balance between precision (0.714714) and recall (0.485584), resulting in a higher F1 score (0.577060). This indicates a better trade-off between false positives and false negatives. Conclusion: Graphical data visualization is done that helps in feature selection of both categorical and non-categorical data. Some features show a clear relationship with the response. This research examines the current and potential applications of AI and ML in marketing, emphasizing how these technologies can automate tasks, reduce costs, and improve workflows. It also explores the role of ML in micro-segmentation and personalized marketing campaigns.