Data Mining Techniques for Customer Segmentation in E-Commerce Platforms: Unlocking the Power of Personalization

It is important in today's complex e-commerce context to provide customers with tailor-made solutions, treatments, offerings to achieve a high customer satisfaction rate. This study looks into use of data mining approach in classification of customers of e-commerce businesses by behavior, preference, and age. Using clustering like K-means, hierarchical and DBSCAN we are able to develop specific groupings of customers that assists in the development of specific marketing techniques. Also, among the techniques, classification models, association rule mining and decision trees are used to forecast the future purchasing behavior and to observe the relationship of many years transactional data. The importance of adopting machine learnings and big data to process large data sets is described in this study for helping firms to make informed decisions to improve their customer relations. Examples of organisations using these techniques are showcased to demonstrate how they help enhance customer loyalty and generate more revenue. This research also important the importance of segmentation using data as well as highlighting the importance of data analysis for changing customer needs. Finally, the paper would be valuable to online retailing stakeholders in enhancing customized e-commerce environments that support close customer ties and sustainable business performance.