Optimizing Predictive Models for Customer Segmentation in E-commerce: A Data Science Approach

Authors

Shafeeq Ur Rahaman

Abstract

This article focuses on customer segmentation optimization in e-commerce with the use of predictive analytics to develop an efficient marketing strategy and provide a personalized customer experience. Using a variety of sophisticated machine learning algorithms, namely clustering, classification, and ensemble methods, different groups of customers will be segregated based on customer behavior, preference, and buying trends. It, therefore, focuses on the integration of data from diverse sources such as transaction history, browsing behavior, and demographic data in building robust predictive models with high accuracy in predicting customer needs and preferences. The benchmark various algorithms for their performance, assess their scalability, and provide actionable insights for businesses on better ways to target their customers, optimize resource allocation, and improve customer retention. These results also point to the importance of refining segmentation techniques in order to drive up customer engagement and profitability in the competitive environment of e-commerce.

Keywords

Customer Segmentation, Predictive Analytics, E-commerce, Machine Learning, Data Science, Clustering, Classification, Personalization, Marketing Strategy, Customer Retention, Behavioral Analytics

Optimizing Predictive Models for Customer Segmentation in E-commerce: A Data Science Approach. Shafeeq Ur Rahaman. 2018. IJIRCT, Volume 4, Issue 4. Pages 1-12. https://www.ijirct.org/viewPaper.php?paperId=2412026