A Data-Driven Approach to Customer Segmentation and Customer Lifetime Value Prediction in Retail and E-Commerce

Patil, Prachi Pradeep (2024) A Data-Driven Approach to Customer Segmentation and Customer Lifetime Value Prediction in Retail and E-Commerce. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rapid advancement of digital technologies has transformed the retail and e-commerce landscape, boosting market competition and reshaping traditional business practices, making it essential for businesses to understand customer needs and behaviors. This proposed research focuses on improving Customer Segmentation and Customer Lifetime Value (CLTV) prediction in the Retail and E-Commerce sectors by integrating RFM (Recency, Frequency, Monetary) analysis with advanced clustering and regression techniques. The primary objective was to assess the effectiveness of these combined methods in enhancing marketing strategies through more accurate customer segmentation and value prediction. The methodology followed the Cross-Industry Standard Process for Data Minin (CRISP-DM) framework, starting with data collection and preprocessing, followed by the implementation of RFM analysis Hierarchical Clustering, Gaussian Mixture Models for segmentation, and Linear Regression, Random Forest, and Support Vector Regression models for CLTV prediction. The findings showed that Linear Regression using Scikitlearn, particularly when applied to log-transformed data, outperformed other models, with a significant improvement in accuracy and error reduction. LazyPredict further confirmed these results, with ElasticNetCV and LassoCV models showing superior performance. Despite limitations related to data quality and industry-specific findings, the research successfully proved the potential of these techniques in optimizing customer relationship management and targeted marketing efforts, contributing valuable insights for the retail and e-commerce industries.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Menghwar, Teerath Kumar UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Marketing > Consumer Behaviour H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Retail Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	25 Aug 2025 09:22
Last Modified:	25 Aug 2025 09:22
URI:	https://norma.ncirl.ie/id/eprint/8608

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