Tailoring Customer Engagement: Advanced Segmentation for Growth in Garden Business

Kedambadi, Punya Nandakumar (2024) Tailoring Customer Engagement: Advanced Segmentation for Growth in Garden Business. Masters thesis, Dublin, National College of Ireland.

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Abstract

The garden industry is comprising of the seasonal sales patterns and eco-conscious customers, faces challenges to segment the customers who are diverse in their characteristics. This research explores the advanced data mining techniques like K-means, DBSCAN, Hierarchical Clustering, and GMM algorithms integrated with RFM analysis to address the challenges. Using the customer purchasing details of the company “My Dream Garden”, the study focuses to evaluate the performance of the algorithms using Silhouette Score, Elbow Method, and Davies-Bouldin Index. The research described DBSCAN method as the most effective to segment the garden customer showcasing the highest Silhouette Score (0.588) confirming that the algorithm can handle noise and irregular cluster shapes. GMM exhibits a score of 0.502 which is a probability clustering while the Hierarchical clustering exhibits a score of 0.477. Despite the popularity of the K-means, this method underperformed for the garden industry to segment the customers with the lowest score of 0.278 struggling hard with the non-linear data. RFM analysis skillfully categorises the customers to “Best Customers” and “At Risk Customers” underlining the need to apply strategies on personalised marketing, customisation and to retain the customer. While DBSCAN proved robust but its reliance on manual parameter tuning and the study's focus on transactional data reveal opportunities for refinement. This research provides a robust framework for customer segmentation in the gardening sector, empowering businesses with insights to enhance customer satisfaction, loyalty, and revenue.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > Economics > Business H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 16:06
Last Modified:	02 Sep 2025 16:06
URI:	https://norma.ncirl.ie/id/eprint/8725

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