IBM-CSRBOX: Customer Segmentation Model for Marketing Targeting
Led development of a clustering-based segmentation model that improved marketing campaign targeting within a sprint-based analytics team.
Alqama Ansari
Quantitative Researcher at Confidential ( Stealth Mode )
From their time as
Data Analytics Intern
Results • 2024 - 2024
Overview
During his internship at IBM-CSRBOX, Alqama led the development of a customer segmentation model using k-means clustering and Principal Component Analysis. The objective was to improve the precision of marketing campaign targeting by identifying distinct customer groups within the dataset.
The Story
During his internship at IBM-CSRBOX, Alqama led the development of a customer segmentation model using k-means clustering and Principal Component Analysis. The objective was to improve the precision of marketing campaign targeting by identifying distinct customer groups within the dataset.
He built and ran a comprehensive data preprocessing pipeline that reduced input noise by 15%, improving model stability before the clustering step. The segmentation model itself improved marketing campaign targeting by 25%, giving the team a more reliable basis for outreach decisions.
He worked within a five-member quantitative analytics team operating on a sprint-based delivery model, maintaining 100% adherence to data quality standards throughout the project. The work gave him early experience in structured, team-based analytics delivery under defined timelines.