Planning and Statistics: The Power of Data

Learn clustering techniques for professionals who want to make data-driven decisions in retail.

What you will learn

Customer segmentation: Group customers with RFM analysis to develop personalised marketing strategies and match the right product mix to each segment.

Store clustering: Cluster stores with similar performance to optimise stock and staffing.

Product clustering: Find product similarities, optimise categories, and generate sales-boosting recommendations.

Real case studies: Python/R applications on retail datasets (Pandas, Scikit-learn).

Best practices: Model selection, metrics (silhouette, elbow method), and interpreting results.

By the end of this course

• You will understand customer behaviour and strengthen CRM strategies
• You will track store and product efficiency with automated reports
• You will have hands-on experience applying data science in retail
• You will gain analytics skills adaptable across sectors
• You will communicate data-driven insights with sales and operations teams

Who should attend?

Data analysts, retail managers, marketing specialists, and ML enthusiasts. Basic Python/R and statistics knowledge is sufficient.