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Retail Customer Segmentation & Intelligence

A customer-intelligence project segmenting 3,900 retail shoppers by value, loyalty, and promotion dependency using Python feature engineering, SQL analysis, and Power BI reporting.

Overview

A Power BI and SQL analytics project focused on customer segmentation, value tiers, promotion dependency, and retention recommendations.

This is the cleanest Data Analyst project, so the page prioritizes dashboard visuals, SQL evidence, and executive takeaways.

Impact

  • Engineered 10+ behavioral features and wrote ~15 SQL segmentation queries over a 3,900-customer enriched dataset.
  • Found that Champions - the top 25% value tier - drive ~49.7% of estimated annual revenue.
  • Delivered a Power BI dashboard, executive summary, and customer-retention playbook.

What it shows

  • Customer segmentation using engineered behavioral features (value score, promo-dependency, value tier).
  • ~15 SQL analytical queries over enriched customer data with window functions and CASE logic.
  • Power BI storytelling with an executive summary and a retention playbook.

Demo plan

  • Published Power BI dashboard or an embedded static dashboard export.
  • A short executive-summary section directly on the page for non-technical readers.

Stack