Store-Level Loss & Sales ETL

SQL ETL + Anomaly Detection

Demo

Compare store-level incident signals against sales metrics using tabs and filters.

  • Pick a region to filter incidents and trends.
  • Switch sales metrics (Total, Online, Drive-up) to change the sales view.
  • Switch incident metrics (Count vs. Proven $) to change the incident view.
  • Use “Reset view” to return to the default slice.

STAR Summary

  • Modeled incident, sales, and HR data in SQL so stores, regions, time periods, and risk measures use consistent definitions.
  • Built Python reporting views that separate frequency, severity, and sales context for faster investigation planning.
  • Used anomaly detection to flag outlier stores, regions, and associate patterns for review.
  • Supported analytics-driven investigations that reduced inventory loss by 24%.
  • Improved theft reporting by 57.6% through clearer dashboards and workflow redesign.
  • Narrowed broad incident data into a short, explainable list of hotspots worth investigating.

Notes

Store, state, and employee identifiers are anonymized; risk rankings normalize incident signals with sales context so high-volume stores are not treated as high-risk by volume alone.

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