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.
Links
STAR Summary
- Situation
- We didn't have a single view of security incidents, theft hotspots, and boycott-driven sales swings.
- Task
- Led the analysis: SQL modeling/ETL, anomaly detection, and reporting.
- Action
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- Joined incident, sales, and HR tables in SQL and automated KPIs with views and stored procedures.
- Built Python dashboards to compare theft vs. sales by format, state, and time.
- Used anomaly detection to flag outlier stores and associates.
- Result
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- Found a cluster of outlier stores with incident rates several times higher than typical peers.
- Flagged regions and stores with substantially higher estimated loss rates (figures anonymized).
- Measured year-over-year sales changes around boycott periods (exact figures anonymized).
Notes
Store, state, and employee identifiers are anonymized in the case study.
