Project library
Store-Level Loss & Sales ETL
Bring store data together to spot unusual loss and sales patterns.
STAR Summary
- Situation
- Leaders needed one reliable way to compare security incidents, theft hotspots, and sales signals without stitching together separate reports.
- Task
- Unify incident, sales, and HR data into consistent reporting that highlights unusual patterns and supports investigation planning.
- Action
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- 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.
- Result
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- 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.
- Kept identifiers anonymized so the case study can show the workflow without exposing people or locations.
Store Loss and Sales
Explore monthly sales, compare incident hotspots, and follow shrink trends.
Open full demo
Start with sales, incident count, and shrink summaries; each metric shows its own source period and population.
- Pick a region to filter the incident chart; the summary statistics retain their stated source populations.
- 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.
