01 The challenge
The client — among the three largest home retailers in LATAM — was losing millions per year to shrinkage, theft, and operational losses they couldn't predict or explain with precision. Reactive reporting arrived late; by the time the pattern was detected, the losses were already a fact.
They needed to move from a reactive model to a predictive one: anticipate events by store, category, and season, with enough lead time to take preventive measures and protect the budget.
02 What we built
ITSense's BI team implemented an advanced predictive analytics system on 10+ years of historical data, integrating multiple operational sources: sales, inventory, physical security, cameras, operational events, weather, commercial calendar.
- Ingestion pipeline that normalizes and versions data from disparate systems into a unified data warehouse.
- ML predictive model — a combination of supervised algorithms + anomaly detection, continuously trained and retrained.
- Actionable dashboard with alerts by store and loss type, prioritized by expected financial impact.
- Preventive measures recommended by the system — patrol-route adjustments, staffing in critical hours, contingency budget.
Tech stack
03 The outcome
The system reached 97% precision in predicting loss events with enough lead time to take preventive and corrective action. Operational efficiency improved and additional controls were established in higher-risk areas.
04 What it meant
"The ability to predict losses with 97% precision let us reduce them, improve operational efficiency, and put extra security measures in higher-risk areas."— BI team · Top-3 LATAM Retailer (NDA client)
A paradigm shift: from retrospective reporting to anticipated decisions by store and season. Proves that with the right data and the right method, retail loss is predictable — not a fixed cost of doing business.
05 Team & Method
Retail & Consumer Cell — led by a senior Data Scientist and ITSense's BI team.
The engagement runs on the ITSense Method with emphasis on Sense (exhaustive ingestion of historical data + interviews with store operations) and Prove (continuous validation against held-out data before pushing the model to production). Model observability from day zero — drift tracking, quarterly retraining.
