Anomaly Detection
Solving for HomeDepot's Pricing, Inventory, & Promo Issues
Client
The Home Depot
Year
2024
My Role
Product Manager
Services
Strategy & Design
Intro
At Home Depot, promotion manipulation and pricing discrepancies were silently draining revenue and eroding margins, to the tune of over $25 million per year. Without an automated system to catch these issues in real time, missed opportunities and costly errors became the norm
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The Problem
Home Depot’s Category Managers faced significant challenges in maintaining accurate pricing, inventory, and promo tracking. Without an automated alert system, they struggled to detect errors in real time, leading to:
Missed Opportunities: Critical pricing adjustments and inventory optimizations were delayed, impacting sales performance.
Revenue and Margin Loss: Undetected discrepancies, including the misuse of unauthorized promotions, led to financial setbacks and inefficiencies.
Manual Firefighting: Teams had to rely on time-consuming, reactive efforts to identify and resolve issues instead of focusing on strategic improvements.
Solution
To address these challenges, I led the design and implementation of an automated anomaly detection system. By working closely with Engineering, Data Science, and Finance teams, we defined business rules, anomaly thresholds, and escalation workflows to ensure the system was both effective and relevant.
Key components of the solution included:
✅ Real-time monitoring of key e-commerce metrics, allowing the system to detect anomalies as they happened.
✅ Business logic integration to filter out irrelevant anomalies, ensuring that only the most critical issues, such as promotional exploitation, were flagged.
✅ A centralized dashboard that offered Category Managers a clear view of detected anomalies, their severity, and recommended actions.
Results
Saved $70M in potential margin loss by detecting pricing and inventory anomalies before they escalated.
Cut manual data checks by 80%, freeing teams to focus on high-value, revenue-driving initiatives.
Increased confidence in analytics tools, leading to greater adoption among Category Managers and Operations teams.
More Case Studies
Anomaly Detection
Solving for HomeDepot's Pricing, Inventory, & Promo Issues
Intro
At Home Depot, promotion manipulation and pricing discrepancies were silently draining revenue and eroding margins, to the tune of over $25 million per year. Without an automated system to catch these issues in real time, missed opportunities and costly errors became the norm
Client
The Home Depot
Year
2024
My Role
Product Manager
Services
Strategy and Design
The Problem
Home Depot’s Category Managers faced significant challenges in maintaining accurate pricing, inventory, and promo tracking. Without an automated alert system, they struggled to detect errors in real time, leading to:
Missed Opportunities: Critical pricing adjustments and inventory optimizations were delayed, impacting sales performance.
Revenue and Margin Loss: Undetected discrepancies, including the misuse of unauthorized promotions, led to financial setbacks and inefficiencies.
Manual Firefighting: Teams had to rely on time-consuming, reactive efforts to identify and resolve issues instead of focusing on strategic improvements.
To address these challenges, I led the design and implementation of an automated anomaly detection system. By working closely with Engineering, Data Science, and Finance teams, we defined business rules, anomaly thresholds, and escalation workflows to ensure the system was both effective and relevant.
Key components of the solution included:
✅ Real-time monitoring of key e-commerce metrics, allowing the system to detect anomalies as they happened.
✅ Business logic integration to filter out irrelevant anomalies, ensuring that only the most critical issues, such as promotional exploitation, were flagged.
✅ A centralized dashboard that offered Category Managers a clear view of detected anomalies, their severity, and recommended actions.
The Solution
Results
Saved $70M in potential margin loss by detecting pricing and inventory anomalies before they escalated.
Cut manual data checks by 80%, freeing teams to focus on high-value, revenue-driving initiatives.
Increased confidence in analytics tools, leading to greater adoption among Category Managers and Operations teams.
