Price Intelligence & Revenue Leakage Detection
Demand Signals · Pricing
Unauthorized discounting rarely shows up as a single alarming number; it shows up as a slow, quiet erosion of margin across thousands of listings.
Revenue leakage is a strange kind of problem: nobody decides to lose margin. It accumulates quietly through unauthorized reseller discounting, MAP violations, and pricing drift that nobody's watching closely enough to catch in real time. Reliable anomaly detection depends entirely on the data infrastructure underneath it: the same kind of collection and validation pipeline described in SARA.
The Build
By the time revenue leakage shows up in a quarterly review, it's been happening for months. Nobody decides to lose margin; it accumulates quietly through unauthorized reseller discounting, MAP violations, and pricing drift.
Marketplace pricing feeds standardized and mapped to products and MAP/MRP, channel and seller policies: the fields that actually determine whether a price is a violation or ordinary pricing activity.
A monitoring system that surfaces pricing anomalies and unauthorized discounting across marketplaces as they happen, rather than as a retrospective finding, combining rule-based policy checks with ML-based anomaly and pattern detection.
Detection: rule-based (MAP/MRP, channel, seller) + ML models (anomaly & pattern detection, seller scoring)
A detection system that cries wolf trains its own users to ignore it.
A threshold tight enough to catch real leakage early is also tight enough to flag every legitimate flash sale and seasonal discount. Distinguishing a genuine violation from ordinary pricing activity matters as much as catching the anomaly itself: an alert nobody trusts gets ignored within a week, and an ignored alert is worse than no alert. The goal was never more alerts: it was alerts a revenue team actually trusts enough to act on.
A snapshot of the kind of signal the system surfaces across a 30-day monitoring window:
Violation Rate by Marketplace
Impact: pricing and revenue teams can act on leakage as it happens rather than discovering it a quarter later: the goal was never more alerts, it was alerts worth trusting.
Chasing pricing leakage across marketplaces?
Happy to talk through anomaly detection design, alerting thresholds, or catching leakage before it hits quarterly numbers.