Abstract: Systems and methods for automating the price adjustments for items in a network-based environment are described in this application. The price adjustment can consider both the price information and non-price information of an item. The price information can include the price of the item charged by a single seller or a group of sellers, as well as a customer's reaction to that price. The non-price information can contain any criteria other than the price of the item, including actual or anticipated inventory levels, initial cost, product life cycle, future demand, etc. The systems and methods can then adjust the price of the item for sale on an automated, or even semi-automated, basis without the need for manual intervention. The systems and methods could also contain a feedback mechanism that tracks the price adjustment and the conditions for making that adjustment, thereby aiding the formulation of pricing strategies for the item.

Abstract: An anomaly detection engine monitors network traffic to detect orders placed by users from an electronic catalog of items, aggregates data about the detected orders by time period, and analyzes the aggregated data to detect anomalies in activity levels associated with specific items in the catalog. To detect whether an anomaly exists in the activity data associated with a given item, a forecasting algorithm, such as an exponential smoothing algorithm, is used to generate an expected order volume for a current time period, and the expected order volume is compared to an actual order volume. Other criteria may also be taken into consideration. If an anomaly is detected, such as a sharp increase in the item's order volume, the anomaly detection engine generates an alert message to notify a catalog administrator, who may then determine whether the anomaly is attributable to an erroneous item description in the catalog.