Faisal Masud - Issaquah WA, US Stacy Saal - Bellevue WA, US Paul D. Ohlhaut - Seattle WA, US Josiah P. Olivieri - Bellevue WA, US Umair Bashir - Issaquah WA, US Xiao Yu - Waterloo, CA Tao Hu - Renton WA, US
Assignee:
Amazon Technologies, Inc. - Reno NV
International Classification:
G06Q 30/00
US Classification:
705 735
Abstract:
Disclosed are various embodiments for optimizing prices of items using current offers. Numerous current offers to sell an item are determined from a subset of sellers in an electronic marketplace. Each one of the subset of sellers is associated with a respective reputational score that meets a minimum threshold. A competitive price is generated for the item based at least in part on an average marketplace price determined from the current offers.
System And Interface For Promoting Complementary Items
Umair Bashir - Issaquah WA, US Gregor A. Moulton - Seattle WA, US Sean M. Scott - Sammamish WA, US
Assignee:
Amazon Technologies, Inc. - Reno NV
International Classification:
G06Q 30/00
US Classification:
705 267, 705 261, 705 271, 705 268, 705 5
Abstract:
A complementary item promoter user interface (UI) is provided that promotes, to a user, items that are complementary to an item of interest to the user. Upon the user's selection of an item of interest, the complementary item promoter UI presents a combination of complementary items selected in accordance with business rules data. The business rules data may be submitted by an administrative user to a content management service that deploys the complementary item promoter UI in a deployment environment. The business rules data may include rules that govern how complementary items are chosen or how they are to be displayed in the complementary item promoter UI.
An auto-replenishment platform may receive consumer data, retailer data and product data for a set time period, via an e-commerce platform of a retailer or manufacturer that sells products and services directly to the public. Using the consumer data, the auto-replenishment platform may determine the auto-replenishment product basket for the consumer. Subsequently, the auto-replenishment platform may generate a consumer model for the consumer, based at least on the consumer data, the retailer data, and the manufacturer data, for predicting the consumer auto-replenishment shopping cart, consumer behavior, and determining alternate auto-replenishment products and discounts for the consumer. The auto-replenishment platform may send the consumer model to the e-commerce retailer and suggest auto-replenishment products and discounts to the consumer.
An auto-replenishment platform may receive retailer, manufacturer, and 3party consumer data on a regular time interval, via their e-commerce platforms. The auto-replenishment platform, via a harmonization engine, may aggregate all data sets, mine the aggregated data, and then cluster the data. Subsequently, the auto-replenishment platform may generate a consumer model for predicting the consumer demand for a product, factors that influence a consumer's perception of convenience or ease in purchasing that product, and for aggregating a consumer's purchased products for shipment or pickup. The auto-replenishment platform may send the consumer model to the retailer, manufacturer, and 3party e-commerce platforms to integrate the auto-replenishment platform into those platforms. Additionally, the auto-replenishment platform may group a consumer's products for shipment which provides additional efficiencies for the customer and retailer/manufacturer/3party in the form of time savings and/or reduced shipping and handling cost and related logistical advantages.