Abstract: An auto-replenishment platform may receive retailer, manufacturer, and 3rd party 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 3rd party e-commerce platforms to integrate the auto-replenishment platform into those platforms.

Abstract: 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.

Abstract: An auto-replenishment platform may receive retailer, manufacturer, and 3rd party 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 3rd party e-commerce platforms to integrate the auto-replenishment platform into those platforms.

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.

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.