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: Described herein are techniques for generating a product strategy that may be implemented by a seller to optimize a product's prominence within a product listing generated by an electronic retailer. In some embodiments, a number of user segments may be generated for a product category into which the product falls. The system then identifies a target user segment for the product from the number of user segments. Once a target user segment has been identified, revisions to product data associated with the product may be determined based upon a set of search terms associated with the target user segment. The revisions may be provided to a seller of the product via a product strategy for implementation, either as a revised product data or as a set of recommended revisions to the product data.

Abstract: Described herein are techniques for generating a product strategy that may be implemented by a seller to optimize a product's prominence within a product listing generated by an electronic retailer. In some embodiments, a number of user segments may be generated for a product category into which the product falls. The system then identifies a target user segment for the product from the number of user segments. Once a target user segment has been identified, revisions to product data associated with the product may be determined based upon a set of search terms associated with the target user segment. The revisions may be provided to a seller of the product via a product strategy for implementation, either as a revised product data or as a set of recommended revisions to the product data.

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.