Provided is a product/service proposal system and computer program product for making, to a user who considers purchase of a product and/or service, a proposal leading the user to purchase a product and/or service using a display serving as communication means between the user and a supplier that supplies a product and/or service. The product/service proposal system includes a user action history acquisition unit configured to acquire a user action history, a parameter setting unit configured to set parameters indicating preferences of at least one of the user and the supplier in a process in which the user considers the purchase of the product and/or service, and a display proposal unit configured to determine a product and/or service to be proposed to the user on the basis of at least one of the user action history and the parameters and to make a proposal leading the user to purchase the determined product and/or service to the user using the display.

Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold. The server determines, for each different channel type, a distance value between the engagement probability value and the corresponding threshold and communicates with a remote computing device via a channel that is associated with an optimal distance value.

A computerized product promotion system for use in a store is provided. The system comprises one or more processors configured to receive a plurality of captured images of a shopper in a current shopping session in the store and process the plurality of captured images to determine one or more products selected by the shopper during the current shopping session. The one or more processors are further configured to determine an identity of at least one target product, selected by a product prediction model, that is estimated to have a threshold minimum likelihood of being purchased by the shopper during a remainder of the current shopping session. The product prediction model receives as input the one or more products selected by the shopper during the current shopping session, and outputs the identity of the at least one target product.

Methods and apparatus for delivering content to an audience member via one or more mediums based on an audience member profile are disclosed. Profile data for audience members may be initially collected from an offline source, such as a registration or subscription database. The profile data may be stored in a dedicated database. The initial profile data may be supplemented periodically with data reflecting online activity by the audience member. The combined offline and online profile data may be used to group the audience members into segments. Audience member segments may be used to identify audience members who are targeted to receive like content. An audience member's inclusion in a segment may be indicated by storing a segment-targeting cookie on the audience member computer. Content may be delivered to the audience member based on identification of the segment in the segment-targeting cookie.

A system and method for order fulfillment includes receiving an order for a plurality of items, initiating collection of the plurality of items for the order, detecting presence of a user at a selected one of a plurality of delivery locations when the plurality of items are ready for delivery, and initiating delivery of the items to the user at the selected delivery location.

In a method for delivering targeted television advertisements based on online behavior, IP addresses indicating online access devices and IP addresses indicating television set-top boxes are electronically associated for a multitude of users. Using user profile information derived from online activity from one of the online access IP addresses, a television advertisement is selected, such as by using behavioral targeting or demographic information, and automatically directed to the set-top box indicated by the set-top IP address associated with that online access IP address. Preferably neither the user profile information nor the electronic association of online access and set-top box IP addresses includes personally identifiable information.

A digital brand asset system is provided enabling a brand owner to create, distribute, maintain, manage, merchandise and analyze smart brand assets. The system enables distribution and sharing of smart brand assets across the websites. The websites can host webpages containing codes representing the smart brand assets. When a user device retrieves a webpage from one of the websites and renders the webpage, it executes the codes and requests the content of the smart brand assets from a brand asset server. Through the brand asset server, a brand owner can control the content and the presentation of the smart brand asset hosted by the websites. The system further enables the brand partners to adjust the content of the smart brand assets based on their needs.

A method including receiving a digital promotion payload from a brand manufacturer for at least one branded consumer packaged good, the digital promotion payload including a digital promotion value associated with the branded consumer packaged good, is provided. The method includes receiving a bid request to the digital promotion engine, providing a bid response to the bid request, the bid response including the digital promotion payload, and receiving, from the supply side platform, a confirmation that the bid response has been selected from one or more bids from different digital advertising entities. The method includes providing a command to the supply side platform to deliver the digital promotion payload to a mobile device accessing a resource from the mobile display publisher, and loading the digital promotion value to a frequent shopper identification in response to a consumer interaction with the digital promotion payload detected from the mobile device.

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.

Techniques and arrangements for performing data analysis in order to generate connections between merchants. For instance, a payment service may determine, based at least in part on transaction information, that a first customer conducted a first transaction at a first merchant followed a subsequent transaction at a second merchant. The payment service may further determine that a second customer conducted a second transaction at the first merchant followed by a subsequent transaction at a third merchant, Based on transaction information associated with the first transaction and the second transaction, the payment service may create a buyer profile including the first customer and second customer. Upon the payment service receiving a request to process a third transaction between the first merchant and the second customer, the payment service can generate a recommendation that the second customer conduct a subsequent transaction to the third transaction at the second merchant rather than the third merchant. The payment service can then send a electronic communication that includes the recommendation to the first merchant or the second customer.