Systems and methods to manage incentive campaigns and automatically approve requests for incentives received from consumers or their proxy based on dynamic parameters defined by an incentive provider are described herein. Users at various levels between an original equipment manufacturer and the consumer may interact and offer input. In various implementations, campaign parameters may be received from an incentive provider and used to initiate and/or otherwise administrate a campaign. Incentive providers may then dynamically modify ongoing campaigns via the system. One or more user interfaces may be generated through which users may interact with the system and visualizations related to a campaign may be presented. Based on the initial or modified parameters for a campaign, the systems and methods described herein may further determine whether to approve, deny, or otherwise respond to requests for incentives received from consumers or their proxy.

A user-interactive digital signage apparatus includes a signage housing, a touch panel display, a card reader, and a controller. The card reader is configured to read card information from a prepaid card presented by a user at the retail space. The controller is configured to obtain map image data for a map image of the retail space using the card information, the map image data including information on one or more affiliated stores that are affiliated with the prepaid card among a plurality of stores in the retail space. The controller is configured to control the touch panel display to display a user-interactive retail space map including one or more map elements corresponding to at least a part of the one or more affiliated stores, in an emphasized display manner with respect to one or more map elements corresponding to one or more non-affiliated stores in the retail space.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

An incentive management system may receive a request for registration of a software application from a software developer and assign at least an API key and an API secret to the software application. The system may further provide, to the software developer, integration instructions including at least the API key and the API secret and provide a developer platform to the software developer, the developer platform configured to receive program information including at one or more actions that may be performed by a user of the software application. The system may monitor messages from one or more application servers, such as via webhooks embedded in the software application, regarding performance of one or more actions identified as actionable by the software developer. The system may periodically determine a cumulative incentive associated with each of the users of the software application and coordinate provision of the incentives.

Systems and methods to manage incentive campaigns and automatically approve requests for incentives received from consumers or their proxy based on dynamic parameters defined by an incentive provider are described herein. Users at various levels between an original equipment manufacturer and the consumer may interact and offer input. In various implementations, campaign parameters may be received from an incentive provider and used to initiate and/or otherwise administrate a campaign. Incentive providers may then dynamically modify ongoing campaigns via the system. One or more user interfaces may be generated through which users may interact with the system and visualizations related to a campaign may be presented. Based on the initial or modified parameters for a campaign, the systems and methods described herein may further determine whether to approve, deny, or otherwise respond to requests for incentives received from consumers or their proxy.

Systems and methods of reducing latency in online network traffic are provided. The system can include a processor and a memory couple to the processor. The memory stores computer-executable instructions, which when executed by the processor, cause the processor to identify an indication of an interaction with a content item presented within a content slot of an information resource, generate a click identifier that uniquely identifies the interaction, transmit a first request comprising a click resource link including the click identifier to a click server, and transmit a second request to access the landing page of the content item independent of transmitting the first request. The second request comprises a generated resource link that identifies a resource identifier of the landing page of the content item and the click identifier.

A system and method is presented for product substantiation and promotion redemption. A purchased product list is transmitted from a retail store system to a promotion analysis server along with a user identifier. The promotion analysis server identifies promotions available to the user, and then considers whether the benefits under the promotions are still available to the user. Discounts for available promotions related to the purchased product list are calculated and transmitted to the retail store system for deduction from the transactions. A promotion product filter can be provided that filters the purchased product list before transmittal. The promotion product filter can include promotions from a plurality of programs. SNAP and Enhanced SNAP benefits can be provided along with the discount benefits.

A system and method is presented for product substantiation and promotion redemption. A purchased product list is transmitted from a retail store system to a promotion analysis server along with a user identifier. The promotion analysis server identifies promotions available to the user, and then considers whether the benefits under the promotions are still available to the user. Discounts for available promotions related to the purchased product list are calculated and transmitted to the retail store system for deduction from the transactions. A promotion product filter can be provided that filters the purchased product list before transmittal. The promotion product filter can include promotions from a plurality of programs. SNAP and Enhanced SNAP benefits can be provided along with the discount benefits.

Embodiments of the present specification provide methods for performing marketing cost control by using a deep reinforcement learning system. One method includes the following: determining a cost of a marketing activity; determining a reward score of reinforcement learning that is negatively correlated with the cost; and returning the reward score to a smart agent of a deep reinforcement learning system, for the smart agent to update a marketing strategy, wherein the smart agent is configured to determine a marketing activity based on the marketing strategy and status of an execution environment of the deep reinforcement learning system.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.