Systems, apparatus, methods, and computer program products are provided for optimized and effective leading messages, which may be an email subject that may provoke a consumer to access the body portion of an email message. A system may include circuitry configured to programmatically determine a predicted access rate for a leading message when the leading message is provided as a portion of a promotional message. Circuitry may be configured to track historical data indicating the access rates of leading messages and/or leading message terms. The circuitry may be configured to leverage the historical data to determine predicted access rates for leading messages, such as based at least in part on historical access rates associated with one or more leading message terms of the leading message.

An optimization apparatus includes: a selection unit that selects, as a correction value, an element having a magnitude equal to or smaller than a predetermined value from among convex hulls of a policy set; an acquisition unit that acquires a result of execution of a second policy executed in a second round, the second round being a round a predetermined round before a first round for executing a first policy that is determined from among the policy set; a calculation unit that calculates an estimated value of a loss vector in the execution of the policy based on the result of the execution and the correction value selected in the second round; an update unit that updates a first probability distribution based on the estimated value; and a determination unit that determines a policy for a next round based on the updated first probability distribution.

Techniques and systems are described to enable users to optimize a digital marketing content system by analyzing an effect of components of digital marketing content on audience segments, environments of consumption, and channels of consumption. A computing device of an analytics system receives user interaction data describing an effect of user interaction with multiple items of digital marketing content on achieving an action for multiple audience segments. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system generates data identifying different aspects that likely had an effect on the achieving an action on the items of digital marketing content, such as components of the items of digital marketing content, environments of consumption, channels of consumption. The analytics system outputs a result based on the data in a user interface.

The present disclosure includes systems, methods, and non-transitory computer readable media that generate and provide return of incremental digital content user interfaces that improve performance and efficiency of multi-channel, multi-region digital content campaigns. In particular, one or more embodiments generate and provide a user interface that comprises a return of incremental digital content expenditure regression curve and return of incremental digital content expenditure point representations that accurately and intuitively detail digital content campaign expenditure efficiency for combinations of channels and regions during multiple time periods in a time window. For example, the resulting return of incremental digital content expenditure user interface effectively utilizes limited computing device display space and resources to enable a publisher to quickly and accurately optimize and project high level expenditure allocation in order to improve digital content campaigns.

In an aspect, a request to display information on a client device of a user can be received. A plurality of features associated with the request can be extracted. The plurality of features can include at least one feature characterizing the user and at least an additional feature characterizing the client device. A feature vector based on the features associated with the request can be generated. A predicted response value of the user to a content presentation can be generated using a predictive model and the feature vector. The predicted response value can characterize a likelihood of the user interacting with the content presentation. A request response value can be determined based on a content presentation impression value and the feature vector. The request response value and the content presentation can be transmitted for display on the client device of the user.

The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.

The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.

The present teaching relates to generating combination distributions for ads. Features are computed based on training data associated with ads, each of which has a plurality of attributes. The training data include asset combinations with past performance thereof for each of the ads. Each combination includes multiple assets representing respective attributes of an ad. The features are used in machine learning to obtain an auxiliary model, which is used to generate combination distributions for each ad based on predicted performance for each combination associated with the ad. Such generated combination distributions are sent to an explore/exploit layer (EEL) for a frontend ad serving engine to draw a combination therefrom for an auction winning ad for rendering on a webpage viewed by a user on a user device.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

A system and method are provided for location-targeting the provision of media distributed by a mobile platform. The method provides a mobile platform with an attached media projection subsystem, and an identifier associated with the media projection subsystem. The media projection subsystem is selectively enabled, the geographic location of the mobile platform is determined, and the identifier and the enablement of the media projection system are verified. Verification information, including the mobile platform (media projection subsystem) location, identifier, and enablement of the media projection subsystem is communicated to a server and stored in a non-transitory memory. A targeting application may direct the system to a target location in cooperation with analyzing the verification information, weighted for factors such as proximate vehicular traffic, line of sight, proximate pedestrian traffic, proximity to cultural events, proximity to cultural facilities, the time of day, and the length of time the media is being projected.