Various embodiments of a framework which allow dynamic testing of many creative content and other messages simultaneously using metrics-based optimization. A “multi-armed bandit” algorithmic approach employed, as an alternative to limited AB-type testing, to automatically select a set of content parameters based on the content parameters' respective probabilities, render the selected parameters to generate content sent to a user, and, after obtaining feedback in the form of user interaction data, update the parameters for future, iterative selection of content parameters. This framework can be used in essentially any setting to allow for the provision of feedback, including user interaction data.

A method and system provide the ability to manage entities of a marketing domain model in a multi-state workflow. Multiple entities are acquired in a content hub. Each entity is a set of data that belongs together as one and includes properties that describe entity details. Relations are created between the multiple entities to give meaning to the marketing domain model. A solution graph is generated that represents all of the multiple entities (nodes) and relations (edges). Inside the solution graph, a state workflow can be created for each node. Nodes can be linked to a state and there are transitions between the states. Multiple non-linear state workflows can be orchestrated by an overall waterfall-based workflow (that is linear and time duration based. A graphical user interface enables management of and renders a representation of the multiple entities, the solution graph, and the workflows.

Systems and methods for targeted advertising to specific consumers are disclosed. A system may include a memory storing instructions; and at least one processor configured to execute the instructions to: receive, over a network, consumer data from a client device; identify a plurality of client-provided consumers from the consumer data; obtain a plurality of unique consumer identifiers corresponding to the plurality of client-provided consumers; and identify at least one first overlapping unique consumer identifier by matching at least one of the plurality of client-provided consumers with at least one publisher-provided consumer provided by a first publisher device of a plurality of publisher devices, the first publisher device having a highest priority among the plurality of publisher devices.

The disclosure relates to an advertisement generation method and system. The advertisement generation method includes: acquiring click data of the advertisements subjected to advertisement exposure triggered by each user from various predetermined data source servers, and extracting picture style features of advertisement background pictures in the advertisement click data; during recommending of advertisements to predetermined users, analyzing the advertisement click data of the various users according to predetermined picture style features and a predetermined first analysis rule to obtain picture style features of advertisement pictures to be recommended corresponding to the various users; and generating recommended advertisements according to the obtained picture style features corresponding to the various users to recommend the recommended advertisements to the corresponding users. The disclosure can objectively or adaptively integrate the picture style features of other advertisements to improve the acceptance of advertisement and improve the advertisement production efficiency.

Portable devices include environmental sensors that generate metrics about the environment (e.g., accelerometers detecting impulses and vibration, and GPS receivers detecting position and velocity). Such devices often use environmental metrics to extract user input directed at the device by the user, and status information about the device and the environment. Presented herein are techniques for using environmental metrics to infer physical activities performed by the user while attached to the device. For example, jogging may be inferred from regular, strong impulses and typical jogging speed; walking may be inferred from regular, weak impulses and typical walking speed; and riding in a vehicle may be inferred from low-level vibrations and high speed (optionally identifying the type of vehicle ridden by the user). Based on these inferences, the device may automatically present applications and/or or adjust user interfaces suitable for the user's physical activity, rather than responsive to user input.

For each service scenario out of a plurality of service scenarios, matching features of a to-be-matched product corresponding to the service scenario are acquired based on user features of users accessing the service scenario. A respective user feature mapping value of the service scenario is calculated based on the matching features of the to-be-matched product corresponding to the service scenario. Out of the plurality of service scenarios, a target service scenario of the to-be-matched product is selected based on the respective user feature mapping value of the service scenario.

Methods and systems are described for selecting an engaging ad campaign among advertising campaigns having different types of bid strategies. In one embodiment, an advertising system designed for selecting relevant and engaging ad campaigns for delivering to a device of a user includes an adaptive decision unit having filter logic for filtering eligible ad campaigns, a storage medium to store instructions of the system, and processing logic coupled to the storage medium. The processing logic is configured to execute the instructions of the system to receive and process an ad request from the device upon initiation of a software application on the device, filter eligible ad campaigns, convert each bid strategy of the filtered ad campaigns into an effective cost-per-mille (CPM) strategy, compare effective CPM strategies for the filtered ad campaigns, and select an ad campaign based on the comparison of the effective CPM strategies.

Content creation and deployment collaboration techniques are described. In one or more implementations, metadata that describes the creation of the content may be associated with the content. The content may then be provided from a content creation service to a content deployment service for deployment as part of a marketing activity. Deployment data obtained from this tracking may be utilized to support a variety of functionality, such as by content creators to determine which of their content has been successfully employed as part of marketing activities, marketers may also use knowledge of the deployment of the content to choose content to be included in a marketing activity as well as select content creators that are best suited to provide this content, used to configure badges, by retailers and manufacturers, and so forth.

A target user directing method and apparatus and provided. The method includes determining a similarity between each of candidate users and a seed user by using a similarity model. A conversion prediction model is used to predict a probability that each of the candidate users performs a conversion operation on to-be-delivered information. One or more target users for the to-be-delivered information are selected from the candidate users according to the similarity that is determined and the probability that is predicted for each of the candidate users. The to-be-delivered information is transmitted to the one or more target users.

A digital ad-buying platform uses counterfactual-based incrementality measurement by implementing randomization and/or a correction for auction win bias to avoid the need to identify counterfactual winner types in the control group. This approach can estimate impact at the individual consumer level. Confidence levels can be determined using Gibbs sampling in the context of causal analysis in the presence of non-compliance.