Systems and methods are provided for generating a predicted number of viewers in support of selecting an advertisement to be displayed during an advertisement break. The method includes: generating, by an advertisement break class identifier module, a plurality of advertisement break classes based on historical advertisement break information and prediction errors generated by a regression model analysis of advertisement break data samples; evaluating, by the advertisement break class identifier module, the plurality of advertisement break classes; redefining and rearranging, by the advertisement break class identifier module, advertisement break classes from the plurality of advertisement break classes based on the step of evaluating which results in an updated set of advertisement break classes; forwarding, by the advertisement break class identifier module, the updated set of advertisement break classes and trained model information to a prediction module; receiving, modifying and returning prediction model information to the prediction module by a training function in the advertisement break class identifier module; generating, by the prediction module, the prediction of a number of viewers for the advertisement break, wherein a regression model uses the updated set of advertisement break classes and data associated with a specific advertisement break to generate the prediction; and sending, by the prediction module, the prediction of a number of viewers for the advertisement break to a scheduler of advertisements.

The present disclosure provides for management of impressions in advertisement campaigns. Impressions may be moved between different impression media based on performance metrics and historical data. Impression budgets may be modified in an active campaign based on forecasts determined using current performance data. Impression budgets may be reallocated between simultaneously active advertisement campaigns.

The present disclosure provides for management of impressions in advertisement campaigns. Impressions may be moved between different impression media based on performance metrics and historical data. Impression budgets may be modified in an active campaign based on forecasts determined using current performance data. Impression budgets may be reallocated between simultaneously active advertisement campaigns.

The present disclosure provides for management of impressions in advertisement campaigns. Impressions may be moved between different impression media based on performance metrics and historical data. Impression budgets may be modified in an active campaign based on forecasts determined using current performance data. Impression budgets may be reallocated between simultaneously active advertisement campaigns.

This disclosure covers methods, non-transitory computer readable media, and systems that determine a cluster effect representing the impact that a user's digital-content exposure has on other users' conversion actions. The disclosed methods, non-transitory computer readable media, and systems detect the downloads, purchases, or other forms of consumption of a featured item that result from users within the same cluster viewing digital content featuring the item. In some embodiments, the methods, non-transitory computer readable media, and systems apply the cluster effect by, for example, generating a conversion report representing the cluster effect or by providing tools that exploit the cluster effect in distributing digital content.

Systems and methods provide a customer engagement platform experimentation framework. Experiments are created to determine which variation of a particular promotion (i.e., an experiment) is most effective. Initially, users are split into buckets that define whether a particular user is in a control group or a treatment group for a particular experiment. Users are assigned into buckets differently for unrelated experiments and similarly for related experiments. This ensures the users are in either the control group or the treatment group for all related experiments. Key performance indicators (KPIs) of users that experienced the experiment are compared with KPIs of users that did not experience the experiment. An assessment of the experiment can then be provided based on the compared KPIs.

A method and apparatus for spawning and management of online surveys is provided. An administration and redirector subsystem (ARS) is used manage ongoing surveys for a plurality of sites. Surveys can be added to or removed from individual sites using the ARS. The ARS also tracks statistics for individual surveys. Redirector tags invite users to participate in surveys and track user survey participation through cookies. The system can be used to conduct advertisement effectiveness studies. Tracker tags and corresponding cookies are used to track users who have viewed content containing the tag. Each study is issued a unique content tag, but uses the same redirector tag.

A system tracks user actions associated with advertisements. The system may receive information in response to a user action. The system may then determine whether the action performed by the user resulted from an advertisement.

This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

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.