Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for updating campaigns. A first entity is identified with a first campaign, including selection criteria for delivery of content associated with the first campaign. At least one comparable second entity is identified, each having a respective campaign including selection criteria for delivery of content associated with the respective campaign. Categories are determined for the first campaign and respective campaigns of the comparable second entities. The determining is based on the selection criteria of a given campaign. Categories determined for the first entity are compared to the categories determined for the comparable second entities, including identifying missing categories not included in the first campaign but included in one or more campaigns of the comparable second entities. The missing categories are used to suggest a change to or adjust a portion of the first campaign.

Systems and methods are described herein for resolving advertisement placement conflicts. Specifically, a number of parameters may be entered into a system in order to distribute advertisements into advertisement slots. In many instances, a combination of these parameters causes a conflict in the system where all the parameters cannot be applied in order to place advertisements into advertisement slots. The conflict may be resolved by using an advertisement assignment model to determine which parameters may be relaxed in order to arrive at an optimal solution that violates a smallest number of parameters having the least priority. When such a solution is found, the advertisement assignment model may be modified and advertisements may be placed into advertisement slots based on the modified advertisement assignment model.

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.

A system and process provide a novel methodology to improve advertising performance by creating and testing new ad copy within advertisements more effectively and efficiently. The system includes an automated technology that produces a virtually unlimited number of predictably high-quality machine-generated advertisements, by combining and recombining the words and phrases (e.g., ad copy) that have proven to be most effective in previous advertisements with new words and phrases that are determined as likely to be effective based on the motivations of the target audience that will be viewing the advertisements. The novel technology is a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results in the form of top performing ad copy and top performing machine-generated advertisements.

A distributed data processing and collection system improves effectiveness of data processing and collection by distributing data processing tasks and data collection tasks across a plurality of first computers, a plurality of second computers, and one or more servers. The system comprises a plurality of the first computers, each associated with a unique identifier, a plurality of the second computers, each of which is configured to detect the unique identifier of any of the first computers and, upon detecting the unique identifier, to collect data associated with the unique identifier, and one or more servers in communication with the first computers to distribute data to be displayed by the first computers, aggregate data collected by the second computers, and determine an effectiveness of the distributed data based on the aggregated data.

Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.

Methods, apparatus, systems and articles of manufacture are disclosed to determine component lifts and synergy from overlapping stimuli. An example disclosed apparatus to allocate advertising campaign resources includes a vehicle segregator to segregate impression counts of an advertisement campaign into vehicle types and segregate measured lift values associated with the impression counts into respective ones of the vehicle types, the segregated measured lift values corresponding to respondents that viewed an advertisement corresponding to one of the respective ones of the vehicle types. The example apparatus further includes a correlation engine to establish correlations between the measured lift values and component lift values, the correlations based on a weighted average of the component lift values and segregated impression counts, the component lift values corresponding to respondents that viewed an advertisement corresponding to one of the respective ones of the vehicle types, a lift calculator to determine the component lift values based on the correlations and a campaign selector to modify computing resource allocation to at least one of the vehicle types of the advertising campaign based on a comparison of the determined component lift values.

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.

A keyword campaign automatically groups keywords for customized override bids for the keyword group. The keywords of a campaign may be analyzed by a computer model to predict membership in a category in addition to the likelihood that the bid of the keyword will be modified. The keyword groups may be automatically generated based on the predictions, and performance metrics are evaluated for the keyword groups at one or more modified bids. The performance metrics of the keyword groups at the modified bids may then be used to set override bids. The automatically generated keyword groups and performance metrics permit a sponsor to intelligently group and customize keyword bids with reduced interface interactions and without requiring individual keyword bid adjustments.

A system includes a processor and a memory including computer program code. The memory and the computer program code are configured to, with the processor, cause the processor to: receive historical campaign data; determine a likelihood that a vendor will accept a card offer by inputting the historical campaign data into a machine learning model that has been trained for at least one campaign using training data comprising past campaign performance data; automatically recommend a campaign strategy tailored for the vendor using the machine learning model, wherein the recommended campaign strategy is an output of the machine learning model that is based on the determined likelihood that the vendor will accept the card offer; and automatically display the recommended campaign strategy output from the machine learning model on a graphical user interface (GUI).