Systems, methods, and non-transitory computer readable media can determine qualitative ratings associated with a plurality of advertisements based on a machine learning model. One or more clusters of the plurality of advertisements can be generated based on representations of the plurality of advertisements. One or more advertisements visually similar to an advertisement can be identified based at least in part on a cluster of the one or more clusters and qualitative ratings of advertisements in the cluster.

Systems, methods, and non-transitory computer readable media can predict one or more qualitative ratings associated with an advertisement based on a machine learning model. One or more advertisements that are visually similar to the advertisement can be identified. At least one difference between the advertisement and the one or more advertisements can be determined. A recommendation for improving the one or more qualitative ratings associated with the advertisement can be provided based on the at least one difference.

A method includes determining a plurality of harvest content items. The harvest content items are ranked based on a performance metric. Matching criterion aspects of the harvest content items are determined. Aspects of a candidate content item are compared with the plurality of harvest content items according to the matching criterion aspects. A subset of the harvest content items that are similar to the candidate content item is determined. A transformation for the candidate content item is selected and applied to the candidate content item to generate a transformed content item.

A system and method for generating advertisement automatically are provided. The system may comprise at least one computer-readable storage medium including a set of instructions; at least one processor in communication with the at least one computer-readable storage medium, wherein when executing the set of instructions, the at least one processor is configured to cause the system to generate a first plurality of ads, the first plurality of ads including a first plurality of advertisement elements and a first plurality of information components; transmit, via a network, the first plurality of ads to a first group of user terminals; determine at least one of a click-through rate, a number of impressions, or a conversion rate for the first plurality of ads; and analyze the at least one of the click-through rate, the number of impressions, or the conversion rate of the first plurality of ads.

Technologies are disclosed for determining a runtime length for an A/B experiment, where the experiment evaluates the desirability of a potential change at a website. The experiment is run for an initial period of time and based upon initial data from the initial period of time, an equation is iteratively solved until a minimal amount of time is determined that that indicates when a statistically significant change will be observed in the data. The experiment is then run for the minimal amount of time.

In some implementations, a computing device may determine, from multiple data sources, multiple event timelines, with each event timeline associated with a customer. Each event in an event timeline represents an interaction between the customer and a vendor of goods and/or services. For N (N>1) marketing campaigns, N augmented timelines may be created for each timeline by augmenting each event timeline with the individual marketing campaigns. Thus, for M (M>1) customers, MN augmented event timelines may be created. A trained machine learning model may perform an analysis of each augmented event timeline to predict results of executing each marketing campaign. The results may include total predicted revenue and total predicted cost resulting from executing each marketing campaign. A particular marketing campaign from the N marketing campaigns may be selected and execution of one or more marketing events may be initiated.

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 instruction to: receive, over a network, client-provided data from a client device; identify at least one consumer by comparing the client-provided data against consumer data recorded in an electronic consumer database; obtain at least one unique consumer identifier for the identified at least one consumer, the at least one unique consumer identifier not including personal identifiable information; generate a target audience pool based on the at least one unique consumer identifier; and deliver, over a network, the target audience pool to the client device to facilitate targeted advertising to specific consumers.

Systems and methods for federating social network content items include determining an engagement value for content items based on previous interactions with content items from first and second content item sources stored in an electronic data storage, certain content items having selection metrics and value metrics according to varying selection schemas and a value schema. A utility value is generated for each of the at least some of the content items, the utility value including an engagement component and a monetary component. A user device displays the content items in an order based on the utility values.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating media assets. A method includes sending a first media asset to a plurality of subscribers of a first channel of a plurality of channels. The first media asset includes a first set of media elements. The method also includes analyzing aggregated performance data associated with the first set of media elements and with the plurality of subscribers. The method further includes generating, by a computer processing device, a second media asset comprising a second set of media elements based on the aggregated performance data. The first set of media elements differs from the second set of media elements. The method further includes sending the second media asset to the plurality of subscribers.

The present disclosure is directed towards systems and methods for adjusting impression inventory within overlapping packages and based on impression inventory reservations. The systems and methods receive attributes of a first package and a second package to create impression inventories for the first package and the second package. Additionally, the systems and methods determine overlap between the impression inventory of the first package and the impression inventory of the second package. Moreover, upon receiving an inventory reservation request, the systems and methods adjust, based on the reservation request, the impression inventories of both the first package and the second package.