A technique for dynamically adjusting a digital advertising campaign during an active campaign flight is discussed. Using feedback from a digital survey over an exposed audience of user populations, brand lift may be calculated on a per ad creative and/or per site basis. User characteristics derived from content consumption patterns may be used to optimize ongoing campaigns and formulate target audiences and target creative formats for new campaigns.

Systems and methods for frequency management, including: an online media service configured to receive a request for a media item, the request comprising a recipient identifier of a recipient, and identify a set of candidate media items ranked based at least partially on relevance to the recipient; and a frequency management service configured to: (i) identify a quantity of impressions associated with a first candidate media item of the set of candidate media items and the recipient identifier over a preceding duration of time, (ii) identify a maximum frequency threshold, (iii) determine, based on the quantity of impressions, that the maximum frequency threshold is exceeded, (iv) exclude the first candidate media item from a result set based on the frequency threshold being exceeded, and (v) provide the result set comprising an identifier of a second candidate media item in response to the request.

Systems and methods for programmatic generation of training data, including: a training data generation engine configured to: identify an image asset corresponding to an entity; identify a training video; select a consecutive subset of frames of the training video based on a procedure for ranking frames on their candidacy for overlaying content; for at least one frame of the subset of frames: perform an augmentation technique on the identified logo image to generate an augmented image asset; overlay at least one variation of the image asset, including the augmented image asset, onto each of the subset of frames to generate a set of overlayed frames; and generate an augmented version of the training video including the overlayed frames; and a model training engine configured to: train an artificial intelligence model for entity detection using the augmented version of the training video.

A system associated with a user experience survey framework for an enterprise may include an enterprise product hierarchy data store that contains information about a hierarchy of product nodes. Each product node may be, for example, associated with a user application. A computer processor of a user experience survey tool may receive from the enterprise an adjustment to the hierarchy of product nodes and store an adjusted hierarchy of product nodes into the enterprise product hierarchy data store. The user experience survey tool may then retrieve user experience survey results for a plurality of user applications. The retrieved user experience survey results are automatically aggregated in accordance with the adjusted enterprise product hierarchy and an aggregation rule selected by the enterprise. An indication of the aggregated user experience survey results may then be output to the enterprise.

Disclosed here is a system that can obtain attributes of an advertisement, where an attribute has a continuous value, and a range of acceptable values is uncertain. The system can create a file including contents that when provided to a predetermined function produce a value of the attribute. Based on the file, the system can generate values corresponding to the attributes. Based on the generated values, the system can create the advertisement. The system can obtain a response data to the created advertisement and can fit a multidimensional function to the attributes and the user response data. Based on the multidimensional function, the system can determine next values and next ranges, where the next values and the next ranges indicate an improvement in the response data.

Methods, systems, apparatus, and articles of manufacture to determine causal effects are disclosed herein. An example apparatus includes a weighting engine to calculate a first set of weights corresponding to a first treatment dataset, a second set of weights corresponding to a second treatment dataset, and a third set of weights corresponding to a control dataset, the weighting engine to increase an operational efficiency of the apparatus by calculating the first set of weights, second set of weights, and third set of weights independently, a weighting response engine to calculate a first weighted response for the first treatment dataset, a second weighted response for the second treatment dataset, and determine a causal effect between the first treatment dataset and the second treatment dataset based on a difference between the first weighted response and the second weighted response, and a report generator to transmit a report to an audience measurement entity.

Certain aspects provide a computer-implemented method for evaluating social media marketing campaigns using artificial intelligence (AI). The method comprises using a large language model (LLM) to generate a plurality of AI personas. Each AI persona represents a different segment of a target audience of a marketing campaign. The method uses a transformer model, a decision tree-based model, and a natural language processing (NLP) model to predict a response, a rating, and a feedback to the marketing campaign for each AI persona that represents a different segment of the target audience. The predicted responses, ratings, and feedback for the AI personas that represent different segments of the target audience are aggregated to form an evaluation of the marketing campaign for each segment of the target audience. The method sends the evaluation of the marketing campaign to a user.

Systems and methods for programmatic generation of training data, including: a training module configured to receive human curation input for brand entity detection, generate hybrid training data by combining programmatic and human generated data, and calculate brand-probability pairs by weighting detection results and human input to improve brand detection accuracy for frequency management; an online media service configured to serve training data to recipients during controlled experiments, calculate quality scores based on performance metrics and human input, and exclude low-quality training data from model training; a model training engine configured to train an artificial intelligence model for brand detection using the hybrid training data weighted by quality scores; and a frequency management service configured to execute the trained model on media items to identify brand identifiers with improved accuracy and regulate serving frequency of brand-associated content to recipients.

Systems and methods for frequency management, including: an online media service configured to (i) receive a request for a media item, the request including a recipient identifier, (ii) identify a set of candidate media items relevant to the recipient, and (iii) obtain a set of cross-device identifiers associated with the recipient identifier, the set corresponding to a household; and a frequency management service configured to (i) identify an aggregate quantity of impressions associated with a candidate media item of the set of candidate media items and the set of cross-device identifiers over a preceding duration of time, (ii) identify a maximum frequency threshold, (iii) determine, based on the aggregate quantity of impressions, that the maximum frequency threshold is exceeded, (iv) exclude the candidate media item from a result set based on the maximum frequency threshold being exceeded, and (v) provide the result set in response to the request.

A computer implemented method for providing social media data to brands comprising pairing a user with a brand in response to the user requesting a connection with the brand. A privacy agreement is made between the user and the brand to facilitate receiving social media data related to the user. A user data package is created that includes at least a portion of the social media data and the user data package includes data based on instructions from the brand. Data is removed from the user data package that is excluded by the privacy agreement to create a filtered data package. The filtered data package is then sent to the brand. Remote social media data is received from a remote social media network in response to the user selecting a link with the remote social media network and the remote social media data is added to the social media data.