This disclosure relates to systems and methods for generating an advertising recommendation. In one example, a method includes determining a statistical performance level threshold for a plurality of advertising entities advertising, identifying one of the advertising entities that fails to meet the statistical performance level threshold, determining a variance associated with the one advertising entity as compared with others of the plurality of advertising entities that do satisfy the performance threshold constraint, generating a recommendation to the one advertising entity that addresses the variance, and transmitting the recommendation to the one advertising entity.

A method of delivering advertising in an online environment includes determining a context of a user operating a client computer to interact with an e-commerce website, where the determined context representing an intent of the user to locate a product for purchase, defining a relation between one or more of a plurality of advertisements and the product based on at least one of a plurality of relevance types, and displaying, to the user, at least one of the advertisements having the relation to the product.

Embodiments are associated with user behavior simulation. A user behavior simulation apparatus may retrieve, from a unit data store, relevant unit data. The simulation apparatus may also retrieve, from a user behavior data store, user behavior data (and train a user interest decay model based on the retrieved user behavior data) along with a unit bundle generation strategy model from a unit bundle generation strategy data store (and initialize control parameters of the unit bundle generation strategy model). The system may then initialize control parameters of an A/B treatment generation strategy model and repeatedly simulate user interest in unit bundles using the relevant unit data, the user interest decay model, the unit bundle generation strategy model, and the A/B treatment generation strategy model. Based on the simulated user interest in unit bundles, statistics associated with bandit strategy results are collected and transmitted when at least one evaluation condition is satisfied.

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include updating, dynamically, the at least one risk-reward score and identifying a recommended stage for the at least one advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the at least one advertisement to the user at the recommended stage.

A system and method for multi-channel dynamic advertisement testing. The system comprises a multi-platform adaptive ad campaign manager, a dynamic advertisement engine, a campaign database, and an omnichannel text-based communicator. The system receives customer interactions with two advertisement test variants, establishes a real-time media stream between a customer device and a second user device, and monitors the media stream to collect data related to effectiveness of the advertisement variants. The system may analyze media stream data together with a plurality of other data types to statistically determine which of the two advertisement variants resulted in better performance based on a variety of advertisement metrics. The system may use the plurality of data and the statistical analysis to suggest an advertisement element to be altered in the next round of advertisement variant testing. This system can combine data collection and analytics for an ad campaign together into one system.

A computer-implemented method is provided for identifying potential individuals to contact in a campaign of interest. The method includes receiving campaign data including description about the campaign of interest and information about the potential individuals to contact for the campaign of interest and selecting a plurality of trained machine learning models from a library of trained machine learning models based on the campaign data. The library of trained machine learning models is created from data of historical campaigns administered, and each of the selected plurality of trained models corresponds to a historical campaign that is within a similarity threshold from the campaign of interest. The method also includes scoring a pool of existing customers using the select plurality of trained machine learning models and identifying the potential individuals to contact in the campaign of interest by ranking the existing customers by their corresponding propensity scores.

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include engaging a user in a primary activity; the primary activity may include a plurality of stages. The operations may include compiling at least one risk-reward score for an advertisement placement of an advertisement for at least one of the plurality of stages. The operations may include identifying a recommended stage for the advertisement; the recommended stage may be based on the risk-reward score. The operations may include displaying the advertisement to the user at the recommended stage.

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.

A system and methods for evaluating whether a public review matches another review is disclosed. The method includes obtaining a private review and a public review of a business. A likelihood the public review matches the private review is evaluated. The evaluation includes evaluating a match distance between text bodies, reviewer names, review dates and ratings in the public review and the private review. Evaluation rules are applied to determine the likelihood of a match.

Apparatus, systems, and methods to identify consumer content exposure are disclosed. An example apparatus includes at least one memory; machine readable instructions; and processor circuitry to at least one of instantiate or execute the machine readable instructions to detect a first exposure of a first consumer to a marketing campaign during a first time period; identify the first exposure as a first recency exposure; detect a second exposure of the first consumer to the marketing campaign during one of the first time period or a second time period; when the second exposure is detected during the first time period, identify the first time period as an iterative frequency exposure; and when the second exposure is detected during the second time period, identify the second exposure as a second recency exposure.