A system for presenting branding opportunity information includes a processing device that includes an input module configured to receive a request from a user for branding opportunity information in relation to a temporary space, and a processing module configured to determine one or more branding opportunities in the temporary space. The processing device also includes an output module configured to provide a display of at least part of the temporary space and a visual representation of each branding opportunity of the one or more branding opportunities, the visual representation showing a location and size of the branding opportunity relative to the temporary space.

Methods and corresponding systems are provided for configuring communications to customers when there are multiple campaigns scheduled in a given period. An optimal number of communications are sent prioritized according to business or performance measures where send times are spaced out in a manner that strives to attain the highest value. An example method includes determining a number of electronic communications, for a plurality of campaigns to send to the particular customer during a particular time period; determining the optimal send time during the particular time period; determining for which campaigns the particular customer is eligible; for each determined optimal time: determining a strategy including selecting an electronic communication for one of the campaigns to send to the eligible particular customer so as to maximize the value over the particular time period; and causing the selected electronic communication to be sent to the particular customer at the determined optimal time.

A delayed grouping (batch) processing of previous campaign delivery pacing decisions and corresponding outcomes (deliveries) is used to configure a new auction experiment iteration. In the new iteration, a campaign that was previously over-delivered is classified as either (a) over-delivered due to incorrect pacing or (b) over-delivered due to auction experiment design. After the delayed processing, the new auction experiment iteration is conducted with a mitigating action taken on the previously over-delivered campaign if the campaign is classified as (b) over-delivered due to auction experiment design. For example, the mitigating action can include removing the campaign from a subsequent iteration of the experiment, or the experiment can be redesigned. By doing so, the over-delivery caused by the campaign due to the auction experiment design is avoided when performing the new auction experiment iteration.

There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.

Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

Provided are methods and systems for optimization and personalization of marketing actions using cloud, hybrid, and quantum-based computing techniques. An example method commences with iteratively selecting, from a pool of prospective clients, at least one subgroup of the prospective clients based on predetermined criteria. The method further includes performing at least one marketing action on the at least one subgroup of the prospective clients. The method then continues with receiving a feedback from a prospective client belonging to the at least one subgroup of the prospective clients in response to the at least one marketing action. The method further includes scoring, by a machine learning technique, the feedback received from the prospective client. The method further includes modifying the at least one marketing action until the at least one marketing action is optimized for the prospective client based on the scoring of the feedback.

Online advertising campaigns are operated responsive to keyword trends. The keywords used by a group of browsers is analyzed periodically over time. A list of the most frequently used keywords is separated into those that have previously appeared on the list, referred to herein as the stable keywords, and those that are newly emerging, referred to herein as the trending keywords. The advertiser selects at least one advertising creative that the advertiser associates with the stable keywords, referred to herein as the stable creative, and at least one advertising creative that the advertiser associates with the trending keywords, referred to herein as the trendy creative. The advertising system then operates the online advertising campaign to deliver the respective stable and trendy creatives in proportion to the frequency of use of the trending versus the stable keywords.

A system and method for optimizing ad campaigns, which considers the relationship of items and immediately takes into account the future estimated impact of optimizations.