A system for executing a code for training an insurance response system, comprising at least one hardware processor adapted to: using a plurality of insurance attribute values, extracted from a received insurance claim to retrieve: at least one inquiry related to the insurance claim and at least one inquiry result, and historical insurance data related to the insurance claim; computing a similarity value indicative of a difference between the at least one inquiry result and a claim result; identifying in the historical insurance data a plurality of insurance conditions relevant to the claim result and the at least one inquiry result; identifying at least one insurance condition effecting the similarity value by analyzing data comprising the at least one inquiry and the plurality of insurance conditions; and training a protocol model of an insurance response system using the at least one insurance condition and the at least one inquiry.

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.

Aspects of the subject disclosure may include, for example, providing to a user device a video content item including at least one scene which includes a plurality of advertisement placement opportunities and determining a preference profile for an individual associated with the user device. Aspects further include selecting a group of matching advertisements having advertisement profiles that match the preference profile for the individual and determining a relative prominence score for each advertisement placement opportunity. Aspects further include ordering the matching advertisements according to prominence information specified for each matching advertisement, wherein the prominence information corresponds to a relative desired prominence specified by an advertiser associated with the matching advertisement. Aspects further include providing the ordered matching advertisements to the user device according to the respective prominence information so that a matching advertisement having a greatest desired prominence is displayed in the video content item at an advertisement placement opportunity having a highest relative prominence score. Other embodiments are disclosed.

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.

Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.

Systems and methods directed to managing computer resource allocation by monitoring signals indicating demand for services utilizing computer resources are described. A method includes maintaining, for each first event of first events, historical registration data and respective parameter values of the first event and identifying, for a second event having an open registration status, respective parameter values of the second event, and registration data for the second event. The method includes computing a similarity score between the second event and each first event of the plurality of first events, based on the respective parameter values of the first event and the second event and the registration data of the second event and the historical registration data of the first event, generating, for the second event, a projected number of entities based on determined information and determining a ranking of the second event.

Systems, methods and computer-readable storage media utilized to prepare experimental datasets for experimental analysis systems. One method includes identifying, by one or more processing circuits, a dataset of a plurality of geographic pairs associated with a geo experiment. The method further includes calculating, by the one or more processing circuits, a difference in input data and a difference in response data between the first geographic region and the second geographic region of each geographic pair. The method further includes calculating, by the one or more processing circuits, a plurality of outcome estimates. The method further includes selecting, by the one or more processing circuits, a first subset of geographic pairs of the plurality of different subsets of geographic pairs based a first outcome estimate of the plurality of outcome estimates that is about a prespecified value on the outcome estimates and providing the selected subset of geographic pairs.

Various implementations of the invention for predicting customer behavior are described. Various implementations of the invention comprise an embedding component configured to receive and embed sequential inputs regarding a plurality of customer interactions with an online presence of a client; a plurality of causal dilated convolutional “CDC” elements configured to receive the embedded sequential inputs and to output a feature vector, where each CDC element comprises two causal dilated convolutions with regularization that is bypassed with a skip connection; a plurality of dense neural network elements configured to receive the feature vector and non-sequential inputs regarding a plurality of other customer interactions with the client, where each of the plurality of dense neural network elements comprises two dense neural networks with regularization that is bypassed with a skip connection; and an output generator configured to receive the output from the plurality of dense neural network elements and to generate a distribution of times over which a particular customer event will occur and/or a likelihood estimation that the particular customer event will occur within a particular time period.

An online system that identifies allocations of both organic and promoted content on a given page. The allocations of page content are compared against one another and configured to prioritize for overall utility based on objective factors that quantify a page “look and feel” as measured by machine learning models. The page allocations are operated on an automatic and continuous basis for each user viewing the page. In some embodiments, the page content allocations are based on individual viewing users stored characteristics.

Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. Send Time Optimization (STO) uses machine learning (ML) to recommend a personalized send time based on a recipient's past engagement patterns. The purpose of the ML model is to learn patterns in the data automatically and use the patterns to make personalized predictions for each recipient. The send time recommended by the model is the time at which the model believes the recipient will be most likely to engage with the message, such as clicking or opening, and use of the send time mode is expected to increase engagement from recipients. Additional customizations include communication-frequency optimization, communication-channel selection, and engagement-scoring model.