Abstract: A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver. The method includes providing the predicted call driver to a call center associated with the company.

Abstract: A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver. The method includes providing the predicted call driver to a call center associated with the company.

Abstract: A system includes instructions for execution by at least one processor, including, in response to an event, obtaining a first set of alerts stored in the alert database corresponding to a first scenario of a set of scenarios and selecting a first model of a set of models corresponding to the first scenario and identifying a first set of features stored in the features database corresponding to the first scenario. The instructions include, for each alert of the first set of alerts, identifying a first identifier included in the alert, retrieving the first set of features of the first identifier from the parameter database, determining a score using the first model based on the retrieved first set of features, and adding the alert and the score to a result list. The instructions include displaying, on a user device, the result list including the first set of alerts and corresponding scores.

Abstract: A system includes instructions for execution by at least one processor, including, in response to an event, obtaining a first set of alerts stored in the alert database corresponding to a first scenario of a set of scenarios and selecting a first model of a set of models corresponding to the first scenario and identifying a first set of features stored in the features database corresponding to the first scenario. The instructions include, for each alert of the first set of alerts, identifying a first identifier included in the alert, retrieving the first set of features of the first identifier from the parameter database, determining a score using the first model based on the retrieved first set of features, and adding the alert and the score to a result list. The instructions include displaying, on a user device, the result list including the first set of alerts and corresponding scores.

Abstract: A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

Abstract: A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

Abstract: A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver.

Abstract: A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver. The method includes providing the predicted call driver to a call center associated with the company.

Abstract: A method includes receiving client data of a client that includes at least one of clickstream data and analytic data of the client. For each of a number of trained machine learning (ML) models corresponding, respectively, to a number of campaigns, campaign-specific features are extracted from the client data, and a campaign interest prediction score is generated by inputting the campaign-specific features extracted for the ML model into the ML model. At least one campaign, from among the plurality of campaigns, is assigned to the client based on the generated campaign interest prediction scores. The clickstream data includes a plurality of pages visited by the client, and the analytic data of the client includes at least one of phone call data, chat message data, email data, or survey data of the client.

Abstract: A method of operating a customer utterance analysis system includes obtaining a subset of utterances from among a first set of utterances. The method includes encoding, by a sentence encoder, the subset of utterances into multi-dimensional vectors. The method includes generating reduced-dimensionality vectors by reducing a dimensionality of the multi-dimensional vectors. Each vector of the reduced-dimensionality vectors corresponds to an utterance from among the subset of utterances. The method includes performing clustering on the reduced-dimensionality vectors. The method includes, based on the clustering performed on the reduced-dimensionality vectors, arranging the subset of utterances into clusters. The method includes obtaining labels for a least two clusters from among the clusters. The method includes generating training data based on the obtained labels. The method includes training a neural network model to predict an intent of an utterance based on the training data.

Abstract: A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver. The method includes providing the predicted call driver to a call center associated with the company.

Abstract: A system includes instructions for execution by at least one processor, including, in response to an event, obtaining a first set of alerts stored in the alert database corresponding to a first scenario of a set of scenarios and selecting a first model of a set of models corresponding to the first scenario and identifying a first set of features stored in the features database corresponding to the first scenario. The instructions include, for each alert of the first set of alerts, identifying a first identifier included in the alert, retrieving the first set of features of the first identifier from the parameter database, determining a score using the first model based on the retrieved first set of features, and adding the alert and the score to a result list. The instructions include displaying, on a user device, the result list including the first set of alerts and corresponding scores.