Abstract: A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by one or more servers configured to provide a user interface comprising a web site, web portal, or virtual portal, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream.

Abstract: The methods, apparatus, and systems described herein facilitate optimizing routing decisions. The methods include retrieving and/or predicting a profile of a current customer and future customers, determining which agents are currently available and which agents are expected to be available, and providing a routing recommendation based on the profile retrieval or prediction for the current customer and the future customers, and the currently available agents' and expected available agents' proficiency at handling customers with the retrieved and/or predicted profiles.

Abstract: A method for analyzing a telephonic communication between a customer and a contact center is provided. According to the method, a telephonic communication is separated into at least first constituent voice data and second constituent voice data. One of the first and second constituent voice data is analyzed by mining the voice data and applying a predetermined linguistic-based psychological behavioral model to one of the separated first and second constituent voice data. Behavioral assessment data is generated which corresponds to the analyzed voice data.

Abstract: The methods and systems described herein predict user behavior based on analysis of a user video communication. The methods include receiving a user video communication, extracting video facial analysis data from the video communication, extracting voice analysis data from the video communication, associating the video facial analysis data with the voice analysis data to determine an emotional state of a user, collecting biographical profile information specific to the user, applying a linguistic-based psychological behavioral model to the spoken words to determine personality type of the user, and inputting the collected biographical profile information, emotional state, and personality type into a predictive model to determine a likelihood of an outcome of the video communication.

Abstract: A method for automatically routing a telephonic communication is provided, and includes receiving a customer identifier and communicating with a customer history database storing historic data and determining whether the received customer identifier corresponds to a stored customer identifier in the customer history database, wherein the customer profile included in the stored historic data is based on a portion of a customer's demographic identity. Historic data corresponding to the stored customer number is identified based on the comparison of the received customer identifier and the stored customer identifier. The telephonic communication is associated with a predetermined communication destination based on the identified historic data.

Abstract: A method for searching for one or more recorded communications between one or more customers and a contact center comprising is provided. The method is implemented by a computer readable medium having a plurality of code segments. One or more communications is analyzed and assessment data is assigned thereto. The assessment data of the communications is searched for at least one search criterion, and at least one communication having assessment data matching the search criterion is identified. A representation of the matching communication is then displayed.

Abstract: The methods and systems described herein predict user behavior based on analysis of a user video communication. The methods include receiving a user video communication, extracting video facial analysis data from the video communication, extracting voice analysis data from the video communication, associating the video facial analysis data with the voice analysis data to determine an emotional state of a user, applying a linguistic-based psychological behavioral model to the voice analysis data to determine personality type of the user, and inputting the emotional state and personality type into a predictive model to determine a likelihood of an outcome of the video communication.

Abstract: A method for automatically routing a telephonic communication to at least one of a plurality of communication destination addresses is provided. The method is implemented by a computer readable medium having a plurality of code segments. The method comprises the step of receiving customer identifier and communicating with a customer history database configured to store historic data. The method also includes determining whether the received customer identifier corresponds to a stored customer identifier in the customer history database, wherein the customer profile included in the stored historic data is based on one or more components of a customer's demographic identity. Historic data corresponding to the stored customer number is identified based on the comparison of the received customer identifier and the stored customer identifier. The telephonic communication is associated with a predetermined communication destination based on the identified historic data.

Abstract: A method for searching for one or more recorded telephone communications between one or more customers and a contact center comprising is provided. The method is implemented by a computer readable medium having a plurality of code segments. One or more telephone communications is analyzed and assessment data is assigned thereto. The assessment data of the telephone communications is searched for at least one search criterion, and at least one telephone communication having assessment data matching the search criterion is identified. A representation of the matching telephone communication is then displayed.

Abstract: The methods, apparatus, and systems described herein are designed to route customer communications to the best agent or best available agent. The methods include receiving a customer communication, predicting a demographic profile of the customer associated with the customer communication, and providing a routing recommendation to route the customer to an agent based on the prediction and historical customer data.

Abstract: A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by one or more servers configured to provide a user interface comprising a web site, web portal, or virtual portal, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream.

Abstract: A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by a contact center, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream. In other embodiments, electronic customer communication data may include data received during a customer communication with a graphical user interface for the contact center.

Abstract: A method for automatically routing a telephonic communication to at least one of a plurality of communication destination addresses is provided. The method is implemented by a computer readable medium having a plurality of code segments. The method comprises the step of receiving customer identifier and communicating with a customer history database configured to store historic data. The method also includes determining whether the received customer identifier corresponds to a stored customer identifier in the customer history database, wherein the customer profile included in the stored historic data is based on one or more components of a customer's demographic identity. Historic data corresponding to the stored customer number is identified based on the comparison of the received customer identifier and the stored customer identifier. The telephonic communication is associated with a predetermined communication destination based on the identified historic data.

Abstract: The methods, apparatus, and systems described herein facilitate optimizing routing decisions. The methods include retrieving and/or predicting a profile of a current customer and future customers, determining which agents are currently available and which agents are expected to be available, and providing a routing recommendation based on the profile retrieval or prediction for the current customer and the future customers, and the currently available agents' and expected available agents' proficiency at handling customers with the retrieved and/or predicted profiles.

Abstract: The methods, apparatus, and systems described herein are designed to route customer communications to the best agent or best available agent. The methods include receiving a customer communication, predicting a personality type of the customer associated with the customer communication, and providing a routing recommendation to route the customer to an agent based on the personality type prediction and historical customer data.

Abstract: The methods, apparatus, and systems described herein are designed to route customer communications to the best available agent. The methods include predicting a personality type of the customer, determining the occupancy level of agents to provide a list of available agents, providing a routing recommendation based on the predicted personality type of the customer and agent data such as proficiency in handling customers with the predicted personality type, and/or analyzing real-time customer-agent interactions.

Abstract: A method for automatically routing a telephonic communication to at least one of a plurality of communication destination addresses is provided. The method is implemented by a computer readable medium having a plurality of code segments. The method comprises the step of receiving customer identifier and communicating with a customer history database configured to store historic data. The method also includes determining whether the received customer identifier corresponds to a stored customer identifier in the customer history database, wherein the customer profile included in the stored historic data is based on one or more components of a customer's demographic identity. Historic data corresponding to the stored customer number is identified based on the comparison of the received customer identifier and the stored customer identifier. The telephonic communication is associated with a predetermined communication destination based on the identified historic data.

Abstract: The methods, apparatus, and systems described herein facilitate optimizing routing decisions. The methods include retrieving and/or predicting a profile of a current customer and future customers, determining which agents are currently available and which agents are expected to be available, and providing a routing recommendation based on the profile retrieval or prediction for the current customer and the future customers, and the currently available agents' and expected available agents' proficiency at handling customers with the retrieved and/or predicted profiles.

Abstract: A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by a contact center, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream. In other embodiments, electronic customer communication data may include data received during a customer communication with a graphical user interface for the contact center.

Abstract: The methods, apparatus, and systems described herein facilitate optimizing routing decisions. The methods include retrieving and/or predicting a profile of a current customer and future customers, determining which agents are currently available and which agents are expected to be available, and providing a routing recommendation based on the profile retrieval or prediction for the current customer and the future customers, and the currently available agents' and expected available agents' proficiency at handling customers with the retrieved and/or predicted profiles.