Abstract: The disclosure is directed to automatically determining product feature requests by analyzing conversations of representatives with customers. A feedback identification system retrieves recordings of various conversations, extracts features of the conversations, and analyzes the features to determine a set of features that is indicative of a feature request. A feature request is a request for adding a specified functionality to a product. The set of features is analyzed to generate a feedback manifest, which includes the feature request (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.

Abstract: The disclosure is directed to automatically determining action items by analyzing conversations of representatives with customers. An action item identification system retrieves recordings of various conversations, extracts features of each of the conversations, and analyzes the features to determine a set of features that is indicative of an action item associated with the corresponding conversation. The set of features is further analyzed to generate the action item in an action item manifest (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.

Abstract: The disclosure is directed to automatically determining deals at risk by analyzing conversations of representatives with customers. A risk identification system retrieves recordings of various conversations, extracts features of each of the conversations, and analyzes the features to determine if any of the conversations includes features that are indicative of a deal discussed in that conversation being at risk. By performing such an analysis of conversations, the risk identification system can identify a number of deals that are at risk and generate a report of such deals and notify a consumer user of the risk identification system of such deals.

Abstract: The disclosure is directed to automatically determining conversation patterns that distinguish a first set of participants from a second set of participants. For example, a first set of participants can be top-performing representatives and the second set of participants can be low-performing representatives. A pattern recognition system analyzes a first set of recordings of the top-performing representatives to extract a first set of features associated with the first set of recordings, and analyzes the first set of features to generate first pattern data that is indicative of a pattern of the conversation of the top-performing representatives. Similarly, the pattern recognition system also generates second pattern data that is indicative of a pattern of the conversation of the low-performing representatives. The pattern recognition system analyzes the first pattern data and the second pattern data to generate distinctive features that distinguish the first pattern from the second pattern.

Abstract: The disclosure is directed to analyzing voice conversations between participants of conversations and coordinating calls between participants, e.g., in order to influence an outcome of the voice conversation. For example, sales calls can be coordinated between specific sales representatives (“representatives”) and customers by routing a sales call from a customer to a specific sales representative, based on their voices and the content of the conversation, with the goal of positively influencing the outcome of the sales call. A mapping between sales representatives and customers that is set to maximize the probability for certain outcomes is generated. This mapping (or pairing) may be fed into either an automatic or manual coordination system that connects or bridges sales representatives with customers. The mapping may be generated either based on historic data or early-call conversation analysis, in both inbound and outbound calls.

Abstract: The disclosure is directed to automatically generating a playlist of conversations having a specified moment. A moment can be occurrence of a specific event or a specific characteristic in a conversation, or any event that is of specific interest for an application for which the playlist is being generated. For example, a moment can include laughter, fast-talking, objections, response to questions, a discussion on a particular topic such as budget, behavior of a speaker, intent to buy, etc., in a conversation. A moment identification system analyzes each of the conversations to determine if one or more features of a conversation correspond to a specified moment, and includes those of the conversations in the playlist having one or more features that correspond to the specified moment. The playlist may include a portion of a conversation that has the specified moment rather than the entire conversation.

Abstract: The disclosure is directed to analyzing voice conversations between participants of conversations and coordinating calls between participants, e.g., in order to influence an outcome of the voice conversation. For example, sales calls can be coordinated between specific sales representatives (“representatives”) and customers by routing a sales call from a customer to a specific sales representative, based on their voices and the content of the conversation, with the goal of positively influencing the outcome of the sales call. A mapping between sales representatives and customers that is set to maximize the probability for certain outcomes is generated. This mapping (or pairing) may be fed into either an automatic or manual coordination system that connects or bridges sales representatives with customers. The mapping may be generated either based on historic data or early-call conversation analysis, in both inbound and outbound calls.

Abstract: The disclosure is directed to analyzing voice conversations between participants of conversations and coordinating calls between participants, e.g., in order to influence an outcome of the voice conversation. For example, sales calls can be coordinated between specific sales representatives (“representatives”) and customers by routing a sales call from a customer to a specific sales representative, based on their voices and the content of the conversation, with the goal of positively influencing the outcome of the sales call. A mapping between sales representatives and customers that is set to maximize the probability for certain outcomes is generated. This mapping (or pairing) may be fed into either an automatic or manual coordination system that connects or bridges sales representatives with customers. The mapping may be generated either based on historic data or early-call conversation analysis, in both inbound and outbound calls.

Abstract: The disclosure is directed to modeling calls in real time, with the goal of helping users, e.g., a sales representative and/or their managers, improve and/or guide the outcome of the calls. The embodiments generate real-time probabilities for possible outcomes of the conversation, as well as highlight specific on-call patterns, which may be either conducive or detrimental to a desired conversation outcome. The generated probabilities and highlighted patterns may be used by the sales representatives and/or their managers to either increase the probability of a desired outcome and/or optimize for call duration with a specific outcome.

Abstract: The disclosure is directed to analyzing voice conversations between participants of conversations and coordinating calls between participants, e.g., in order to influence an outcome of the voice conversation. For example, sales calls can be coordinated between specific sales representatives (“representatives”) and customers by routing a sales call from a customer to a specific sales representative, based on their voices and the content of the conversation, with the goal of positively influencing the outcome of the sales call. A mapping between sales representatives and customers that is set to maximize the probability for certain outcomes is generated. This mapping (or pairing) may be fed into either an automatic or manual coordination system that connects or bridges sales representatives with customers. The mapping may be generated either based on historic data or early-call conversation analysis, in both inbound and outbound calls.