Abstract: A conference management system facilitates data compliance in recording conversations between users. A host user can send an electronic invitation for a meeting to participants. Upon accessing the invitation, the participants can be presented with two options to join the conference—a first option using which a participant can join the meeting by providing consent to recording the meeting and a second option using which the participant can join the meeting by opting-out of recording of the meeting. When a participant opts-out of the recording of the meeting, the conference management system ensures that the recording is performed in compliance with a data compliance policy applicable to the participant who opted out of recording.

Abstract: A conference management system (“system”) facilitates data compliance in recording conversations between users. A host user can send an electronic invitation for a meeting to participants. Upon accessing the invitation, the participants can be presented with two options to join the conference—a first option using which a participant can join the meeting by providing consent to recording the meeting and a second option using which the participant can join the meeting by opting-out of recording of the meeting. When a participant opts-out of the recording of the meeting, the conference management system ensures that the recording is performed in compliance with a data compliance policy applicable to the participant who opted out of recording.

Abstract: A conference management system (“system”) facilitates data compliance in recording conversations between users. A host user can send an electronic invitation for a meeting to participants. Upon accessing the invitation, the participants can be presented with two options to join the conference—a first option using which a participant can join the meeting by providing consent to recording the meeting and a second option using which the participant can join the meeting by opting-out of recording of the meeting. When a participant opts-out of the recording of the meeting, the conference management system ensures that the recording is performed in compliance with a data compliance policy applicable to the participant who opted out of recording.

Abstract: A conference management system (“system”) facilitates data compliance in recording conversations between users. A host user can send an electronic invitation for a meeting to participants. Upon accessing the invitation, the participants can be presented with two options to join the conference—a first option using which a participant can join the meeting by providing consent to recording the meeting and a second option using which the participant can join the meeting by opting-out of recording of the meeting. When a participant opts-out of the recording of the meeting, the conference management system ensures that the recording is performed in compliance with a data compliance policy applicable to the participant who opted out of recording.

Abstract: A conference management system (“system”) facilitates data compliance in recording conversations between users. A host user can send an electronic invitation for a meeting to participants. Upon accessing the invitation, the participants can be presented with two options to join the conference—a first option using which a participant can join the meeting by providing consent to recording the meeting and a second option using which the participant can join the meeting by opting-out of recording of the meeting. When a participant opts-out of the recording of the meeting, the conference management system ensures that the recording is performed in compliance with a data compliance policy applicable to the participant who opted out of recording.

Abstract: A feedback identification system to automatically determine customer pain points by analyzing conversations of representatives with customers. The 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 customer pain point. A customer pain point is a problem the customer is facing with a product. The set of features is analyzed to generate a feedback manifest, which includes the customer pain point (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.

Abstract: A feedback identification system to automatically determine product feature requests by analyzing conversations of representatives with customers. The 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: A product functionality identification system to automatically determine product features that are a favorite of customers by analyzing conversations of representatives with the customers. The product functionality 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 favorite functionalities of a product for one or more customers. A favorite functionality is one of multiple product features that is determined to be a favorite of one or more customers. The set of features is further analyzed to generate a favorite functionality manifest, which includes information regarding the favorite functionalities (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.

Abstract: An action item identification system automatically determines action items by analyzing conversations of representatives with customers. The 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: A risk identification system automatically determines deals at risk by analyzing conversations of representatives with customers. The 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: A pattern recognition system (“system”) automatically determines 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. The 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 system also generates second pattern data that is indicative of a pattern of the conversation of the low performing representatives. The 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 automatically determining product features that are a favorite of customers by analyzing conversations of representatives with the customers. A product functionality 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 favorite functionalities of a product for one or more customers. A favorite functionality is one of multiple product features that is determined to be a favorite of one or more customers. The set of features is further analyzed to generate a favorite functionality manifest, which includes information regarding the favorite functionalities (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 customer pain points 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 customer pain point. A customer pain point is a problem the customer is facing with a product. The set of features is analyzed to generate a feedback manifest, which includes the customer pain point (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 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.