1. A method for computing online metrics for customer relations management, comprising: querying historical linguistic data sets of exchanges between customers and customer relations representatives over social media; extracting from the historical linguistic data sets conversations between said customers and said customer relations representatives; wherein extracting conversations uses a customer relations representative identifier and extracts all messages sent by said customer relations representative identifier or to said customer relations representative identifier; wherein said messages are grouped by a customer using a customer associated identifier; ordering chronologically a list of all said messages by said customer associated identifier; extracting from the list of all said messages groups of messages, wherein each group of messages is related to an individual subject; aggregating said groups of messages into pseudo synchronous conversations; separating each pseudo synchronous conversation of the pseudo synchronous conversations into one or more segments and associating one or more classes to each of the one or more segments; labeling each said segment in said each pseudo synchronous conversation according to a conversation analysis; and, labeling each said segment in said each pseudo synchronous conversation according to an engagement analysis.
1. A method for computing online metrics for customer relations management, comprising: querying historical linguistic data sets of exchanges between customers and customer relations representatives over social media; extracting from the historical linguistic data sets conversations between said customers and said customer relations representatives; wherein extracting conversations uses a customer relations representative identifier and extracts all messages sent by said customer relations representative identifier or to said customer relations representative identifier; wherein said messages are grouped by a customer using a customer associated identifier; ordering chronologically a list of all said messages by said customer associated identifier; extracting from the list of all said messages groups of messages, wherein each group of messages is related to an individual subject; aggregating said groups of messages into pseudo synchronous conversations; separating each pseudo synchronous conversation of the pseudo synchronous conversations into one or more segments and associating one or more classes to each of the one or more segments; labeling each said segment in said each pseudo synchronous conversation according to a conversation analysis; and, labeling each said segment in said each pseudo synchronous conversation according to an engagement analysis. 5. The method of claim 1 , wherein if the lapsed time between consecutive messages is less than three days, then identify pseudo conversation as same conversation.