18. A non-transitory machine-readable storage medium having embodied thereon instructions executable by one or more machines to perform operations, comprising: querying historical linguistic data sets; extracting from the historical linguistic data sets conversations between customers and customer relations representatives; wherein extracting the 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; labeling each said segment in said each pseudo synchronous conversation according to an engagement analysis; wherein said historical linguistic data sets are asynchronous and decentralized; and, wherein if a lapsed time between consecutive messages is greater than or equal to a predeterminable time period, then identify a pseudo conversation as two different conversations.
18. A non-transitory machine-readable storage medium having embodied thereon instructions executable by one or more machines to perform operations, comprising: querying historical linguistic data sets; extracting from the historical linguistic data sets conversations between customers and customer relations representatives; wherein extracting the 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; labeling each said segment in said each pseudo synchronous conversation according to an engagement analysis; wherein said historical linguistic data sets are asynchronous and decentralized; and, wherein if a lapsed time between consecutive messages is greater than or equal to a predeterminable time period, then identify a pseudo conversation as two different conversations. 19. The method of claim 18 , further comprising: computing engagement metrics from said labels of each said segment, wherein said engagement metrics are selected from the group consisting of: resolution rate, properly handled threads rate, customer hang-up, conversion rate, and happy customer rate.