BACKGROUND

An organization may use any number of computing systems, communications networks, data storage devices, or other types of systems to provide service to individuals. An organization may also employ service representatives that use the various systems to assist individuals in service sessions that are conducted over the telephone, in a video conference, through text chat sessions, in person, and/or over other communication channels. Organizations may strive to provide an efficient and productive service interaction between service representatives and the individuals being serviced, while maintaining an appropriate quality level for the service provided by service representatives.

SUMMARY

Implementations of the present disclosure are generally directed to determining one or more metrics that describe a service session. More specifically, implementations are directed to using machine learning techniques, such as deep learning that includes classification, clustering, and/or other techniques, to determine metric(s) for a service session and a customer experience.

In general, innovative aspects of the subject matter described in this specification can be embodied in methods that includes actions of receiving, by at least one processor, a session record of communications between a service representative (SR) and an individual during a service session. The methods include the actions of providing, by at least one processor, the session record as input to at least one computer-processable model that determines, based on the session record, at least one metric for the service session, the at least one model having been trained, using machine learning and based at least partly on survey data for previous service sessions, to provide the at least one metric associated with the individual's experience. The methods include the actions of associating, by at least one processor, the metric of the individual's experience with the individual. The methods also include the actions of communicating, by at least one processor, the at least one metric for presentation through a user interface of a computing device.

Implementations can optionally include one or more of the following features, alone or in combination. The service session may be an audio call between the CSR and the individual. The session record may include an audio record of at least a portion of the audio call. The methods may include developing, by at least one processor, the at least one model based on training data that describes, for each of a plurality of previous service sessions: a previous session record of communications during a respective previous service session, and at least one survey score provided to rate the respective previous service session. The respective previous service session may be an audio call; and the previous session record may include an audio record of at least a portion of the audio call. The methods may include the actions of updating the customer's requirements based on metric and selecting another service representative to interact with the individual on a subsequent service session based in part on the updated customer requirements. The methods may include the actions of receiving a request for a service session from a first individual, identifying a second individual similar to the first individual, and connecting the first individual with another service representative, where the other service representative is selected, at least in part, based on the metrics associated with the second individual.

Other implementations of any of the above aspects include corresponding systems, apparatus, and computer programs that are configured to perform the actions of the methods, encoded on computer storage devices. The present disclosure also provides a computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein.

Implementations of the present disclosure provide one or more of the following advantages. Through use of machine learning developed models to determine metrics that are predicted survey results, implementations provide a technique to determine survey results in a service environment even in instances when the individual being serviced does not complete a survey. Moreover, the model(s) enable an objective determination of survey results across a wider range of individuals than would ordinarily complete survey results, thus providing more accurate, objective, and comprehensive view of the service provided in a service environment. By providing more accurate survey results through use of predictive modeling, implementations may avoid repeatedly requesting that individual(s) complete surveys following a service session, thus avoiding the expenditure of processing power, network bandwidth, storage space, active memory, and/or other computing resources that may be expended in traditional service environments. In instances where the predictive model(s) are sufficiently developed to enable session metric(s) to be determined without surveying individuals, implementations also avoid the expenditure of computing resources that would otherwise be consumed to survey individuals following a service session.

It is appreciated that aspects and features in accordance with the present disclosure can include any combination of the aspects and features described herein. That is, aspects and features in accordance with the present disclosure are not limited to the combinations of aspects and features specifically described herein, but also include any combination of the aspects and features provided.

The details of one or more implementations of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the present disclosure will be apparent from the description and drawings, and from the claims.

