Techniques for behavioral pairing in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a contact center system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, a plurality of contacts available for connection to an agent; determining, by the at least one computer processor, a plurality of preferred contact-agent pairings among possible pairings between the agent and the plurality of contacts; selecting, by the at least one computer processor, one of the plurality of preferred contact-agent pairings according to a probabilistic network flow model; and outputting, by the at least one computer processor, the selected one of the plurality of preferred contact-agent pairings for connection in the contact center system.

One example method of operation may include identifying one or more call parameters associated with each of a number of calls received over a fixed period of time, assigning scores to each of the calls based on the one or more identified call parameters for each of the plurality of calls, assigning one or more of the calls to a scam call category based on the assigned scores, and responsive to the assigning of the one or more of the calls to a scam call category, determining whether a number of remaining calls of the calls, which are not assigned to the scam call category, have increased or decreased beyond a deviation margin of a target percentage of calls.

A system and method for optimizing callback times to increase the success rate of callbacks while managing overflow of calls to alternate sites when callbacks are unsuccessful. The system and method use a pacing algorithm to determine when callbacks are likely to be successful from a preferred contact site, routing to alternate callback sites when callbacks are unsuccessful, and preferences for re-routing back to the preferred site when a callback is successful and the agent with whom the caller has interacted previously is available.

The system described herein can automatically match, link, or otherwise associate electronic activities with one or more record objects. For an electronic activity that is eligible or qualifies to be matched with one or more record objects, the system can identify one or more set of rules or rule sets. Using the rule sets, the system can identify candidate record objects. The system can then rank the identified candidate record objects to select one or more record objects with which to associate the electronic activity. The system can then store an association between the electronic activity and the selected one or more record objects.

A system and method for investigating an issue may include defining a set of phrase categories; associating each of a set of recorded interactions with at least one phrase category; receiving a selection of a first time interval and a second time interval; selecting a reference phrase category; calculating, for at least some of the phrase categories in the set of phrase categories a correlation differential based on a trend of the phrase category and a trend of the reference phrase category and, if a correlation differential of a category is larger than a threshold then including the category in the reference category.

The present disclosure is directed to methods including obtaining a location data of a source; obtaining at least one performance measure; correlating the location data and the at least one performance measure to obtain a correlation; and analyzing the correlation to obtain an analysis report. The present disclosure is further directed to systems that include a source related to a contact center, and a processor configured to: obtain a location data of the source; derive at least one performance measure; correlate the location data and the at least one performance measure to obtain a correlation; and analyze the correlation to obtain an analysis report.

Systems and methods for using secondary communication channel information, such as RTCP information to facilitate correlating business information to allow gateway recording. Using information in this secondary communication channel the recording system can correlate the business metadata either directly (e.g., using a phone extension) or through one or more indirect operations (e.g., a call ID-to-universal call id-to-phone extension). Additionally, information in the primary communication channel may be used to perform an indirect operation to correlate auxiliary business information (e.g., using the CALLID or UCID in SIP signaling to resolve the agent's phone extension and thereby access auxiliary business information.)

A method and system enables converting Application-to-Person (A2P) messaging to Person-to-Person (P2P) messaging. Customer replies to short code A2P messages are diverted to alternate destinations in an enterprise. Enterprise destinations include call center operations and data enabled devices in various departments in an enterprise. Information collected from the reply messages can be used to determine the appropriate destination and can be used to analyze support and marketing statistics.

A computer-implemented method includes identifying a mobile phone. The method includes identifying one or more input devices. The one or more input devices are associated with the mobile phone. The method includes collecting behavior information from the input devices. The method includes applying machine learning to the behavior information to yield a schedule.

In some embodiments, a system and method for generating phrase based categories of interactions may include obtaining a base category, the base category including one or more phrases. A base trend for the base category may be generated based on a frequency of appearance of at least one of the one or more phrases in a set of recorded interactions; a candidate phrase may be obtained and a candidate trend may be generated for the candidate phrase based on a frequency of appearance of the candidate phrase in the set of recorded interactions; a correlation level may be determined by relating the candidate trend to the base trend; and, if the correlation level is greater than a threshold level then the candidate phrase may be included the in the base category.