A sentiment-based score is determined for a contact center engagement between a first contact center service operator and a contact center user. The sentiment-based score is indicated within a graphical user interface displaying information associated with multiple contact center engagements at a device of a second contact center service operator. Based on a request to participate in the contact center engagement received from the device of the second contact center service operator via the graphical user interface, information associated with the contact center engagement is transmitted to the device of the second contact center service operator, and a contact center session involving a device of the contact center user and the device of the second contact center service operator is established.

A computerized-method for prioritizing agents for working-from-office, in a hybrid contact-center work-environment, is provided herein. The computerized-method includes operating an Agent-Work-From-Office (AWFO) Prioritization Analytics module. The AWFO Prioritization-Analytics-module includes: (i) for each agent in the data store of agents' metrics calculating an Agent Health (AH) score; (ii) when the AH score is ‘1’ then calculating: (a) Agent Home Productivity (AHP) score; (b) Agent Skills Prioritization (ASP) score; and (c) agent's preferences to work from office indicator; (iii) determining an AWFO-score based on the AHP score, the ASP score and the agent's preferences to work from office indicator: (iv) adding the determined AWFO-score to a list-of-agents-AWFO-score and after the calculating of the AH score for all agents, sending the list-of-agents-AWFO-score to a WFM-application to allocate by the WFM-application agents to work-from-office according to a preconfigured office capacity and a scheduled-shift requirements by selecting agents which are having a lowest AWFO-score.

A computerized-method for prioritizing agents for working from office, in a hybrid contact center work environment, is provided herein. The computerized-method includes operating an Agent Work From Office (AWFO) Prioritization Analytics module. The AWFO Prioritization Analytics module includes: (i) for each agent in the data store of agents’ metrics calculating an Agent Health (AH) score; (ii) when the AH score is ‘1’ then (a) calculating: (a.i) Agent Home Productivity (AHP) score; (a.ii) Agent Skills Prioritization (ASP) score; and (a.iii) agent’s preferences to work from office indicator; (b) determining an A WFO score based on the AHP score, the ASP score and the agent’s preferences to work from office indicator; and (c) sending the determined A WFO score to a Workforce Management (WFM) application to be presented via a User Interface (UI) thereof.

Dynamically evaluating effectiveness of real-time messages is provided. A scalable signal reconstruction analysis of real-time message content in a question-and-answer format posted in a social network is performed. A message effectiveness evaluator model is generated based on the scalable signal reconstruction analysis of the real-time message content in the question-and-answer format. An effectiveness score corresponding to the real-time message content in the question-and-answer format is generated using the message effectiveness evaluator model.

A computerized-method for identifying nonadherence to contact-center practice rules is provided herein. The computerized-method includes operating a data-analyzer module. The said data-analyzer module includes: (i) retrieving a set of rules; (ii) monitoring activities of each user via one or more product-applications by receiving a stream of data related to the activities from the one or more applications. The stream of data may include details of the monitored activities; (iii) comparing details of the monitored activities with each rule in the set of rules to identify one or more activities that are breaching a rule from the set of rules; (iv) for each activity of a related-user from the identified one or more activities: (a) notifying the related-user about a guideline-breaching via a computerized-device of the related-user; and (b) updating the data store of exceptions with the details of the activity that breached the rule from the set of rules.

A pest monitoring system and associated method includes pest monitoring devices for placement at a site and generating a signal upon detection of a pest, and a pesticide dispensing unit including a reservoir housing a diluent and a pesticide module housing a pesticide. A computer device remotely disposed to the pest monitoring devices receives generated signals therefrom, analyzes environmental and historical factor data for the site to determine an amount and placement location of the pest monitoring devices for monitoring the site, and directs deployment of the pest control monitoring devices according to the analysis. The computer device also receives data from the pesticide dispensing unit including an amount or rate of the pesticide substance dispensed from the pesticide module, to form treatment having a concentration of the pesticide substance in the diluent, the treatment being dispensed from the pesticide dispensing unit to treat a detected pest at the site.

A system is described for calculating and outputting micro invisible lost time (MILT). The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. Time-stamp data that includes values of drilling parameters may be received about a drilling operation, and the values of drilling parameters may be classified into a rig state that includes rig activities. For each rig activity, an actual completion time may be determined and compared to an expected completion time for determining a deviation. At least one deviated activity, in which the deviation is greater than a threshold, may be determined. Deviations may be combined into MILT that can be output for controlling the drilling operation.

A system and method of combining table management software and customer experience skills by generating profiles of patrons, servers, and restaurants by using machine learning algorithms on and location data in those profiles to build more intimate relationships between patrons, food service establishments, and food service professionals. Trait matching provides optimized matchmaking between patrons and servers who share certain commonalities or affinities while also balancing the table management operations. Machine learning algorithms may be used to identify patterns of commonality that would not otherwise be recognized. This system allows patrons to choose servers at an establishment over a plurality of electronic devices by using the cluster analysis results. It provides servers more lateral work experience between participating restaurants, and restaurants more power to operate smoothly and build highly cohesive teams.

In some implementations, a system may collect historical interaction information associated with previous service interactions involving users that received the service. The system may determine, based on the historical interaction information, an impact score associated with an attribute of a group of the users satisfies a degradation threshold. The system may receive a request involving a user receiving the service, wherein the request indicates that the user is associated with the attribute. The system may determine, based on the user being associated with the attribute and the impact score satisfying the degradation threshold, a parameter that reduces a probability that the user experiences the degradation of the performance metric when receiving the service. The system may perform an action associated with the parameter and the user.

Disclosed are system and methods for digitally capturing, labeling, and analyzing data representing shared experiences between a service provider and a customer. The shared experience data is used to identify, test, and implement value-added improvements, enhancements, and augmentations to the shared experience and to monitor and ensure the quality of customer service. The improvements can be implemented as customer service process modifications, precision learning and targeted coaching for agents rendering customer service, process compliance monitoring, and as knowledge curation for a knowledge bot software application that facilitates automation of tasks and provides a natural language interface for accessing historical knowledge bases and solutions.