Method of integrating artificial intelligence systems to dynamically adjust wrap-up time
The present disclosure provides, among other things, a method of managing a wrap-up time in a contact center, the method including: receiving, by an agent of the contact center, a communication having a variable associated with the communication; receiving an input from a source external to the contact center; determining that the variable is related to the input; based on the relation of the variable to the input, determining an updated wrap-up time; storing the updated wrap-up time and the input in a database including timing variables; enabling a machine learning process to analyze the database; providing the updated wrap-up time to the agent as an amount of time rendered on a display to the agent; and updating a data model used to automatically determine wrap-up times based on the analysis of the machine learning process.
1. A method of managing a wrap-up time in a contact center, the method comprising:
receiving, by an agent of the contact center, a communication having a variable associated with the communication;
receiving an input from a source external to the contact center;
determining that the variable is related to the input;
based on the relation of the variable to the input, determining an updated wrap-up time;
storing the updated wrap-up time and the input in a database comprising timing variables;
enabling a machine learning process to analyze the database;
providing the updated wrap-up time to the agent as an amount of time rendered on a display to the agent; and
updating a data model used to automatically determine wrap-up times based on the analysis of the machine learning process.
2. The method of claim 1 , further comprising:
receiving input from the agent during the wrap-up time, wherein the receiving the input comprises at least one of storing a result and automatically reporting a result; and
displaying an end of the updated wrap-up time to the agent.
3. The method of claim 1 , wherein the input from the external source is received during the communication.
4. The method of claim 1 , wherein the machine learning process retrieves data related to the variable from the timing variables database, and wherein the updated wrap-up time is based on the data.
5. The method of claim 1 , wherein the machine learning process performs an analysis of a sentiment contained in the communication.
6. The method of claim 1 , wherein the data model is used to automatically classify communications, and further comprising providing a second updated wrap-up time to an agent associated with a second communication based on the automatic classification and the input, wherein the second updated wrap-up time is a second amount of time rendered on the display to the agent.
7. The method of claim 1 , wherein the variable changes during the communication and wherein the machine learning process performs the analysis over a timeframe that includes the changes in the variable.
8. The method of claim 1 , wherein the updated wrap-up time is determined based on a rule of the contact center considered in combination with the analysis by the machine learning process.
9. The method of claim 1 , further comprising:
receiving a secondary input from a second source external to the contact center, wherein the second source is one of a news feed, a social media feed, and a business rule, and wherein the updated wrap-up time is determined by the machine learning process and based on both of the input and the secondary input.
10. The method of claim 1 , wherein the machine learning process analyzes the database to obtain the input before a start of the communication.
11. A communication system, comprising:
a processor; and
computer memory storing data thereon that enables the processor to:
receive, by an agent of the contact center, a communication having a variable associated with the communication;
receive an input from a source external to the contact center;
determine that the variable is related to the input;
based on the relation of the variable to the input, determine an updated wrap-up time;
store the updated wrap-up time and the input in a database comprising timing variables;
enable a machine learning process to analyze the database;
provide the updated wrap-up time to the agent as an amount of time rendered on a display to the agent; and
update a data model used to automatically determine wrap-up times based on the analysis of the machine learning process.
12. The communication system of claim 11 , wherein the processor is further enabled to receive input from the agent during the wrap-up time, wherein the receiving the input comprises at least one of storing a result and automatically reporting a result.
13. The communication system of claim 11 , wherein the input from the external source is received during the communication.
14. The communication system of claim 11 , wherein the machine learning process retrieves data related to the variable from the timing variables database, and wherein the updated wrap-up time is based on the data.
15. The communication system of claim 11 , wherein the machine learning process is further enabled to determine the updated wrap-up time based on a sentiment contained in the communication.
16. The communication system of claim 11 , wherein the data model is used to automatically classify communications, and the processor is further enabled to provide a second updated wrap-up time to an agent associated with a second communication based on the automatic classification and the input.
17. The communication system of claim 11 , wherein the processor is further enabled to invoke a reporting function when the updated wrap-up time is provided to the agent or when the data model is updated.
18. The communication system of claim 11 , wherein the updated wrap-up time is determined based on a rule of the contact center considered in combination with the analysis by the machine learning process.
19. The communication system of claim 11 , wherein the processor is further enabled to receive a secondary input from a second source external to the contact center, wherein the second source is one of a news feed, a social media feed, and a business rule, and wherein the updated wrap-up time is determined by the machine learning process and based on both of the input and the secondary input.
20. A contact center, comprising:
a server comprising a processor and a wrap-up timing engine that is executable by the processor and that enables the processor to:
receive, by an agent of the contact center, a communication having a variable associated with the communication;
receive an input from a source external to the contact center;
determine that the variable is related to the input;
based on the relation of the variable to the input, determine an updated wrap-up time;
store the updated wrap-up time and the input in a database comprising timing variables;
enable a machine learning process to analyze the database;
provide the updated wrap-up time to the agent as an amount of time rendered on a display to the agent; and
update a data model used to automatically determine wrap-up times based on the analysis of the machine learning process.