Predicting Contact Center Agent Demand Using Multiple Modeling Engines

1 . A method, comprising:

receiving a request to determine a number of agents working at a future time;

generating, using a combination engine, a combination of one or more modeling engines from multiple modeling engines;

determining, using the combination of the one or more modeling engines, the number of agents; and

providing an output representing the number of agents.

2 . The method of claim 1 , comprising:

obtaining, at the future time, contact center data of the contact center, wherein the contact center data comprises at least one of a number of agents currently working, engagement volume data, engagement length data, contact center user wait time data, or contact center agent availability data; and

training, using online learning, the combination engine and the multiple modeling engines based on the obtained contact center data.

3 . The method of claim 1 , wherein the output comprises a prompt to accept an overtime assignment that is transmitted to a device of at least one agent.

4 . The method of claim 1 , wherein the output comprises a graphical indication of the future time, a graphical indication of the number of agents, and a graphical indication of a number of agents currently scheduled to work at the future time.

5 . The method of claim 1 , comprising:

receiving an input representing a service level target, wherein the number of agents is determined based on the service level target, wherein the service level target represents a proportion of contact center users who are connected to a contact center agent within a given time period after requesting connection to the contact center agent.

6 . The method of claim 1 , wherein the multiple modeling engines comprise at least one of a weighted weekly moving average engine, a long short-term memory engine, or a time-series forecasting engine.

7 . The method of claim 1 , comprising:

determining a number of agents who were working at the future time and a service level of the contact center at the future time; and

training the multiple modeling engines based on the number of agents who were working and the service level.

8 . A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:

receiving a request to determine a number of agents working at a future time;

generating, using a combination engine, a combination of one or more modeling engines from multiple modeling engines;

determining, using the combination of the one or more modeling engines, the number of agents; and

providing an output representing the number of agents.

9 . The computer readable medium of claim 8 , the operations comprising:

obtaining, at the future time, contact center data of the contact center, wherein the contact center data comprises at least one of a number of agents currently working, engagement volume data, engagement length data, or contact center agent availability data; and

training, using online learning, the combination engine and the multiple modeling engines based on the obtained contact center data.

10 . The computer readable medium of claim 8 , wherein the output comprises a prompt to accept an assignment that is transmitted to a device of an agent.

11 . The computer readable medium of claim 8 , wherein the output comprises a graphical indication of the future time and a graphical indication of the number of agents.

12 . The computer readable medium of claim 8 , the operations comprising:

receiving an input representing a service level target for determining the number of agents, wherein the service level target represents a proportion of contact center users who are connected to a contact center agent within a given time period after requesting connection to the contact center agent.

13 . The computer readable medium of claim 8 , wherein the multiple modeling engines comprise at least one of a weighted weekly moving average engine or a long short-term memory engine.

14 . The computer readable medium of claim 8 , the operations comprising:

determining a number of agents who were working at the future time and a service level of the contact center at the future time; and

training the multiple modeling engines based on the number of agents who were working at the future time and the service level at the future time.

15 . An apparatus comprising:

a memory; and

a processor configured to execute instructions stored in the memory to:

receive a request to determine a number of agents working at a future time;

generate, using a combination engine, a combination of one or more modeling engines from multiple modeling engines;

determine, using the combination of the one or more modeling engines, the number of agents; and

provide an output representing the number of agents.

16 . The apparatus of claim 15 , the processor configured to execute the instructions stored in the memory to:

obtain, at the future time, contact center data of the contact center, wherein the contact center data comprises at least one of a number of agents currently working, contact center user wait time data, or contact center agent availability data; and

train, using online learning, the combination engine and the multiple modeling engines based on the obtained contact center data.

17 . The apparatus of claim 15 , wherein the output comprises a prompt, to a device of at least one agent, to work at the future time.

18 . The apparatus of claim 15 , wherein the output comprises a graphical indication of the number of agents and a graphical indication of a number of agents presently scheduled to work at the future time.

19 . The apparatus of claim 15 , the processor configured to execute the instructions stored in the memory to:

receive an input representing a service level target, wherein the number of agents is determined based on the service level target.

20 . The apparatus of claim 15 , wherein the multiple modeling engines comprise a weighted weekly moving average engine.