Contact Center Device Queue Wait Time Estimation
At a server, a user device is added to a queue of devices stored at the server. A number of devices, of the queue of devices, preceding the user device in the queue is determined. A number of contact center agent devices is determined. An estimated wait time for the user device is calculated based on the number of devices preceding the user device in the queue, the number of contact center agent devices, and wait times of devices, distinct from the user device, in the queue.
1 . A method comprising:
adding, at a server, a user device to a queue of devices stored at the server;
determining, at the server, a number of devices, of the queue of devices, preceding the user device in the queue;
determining, at the server, a number of contact center agent devices; and
calculating, at the server, an estimated wait time for the user device based on the number of devices preceding the user device in the queue, the number of contact center agent devices, and wait times of devices, distinct from the user device, in the queue.
2 . The method of claim 1 , wherein determining the number of contact center agent devices comprises:
determining the number of contact center agent devices based on network connection data between the server and the contact center agent devices, the network connection data including a connection status.
3 . The method of claim 1 , wherein calculating the estimated wait time comprises:
computing, using a statistical engine, a value based on the wait times of the devices distinct from the user device; and
calculating, using a combination engine, the estimated wait time based on a combination of the value and a quotient of the number of devices preceding the user device in the queue divided by the number of contact center agent devices.
4 . The method of claim 1 , wherein calculating the estimated wait time comprises:
calculating, using a combination engine, the estimated wait time based on a mean of (i) an average hold time multiplied by a quotient of the number of devices preceding the user device in the queue divided by the number of contact center agent devices, as computed every threshold time period, (ii) the average hold time multiplied by the quotient of the number of devices preceding the user device in the queue divided by the number of contact center agent devices, as computed at a call termination of one of the devices preceding the user device in the queue, and (iii) a value computed by a statistical engine based on the wait times of the devices distinct from the user device.
5 . The method of claim 1 , further comprising:
receiving, at the server and from the user device, a request for a contact center engagement, wherein the request comprises natural language text; and
identifying, using a natural language processing engine and based on the natural language text, the queue from among a plurality of queues.
6 . The method of claim 1 , wherein calculating the estimated wait time comprises:
calculating the estimated wait time once every threshold time period.
7 . The method of claim 1 , wherein calculating the estimated wait time comprises:
calculating the estimated wait time based on a connection of a device, of the devices distinct from the user device, to a contact center agent device.
8 . The method of claim 1 , wherein calculating the estimated wait time comprises:
calculating the estimated wait time based on a change in the number of contact center agent devices.
9 . The method of claim 1 , further comprising:
transmitting, to the user device, an output associated with the estimated wait time, wherein the output causes a color-coded display, at the user device, of the estimated wait time.
10 . The method of claim 1 , wherein calculating the estimated wait time comprises:
calculating, using a predictive artificial intelligence model, the estimated wait time based on at least one of a predicted average hold time, a position of the user device in the queue, the number of contact center agent devices, a time of day, a day of a week, or a calendar date.
11 . At least one non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising:
adding, at a server, a user device to a queue of devices stored at the server;
determining, at the server, a number of devices, of the queue of devices, preceding the user device in the queue;
determining, at the server, a number of contact center agent devices; and
calculating, at the server, an estimated wait time for the user device based on the number of devices preceding the user device in the queue, the number of contact center agent devices, and wait times of devices, distinct from the user device, in the queue.
12 . The at least one non-transitory computer readable medium of claim 11 , wherein a combination engine calculates the estimated wait time based on a combination of the number of devices preceding the user device in the queue divided by the number of contact center agent devices and a number computed by a statistical engine based on the wait times of the devices distinct from the user device.
13 . The at least one non-transitory computer readable medium of claim 11 , wherein a combination engine calculates the estimated wait time based on a mean of (i) an average hold time multiplied by a quotient of the number of devices preceding the user device in the queue divided by the number of contact center agent devices, as computed every threshold time period, (ii) the average hold time multiplied by the quotient of the number of devices preceding the user device in the queue divided by the number of contact center agent devices, as computed at a call termination of a device of the devices preceding the user device in the queue, and (iii) a value computed by an artificial intelligence engine based on the wait times of the devices distinct from the user device.
14 . The at least one non-transitory computer readable medium of claim 11 , the operations comprising:
receiving, at the server and from the user device, a natural language request for a contact center engagement; and
identifying, using a natural language processing engine and based on the natural language request, the queue from among a plurality of queues.
15 . The at least one non-transitory computer readable medium of claim 11 , wherein calculating the estimated wait time comprises calculating the estimated wait time once every preset time period.
16 . The at least one non-transitory computer readable medium of claim 11 , wherein calculating the estimated wait time comprises calculating the estimated wait time based on connection of a device in the queue with a contact center agent device.
17 . The at least one non-transitory computer readable medium of claim 11 , the operations comprising:
transmitting, to the user device, an output associated with the estimated wait time, wherein the output causes a color-coded display, at the user device, of the estimated wait time.
18 . A system comprising:
memory hardware storing instructions; and
processing circuitry configured to execute the instructions to:
add, at a server, a user device to a queue of devices stored at the server;
determine, at the server, a number of devices, of the queue of devices, preceding the user device in the queue;
determine, at the server, a number of contact center agent devices; and
calculate, at the server, an estimated wait time for the user device based on the number of devices preceding the user device in the queue, the number of contact center agent devices, and wait times of devices, distinct from the user device, in the queue.
19 . The system of claim 18 , comprising a combination engine that calculates the estimated wait time based on at least one of: the number of devices preceding the user device in the queue divided by the number of contact center agent devices or a value computed by a statistical engine based on the wait times of the devices distinct from the user device.
20 . The system of claim 18 , comprising a predictive artificial intelligence model that calculates the estimated wait time based on at least one of a predicted average hold time, a position of the user device in the queue, the number of contact center agent devices, a time of day, a day of a week. or a calendar date.