Dynamic occupancy predictive routing and methods
The methods, apparatus, and systems described herein facilitate dynamic occupancy routing decisions. The methods include predicting a likelihood, based on day or time of day, that a customer having a retrieved or predicted profile is expected to initiate a customer communication; receiving, by one or more processors, the customer communication; providing a list of currently available agents and expected available agents, wherein the currently available agents are selected by excluding agents that (i) exceed a predetermined work threshold; and (ii) have exceeded a predetermined occupancy level; providing a routing recommendation to a communication distributor based on the predicted likelihood that the customer will initiate the customer communication on a day or at a time of day, and currently available agents' and expected available agents' proficiency at handling customers with the retrieved or predicted profile; and routing the customer communication via the communication distributor to an agent based on the recommendation.
1. A system configured to optimize routing of incoming customer communications, which comprises a node comprising a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, where the plurality of instructions, when executed:
predict a likelihood, based on day or time of day, that a customer having a retrieved or predicted profile is expected to initiate a customer communication;
provide a list of currently available agents and expected available agents, wherein the currently available agents exclude agents that (i) exceed a predetermined work threshold; and (ii) have exceeded a predetermined occupancy level;
provide a routing recommendation to a communication distributor based on the predicted likelihood that the customer will initiate a customer communication on a day or at a time of day, and currently available agents' and expected available agents' proficiency at handling one or more customers with the retrieved or predicted profile; and
route the customer communication via the communication distributor to an agent based on the routing recommendation.
2. The system of claim 1 , wherein excluding an agent that has exceeded a predetermined occupancy level is determined by instructions that, when executed, selects agents who have worked less than a group of agents over a specified period.
3. The system of claim 2 , which further comprises instructions that, when executed, adjusts the predetermined occupancy level to ensure that each agent achieves at least a minimum occupancy by routing a customer communication to the agent with the lowest current occupancy level.
4. The system of claim 2 , wherein the predetermined occupancy level is temporarily adjusted to an adapted threshold to route a customer communication to the agent with the lowest current occupancy level.
5. The system of claim 1 , wherein the instructions that, when executed, predict a likelihood, further comprise instructions that, when executed, create a current probability matrix based at least on the day or time of day.
6. The system of claim 5 , wherein the current probability matrix is updated with actual customer routing information based on the routed customer communications.
7. The system of claim 1 , wherein the instructions that, when executed, return a list of currently available agents and expected available agents further comprise instructions that, when executed, dynamically monitor occupancy level of each agent to determine real-time availability and performance metrics.
8. The system of claim 1 , wherein the retrieved or predicted customer profile comprises one or more of a personality type, task type, likelihood of purchase, contact time, likelihood of attrition or account closure, and/or customer satisfaction.
9. The system of claim 1 , wherein, if a currently available agent is proficient at handling a current customer and an expected future customer, and an expected available agent is proficient at handling a current customer and is not as proficient at handling the expected future customer, then the instructions that, when executed, provide the routing recommendation recommend routing the current customer to the expected available agent.
10. A system configured to optimize routing of incoming customer communications, which comprises a node comprising a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, where the plurality of instructions, when executed:
predict a likelihood, based on day or time of day, that a customer having a predicted profile is expected to initiate a customer communication;
return a list of currently available agents, wherein the list of currently available agents is based on occupancy and is ordered by agents who have worked the least over a specified period;
provide a routing recommendation to a communication distributor based on the predicted likelihood that the customer will initiate a customer communication on a day or at a time of day, and currently available agents' proficiency at handling one or more customers with the predicted profile; and
route the customer communication via the communication distributor to an agent based on the routing recommendation.
11. The system of claim 10 , wherein the routing recommendation is further based on speed so as to serve an increased number of customers over a pre-selected time period.
12. A system configured to optimize routing of incoming customer communications, which comprises:
a database to retrieve or predict a first profile of a current customer, and predict a second profile of a future customer, wherein the second profile is based on day or time of day that the customer is expected to initiate a customer communication;
a governor processor to rank currently available agents and expected available agents based on their proficiency at handling customers with the retrieved or predicted profiles, wherein the currently available agents exclude agents that (i) exceed a predetermined work threshold; and (ii) have exceeded a predetermined occupancy level; and
a routing apparatus to match each customer communication to an agent based on the retrieved or predicted first profile of the current customer and the predicted second profile of the future customer, and the rankings of the currently available agents and the expected available agents, wherein the routing apparatus comprises a communication distributor that routes each customer communication to an agent based on the routing apparatus match.
13. An analytics center comprising the system of claim 12 .
14. A method to optimize routing incoming customer communications, which comprises:
predicting a likelihood, based on day or time of day, that a customer having a retrieved or predicted profile is expected to initiate a customer communication;
receiving, by one or more processors, a customer communication;
providing a list of currently available agents and expected available agents, wherein the currently available agents are selected by excluding agents that (i) exceed a predetermined work threshold; and (ii) have exceeded a predetermined occupancy level;
providing a routing recommendation to a communication distributor based on the predicted likelihood that the customer will initiate the customer communication on a day or at a time of day, and currently available agents' and expected available agents' proficiency at handling one or more customers with the retrieved or predicted profile; and
routing the customer communication via the communication distributor to an agent based on the routing recommendation.
15. The method of claim 14 , wherein the retrieved or predicted profile comprises one or more of personality type, task type, likelihood of purchase, contact time, likelihood of attrition or account closure, and customer satisfaction for the customer and the future customer.
16. The method of claim 14 , wherein excluding an agent that has exceeded a predetermined occupancy level is determined by selecting agents who have worked less than a group of agents over a specified period.
17. The method of claim 16 , which further comprises adjusting the predetermined occupancy level to ensure that each agent achieves at least a minimum occupancy by routing a customer communication to the agent with the lowest current occupancy level.
18. The method of claim 16 , wherein the predetermined occupancy level is temporarily adjusted to an adapted threshold to route a customer communication to the agent with the lowest current occupancy level.
19. The method of claim 14 , wherein the predicting a likelihood comprises creating a current probability matrix based at least on the day or time of day.
20. The method of claim 19 , wherein the current probability matrix is updated with actual customer routing information based on the routed customer communications.
21. The method of claim 14 , wherein the providing a list of currently available agents and expected available agents further comprises dynamically monitoring occupancy level of each agent to determine real-time availability and performance metrics.
22. The method of claim 14 , wherein, if a currently available agent is proficient at handling a current customer and an expected future customer, and an expected available agent is proficient at handling a current customer and is not as proficient at handling the expected future customer, then providing the routing recommendation to recommend routing the current customer to the expected available agent.