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Additionally attributes such as country, department, and skills are defined on the queue.

Our Customer Support teams address concerns and issues from safety issues to general FAQs.

The teams delight our customers through quick resolutions, resulting from world-class support framework and an efficient workforce routing system.

This was particularly important for handling safety issues that were not impacted due to other low-priority requests like enquiries.

So our goal for the in-house solution was to ensure that we were able to configure the priority of the request queues.

Fortigate Rules Plex Media Server Full Controls Over

Building this in-house meant Grab had full controls over the design and configuration over routing.

Here are a few sample use cases that were addressed by customisation.

So, we introduced another layer of grouping for agents that share the same configuration.

For example, which queues the agents receive chats from and what the proficiency and max concurrency should be.

This limit was based on factors like the number of incoming chats and the agent performance over the last hour.

So we released an enhancement on the routing system to reroute chats handled by unavailable agents (due to disconnection for an extended period) to another available agent.The seamless experience helped increase customer satisfaction.

With the custom implementation, we were able to add more granular metrics which are very useful to assess the agent productivity and performance and helps in planning the resources ahead of time, and this is why reporting and analytics were so valuable for workforce planning.

With the custom routing solution, we were able to create customised statuses to reflect the time the agent spent in a particular status due to chat connection issues and failures and reflect this on dashboards for immediate attention.

We then automated this process with a custom implementation.

The incoming requests cannot simply be assigned to the first available agent.

The issue with this approach is that we would eventually run out of agents to serve the high-priority requests.

As a result, the low-priority requests are directly processed instead of waiting for the queue to fill up before processing high-priority requests.

Because of this queuing issue, prioritisation of requests is critical.

Reallocations happen when a chat is transferred to another agent or when it was not accepted by the allocated agent.

When reallocated, it goes back to the queue with a higher priority.

There is also a cooldown period of 100ms before it moves on to receive a message from a different priority queue.

We addressed this by having multiple workers running concurrently.

To ensure that this doesnt happen, the workers are forced to run out of sync by tweaking the order in which priority queues are processed, such that when worker1 is processing a high-priority queue request, worker2 is waiting for a request in the medium-priority queue instead of the high-priority queue.

On top of that, we wanted to ensure that there is a limit on the number of requests that a queue can accept at a time, guaranteeing that the system isnt flushed with too many requests, which can lead to longer waiting times for request allocation.