Queuing and routing customer requests in Pega Chat
Queuing and routing customer requests in Pega Chat
|Description||Recommendations for balancing chat queues and agent workloads.|
|Version as of||8.4|
|Capability/Industry Area||Pega Chat|
For text-based interactions between customer service representatives (CSRs) and customers, a chatbot is usually the first point of contact and it is always available. When escalation to a human agent is sought by the customer (or triggered by the chatbot), Pega queuing and routing logic kick in.
The first expected action from the customer will be queue selection, which is generally a proxy for intent expression. A chat queue is manned by one or more agents with a capacity to work on one or more text-based customer requests at the same time: concurrency is a configurable option at the agent, queue, and global level.
Separate queues are needed for business functions that need a distinct set of skills in a service representative. For example, billing could be a queue to direct all billing-related inquiries. Avoid configuring too many queues because this could result in spreading the available agent capacity too thinly across the queues. Chat requests are not prioritized by queue - all incoming requests are given equal weight, which might lead to more urgent customer requests remaining queued for longer periods of time. It is also possible for customers to be denied a route to an agent on a particular queue because there are no agents associated with that queue with free capacity.
As customer wait times and rejection rates increase, even with a sizable agent pool, it could point to too many queues. Five or fewer is usually a good number to target, but it depends on other factors such as handle times, number of agents, concurrency limits, etc.
Queues work in the same way for all messaging channels including asynchronous channels such as Facebook Messenger, WhatsApp, etc. Unless it is essential to have a queue specific to the source channel, use the same queue for a particular business function across multiple channels. This helps prevent the proliferation of queues, whose implications are detailed above.
Customer wait time on queues
While queuing customer requests, Pega considers not only the current un-utilized capacity of the agent pool, but also the potential capacity that will be freed up to consume queued chats within a configured maximum wait time of a customer. By always estimating the wait time for a customer and evaluating this estimate against the maximum wait time (as configured by the business, keeping customer experience in mind), you ensure that an optimal balance is achieved between responsiveness to customer requests and contact center capacity at any given point in time.
Maximum wait time
This configuration ensures that your customers will never spend egregiously long times waiting in the queue to be connected with an agent. Setting too low a limit will lead to customers being denied a place in the queue too often. So, it is important to find the optimal configuration after taking into account your business objectives.
Configure the maximum wait time to be greater than the average handle time of customer interactions. A wait time between 300 and 600 seconds is generally agreeable to most customers. For example, setting the maximum wait time to 600 seconds would ensure that in cases where customers are expected to wait for longer than 10 minutes, the chat application would deny them service and request to retry later.
Expected wait time
Key factors that impact the expected wait time:
· Capacity of the agent pool at any point in time (this would be the active agents multiplied by the concurrency allowed for each agent on the queue)
· Number of queued chats ahead of the customer in question
· Number of active chats that the agent pool is currently working on
· Average time to handle a single chat
A good balance of these two configurations (image above) would result in the most optimal data set to utilize for computing the expected wait time. Settling for too small a set of previous interactions or waiting for too large a set can skew the expected wait time. These settings are available in App Studio > Settings > Chat and Messaging > Chat and messaging configuration.
Workload-based routing, which is the default option, works best in cases where customer interactions are of low complexity and high volume in nature. Selecting this option routes new requests to agents who have fewer active chats. Choose this option if your objective is to uniformly distribute the incoming customer requests among all available agents. Workload-based routing is applicable where
- customer issues are generic and not differentiated or complex to require very specific set of skills
- agent compensation is directly tied to the amount of work they have handled and it is important to distribute work uniformly
The skill-based routing option routes new requests to the CSR with the highest skill level of all the CSRs who are available to take on more requests. Choose this option if customer interactions are of high complexity and low volume in nature. Skill-based routing is applicable where
- customer issues are complex and non-generic, requiring differentiated skills in a CSR
- SLAs need to be adhered to and therefore customer issues require the attention of the most skilled CSR available
In cases where you need to support blended agents, you can opt for third party routing. With this option enabled, the responsibility of routing incoming chat requests will be delegated to a third-party routing service, which could be the same service that handles the routing of incoming calls. This type of routing helps centralize your routing logic in an external service to support agents who can handle both call and text-based customer interactions.
Service representatives are typically expected to handle multiple text-based conversations at the same time (concurrently). Pega Chat allows you to define this limit at three different levels:
- Global: a value that applies to all the CSRs handling text-based interactions
- Queue: a value that can be configured at the queue level to specify the maximum number of interactions, on the particular queue, that a CSR can concurrently handle
- Agent: an option for service managers to define concurrency limits at the individual CSR level to suit the agent's experience and competence
Conditional screen pop behaviors
To ensure that the wait time estimates communicated with the customer are honored, it is essential that the agents accept chat offers as expected. A level of certainty can be achieved by toggling on the three configurations related to screen pop behaviors at App Studio > Settings > Chat and Messaging > Routing.
It is recommended to use the metadata captured for each messaging interaction to automatically direct requests to specific queues. Identified language, message type, and channel data can be used to decide on a queue based on the intelligent routing configurations. Using the metadata avoids the need to expose queue names that are too specific to customers, for example, Billing-German-Twitter-Public. The customer can simply be shown the Billing queue and the metadata can be used to select the more specific queue internally.