Queuing and routing customer requests in Pega Chat
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For text-based interactions between service representatives 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 which need distinct set of skills in a service representative. For e.g. Billing could be a queue to direct all billing related inquiries. It is recommended to not configure too many queues as this may result in spreading the available agent capacity too thinly across the queues. As queues cannot be prioritized, all incoming requests will be given equal weight, and this may at times 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 no available agents have any free capacity.
As customer wait times and rejection rates increase, even with a sizeable agent pool, it could point to too many queues. Five or fewer is usually a good number to target but it is dependent on other factors such handle times, number of agents, concurrency limits etc.
Queues work the same 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, it is recommended to 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 unutilized capacity of the agent pool, but also the potential capacity that will 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 can ensure that an optimal balance is achieved between responsiveness to customer requests and contact center capacityat any given point in time.
Maximum wait time
This configuration ensures that your customers will never spend egregiously long times waiting on the queue to connected with an agent. Setting too low a limit will lead to customers being denied a place on the queue too often. So, it is important to find the optimal configuration after taking into account your business objectives.
It is recommended to configure the maximum wait time to greater than the average handle time of customer interactions. A wait time between 300 and 600 seconds is generally agreeable to most customers. For e.g. setting maximum wait time to 600 seconds would ensure in cases where customers are expected to wait for longer than 10 times, the Chat application would deny them service and request to retry later.
Expected wait time
Key factors that impact the expected wait-time:
· the capacity of the agent pool at any point in time (this would be 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 App Studio > Settings > Chat and Messaging > Chat and messaging configuration
Workload based routing
Workload based routing, which is enabled as the default option, suits 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
Skill based routing
Skill based routing: Selecting this 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 hence 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 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: this value applies to all the CSRs handling text-based interactions
- Queue: a value can be configured at the queue level to specify the maximum number of interactions, on the particular queue, a CSR can concurrently handle
- Agent: Service managers are provided with an option to define concurrency limits at the individual CSR level to suit the agent's experience and competence
Conditional screenpop behaviors
In order 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 screenpop behaviors at App Studio > Settings > Chat and Messaging > Routing
It is recommended to utilize the metadata captured for each messaging interaction to auto-direct the requests to specific queues. Identified language, message type and channel data can be used to decide a queue based on the intelligent routing configurations. This avoids the need to expose queue names that are too specific to customers for e.g. 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.