Advanced routing overview


When you create a queue, you can set evaluation and routing methods. PureCloud offers two routing methods: standard and advanced. Standard routing sends interactions to agents based on the evaluation method you select. Advanced routing also sends interactions to agents according to the selected evaluation method. Advanced routing also includes the option of routing to preferred agents first, and also according to the bullseye routing rules that you configure. Preferred agent routing and bullseye routing are optional settings.

Note: Supported channels include email, chat, voice, messaging, voicemail, and callbacks. Outbound campaigns and scheduled callbacks are not supported.

Preferred agent routing

Preferred agents are agents that you select to handle interactions based on characteristics you determine, such as skill level, responsibility, or customer relationships. For example, route interactions to agents with higher levels of product knowledge, with historical information about a case, or who are responsible for a particular account.

Preferred agent routing enables you to first route interactions to a designated pool of these preferred agents. If no preferred agents are available based on your configuration rules, PureCloud routes the interaction to a wider pool of agents. 

In PureCloud, you can specify preferred agent routing behavior as you set up queue configuration. You can create up to six rings to expand the selection pool gradually when PureCloud does not find a match on a previous ring. For each ring, you can also route interactions to all preferred agents for a specific length of time. For more information, see Create and configure queues. In Architect, you can create up to 20 preferred agent and agent score pair collections that supports up to 20 agent score pairs. For more information, see Transfer to ACD action.

Agent scores allow you to specify the priority of preferred agents. If for example, you have agents that you prefer most, set their scores higher, and set backup agents lower. If you don’t have a preference, set the score to 100. For more information, see Set up preferred agents and scores in Architect.

All interactions associated with preferred agent routing are also associated with a queue, so metrics and details appear in supervisor views, analytics data, and reports.

Note: For email and messaging interactions, and inbound callbacks, when you use preferred agent routing, PureCloud no longer attempts to route the interaction to the last agent who handled it. Scheduled callbacks, however, are unaffected by preferred agent routing.

Set up preferred agents and scores in Architect

In Architect, to influence routing you can create an agent score pair collection that supports up to 20 agent score pairs. An agent’s score represents the agent’s priority and can be from 0 to 100, with the highest score representing the most preferred agent. If you have agents that you prefer over others, set their score to 100. Set backup agents to 90. If you don’t have a preference, set the score to 100. For example, assume you have a case owner who is the most preferred agent, and you have three additional backup agents. Set the case owner’s score to the maximum of 100, and then set the backup agents to a lower score, such as 90.

To create an individual agent score pair value, use the MakeAgentScorePair function. For example, the following expression in a Transfer to ACD action’s Preferred Agent setting creates an agent score pair collection with two agent score pairs. The first user has a score of 100 and the second has a score of 90:

MakeList(
 MakeAgentScorePair(
  FindUserById("<put_user_guid_string_here>"),
  100
 ),
 MakeAgentScorePair(
  FindUserById("<put_user2_guid_string_here>"),
  90
 )
)

You can use a MakeListAgentScorePair function that takes a collection of users along with an integer collection of scores to associate with the users. Following that method, this example generates the same value as the previous expression:

MakeListAgentScorePair(
 MakeList(
  FindUserById("<put_user_guid_string_here>"),
  FindUserById("<put_user2_guid_string_here>")
 ),
 MakeList(
  100,
  90
 )
)

You can also use an Update Data action to assign variables. Then, use those variables to supply individual user values or user collections, an individual integer score or integer collections, and agent score pairs or collections. For more information about the MakeAgentScorePair, MakeListAgentScorePair, MakeList, FindUserById, and other functions, see Architect’s Expression Help feature. Each function provides more details and examples.

Note: If the action encounters an agent score pair collection supplied with more than 20 items, call flows take the Failure output at runtime. If you bind a variable to the errorType failure output, the value contains the “TooManyPreferredAgents” string value. In email, chat, and message flows, Architect implements flow error handling.

Bullseye routing

With bullseye routing, you can route interactions to agents based on routing rules that you create during queue configuration. With the skills routing method, an agent must have the required skills to be eligible for an interaction. The bullseye routing method is similar. However, it can expand the agent selection pool when no agent with the required skills is available within the configurable amount of time. Think of the expansion as a set of concentric rings, similar to the rings on a target, with each ring being a fallback for the previous ring.

Create up to six rings that expand the selection pool gradually and optionally remove specific skills. The selection pool automatically includes preferred agents, regardless of assigned skills. For more information, see Bullseye routing and Create and configure queues.

Note: PureCloud includes preferred agents in bullseye routing. If you use preferred agents along with bullseye routing, PureCloud considers preferred agents for routing whether they have the required skills or not. For more information, see Preferred agent routing behavior.