rulebase-co Routing audit
Routing failures are invisible in every headline CX metric and they are expensive in all of them. A misrouted contact adds handle time to two people, adds wait time for the customer, and usually adds a repeat contact — but it shows up as slightly worse averages everywhere rather than as a routing problem anywhere.
The audit makes it visible and, importantly, separates the failures that are worth fixing from the ones that are normal.
Reassignment is not automatically a failure
Distinguish these before counting anything, because lumping them together produces a scary number that nobody can act on:
- Designed escalation — tier 1 to tier 2, or to a specialist queue. Working as intended. Expected volume, and if it is lower than expected that is its own finding.
- Load balancing — moved because someone was unavailable. Neutral.
- Misroute — arrived somewhere that could never have handled it. A failure of the routing rule.
- Ping-pong — three or more hops, or a return to a queue that already had it. Always a failure, and the customer experience is far worse than the hop count suggests.
The metric worth tracking is misroute rate and ping-pong rate, not raw reassignment rate.
What to measure
Hop count distribution. Most contacts should be zero or one hop. Report the share at 0, 1, 2, and 3+. The 3+ tail is small and carries a wildly disproportionate share of complaints — check it against complaint and escalation records and quantify the link.
Time lost per hop. Wall-clock between arrival at a queue and reassignment out of it. This is dead time from the customer's point of view, and totalling it across the window converts a routing problem into a number leadership acts on.
Misroute origin. For misroutes, where did they come from? Almost always one of:
- a routing rule matching on the wrong signal
- an intake form whose categories do not match the queues
- a customer-facing menu whose labels do not mean what customers think
- a channel with no routing at all, defaulting everything to one queue
Group misroutes by first queue and by contact driver; the clusters name the broken rule.
Unassigned open work. Contacts sitting in a queue with no owner. Cluster them by queue, tag, time of day and channel. This is usually a rule gap rather than a people problem — a category with no assignment rule, or a queue whose only member left.
Queues nobody owns. Enumerate queues and check each has a current owner, current members, and recent activity. Dormant queues that still receive traffic are a reliable source of abandoned contacts, and they are invisible until someone lists them.
First-time-right rate, per contact driver: the share handled entirely by the queue that first received it. More useful than a global routing accuracy figure because it points at the specific rules to change.
The signal people miss: routing that looks fine because it never fires
A queue with zero misroutes may have perfect rules, or may be receiving nothing because an upstream rule is swallowing its traffic. Check queue volumes against expectation:
- A queue whose volume dropped sharply is usually an upstream rule change, not a demand change.
- A catch-all queue growing steadily means specific rules are decaying.
- Catch-all volume as a share of total is the single best health metric for a routing configuration, and it is rarely tracked. Rising catch-all share means the rules are falling behind the product.
Traps
- Reassignment data quality varies enormously. Some helpdesks record every assignment change, some only the current assignee, some record automation as a reassignment. Establish what the event log actually contains before computing hop counts — a system that only stores the current assignee cannot support this analysis at all, and saying so is the correct output.
- Automated reassignments by rules or schedulers inflate hop counts and are not human failures. Separate them.
- Team reorganisations inside the window make queue-level trends meaningless. Check for renames and merges first.
- A transfer with a good handoff note is a different experience from a silent one. Where notes exist, measure the share of transfers that carry context; a transfer chain with no handoff notes is why the customer repeats themselves four times.
- Do not attribute misroutes to the agent who transferred. They usually did the right thing with a contact that should never have reached them. The finding belongs to whoever owns the rule.
Present results to the user
- What the data supports — whether the assignment event history is complete enough for hop analysis, stated first.
- Hop distribution, with the 3+ tail called out and its link to complaints.
- Misroute and ping-pong rates, separated from designed escalation and load balancing.
- Time lost to routing, totalled across the window.
- Misroute clusters by origin queue and contact driver, each mapped to the rule, form or menu that produced it.
- Unassigned work and unowned queues, as a specific list with owners to assign.
- Catch-all share and its trend.
- Ranked fixes, by time recovered, each naming the rule or form to change — not the people.