Support Queue Triage in Salesforce
A B2B SaaS company with 2,000+ customers had a support queue problem in an inherited Salesforce setup. Simple issues, billing lookups, and feature how-tos buried the support team. Complex issues waited behind cases that should have been classified and routed earlier.
Key Outcome
72% of tier-1 cases were triaged without manual rework. Average wait time dropped from 8 minutes to under 1.
Workflow Design
The Problem
The 6-person support team handled 300+ requests per day. Case classification was manual, queue placement was inconsistent, and agents spent as much time fixing intake as solving issues.
- 60% of requests were repeat questions with documented answers
- Average wait during peak hours exceeded 8 minutes and pushed customers into side channels
- Triage quality varied by agent, which meant inconsistent case fields and queue routing
- Enterprise customers with real problems queued behind tier-1 issues and poorly triaged cases
Hiring more agents wasn't the answer—it took 3 months to onboard someone, and turnover was 40% annually. The team needed leverage, not headcount.
Results
Measured after 90 days in production:
Containment Rate
72% triaged without rework
Cases routed without human rework
Wait Time
8 min → <1 min
Average time to first response
Agent Capacity
3x complex tickets
Human agents now handle higher-value issues
CSAT
+18 points
Post-interaction satisfaction score
Is This for You?
This pattern works best when:
- Your support team handles 100+ requests per day
- A significant portion (50%+) are repeatable tier-1 issues with documented answers
- Wait times spike during peaks and hurt customer satisfaction
- Your best agents are stuck fixing intake quality instead of handling complex issues