Your customers are waiting. Your agents are drowning.
Both problems have the same solution.
The economics of human-only support are broken. Every ticket costs $22, customers wait hours for a first response, and your best agents spend 80% of their time on repetitive tier-1 issues. Here is exactly how to fix it.
4 hours
Average first response time
Industry average across mid-market support teams
$22
Cost per support ticket
Forrester Research
73%
Customers hate repeating themselves
Salesforce State of Service report
Ticket Triage & Resolution Pipeline
The Problem
Your support team receives 300 tickets a day. Every ticket needs to be read, categorised, routed to the right specialist, researched against your knowledge base, and answered. Your agents spend 80% of their time on tier-1 issues that have documented answers. Meanwhile, complex tickets sit in queue while customers grow increasingly frustrated.
Solution Stack
Customer Support Triage Team
3 agents: Billing Support + Technical Support + Account Management
Hierarchical Routing
Supervisor routes to the right specialist
4 Knowledge Bases
Product docs, billing policies, troubleshooting guides, account procedures
Table: Support Tickets
12 columns including priority, category, status, resolution, CSAT
Deployed to Web Chat + Slack + Teams
Customers reach you on any channel
Trigger: Row Created
New ticket arrives, triage begins instantly
Manual reading
Human reads every ticket, categorises manually
Manual KB search
Agent searches knowledge base, copies/pastes answers
Slow routing
Escalations sit in queue, no priority scoring
4 hours to first response
Customers wait, frustration builds, CSAT drops
AI supervisor routes
Ticket classified and routed to specialist in seconds
Automatic KB + CRM search
Specialist agent searches all sources simultaneously
Intelligent prioritisation
Severity, customer tier, and sentiment all factor in
2 minutes to first response
60–65% of tickets auto-resolved without human touch
ROI Calculation
| Monthly ticket volume | 1,000 tickets/month |
| AI-handled tickets (60%) | 600 tickets/month |
| Cost per ticket: before | $22.00 |
| Cost per ticket: after | $2.50 |
| Savings per ticket | $19.50 |
| Monthly savings | $11,700 |
| Annual savings | $140,400 |
First response time 4 hrs \u2192 2 min = 34% CSAT improvement
Customer Health Monitoring
The Problem
You find out a customer is about to churn when they send the cancellation email. By then, it’s too late. The warning signs were there for weeks — declining usage, increasing support tickets, missed check-ins — but nobody was watching. Your CSMs manage 40+ accounts each and cannot manually track health signals across every customer, every week.
Solution Stack
Customer Health Score Agent
Pulls usage data, support history, engagement signals
Customer Health Dashboard Table
9 columns: company, health score, usage trend, ticket count, NPS, last contact, risk level, CSM, actions
Condition Trigger: Score < 60
At-risk accounts flagged immediately
Schedule Trigger: Monday 7 AM
Weekly health recalculation across all accounts
Notifies CSM in Slack
Right person alerted with context and recommended actions
Reactive discovery
You find out a customer is churning when they send the cancellation email
Manual health checks
CSMs manually review accounts quarterly at best
No early warning system
Declining engagement goes unnoticed for weeks
Lost revenue
By the time you intervene, the customer has already decided to leave
Proactive detection
Health scores recalculated weekly, risk flagged instantly
Automated monitoring
Every account tracked continuously without manual effort
Immediate alerts
CSM notified in Slack the moment a score drops below threshold
Retention playbooks
Recommended actions accompany every alert
ROI Calculation
| Accounts saved per month | 5 accounts |
| Average ARR per account | $20,000 |
| Retained revenue per month | $100,000 |
| Retained revenue per year | $1,200,000 |
One prevented churn pays for the entire platform.
Two solutions. $140K+ in annual ticket savings. $1.2M in retained revenue.
Deployed in days, not months.
Which solution does your operations team need most?
Pick one. We’ll show you exactly how to deploy it — the agents, the tables, the employee configuration — in 30 minutes. Running in production this week.
Tell us your department and we’ll walk you through the exact configuration for your use case.