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Customer Service

AI-Powered Support Automation

A mid-market SaaS company (500K+ customers, 45-person support team)

$2.8M annual savings by automating 68% of customer inquiries

KEY RESULTS

$2.8M
Annual savings: $1.9M from avoided headcount (didn't hire 16 planned agents), $900K from reduced cost-per-ticket
68%
Of all tickets now fully resolved by AI—no human touch. Up from 0% pre-implementation.
90 sec
Median first response time, down from 24+ hours. 99th percentile: 4 minutes.
32%
CSAT improvement: from 3.6 to 4.4 stars. AI-only resolutions score 4.5 stars.

Before & After

✗ Before

  • •Average first response time: 24+ hours during peak periods
  • •CSAT score: 3.6 stars (down from 4.2)
  • •Agent turnover: 35% annually
  • •Cost per ticket: $12.40 (industry avg: $10-14)
  • •Tickets requiring escalation: 45%
  • •Resolution time: 4.2 days average

✓ After

  • •Average first response time: 90 seconds
  • •CSAT score: 4.4 stars
  • •Agent turnover: 18% (industry: 30-45%)
  • •Cost per ticket: $3.80 (69% reduction)
  • •Tickets requiring escalation: 12%
  • •Resolution time: 1.8 days average

The Challenge

This B2B SaaS company grew from 200K to 500K customers in 18 months. Their support team couldn't scale proportionally—headcount grew 1.5x while ticket volume grew 3x. The math was brutal: at $12.40 per ticket and 15,000 tickets/month, they were burning $186K monthly on support with quality declining.

PAIN POINTS

  • ×Password resets, billing questions, and feature how-tos consumed 70% of agent time—the same 50 questions answered 10,000+ times monthly
  • ×New agents took 6 months to reach full productivity; high turnover meant perpetual training overhead
  • ×Enterprise renewal conversations derailed by support complaints—three $200K+ accounts churned citing support as primary reason
  • ×Peak periods (Monday mornings, product releases) created 72-hour response backlogs
  • ×Knowledge base existed but agents couldn't search it fast enough during live chats

Technology Stack

  • →RAG-based chatbot with GPT-4 for natural language understanding
  • →Vector database (Pinecone) indexing 2,400 knowledge base articles
  • →Custom intent classification model trained on 50K historical tickets
  • →Zendesk integration via API for seamless ticket handoff
  • →Real-time sentiment analysis to detect frustrated customers

Implementation Approach

1

Phase 1: Knowledge Base Vectorization (Weeks 1-2)

Indexed all 2,400 knowledge base articles, 180 product docs, and 50K historical ticket resolutions into a vector database. Built semantic search that finds relevant content in <100ms. Tested retrieval accuracy against 500 sample queries—hit 94% relevance in top-3 results.

2

Phase 2: Intent Classification & Routing (Weeks 3-4)

Trained a classifier on historical tickets to identify 47 distinct intents. High-confidence intents (password reset, billing inquiry, feature lookup) route to auto-resolution. Low-confidence or complex intents route to human agents with full context attached. Built escalation triggers for negative sentiment, VIP accounts, and compliance-related queries.

3

Phase 3: Auto-Resolution Engine (Weeks 5-8)

Built resolution workflows for the top 20 ticket types (covering 68% of volume). Password resets: automated with identity verification. Billing questions: real-time account lookup and natural language explanation. Feature how-tos: step-by-step guides generated from docs with screenshots. Each workflow includes confidence scoring—only resolves if confidence >95%.

4

Phase 4: Agent Assist Dashboard (Weeks 9-10)

For tickets requiring human review, agents see: AI-generated response draft, relevant KB articles ranked by relevance, customer history summary (plan, tenure, recent tickets), and suggested resolution category. Reduced average handle time from 18 minutes to 11 minutes.

5

Phase 5: Monitoring & Optimization (Ongoing)

Daily review of AI-handled tickets for quality. Weekly retraining on new edge cases. Monthly analysis of escalation patterns to identify automation opportunities. Built anomaly detection to surface trending issues before they become ticket avalanches.

Results Breakdown

$2.8M

Annual savings: $1.9M from avoided headcount (didn't hire 16 planned agents), $900K from reduced cost-per-ticket

68%

Of all tickets now fully resolved by AI—no human touch. Up from 0% pre-implementation.

90 sec

Median first response time, down from 24+ hours. 99th percentile: 4 minutes.

32%

CSAT improvement: from 3.6 to 4.4 stars. AI-only resolutions score 4.5 stars.

Key Learnings

  • 1.The 80/20 rule holds: 20 ticket types covered 68% of volume. Start there, not with edge cases.
  • 2.Confidence thresholds matter more than accuracy. We'd rather escalate 100 tickets than auto-resolve 1 incorrectly. Set threshold at 95%.
  • 3.Agent trust is earned, not assumed. We gave agents override authority and reviewed their overrides weekly. Override rate dropped from 15% to 3% over 60 days as they learned to trust the system.
  • 4.Proactive > reactive: trending issue detection prevented two potential ticket avalanches during product bugs.
  • 5.Don't hide the AI. Customers rated transparent 'I'm an AI assistant' higher than attempts to seem human.

Industry Context

Klarna's AI assistant handles 2.3M conversations monthly—equivalent to 700 FTEs—with customer satisfaction on par with human agents. Industry benchmarks show AI support can reduce costs by 30% while improving response times by 40-70%. The key differentiator isn't the AI model; it's the quality of the knowledge base and the sophistication of the routing logic.

TIMELINE

75 days from audit to full deployment

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