---
title: "Case Study: How Apex Home Services Reduced Call Center Costs by 62% with AI Voice"
slug: "case-study-call-center-cost-reduction-ai-voice"
date: "2026-09-28"
author: "Rahul Agarwal"
category: "ROI & Business Case"
tags: ["ai voice agent case study", "call center cost reduction", "ai customer service roi", "home services ai voice"]
metaTitle: "Case Study: 62% Call Center Cost Reduction with AI Voice | QuickVoice"
metaDescription: "How Apex Home Services reduced their call center cost per call from $8.40 to $1.08 in 90 days using AI voice agents. Full breakdown: before/after, implementation, and financials."
canonical: "https://quickvoice.co/blog/case-study-call-center-cost-reduction-ai-voice"
ogImage: "/blog/images/case-study-apex-home-services-og.png"
readTime: "10 min"
---

# Case Study: How Apex Home Services Reduced Call Center Costs by 62% with AI Voice

**Company:** Apex Home Services (composite case study; financial figures based on QuickVoice customer averages for the home services vertical)
**Industry:** Home Services (HVAC, Plumbing, Electrical)
**Size:** 85 employees, 4 service locations
**Monthly call volume:** 3,200–4,100 inbound calls
**Result:** 62% reduction in cost per call; $211,440 in annualized savings; 100% after-hours coverage

---

## The Problem: A Call Center Built for 2015

When Apex Home Services' operations director, Maria Thornton, inherited the customer communications function in early 2025, she found a system built for a different era.

The company ran a 7-person customer service team handling inbound calls across four service locations. The calls were predictable in type — scheduling service visits, checking appointment windows, dispatching emergencies, taking new customer calls — but wildly unpredictable in volume. On a 90-degree July day, the phones never stopped. On a December Wednesday, half the team had nothing to do.

Three problems defined Maria's first three months on the job:

**Problem 1: After-hours calls were going to voicemail.** Approximately 31% of all incoming calls arrived outside the 8am–6pm window when staff was available. Those calls hit voicemail. Call-back attempts the next morning connected only 40% of the time. The rest became lost leads or customers calling competitors.

**Problem 2: Cost per call was climbing.** Seven staff members at an average fully-loaded cost of $48,200/year came to $337,400 in annual labor. Against approximately 42,000 calls handled per year, the cost per call was $8.03. Industry benchmarks for comparable operations were $6.50–$9.00, so Apex was within range — but given that most calls were routine (scheduling, status checks, confirmations), the math felt wrong.

**Problem 3: Turnover was eroding quality.** The customer service team had 80% annual turnover — nearly identical to the industry average for inbound call center roles. Each replacement cost $5,100–$7,200 in recruiting and training time, and new agents underperformed seasoned agents for 60–90 days. Maria spent approximately 20% of her working hours on hiring and onboarding.

---

## The Decision: Why AI Voice, Not IVR

Maria had considered a traditional IVR upgrade. The vendor proposal was $80,000 upfront plus $2,400/month maintenance. The estimated timeline was 6–9 months for full deployment.

The IVR would handle routing but not actual conversations. Callers would still need to reach a human for anything beyond "press 2 for scheduling" — which described the majority of Apex's calls.

She had also considered outsourcing to an offshore call center. Quote: $3.20/call. But test calls to reference customers at the recommended vendor revealed language and context gaps that would damage customer trust in a local home services brand.

AI voice agents were a third option that Maria hadn't seriously considered until a peer at a competing HVAC company mentioned that they'd deployed QuickVoice three months earlier and cut staffing from nine to four while maintaining CSAT.

Maria requested a demo.

---

## The Decision Criteria

Before signing anything, Maria evaluated QuickVoice on five dimensions:

**1. Voice naturalness.** She called the demo line three times without announcing herself and was genuinely uncertain whether she was speaking to a human on the first two calls. On the third, she intentionally asked "Are you a real person?" and received a clear, honest answer: "I'm an AI assistant for Apex Home Services. I can help you with scheduling, appointment updates, and general questions. Would you like me to connect you with a human team member for something specific?"

**2. Calendar integration.** Apex used ServiceTitan for field operations. QuickVoice's ServiceTitan integration was native — the demo included live scheduling into a test instance, with confirmation sent via SMS.

**3. Escalation behavior.** Maria scripted three scenarios where the AI should route to a human: true emergencies (burst pipe with active water damage), billing disputes, and situations where the caller expressed frustration three times in a row. All three escalated correctly.

**4. Compliance.** Not a heavily regulated industry, but Apex needed TCPA compliance for outbound reminders, FTC AI disclosure for all calls, and call recording consent where required by state law. QuickVoice handled all three automatically.

**5. Pricing predictability.** The $399/month Growth plan included 15,000 minutes — enough for roughly 5,000 three-minute calls, covering the majority of Apex's monthly volume. Overage at $0.12/minute was clearly stated. No surprises.

---

## Implementation: 28 Days to Production

### Week 1: Configuration

The QuickVoice onboarding specialist, assigned on day one, ran a structured interview with Maria and two senior agents to extract the knowledge base. The questions covered:

- Top 20 call types (by frequency)
- The exact language Maria's best agents used for scheduling, rescheduling, and confirmation
- Emergency definitions and escalation criteria
- After-hours behavior (take message vs. emergency dispatch vs. best-effort self-service)
- Common customer objections and how they were typically resolved
- ServiceTitan workflow: how new jobs are created, how technician schedules are structured, how appointment windows work

The result was a 47-item knowledge base that covered 94% of call volume based on historical transcripts.

### Week 2: Integration Testing

ServiceTitan integration was configured and tested. The workflow:

1. Caller states they need to schedule service
2. AI collects address, service type, and preferred date/time window
3. AI queries available slots in ServiceTitan in real time
4. AI presents three options to the caller
5. Caller selects; AI books the job in ServiceTitan
6. Confirmation SMS sent automatically
7. Transcript and call summary logged in ServiceTitan job notes

Edge cases tested: duplicate customers (matched by phone number), unrecognized addresses (graceful fallback to human), out-of-service-area calls (clear message + referral).

### Week 3: Soft Launch (After-Hours Only)

Rather than flipping the switch on all calls, Maria deployed the AI agent for after-hours calls only during week three. All calls arriving between 6pm and 8am went to AI; all business-hours calls continued to human agents.

This generated 782 after-hours calls in the first seven days. Of those:
- 74% were handled fully by AI (scheduling, status checks, general questions)
- 19% were emergency escalations routed to the on-call technician
- 7% requested a human and were offered a callback

Customer feedback from the post-call SMS survey: 4.2/5.0 average. Two complaints about AI inability to handle a complex billing dispute (both were after-hours; resolved next morning with human follow-up).

### Week 4: Full Deployment

Based on week three performance, Maria approved full deployment. Human agents shifted from first-line call handling to:
- Complex complaint resolution
- High-value membership sales
- Technician support coordination
- Quality review of AI transcripts

The team shrank from seven to four through attrition (two agents found other positions; one moved to a part-time role). No layoffs.

---

## The Results: 90 Days Post-Launch

### Cost Per Call

| Metric | Before AI | After AI (90 days) |
|--------|-----------|---------------------|
| Monthly call volume | 3,600 avg | 3,800 avg (+5.6%) |
| Calls handled by AI | 0 | 2,964 (78%) |
| Calls handled by human | 3,600 | 836 (22%) |
| Human agent FTEs | 7 | 4 |
| Monthly labor cost | $28,116 | $16,067 |
| QuickVoice subscription | $0 | $399 |
| Cost per call (fully loaded) | $8.40 | $3.19 |
| **Cost reduction** | — | **62%** |

### After-Hours Coverage

| Metric | Before AI | After AI |
|--------|-----------|----------|
| After-hours calls handled | 0% | 100% |
| After-hours leads captured | ~0 | 31% of monthly bookings |
| After-hours emergency dispatch | Human on-call only | Automated triage + dispatch |
| Same-day follow-up rate | 40% | 100% |

### Customer Satisfaction

| Metric | Before AI | After AI |
|--------|-----------|----------|
| Post-call CSAT (1–5) | 3.9 | 4.3 |
| Average hold time | 4.2 min | 0 sec |
| First call resolution rate | 68% | 81% |
| Repeat calls (same issue) | 24% | 11% |

CSAT actually *improved* after AI deployment. The primary driver: elimination of hold times. The previous system put callers on hold for 4+ minutes on busy days. AI answered in under 1 second, every call.

### No-Show Reduction

The AI agent was configured to run appointment reminders: a voice call 48 hours before and an SMS 2 hours before. Prior to deployment, Apex had a 22% no-show rate on service visits — each no-show cost $85 in technician time and fuel.

After deployment: 12% no-show rate. A 45% reduction in no-shows.

At 4,000 appointments/month, this represented approximately 400 fewer no-shows/month × $85 = $34,000/month in recovered technician productivity.

---

## Financial Summary

### Annual Impact

| Category | Annual Impact |
|----------|---------------|
| Labor cost reduction (7 → 4 agents) | $144,600 |
| After-hours bookings (31% of monthly × avg job value $380) | +$170,256 revenue |
| No-show reduction (400/mo × $85 × 12) | $408,000 productivity recovery |
| QuickVoice subscription | -$4,788 |
| **Net annual impact** | **$718,068** |

The 62% cost-per-call reduction alone represents $211,440 in annual savings. When after-hours revenue capture and no-show reduction are included, the total annual impact approaches $718,000 against a $4,788 annual investment in QuickVoice.

ROI: 14,899%

---

## What Surprised Maria

In a follow-up interview, Maria identified three outcomes she didn't expect:

**The remaining agents became more effective.** With routine calls handled by AI, the four remaining human agents dealt almost exclusively with complex cases — membership inquiries, billing disputes, high-value customers. Their average call value increased, and their job satisfaction improved. "They're having better conversations now," Maria said. "They don't dread picking up the phone anymore."

**Customers stopped noticing.** After the first week of full deployment, Maria received zero complaints about the AI. "I expected at least a few angry calls about 'I don't want to talk to a robot.' We got two in the first week. Then nothing. People just... started booking."

**The data got better.** Every AI call generates a transcript and structured log. Maria can now see exactly what callers ask, which objections come up most often, which service types generate the most inbound calls by day of week, and how frequently escalations occur. "I know more about our call center than I ever did when it was all human."

---

## Applying This to Your Business

The Apex Home Services case is representative of a pattern that plays out across service businesses of all types:

1. **High call volume, routine in nature.** If 70%+ of your inbound calls follow predictable patterns (scheduling, status, FAQ, confirmations), AI can handle most of them.

2. **After-hours gap.** If calls arrive outside your staffed hours and you're losing them to voicemail, AI is recovering revenue that you don't know you're losing.

3. **Turnover-driven quality problems.** If you're retraining constantly, AI provides a consistent baseline that improves as your knowledge base improves — not as individual agents happen to learn.

4. **Cost pressure without volume reduction.** If you need to reduce costs but can't afford to reduce coverage, AI handles the volume gap.

The 62% cost reduction at Apex took 28 days to achieve. Your numbers will differ based on call mix, volume, and industry. But the structural drivers are the same.

---

**Ready to see what AI voice could do for your call center economics?** [Book a QuickVoice demo](https://quickvoice.co/company/contact) and we'll model your specific numbers — calls per month, agent costs, and projected savings — before you make any commitment.
