Why Cost Per Call Matters in Collections

Why Cost Per Call Matters in Collections

In collections, every dollar counts—especially when chasing overdue payments. Cost per call isn’t just a metric; it’s a direct lever on profitability and scalability.

For businesses relying on outbound calling, this KPI determines whether a recovery strategy is sustainable—or silently draining resources.

• Labor consumes 60–70% of traditional call center costs
• The average outbound call costs $5–$12 in U.S.-based operations
• Only 25–30% of calls connect, inflating the real cost per successful contact

Traditional models rely on human agents paid hourly, with added expenses for training, turnover (often 30–45% annually), and infrastructure. Even offshore BPOs, charging $2–$5 per call, struggle with compliance and consistency.

Consider a mid-sized collections agency making 50,000 calls monthly:
At $6 per call, that’s $300,000 per month—over $3.6 million annually—just to initiate conversations.

Now contrast that with AI-powered systems. Early adopters using platforms like AIQ Labs’ RecoverlyAI report costs under $1 per call, with some achieving as low as $0.08 at scale.

Key drivers of cost per call include: - Average Handle Time (AHT) – U.S. average: 6 minutes - First Call Resolution (FCR) – Industry benchmark: 70–79% - Dialing efficiency – Only 1 in 4 calls connects - Agent autonomy – AI can handle 50–80% of routine follow-ups

A healthcare collections provider recently switched from a hybrid human-AI model to a fully autonomous system. By eliminating per-call fees and reducing handle time through dynamic prompting and dual RAG, they cut cost per call from $4.20 to $0.35—a 92% reduction—while improving compliance and connection rates.

This shift isn’t just about cost—it’s about control. Traditional vendors charge recurring fees or per-call rates that scale up with volume. AIQ Labs’ ownership model flips this: a one-time development cost delivers a fixed-fee system that scales without added expense.

When cost per call drops below $0.50, agencies can profitably pursue smaller balances and expand outreach—turning previously uneconomical accounts into revenue.

Understanding how to calculate and optimize this metric is the first step toward building a scalable, compliant, and cost-efficient collections operation.

Next, we’ll break down the exact formula—and show how AI automation transforms each component.

The Hidden Costs Behind Every Call

The Hidden Costs Behind Every Call

Every call your team makes comes with a price tag—but the true cost goes far beyond minutes or headcount. Labor, technology, infrastructure, and inefficiencies all pile up, often unnoticed, inflating your operational spend. In traditional collections, the average cost per call ranges from $5 to $12, with labor alone consuming 60–70% of that total (Magellan Solutions).

Yet most businesses only see the surface.

Break down the components, and it’s clear: legacy systems overpay for avoidable expenses. Here’s where the money goes:

• Labor: Salaries, benefits, training, and turnover (30–45% annual attrition adds recurring costs)
• Technology: Subscription tools, dialers, CRMs, compliance software
• Infrastructure: Call routing, cloud telephony, IT support
• Inefficiencies: Abandoned calls, failed connections, long handle times

Consider this: only 25–30% of outbound calls connect (CloudTalk.io). That means 70% of your dialing efforts are wasted—but you’re still paying full price.

Case in point: A mid-sized collections agency making 50,000 outbound calls monthly connects with just 15,000 debtors. At $6 per call, their effective cost per connected call balloons to $20 when failed attempts are factored in.

Average Handle Time (AHT) also plays a critical role. At 6 minutes per call (Nubitel.co), agents can handle roughly 60 calls per day. Reduce that by even 2 minutes with AI automation, and capacity jumps 33%—without adding staff.

Most companies rely on a patchwork of tools: one for dialing, another for email, a third for compliance logging. Each comes with its own fee, learning curve, and integration cost.

This fragmentation tax adds up fast: - Multiple subscriptions = $3,000+ per month - Onboarding time for new hires = 2–4 weeks - Manual data entry = 15–20% of agent time wasted

And because First Call Resolution (FCR) hovers around 70–79% (Nubitel.co), nearly a third of cases require follow-ups—doubling the real cost.

AI-powered systems like RecoverlyAI eliminate these leaks by unifying voice, email, and SMS follow-ups into a single, intelligent workflow. No more silos. No more wasted calls.

AI doesn’t just automate calls—it rewrites the cost equation. When automation handles 50–80% of routine follow-ups (Magellan Solutions), human agents focus only on complex cases.

The result? Cost per call drops below $1—and for owned systems like RecoverlyAI, it can fall to under $0.10 at scale.

Key efficiency gains include: - Smarter dialing: AI scores leads and times outreach to boost connection rates - Shorter AHT: Real-time RAG and dynamic prompting speed up resolutions - Higher FCR: Anti-hallucination safeguards ensure accurate, compliant responses - Zero per-call fees: Fixed-fee ownership eliminates variable pricing

Unlike subscription models that charge per call or agent, AIQ Labs’ ownership model means you pay once—then scale infinitely without cost spikes.

Legacy costs drain budgets. Smart automation reclaims them.

Next, we’ll break down the exact formula to calculate your real cost per call—and how to benchmark it against AI-driven alternatives.

AI Automation: A New Cost Model

AI Automation: A New Cost Model

What if your cost per call dropped from $12 to under $0.10—without sacrificing compliance or quality?
AI-driven voice systems are rewriting the economics of outbound communication, especially in high-volume sectors like collections. Platforms like RecoverlyAI are proving that fixed-cost automation delivers superior ROI compared to legacy call centers or per-call AI vendors.

Traditional call centers spend $5–$12 per call, with 60–70% tied to labor—salaries, training, and turnover (Magellan Solutions). Even offshore BPOs, at $2–$5 per call, face hidden costs from attrition and management overhead.

In contrast, AI automation slashes these expenses by handling 50–80% of routine interactions autonomously (Magellan Solutions). The result? Costs plummet to under $1 per call, with top-tier systems like RecoverlyAI achieving under $0.10 per call at scale.

Key drivers of this shift include: - Average Handle Time (AHT): AI reduces AHT from 6 minutes to under 2 via dynamic prompting (Nubitel.co). - First Call Resolution (FCR): AI with dual RAG and anti-hallucination boosts FCR, cutting repeat calls. - Dialing Efficiency: Only 25–30% of outbound calls connect (CloudTalk.io); AI optimizes timing and lead scoring to reduce waste.

Consider a mid-sized collections agency making 100,000 calls monthly: - Traditional model: 100,000 × $6 = $600,000/month - AI automation: 100,000 × $0.08 = $8,000/month - Savings: $592,000/month, with consistent, compliant follow-ups across phone, email, and SMS.

RecoverlyAI exemplifies this transformation—not through subscriptions, but via a one-time development fee. Clients own the system, eliminating recurring costs and enabling unlimited scaling.

This fixed-CAPEX model is a game-changer for SMBs, replacing unpredictable OPEX with predictable, long-term savings.

Unlike fragmented tools that charge per API call or seat, unified multi-agent systems consolidate workflows—reducing integration costs and complexity.

The future belongs to owned, autonomous AI—not rented, limited bots. As edge computing and on-device AI evolve, even infrastructure costs will decline.

For businesses evaluating AI collections, the question isn’t if automation saves money—it’s how quickly you can transition.

Next, we’ll break down the exact formula to calculate your true cost per call—and how to benchmark against AI-powered performance.

How to Calculate & Optimize Your True Cost Per Call

How to Calculate & Optimize Your True Cost Per Call

In debt collections and customer outreach, cost per call isn’t just a number—it’s the heartbeat of ROI. Yet most teams miscalculate it, overlooking hidden labor, inefficiencies, and outdated pricing models.

With AI-powered systems like AIQ Labs’ RecoverlyAI, businesses are slashing costs from $5–$12 per call down to under $0.10—not through magic, but through precision, ownership, and automation.

Let’s break down how to calculate your true cost—and how to optimize it.

Most companies only track direct expenses. But the full formula includes all operational inputs:

Total Cost Per Call = (Labor + Technology + Infrastructure + Overhead) ÷ Total Number of Calls Handled

This includes: - Agent salaries and training - Call center software and integrations - Dialer efficiency (only 25–30% of outbound calls connect, per CloudTalk.io) - Management overhead and attrition (industry turnover: 30–45% annually, Magellan Solutions)

Traditional centers spend 60–70% of their budget on labor alone (Magellan Solutions). That’s why automation isn’t optional—it’s essential.

Example: A mid-sized collections agency makes 50,000 calls monthly at $7.50 per call. Their total monthly cost? $375,000—mostly in wages and fragmented tech tools.

Per-call or subscription-based AI platforms may seem affordable at first—but they scale poorly.

RecoverlyAI flips the script: instead of paying per interaction, clients own the system outright. For a flat development fee—say, $15,000—that cost drops to $0.08 per call at 187,500 calls.

That’s 98% lower than traditional models.

AI doesn’t just cut labor—it optimizes the entire calling lifecycle.

Focus on these KPIs to drive down costs: - Average Handle Time (AHT): Reduce from 6 minutes (Nubitel.co) with AI that retrieves data in real time. - First Call Resolution (FCR): Boost from 70–79% using anti-hallucination logic and dual RAG validation. - Dialing Efficiency: Only 25–30% of calls connect (CloudTalk.io). AI improves this with smart scheduling and multi-channel follow-up (SMS/email). - Agent Autonomy: Fully autonomous AI agents handle 50–80% of routine calls (Magellan Solutions), minimizing human escalation.

Mini Case Study: A financial services firm replaced 15 agents with RecoverlyAI.
- Before: $6.80 per call, 42% connection rate, 68% FCR
- After: $0.09 per call, 76% effective reach (via SMS/voice combo), 82% FCR
- Savings: Over $300,000 annually

The biggest shift? Moving from OPEX to CAPEX.

Instead of monthly SaaS fees or per-call billing, RecoverlyAI uses a fixed-fee ownership model. You pay once. Then scale infinitely.

This eliminates: - Subscription creep (e.g., $3,000+/month for 10+ tools) - Per-seat licensing - API call overages

And it enables: - Predictable budgeting - Full data control - Regulatory compliance (proven in legal, healthcare, finance)

Result: A system that gets cheaper with every call.

Next, we’ll show how to benchmark your current operations—and build a compelling ROI case for switching to AI-driven collections.