In pay-per-call, quality is money. A publisher sending weak leads and a buyer's agents fumbling good ones look the same on a revenue report — both just show fewer sales. The only way to tell them apart is to actually review the calls, and the only way to review calls at scale is to let AI do the first pass.
Why manual QA can't scale
Listening to calls is the traditional way to check quality, and it works — for the handful of calls a person has time for. At any real volume, sampling a few calls a day tells you almost nothing about the thousands you didn't hear. Worse, the calls you happen to pick aren't the ones most worth hearing. The expensive problems in pay-per-call are quiet: a source that quietly stops converting, an agent who talks past every close, a compliance slip that only surfaces when a buyer complains.
What AI call QA does
AI call QA automates the first pass over every call so a human only has to look at the ones that matter. In practice that means four things happen automatically to each recorded call:
- Transcription. The call is turned into text. For telephony, transcribing the agent and the customer on separate channels is far more accurate than trying to split one mixed recording, and it makes clear who said what.
- Scoring. The transcript is graded across the parts of a call that decide the outcome — greeting, compliance, discovery, and close — so you can see where a conversation went wrong, not just that it did.
- Risk flagging. Calls that look risky or poorly handled are flagged for a human to review, so compliance and coaching effort goes where it's needed.
- Summary and disposition. Each call gets a short summary and an outcome label, so you can triage a day's calls without listening to any of them.
How call scoring works
A useful score isn't a single number — it's a breakdown. Splitting the call into greeting, compliance, discovery, and close means a low score points at a specific failure: a strong opener that never discovers the caller's need, a great pitch with a missing close, or a compliant call that simply had a weak lead behind it. That granularity is what turns QA from a report card into a coaching tool. When you can see that engaged, qualified callers are being dropped at the close, you know it's a buyer-side skill problem; when calls fall apart at discovery on a particular source, you know it's a lead-quality problem.
The metrics that matter
A few numbers do most of the work once every call is scored:
- Sold rate against billed calls. Always measure conversions as a share of paid calls, never total calls. A buyer only pays for the calls it accepts, so unbilled calls don't belong in the denominator — including them makes agents look worse than they are.
- Engaged-but-not-closed. Long, qualified calls that didn't sell are the smoking gun for a closing problem, and the highest-value calls to actually listen to.
- Disposition mix. The spread of outcomes — sold, not interested, unqualified, callback — separates a lead-quality issue from a handling issue at a glance.
- Risk-flag rate. The share of calls that trip a compliance or handling flag, per source and per buyer, tells you where to spend review time.
Turning QA into decisions
Scoring every call is only useful if it changes what you do. The payoff is being able to coach agents with evidence, catch compliance risk before a buyer does, and settle the eternal "is it the lead or the close?" argument with the actual calls in hand. In Clario, this is what AI QA on every call and Quality Insights do: every call is transcribed and scored, and any publisher-and-buyer pairing can be diagnosed in one view — with sold rate measured against paid calls, the worst-handled engaged calls surfaced first, and a written narrative you can forward. Full visibility into call quality, across 100% of your traffic, without listening to any of it at random.