Action register — the top fixes, ranked by money on the table
Each fix: the team that owns it, the specific change, the lost calls that prove it, and the estimated first-year revenue it could recover. Every action was adversarially re-checked against the underlying calls. Dollar figures are estimates — assumptions in Methodology.
The headline: every lost call has a reason
How every call resolved
100% of calls categorized — no “unknown” bucket.
Recoverable vs. structural
Losses a better-handled call could have saved.
Loss reasons over time
Monthly count of lost leads + cancellations by reason.
Deeper loss breakdown
Sub-type detail for the top loss categories — populated where transcripts or summaries contained enough signal.
Where losses come from
Loss rate by traffic source (lost ÷ all calls on that source).
Signals
Operational spotlight — call-handling failures
The most recoverable losses: prospects lost to how the call was handled (hold, dropped transfer, unanswered question, promised callback).
Voice-of-customer quote bank
De-identified verbatim excerpts. Names, phones, emails and addresses are masked.
Revenue at stake by loss reason
Estimates. Per lost call: first-year customer value × probability the call closes/saves if the underlying issue is fixed. Assumptions in Methodology.
Who owns the fix × why we lose
Losses by accountable team and loss reason.
Loss reason × traffic source
Paid losses cost ad dollars; organic losses cost only the opportunity.
Recoverability × buyer readiness
Lost leads only — where the savable, ready-to-buy callers are.
Call-handling failures by month
Is callback-flaking getting better or worse?
Call-level explorer — every loss, one row
All losses, de-identified (names, phones, addresses masked). Filter by any dimension; search hits summaries and quotes.