From first ring to closed revenue, in one line
Most call analytics stop at the call. Operon follows the conversation into the booking, into the completed job, and into the invoice, so you can finally answer which marketing dollar produced which dollar of revenue.
Every dollar here traces back to a specific conversation. The dashed line is the four-month average before Operon answered a single call.
What it actually does
Attribution that reaches revenue
Every inbound number, campaign, and landing page tied to booked appointments and closed dollars. Not calls, not leads. Dollars.
Leak analysis by hour and location
See exactly where bookings fall out: which hours, which stores, which service lines, and which conversational moment loses the caller.
Every call scored, none sampled
Full transcription with intent, sentiment, objection type, and disposition on 100% of calls. The QA process that used to cover 2% now covers everything.
Cost per booked job, not per lead
Ad spend joined to booked revenue by source, so you can shift budget on evidence instead of on the last thing your agency said.
Slice it any way you run the business
By location, service line, technician, campaign, language, hour, and day. Save views, share them, and schedule them to land in an inbox.
Your data, exportable
Streaming export to Snowflake, BigQuery, and S3, plus a full REST API. Nothing about your data is locked inside our product.
The first report usually changes a budget
Within two weeks of going live, every customer gets a leak report. It shows which paid channels generate calls that never convert, which hours are structurally understaffed, and which locations are losing revenue for reasons that have nothing to do with demand.
- Revenue lost to unanswered calls, by hour and location
- Paid channels ranked by cost per booked job, not cost per call
- Service lines where quoting behavior differs from your price book
- Locations where the gap is staffing rather than demand
Not cost per lead and not cost per call. Spend divided by the jobs that actually landed on the schedule, traced from the ring that produced them.
The numbers your weekly meeting actually needs
Prebuilt views for the metrics operators run on, so nobody has to build a dashboard before they can answer a basic question. Every view drills down to the individual call and its transcript.
- Answer rate, booking rate, and average job value by location
- Escalation volume and reason, ranked
- Brain gaps by frequency and revenue impact
- Scheduled email and Slack delivery on any saved view
One week of inbound volume at a six-location operator. Bar height is calls offered; the filled portion is calls actually answered.
Rollout across 34 locations took eleven days. The part I did not expect was the reporting. I finally know which marketing spend produces booked revenue and which produces ringing phones.
Details worth knowing
It complements it. We write the structured outcome back into your CRM so your existing reports get better, and we cover the part your CRM has never seen, which is what happened on the call itself.
Yes. Streaming export to Snowflake, BigQuery, or S3, plus a REST API and webhooks. Enterprise plans include a dedicated schema and historical backfill.
From your go-live date forward by default. If you have existing call recordings we can backfill and score them so your first report has a real baseline.