Executive insights · Call analytics

How call analytics found the fee costing SOS Plumbing its jobs, and bookings rose 375%

Call analytics

95bookings

in 90 days, up from 20, after adopting AI-recommended pipeline fixes.

Blocker found$399 call-out feeFee droppedBookings climb
Booked beforeBooked after the fix
20 → 95booked jobs, 90 days before vs after
20% → 35.2%of calls that became a booking
$0extra ad spend

SOS Plumbing & Gas, a plumbing and gas business in Adelaide, was getting calls from its Google ads and search listings, but only one caller in five booked a job.

01The problem

In the 90 days before the change, 100 calls produced 52 qualified leads and just 20 bookings. Lead volume was rising, and so was the cost of each lead.

02What was built

Every inbound call was recorded and tracked. Recordings, call notes and AI call summaries were then reviewed to identify where callers dropped out.

The pattern repeated call after call: people described the job, then hung up when the $399 call-out fee came up, often to ring a competitor that did not charge one. On 26 February 2026 the agency recommended dropping the fee in business hours and keeping it after hours. The owner agreed.

03How it works

  1. 01Record every call

    Call tracking logged each inbound call from ads and search, with the recording and the outcome.

  2. 02Find the drop-off

    Reviewing the calls showed callers walking away at the moment the call-out fee came up.

  3. 03Make one decision

    Drop the fee in business hours, when callers compare plumbers, and keep it after hours for urgent jobs.

  4. 04Measure the result

    Compare the 90 days before and after, with no change to ad spend or campaigns.

04What changed

In the 90 days after the change, booked jobs rose from 20 to 95 and the booking rate from 20% to 35.2%. Calls rose from 100 to 270 and qualified leads from 52 to 187, with no increase in ad spend and no new campaigns.

What this could look like for your clients

Your clients’ call recordings already show why customers say no. A similar review could find the one price, policy or script line that is costing them jobs, and put a number on fixing it.

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