By Greg Allan, tradesman and creator of Data First Coaching
Why do customers say no to extra work found on the hoist?
Most customers do not say no because the price is wrong. They say no because the call comes out of nowhere, mid shift, while they are at work or in a meeting, and the mechanic on the phone is trying to explain a failed scan tool reading or a roadworthy item in thirty seconds flat.
A technician finds worn brakes or a fault during a logbook service and rings straight away. The customer cannot see the car, cannot see the part, and has no time to think. Under pressure, most people default to no, or to "just do what was booked." The workshop reads that as the customer being tight. Often it is really a communication problem, not a trust problem, and it shows up again on the next booking.
What does a knocked back approval actually cost the workshop?
It costs more than the missed job. A technician left standing at the hoist waiting on an answer is not billing, an apprentice is not learning on paid work, and parts already pulled for the job may go back on the shelf unused. That time comes straight off the labour rate the workshop is trying to protect.
This is where gross profit margin gets eaten, not on a spreadsheet at the end of the month, but at the exact moment a mechanic is standing next to a ute waiting for a yes or a no. A booking that was meant to take an hour now takes two, the next car on the hoist is delayed, and the whole day's schedule slides. Treat every declined quote as only lost revenue and the workshop keeps losing the same margin every week without knowing where it actually goes.
How do you present extra work so more customers say yes?
You get more yeses when you stop presenting one price and start presenting options. Group what the technician found into what is urgent for safety, what is recommended before the next service, and what can wait, and put a photo against each one so the customer is not taking your word for it over the phone.
A fleet manager approving brake work on five utes wants a different conversation to a private owner bringing in their car for the first logbook service. Build the good, better, best options around who is actually buying, not a script you use on everyone. That is what protects your gross profit margin at the point of quoting, which is the only point that matters, because once the car leaves the workshop that margin is set and cannot be recovered later.
What does your own job data say about which extra work actually gets approved?
Your own numbers, not your memory, hold the real answer. Which types of extra work get approved most, which technician's recommendations customers trust fastest, which fleet accounts approve almost everything and which private customers need more time, all of that sits in the jobs the workshop has already run.
Most mechanics answer this question from a gut feeling built on the last bad day, not from what has actually happened across every booking this year. A memory of one awkward call with a customer who argued over a tyre price is not the same as what the data shows across every roadworthy, every diagnostic, every warranty job the workshop has done. Data first thinking means the next quote for extra work is built on what has actually worked for this workshop, not on a feeling from last week.
How can AI agents help get the approval before the car comes off the hoist?
An AI agent is software that does a job the way you would do it yourself, using your own prices, your own wording and your own rules, so it is not generic. For a mechanic, that can mean the moment a technician logs a found fault on the scan tool, an agent puts together the explanation and the photo and sends it to the customer in the workshop's own words, before the phone call ever has to happen under pressure.
AI agents do not decide what work needs doing. A technician and the scan tool still find the fault. What agents change is the gap between the car going up on the hoist and the customer having the information they need to say yes, calmly, in their own time, instead of on the spot. That gap is where most knockbacks happen, and it is the gap worth closing first.
How does Data First Coaching approach this?
Data First Coaching, built by Greg Allan, starts from the workshop's own job data through the Trade Business Intelligence, not from a general theory about customers and extra work. It looks at which jobs, which technicians, which customer types and which booking patterns are actually behind low approval rates, because the real cause is often different to what it looks like on the surface.
From there, the how, the systems, the wording, the agent builds, is worked through inside the Academy, with members. The Academy is for owners who are open minded, innovative and willing to share what they learn, because the Trade Business Intelligence is powered by every member, for every member.
