By Greg Allan, tradesman and creator of Data First Coaching
Why does an excavator that's booked solid still feel like it's bleeding money?
An excavator can run flat out, week after week, and the business can still be going backwards on it, because being busy and being profitable are two different things. The machine looks fine on the dry hire rate and the diary looks full, but the owner's bank balance tells a different story. That gap is where most earthmoving contractors get caught out.
The problem is that most tracking stops at machine hours against the quoted rate. Fuel gets paid from the fuel card, servicing gets booked when it's due, a breakdown eats a day, float time between sites gets absorbed somewhere in the week, and none of it ever gets properly charged back against the site cut or trench that the machine was actually working on that day. It all sits in overhead instead of sitting against the job. So the job looks like it made money, because on paper it did, and the business still loses money, because the machine's real running cost was never counted against it.
What's hiding between the dry hire rate and what the job actually costs?
The dry hire rate is a price, not a cost. What it actually costs to run that excavator on a given site cut, trench or bulk earthworks job includes fuel burn for that machine on that ground, servicing wear building up in the background, any breakdown downtime, the float to get it there and back, tipping fees if the job carries them, and the operator's hours including the ones spent moving the machine rather than digging.
Most earthmoving contractors already separate dry hire from wet hire pricing. Far fewer separate the true cost of running the machine from the rate they charge for it. Pricing a machine off a flat hourly rate, or a flat markup on what the job seems to need, is a guess dressed up as a number. A job that looks like it carries a healthy markup on paper can still return a much thinner margin once the real running costs land on it, the same trap that catches a trade owner who marks up materials without checking what actually lands in the margin.
Is comparing dry hire rates against wet hire rates enough to answer this?
No. Comparing rates tells an owner what the market is charging, not whether their own excavator covered its own cost on their own jobs. Two earthmoving contractors can charge the same wet hire rate and get completely different results, because one is covering breakdown time, servicing and float against the job and the other is letting it all disappear into overhead.
Rate comparison is useful for setting a price. It says nothing about whether the machine paid for itself once it actually went to work. That answer only lives in what happened on the job, not in what the market charges for a similar one.
What number in your own job data actually tells you if the excavator is paying for itself?
The number that tells the truth is the gap between what a job returned and what the machine truly cost to run on that job, once fuel, servicing, breakdown time, float and the operator's full hours are properly attributed to it. Not the quote. Not the dry hire rate. The actual return, job by job, machine by machine.
This is a data first way of answering the question, because it starts from what your own jobs actually did rather than from a feeling about whether the excavator earns its keep. A machine can look fine on average across a busy month and still be quietly losing money on the wet days, the trenching jobs with awkward access, or the sites where float time ate more hours than anyone noticed. You only see that pattern when you look at the job data itself, not the invoice totals.
How can AI agents take this work off your plate?
AI agents can carry the non-billable tracking that most earthmoving contractors never get to, because it's fiddly and there's always a machine to service or a crew to run. 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.
Here, that means an agent pulling fuel use, servicing cost, breakdown time, float time and operator hours onto the actual job they belong to, instead of letting them sit in general overhead where they quietly erode margin without anyone noticing. AI agents don't replace your judgement on whether to keep a machine, send it to a different site, or put it up for sale. They give you the real number to judge it against, so the decision is made on this week's data instead of last year's impression of how the excavator performs.
How does Data First Coaching approach this?
Data First Coaching, built by Greg Allan, starts from your own job and finance data through the Trade Business Intelligence, not from a conversation about how your earthmoving business feels like it's running. On a question like this, it looks at where the gap between quoted margin and actual return on each machine really starts, whether that's in how float time is costed, how breakdown hours are absorbed, or how servicing gets buried in overhead instead of sitting against the jobs that wore the machine out. Greg has said this kind of structured, repeatable approach is “a really powerful way to quote because when you do start delivering it, people are used to receiving it this way.” The how, built around your own excavators, your own jobs and your own crew, is worked through inside the Academy, with members, because the Trade Business Intelligence is powered by every member who is open minded, innovative and willing to share.
