Insights
23 April 2026 · Dan Branwood
In Part One of this series, we looked at the cost of not having the right data. The delays, the margin erosion, the decisions made on assumptions that prove expensive.
Part Two is different. This is about what happens when businesses do have the data, and what they do with it. Because the most valuable thing about good contract profitability data isn’t what it tells you about the past. It’s what it enables you to do right now.
The businesses that are genuinely using their data well aren’t necessarily the largest or the most sophisticated. They’re the ones asking the right questions of the information they already have.
Think about how you currently assess whether a contract is performing well. Most businesses start with rates. We’re charging X per hour, our cost is Y, so the margin is Z.
The problem is that rates tell you what should be happening. They don’t tell you what is happening. The gap between those two things is where profitability gets lost.
Businesses using contract profitability data properly aren’t just looking at rates. They’re looking at gross margin across multiple layers simultaneously:
Each of these layers tells you something different. Together, they give you a genuinely complete picture of where your business makes money and where it doesn’t.
One of the most common discoveries businesses make when they start analysing contract profitability properly is that their instincts about which work is good work are often wrong.
A good example: a business operating across multiple regions compares two contracts. Contract A has lower agreed rates. Contract B has better rates. On paper, Contract B looks like the better business. But when they look at the actual gross margin, Contract A is outperforming Contract B consistently.
Why? Travel time. Contract B, despite its attractive rates, is being serviced by crews travelling significant distances. The productive hours on site are being eaten up before the team even arrives. The rates look good. The profitability doesn’t.
With the right data, that discovery takes minutes. Without it, that contract can run at below-target margin for months or years before anyone connects the dots.
And once you can see it, you can do something about it. Could a different crew reduce travel time? Would opening a depot in that region change the economics? Is the rate negotiable, given the genuine cost of delivery? These are now business decisions, not guesses.
The rate on a contract tells you what should happen. Gross margin data tells you what is happening. The businesses that know the difference are the ones that stay ahead.
Headline gross margin figures are useful. But the real value comes when you can drill into them.
Labour is the obvious starting point, because it’s usually the largest variable cost and the most controllable. But labour as a single total figure doesn’t tell you much. Broken down by task, by team, by day, it tells you a great deal.
The same applies to vehicles and plant. Which assets are generating productive output versus sitting idle? Are the right machines being deployed on the right contracts? What is the real cost of a particular piece of equipment per job, not per week?
Materials are another layer. When material costs are recorded at job level rather than aggregated across a period, patterns emerge around waste, specification changes, and supplier performance that are completely invisible in a rolled-up cost view.
Every one of these layers can be used to drive the same outcome: higher productivity, better margin, more profitable contracts. But only if the data is there, and only if someone is looking at it.
What analysis reveals
The number of data layers businesses typically need to analyse before identifying the true root cause of a margin issue. Headline figures rarely tell the whole story.
Profitability data becomes significantly more powerful over time. A single month’s figures tell you something. Twelve months of comparable data tell you much more. Three years of it gives you a genuine forecasting foundation.
For businesses where external factors affect performance, this is particularly valuable. Weather-dependent operations are a clear example. A business in grounds maintenance or surfacing can have January and February wiped out by poor conditions. That’s not a management failure, it’s a reality of the sector.
But without data, it’s impossible to distinguish a bad January caused by weather from one caused by operational inefficiency. With year-on-year comparisons, the pattern is visible. You can show that January has historically underperformed and by how much, that March typically recovers, and that your budget assumptions need to reflect that reality rather than assume a smooth linear progression through the year.
This is the difference between a forecast that gets revised every month because reality keeps diverging from it, and a forecast that holds up because it was built on evidence.
When everything is backed up by data, nothing is anecdotal anymore. You are not estimating. You are not working from experience. You are working from fact.
For most field service businesses, budget season is an exercise in collective memory. What did we roughly turn over last year? What were our costs roughly? What do we think next year will look like?
The businesses doing this well take a very different approach. They build their budgets from the bottom up, using actual job-level performance data as the input. They know their margin by work type, by sector, by depot. They know their seasonal patterns. They know which contract types carry risk and which deliver reliably.
That means their 6+3 forecasts, or whatever planning horizon they work to, are grounded in something real. When they say they expect a revenue spike in a particular quarter, they can point to three years of data showing the same pattern. When they plan for a quieter period, they’ve quantified what that looks like and built their cost base to match.
This is not sophisticated finance. It is simply the discipline of using the information your operations are already generating, rather than setting it aside and relying on instinct.
Planning impact
The forecasting model used by field service businesses that run on data, built on actual job-level performance rather than estimates. When the inputs are accurate, the outputs are reliable.
There is a distinction worth drawing out here. Many businesses have reports. Far fewer have intelligence.
A report tells you what happened. Intelligence tells you what it means and what to do about it.
The difference lies in how the data is structured and how accessible it is. A monthly summary report that arrives ten days after the period end is a historical document. Job-level data that you can query in real time, drill into by any dimension you choose, and compare against previous periods is a decision-making tool.
Businesses that have made this shift describe a fundamental change in how they operate. Conversations that used to be based on opinion become conversations based on evidence. Decisions that used to take weeks because no one could find the right numbers happen in an afternoon. And the confidence to walk into a contract renegotiation knowing exactly what your costs are and where the margin sits is not a small thing. It changes the outcome.
You don’t need to rebuild your entire operation to start using data better. But you do need three things in place:
The data your teams are generating every day is one of the most valuable assets your business has. The question is whether it is working for you or just sitting in a system waiting to be looked at.
In Part Three of this series, we look at a real-world example of a business that changed how it operates using contract profitability data, and what that change actually looked like in practice.
Triangle Software’s Formulate platform gives field service businesses the contract profitability visibility they need to move from reactive reporting to genuine business intelligence. To find out more, visit trianglesoftware.co.uk or speak with a member of our team.
[googlesitekit-end-of-content]
Insights
The businesses that get caught out by winter usually aren’t the ones without a plan, they’re the ones whose plan was built for the winter that used to happen rather than the one that’s actually coming, and that gap tends to open up faster than most people expect.
When did you last ask yourself how confident you really are that every safety check your field teams ran last week actually happened? For most businesses managing mobile workforces, that question lands uncomfortably.
There is a version of the AI conversation that stays firmly in the world of dashboards, roadmaps and capability announcements.
Every operations director knows the feeling, because it tends to arrive at the same time every week, usually just as the weekend’s jobs have landed and the Monday morning report is sitting open on screen, and something about the numbers simply doesn’t sit right.