Contract Profitability Part Two

What Good Data Actually Looks Like in Practice

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.

The Questions That Change How You Run Contracts

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:

• At contract level: Is this specific contract delivering the margin we expected?
• At depot or team level: Are some parts of the operation consistently outperforming
  others?
• At work stream level: Which types of work, by category, are most and least
  profitable?
• By sector: Does working for local authority clients perform differently to national or
  private clients?
• By customer relationship: Are you more profitable working directly for an end
  customer or indirectly through a principal contractor?

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.

When the Rates Lie to You

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.

Drilling Down: Where the Detail Lives

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.

Year on Year, Month on Month

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.

Budgeting With Confidence

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.

The Shift From Reporting to Intelligence

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.

What This Means Practically

You don’t need to rebuild your entire operation to start using data better. But you do need three things in place:

• Consistent, accurate data capture at job level, recorded at the point of work rather
  than reconstructed afterwards.
• The ability to view that data across multiple dimensions: by contract, by team, by
  work type, by period.
• A regular discipline of reviewing it, comparing it against previous periods and against
  estimates, and acting on what it shows.

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 transformed 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.