Price increase modeller

Putting prices up is one of the highest-leverage decisions a trades business can make, and one of the most nerve-wracking — because the obvious fear is losing customers over it. This tool models both sides at once: the extra revenue from higher prices, and the revenue given back if a slice of customers walk. Set the increase and your best guess at the loss, and it shows the net effect on monthly revenue — not just the upside, the whole picture.

Why small increases usually win anyway

The maths behind a price rise is more forgiving than it feels. A 5% increase only needs a small slice of customers to leave before it stops paying off — and in most trades, the number who actually leave over a modest, well-explained increase is far lower than people fear. The customers most likely to push back on price are often the ones costing the most in hassle, callbacks and haggling anyway, so losing a few of them isn't always the loss it first looks like.

A worked example

Take £10,000 a month in revenue, a 5% price increase, and a cautious guess that 5% of customers don't come back. New revenue lands at £9,975 — a small net loss of £25, because at this ratio the loss almost exactly cancels the rise. Push the increase to 10% with the same 5% customer loss and it clears £10,450, a gain of £450 a month. The lesson isn't "5% is too risky" — it's that the loss rate has to climb a lot further than most trades actually see before a sensible increase stops paying off, and it's worth running your own numbers through rather than guessing.

The other direction: what a discount actually costs

This tool is the mirror image of a common mistake — knocking money off a price to win a job without working out what that discount really costs against the margin. If a discount is on the table instead of a rise, the discount impact calculator runs the same kind of maths in reverse, showing how many extra jobs a price cut needs to bring in just to stand still.

What this isn't

The customer loss percentage here is a guess, not a measurement — nobody knows exactly how many people will leave until the letter goes out, so treat the result as one scenario among several, and try a few different loss rates to see how sensitive the outcome really is. It also treats the change as instant, when in practice customers drift off gradually over months rather than all on the day the new price lands, so the dip in revenue is usually spread out rather than immediate.