Pricing a product you've never sold is the one pricing decision you can't automate your way out of. There's no sales history, no conversion rate, no margin performance to point a rule at — just an educated guess that sets the trajectory for everything that follows. The good news is that "no history" doesn't mean "no information." Three things about the product are knowable on day one, and together they get you to a confident opening price. Then the first weeks of real sales tell you where to settle.
The stakes are real, which is why the guess is worth getting right.
McKinsey's often-cited finding is that a 1% improvement in price yields roughly an 8% increase in operating profit — nearly 50% more impactful than cutting variable costs by the same amount.
On a new product, that 1% starts on day one.
But the anxiety most teams feel — "I have no data, so I'm just picking a number" — is misplaced.
You're not picking a number.
You're triangulating a range from what you already have on hand, then refining it the moment real data arrives.
Competitive pricing, discount campaigns and insights in one system.
Even with zero history, a new product isn't a blank page.
Three inputs are available on day one, and each one decides a different part of the opening price.
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Your cost sets the floor, the same way cost-plus pricing anchors the bottom of the range.
Landed cost plus the minimum margin you need is the lowest price you'd ever accept — and it's the one number you know with certainty on a brand-new product, because you're buying or making it.
Your competitors set the range.
What do similar products from rivals sell for? That tells you the band shoppers already expect to pay, and on a comparable product it's the strongest signal you have.
And your positioning sets where in that range you land — premium, mid-market or value, adjusted for anything you offer that rivals don't, like faster shipping or a better warranty.
Floor from cost, range from competitors, exact spot from positioning.
That's not a guess — it's an informed opening bracket, built from real information you had all along.
The most useful shift is to stop thinking of the launch price as a decision you have to nail and start thinking of it as a starting point you'll refine.
You're setting a sensible bracket, not carving a number in stone.
The teams that price new products best don't guess more accurately than everyone else — they just plan from the outset to adjust as the first real signals come in.
That reframe kills the anxiety.
You don't need the launch price to be perfect, because it isn't permanent.
It needs to be close enough — inside the right range, above your floor — so that the early sales data has something sensible to correct from.
A defensible opening bracket you'll refine beats a "perfect" price you agonised over and then never revisit.
One caution the reframe comes with: be deliberate about launching low.
A heavy introductory discount can win early volume, but it anchors customers to a price you may never escape, and it tends to attract deal-seekers with lower repeat value.
If you open low, know that you're doing it and why.
Once the product is live, it starts generating exactly what it lacked: data.
Sell-through, conversion, how it moves relative to the competitors you benchmarked against. That's your signal to refine within the bracket — nudge up if it's flying at the opening price, ease down if it's stalling, and watch how each move affects both units and margin.
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The launch window is the best testing opportunity the product will ever have — there's natural attention and no historical price for customers to anchor against.
This is the phase to be actively involved, because it's the phase a rule can't help with yet: there's nothing for competitive automation to stand on until the product has behaved in the market for a while.
Here's where new-product pricing rejoins the rest of your catalogue.
Once the product has enough history that its behaviour is predictable — it's found its competitive position, its margin performance is clear — it no longer needs your hands on it.
That's the moment to graduate it from manual to automated pricing: hand it to your competitive rules, with the cost-based floor still underneath.
This is exactly the line the automation framework draws — keep pricing manual where there's no data for a rule to act on, automate once there is.
A new product is the clearest case of "keep manual, for now." It graduates the day it earns a history, not before.
The reason this transition is usually clumsy is that the launch price lives in one place — a spreadsheet, someone's head — and the automated pricing lives somewhere else, so "graduating" a product means manually migrating it.
In a single system, there's nothing to migrate.
That's how Reprice handles it.
A new product gets a cost-based floor from the moment it's added, so even during the manual phase it can never be sold below margin.
You set the opening price, adjust it as the first weeks of data arrive, and when it's ready, switch it onto competitive rules — same product, same pricing software, no migration.
It's the natural endpoint of getting pricing off spreadsheets: even your newest products are protected and positioned from day one, and automated the moment they've earned it.
Competitive pricing, discount campaigns and insights in one system.
The 1%-price / 8%-profit figure is a widely cited McKinsey estimate. Reprice is informed by The Black Friday Freeze, our survey of 180 European e-commerce companies.
