Every price change is a trade between margin per unit and the volume you keep. Price optimization consulting puts numbers on that trade for each segment you sell to, then sets the targets and floors that let your team negotiate with the numbers in hand.
Textbook price optimization estimates how demand responds to price, then picks the price that maximizes profit. Retailers and online marketplaces can do this because they see thousands of transactions at posted prices and can test a change by the afternoon.
Most B2B companies cannot, for three reasons.
So in B2B, pricing optimization works on something you can observe: the spread of net prices you already achieve, how often you win at each level, and what each deal costs to serve. The output is not a single optimal price. It is a target and a floor for each segment and product family, and the rules for moving between them.
Before any price moves, one calculation frames the whole conversation. For a price change of ΔP (as a percentage) on a product with contribution margin CM (also a percentage), the change in volume that leaves total gross profit exactly where it was is:
Break-even volume change = minus ΔP divided by (CM + ΔP)
A price increase shows how much volume you can lose and still be better off. A price cut shows how much extra volume you must win just to stand still.
| Contribution margin | Cut 10% | Cut 5% | Cut 2% | Raise 2% | Raise 5% | Raise 10% |
|---|---|---|---|---|---|---|
| 20% | +100.0% | +33.3% | +11.1% | minus 9.1% | minus 20.0% | minus 33.3% |
| 30% | +50.0% | +20.0% | +7.1% | minus 6.3% | minus 14.3% | minus 25.0% |
| 40% | +33.3% | +14.3% | +5.3% | minus 4.8% | minus 11.1% | minus 20.0% |
| 50% | +25.0% | +11.1% | +4.2% | minus 3.8% | minus 9.1% | minus 16.7% |
Read one row carefully. At a 30% contribution margin, a 5% discount has to win 20% more volume to break even. A 5% increase stays profitable unless it loses more than 14.3% of volume. The asymmetry is why discounting to win share so often destroys profit: the volume needed to justify it is rarely there, and in contracted B2B markets a competitor usually matches the cut before the extra volume arrives.
The table also tells you where to look first. Low-margin product lines are the most sensitive to discounting and the most forgiving of increases. High-margin lines leave less room for error on increases and make discounting look cheaper than it is.
Illustrative example. One product family, one customer segment (mid-sized contractors), list price $200, variable cost $120 per unit, 20,000 units a year. Net prices already vary widely, so we group last year’s quotes into five bands and look at two things for each: how many units sold there, and how often quotes at that level were won.
| Net price band | Average net price | Units sold | Quote win rate |
|---|---|---|---|
| Below $158 | $152 | 3,000 | 68% |
| $158 to $166 | $162 | 5,000 | 64% |
| $166 to $174 | $170 | 6,000 | 61% |
| $174 to $181 | $177 | 4,000 | 49% |
| Above $181 | $185 | 2,000 | 31% |
Revenue is $3,364,000, an average of $168.20 per unit, and gross profit after variable cost is $964,000.
Quote win rate by net price band
Win rates barely moveThe sharp drop above $174
Floor $158
The floor comes from cost, then from the data.
Variable cost is $120 and serving this segment costs a further $14 a unit in freight, support, and order handling, so the lowest price that still earns a 15% margin over those costs is $134 divided by 0.85, or $157.60. The floor is set at $158. Everything in the first band is below it.
Target $174
The target comes from the win rate.
Win rates barely move between $152 and $174, falling only from 68% to 61%, then drop sharply to 49% above $174. That shape says buyers in this segment do not start resisting until about $174, so the target price for a standard deal is $174, and reps who quote below it are giving away margin the buyer was not asking for.
What the new rules are worth. Say the business moves every first-band account up to the $158 floor and loses 10% of that volume (300 units), and nudges the second band up by $2 to $164 with no loss. The three upper bands are left untouched for now, so none of the gain below depends on reaching the target.
The first band had room to spare: at a 21% margin and a price rise of about 4%, it could have lost up to 15.8% of its units before profit fell. That is the first step only. If half the units in the $166 to $174 band move to the $174 target at renewal, a further 3,000 units gain $4 each, adding $12,000. The compounding comes from applying the same rules to every segment and product family, not from one dramatic list price change.
A company with ten product families and six customer segments has 60 combinations, and optimizing all of them at once is how projects stall. We rank them on three measures and start with the top five or six.
| Measure | Why it matters | How it is read |
|---|---|---|
| Revenue in the combination | A 2% gain on a large base is worth more than a 10% gain on a small one | Last 12 months of invoiced revenue |
| Spread of net prices | Wide dispersion between similar accounts means money is being left for no reason | Interquartile range of net price as a share of the median |
| Margin sensitivity | Low-margin lines gain most from small price moves, as the break-even table shows | Contribution margin after cost to serve |
A combination with high revenue, a wide spread, and a thin margin goes to the top of the list. One with a narrow spread is already priced consistently, whatever its level, and usually waits. This ranking is also how the first results arrive within a quarter: the early segments are chosen because they are the ones most likely to move.
The same lens applies to price increases. An across-the-board increase treats a tightly priced segment and a loosely priced one the same way. Differentiated increases, larger where the spread is wide and smaller where prices already sit near target, recover more margin with less churn, and they are easier for sales to explain because each one has a reason attached.
A price optimization engagement ends with tools your team uses every week, not a model only an analyst can run.
Behind these sit a price segmentation that reflects how customers actually differ, and cost-to-serve estimates so that floors reflect what each segment really consumes.
Most companies searching for price optimization have been shown software first. Software is good at some parts of the problem and poor at others, and it is worth being honest about which.
Part of the problem
Software does it well
Needs judgment first
Our view is that the rules come first and the system second. Once the segmentation, targets, and floors are agreed, PricePro© can carry them into daily quoting, recommend prices within the agreed bands, and alert when realized prices drift. Bought before the rules exist, price optimization software automates whatever habits are already in place.
A rigging hardware distributor whose selling prices varied from branch to branch replaced them with standard prices protected by floors. The groundwork was a cut of its sales history by product category, geography, account size, and loyalty, plus customer research which found that larger and smaller buyers reacted to price very differently. Over the first year the client reported an 8.1% gain in gross margin.
A business and financial services provider with 18,000 US customers found that two comparable clients, with similar volume and revenue, paid $7,700 and $3,700 a year for the same kind of service. Our recommendations there were value-based tiers, closing the gaps between similar customers, and targeted increases of 11% to 25% set by each account’s pricing history and stickiness. Those were recommendations; the client’s results are not reported here.
Acustrategy’s senior team has run pricing work since 2008, and a price optimization engagement typically takes six to 10 weeks from data request to agreed segment guidance.
Before the next increase, discount, or promotion, run it through the break-even table and your own win rates. Talk to us about doing that for one product family and setting the targets and floors that follow from it.
A price increase moves the whole list by a percentage. Price optimization changes where individual segments and deals land relative to their value and cost, so some prices rise, some hold, and a few may fall. In the example above, revenue fell slightly while profit rose, which a blanket increase would never produce.
Yes, with different methods. With a few hundred accounts, quote-level win rates and cost to serve do most of the work. Where the data is too thin, customer research on willingness to pay or a controlled test fills the gap. We say which parts of a recommendation rest on data and which on judgment.
From cost first: variable cost plus the cost of serving the segment, grossed up to the minimum margin the business will accept. Then we check it against the data. If a large share of volume sits below the cost floor, the problem is a product or segment decision, not a pricing rule, and we raise it as such.
Net prices, almost always. In negotiated markets list price is an anchor; the money is made or lost in the distance between list and net. List prices are then reset so that the typical negotiation lands near target. In the worked example above, list is $200 and no band averages above $185.
Some will try. The break-even table is the check on whether matching them makes sense. In most B2B markets buyers weigh availability, service, and reliability alongside price, and our competitive pricing analysis normalizes competitor quotes for those differences before anyone reacts to a headline number.
New quotes follow the rules as soon as they are in place, so win rate and realization on new business move within the first quarter. Margin on the installed base follows as contracts renew. That is why the worked example books the move to the $174 target at renewal, not on day one.
