How executives and deal teams can test the price assumption inside a market size before it shapes an investment decision
Most market sizing debates are about volume. How many units are installed, and how quickly will buyers adopt? Those questions absorb most of the fieldwork and most of the discussion when results are presented.
The average selling price rarely gets the same attention. In many of the models we review, it appears once, in a methodology note, often as last year’s internal ASP or a figure carried over from a secondary report. From there, it gets multiplied through the model and is seldom revisited, even though that one figure stands in for every price in the market.
That imbalance is hard to justify. In any market sized in dollars, price carries the same weight as volume. Teams may spend weeks testing installed base, adoption, replacement cycles, and share assumptions, while accepting the price attached to those units with comparatively little debate. Yet that price rests on the same questions a pricing study is designed to answer.
Every Market Size Is Also a Pricing Assumption
The reason for the imbalance is practical. Counting units, facilities, or buyers requires fieldwork, and fieldwork is where most of the effort in a sizing study goes. Price, by comparison, feels like something the organization already knows.
Consider a simple illustration.
- A market of 2 million units at an average price of $400 is an $800 million market.
- The same 2 million units valued at an assumed $340 is a $680 million market.
- The volume did not change, yet $120 million of opportunity appeared or disappeared because of a number that may never have been questioned.
Our own work shows the same effect. In a recent study of a B2B software platform market, the initial average price assumption for one platform type was $30,000 to $60,000. Primary research put it at $120,000. Had the desk estimate stood, that segment would have been valued at a quarter to half of what the research supported.
The volume estimate was not the only problem. The price attached to that volume was.
The next challenge is that there is rarely a single market price to validate in the first place.
A Market Price Is an Average of Averages
Each competitor sells at its own ASP, shaped by its positioning, product mix, channels, and customers. Branded, private label, and imported products often sit at very different price points within the same category. The market-level price is an average of those prices, and the way it is averaged changes the answer.
A simple average gives every competitor the same weight, whatever its volume. When one player prices well above the rest of the field, a simple average pulls the market price toward that player even if it sells relatively few units.
Another consideration:
- A market of 2 million units with three competitors.
- One sells 200,000 units at $1,000. The other two each sell 900,000 units, at $400 and $350.
- A simple average of the three prices is about $583, which puts the market at roughly $1.17 billion.
- Weighing each price by its share of units gives an average of $437.50 and a market of $875 million.
The units are identical in both cases, and the difference of nearly $300 million comes entirely from how the prices were averaged.
Weighting by unit share requires price and volume estimates for each major competitor, which takes more work than pulling a single figure from a report. It also connects competitive intelligence and pricing research directly to the market size. A model built on one blended price skips that step.
Where the Price Assumption Usually Comes From
When we review market models built internally or inherited from earlier work, the price input tends to come from one of four places: the company’s own selling price, a list price, a single competitor’s price, or a figure from a secondary report. Each is a reasonable starting point, but each answers a slightly different question than the one the market size is meant to answer.
Internal selling price reflects your own product mix, your customers, and your discount structure. It describes your price, not the market’s. A premium-positioned company that sizes the market at its own ASP will overstate the market. A value-positioned company will understate it.
List price ignores what happens after the quote. Discounts, rebates, distributor margins, and contract pricing all sit between the published number and the realized one, and in many B2B categories that gap is substantial.
A competitor’s price is a single data point from a single position in the market. It may be accurate for that competitor and misleading for everyone else.
Secondary reports often carry a price embedded in their totals without stating it. They also tend to leave out the channel level, so it is unclear whether the market is valued at the manufacturer’s selling price, the distributor’s price, or the installed price paid by the end user.
Trade data adds a quieter version of the same problem. U.S. imports are recorded at customs value, which excludes freight and duties, so combining them with domestic shipment values understates what buyers pay for imported products, a gap that has widened as tariffs have risen. Mixing those levels within the same model is one of the more common errors we encounter, and one of the hardest to spot after the fact.
Estimates of the same market can sit very far apart. In one water treatment study, they ranged from $3 billion to $15 billion globally. A range that wide usually reflects different assumptions about price, product mix, and what is being counted, and those assumptions are rarely stated alongside the total.
There is nothing wrong with starting from any of these sources. Problems begin when the starting figure is carried into the final model without being tested.
Price Also Decides How Much of the Market Is Reachable
Up to this point, the issue has been how price affects the value assigned to a market. Price also determines which units belong in the reachable market at all.
At any given price point, some buyers will not participate. They have tighter budgets, lower-cost alternatives, or applications where a premium feature adds little value. A total addressable market sized at a premium price includes volume that is not addressable at that price.
Willingness-to-pay research connects the two disciplines directly. Methods such as Van Westendorp, Gabor-Granger, and conjoint analysis show how demand changes as price changes. The same demand curve that informs a pricing decision also defines the serviceable portion of the market at each price level the business is considering.
When Market Growth Is Really Price Growth
Inflation, tariffs, and input cost increases have pushed prices up in many categories over the past several years. When those increases flow through to the market total, reported growth begins to reflect price as much as demand.
A market that grew 8% in dollar terms while average prices rose 6% grew only about 2% in units. Both figures are correct, but they point toward very different business conclusions.
Mix can move the average without any change in list prices. When buyers trade down from premium to value tiers or to private label, the market’s average price falls even if every tier holds or raises its price. Separating tier mix from price changes shows whether demand is changing or only the average.
Most of the decisions that follow a market sizing study are volume decisions. Capacity, sales coverage, distribution investments, and acquisition theses depend on how many more units the market will absorb, not on how much more each unit costs. Projecting price-driven growth forward also assumes those prices will hold. If costs ease or competitors respond, some of that growth may reverse. Global markets add currency to the list: a market reported in U.S. dollars can grow or shrink with exchange rates while local demand holds steady.
Applying Minimax Thinking to the Price Inside the Model
In our recent articles on minimax thinking, we argued that pricing decisions should be judged by the consequences of being wrong as well as by their most likely outcome. The same discipline applies to the price assumption inside a market model.
What happens to the business case if the average price is 10% or 15% lower than assumed? Does the opportunity still clear the hurdle the business has set? Which parts of the price input carry the most uncertainty?
The direction of the error matters as well. If the price in the model is too high, the business may build capacity it cannot fill, set sales targets it cannot meet, or pay for an acquisition based on revenue that never existed. If the price is too low, the more likely result is underinvestment and missed share.
Both outcomes are undesirable, but they are not equally recoverable. A plan built on a conservative market can usually be scaled up once the market proves itself. Capacity that has already been built, or a purchase price that has already been paid, is far harder to unwind. That asymmetry belongs in the planning discussion before the decision is made.
Treat Price as a Variable, Not a Constant
Teams can keep the methods they already use. What changes is that the price input receives the same rigor as the volume input already gets.
In practice, that starts with modeling price as a range rather than a single number. Triangulated market sizing already checks volume against several independent sources, and the price input deserves the same treatment. Internal transaction data, distributor and customer interviews, competitive checks, and willingness-to-pay research each provide a different view. Agreement among those sources raises confidence in the number. Disagreement is informative too, because it shows where the price assumption needs more work.
It also means building the market price from competitor-level prices weighted by unit share, stating the channel level at which the market is valued, separating volume growth from price growth in any forecast, and running price scenarios alongside volume scenarios. In many models, the price scenario moves the answer more than any of the volume assumptions the team spent weeks validating.
Two Studies, the Same Prices
Market sizing and pricing research are often commissioned separately, by different teams and for different purposes. Yet they rely on the same prices. When the market model assumes one price and the pricing strategy assumes another, both can look sound on their own and still contradict each other in the plan.
The two studies do not need to become one, but they should be grounded in a consistent view of what customers pay, how prices vary across competitors and segments, and how demand changes at different price points. Many of the questions explored in pricing research are the same questions embedded within the assumptions of a market model, even when the two efforts are commissioned for different reasons.
While determining market volume (both units and dollars) may require more fieldwork than price, the price assumptions still deserve the same scrutiny. Every unit in the model is multiplied by it, and seemingly small differences in that assumption can materially change the answer.
