Market size and competitive position tell you whether a target’s growth is plausible. Its invoices tell you whether that growth is real. We read the pricing inside the numbers, pre-LOI through diligence, and size what it means for the deal and for the first 100 days.
A standard commercial due diligence answers outside-in questions: how large the market is, how fast it grows, who competes, and whether the target’s position is defensible. Those questions matter. They do not reveal whether the target’s revenue depends on concessions that a new owner will inherit, or whether it is charging far less than its customers would pay.
A pricing lens in due diligence adds inside-out questions, answered from the target’s own transaction data:
Our commercial due diligence services are built around those five questions. The tests can stand alone or sit inside a broader commercial due diligence led by another adviser. Either way, the findings are written for the investment committee and converted into post-close actions.
The first test is the simplest and often the most revealing. A management presentation might say that revenue grew 13% on the back of a price increase. The invoice data can show how much of that is true.
Illustrative example. A target sells two product lines: a core line and an entry line.
| Year 1 units | Year 1 price | Year 1 revenue | Year 2 units | Year 2 price | Year 2 revenue | |
|---|---|---|---|---|---|---|
| Core line | 300,000 | $150.00 | $45.00M | 290,000 | $156.00 | $45.24M |
| Entry line | 350,000 | $100.00 | $35.00M | 460,000 | $98.50 | $45.31M |
| Total | 650,000 | $80.00M | 750,000 | $90.55M |
Revenue grew by $10.55 million, or 13.2%, and the core line’s price did rise 4%. The price-volume-mix decomposition tells a different story from the headline:
| Effect | How it is calculated | Value | Share of growth |
|---|---|---|---|
| Volume | 100,000 more units at the Year 1 average price of $123.08 | +$12.31M | 117% |
| Mix | Year 2 units at Year 1 prices ($89.50M), less the volume-adjusted base ($92.31M) | -$2.81M | -27% |
| Price | Year 2 revenue less Year 2 units at Year 1 prices | +$1.05M | 10% |
| Total | Volume, mix, and price effects added together | +$10.55M | 100% |
Price contributed $1.05 million, about 1.2% on a like-for-like basis, because the core increase was partly offset by promotional discounting on the entry line. The growth came from volume, and most of that volume was in the cheaper line, which is why mix is negative. The entry line’s share of units rose from 54% to 61%.
None of that makes the target a bad asset. It changes the questions: whether entry-line buyers upgrade over time or stay on promotional pricing, what the entry line earns at contribution level, and whether the core line’s 4% increase would hold without the discounting elsewhere. A thesis built on pricing power needs the price effect to carry weight; here it does not yet. Our glossary explains the method in more detail under price-volume-mix analysis.
Each test uses data the target already holds. Together they give a view of revenue quality and pricing headroom that management interviews and a CIM will not.
Test 1
What it reveals: How much growth came from price
The red flag: Growth attributed to price that the invoices do not show
Test 2
What it reveals: Whether announced increases reached net price
The red flag: Less than half of each announced increase held a year later
Test 3
What it reveals: Whether pricing follows rules or history
The red flag: A wide spread that volume and cost to serve do not explain
Test 4
What it reveals: Dependence on concessions to hit targets
The red flag: Discount depth rising at quarter ends, or in the months before the sale process
Test 5
What it reveals: Pricing power in the installed base
The red flag: Renewals flat or lower in price; subscription net revenue retention held up only by seat growth
Test 6
What it reveals: How much revenue can be repriced after close
The red flag: Large share under price caps, no escalators, or most-favored-customer clauses
Test 7
What it reveals: Exposure in the largest accounts
The red flag: The top customers on the deepest discounts, with renewals falling inside the hold
Test 8
What it reveals: What customers would pay, and why
The red flag: Customers cannot articulate the value, or losses on price are rising
Tests one to seven are read from the data. Test eight needs a focused set of customer conversations, covering won and lost accounts across the main segments, to test willingness to pay and what buyers compare the target against.
Test three often produces the most defensible upside number in a pricing diligence, because it asks the target’s customers to pay nothing more than comparable customers already pay.
Illustrative example. One segment of a target, $40 million of revenue, split into four equal quartiles of customers ranked by net price. Net price is expressed as an index where list price is 100, and customers are compared only with others of similar size buying similar products.
| Quartile | Average net price index | Revenue at current prices |
|---|---|---|
| Lowest priced | 78 | $10.0M |
| Second | 84 | $10.0M |
| Third | 88 | $10.0M |
| Highest priced | 93 | $10.0M |
Two bounds follow. Moving the lowest-priced quartile up to the second quartile’s level is a 7.7% increase on $10 million, or about $0.77 million. Moving both lower quartiles up to the third quartile’s level is 12.8% on the first and 4.8% on the second, about $1.76 million in total. So the gross headroom in this segment sits between roughly 1.9% and 4.4% of its revenue, before any volume response.
The number then gets tested rather than taken. Some of the lowest-priced customers will have a genuine reason to be there, such as a volume commitment, a strategic reference account, or a lower cost to serve, and they come out of the base. Contract terms decide how quickly the rest can move, so the headroom is phased by renewal date. And a volume assumption is applied by segment, informed by how customers reacted to past increases. What reaches the investment committee is the net range, with each adjustment shown, not the gross figure.
A pricing diligence is only as good as the data behind it, and the request is where time is won or lost under exclusivity. This is the list we send, in priority order:
Where the buyer competes with the target, competitively sensitive pricing data should go through a clean team under counsel’s rules, and the analysis is shared with the deal team only in aggregated form. We work within whatever protocol the deal counsel sets.
The output is written for the investment committee, and every finding carries a label saying whether it is firm or directional given the data provided.
A revenue quality verdict:
how much of the growth is repeatable, how much depends on concessions, and what the price-volume-mix looks like by year.
A headroom range:
the pricing upside by segment or product line, sized as a range of EBITDA with the volume assumptions stated.
The risks:
pricing that could unwind after close, such as contracts about to renew at the deepest discounts or growth that relies on promotional prices.
The first 100 days:
the pricing actions the thesis depends on, in order, so the diligence output becomes the first draft of the private equity value creation plan.
A rapid pricing assessment can run pre-LOI on a limited data set to test whether the pricing thesis is worth pursuing, with the full set of tests following in confirmatory diligence.
Our commercial due diligence consulting concentrates on the part of the thesis that sits closest to pricing. It is deliberately narrower than a full market study, and it is designed to fit next to one.
| Question | Outside-in commercial due diligence | The pricing lens |
|---|---|---|
| Is the market growing? | Market sizing and growth drivers | Not covered |
| Is the target’s position defensible? | Competitive mapping, share, differentiation | Whether customers pay for that differentiation |
| Is the growth real? | Management case against market growth | Price-volume-mix and realization in the invoices |
| Can revenue be repriced? | Rarely examined | Contract terms, escalators, caps |
| Where is the upside after close? | Market expansion, new products | Pricing headroom, leakage, and discount discipline |
When both workstreams run together, we share the customer conversation program rather than contacting the same customers twice.
Tell us about the target and the timeline, and we will tell you which of the eight tests matter most for your thesis and what data we would need first.
The timeline follows the exclusivity window. The data request goes out on the first day, and the first read of dispersion and price-volume-mix follows as soon as clean invoice data arrives. Customer conversations run in parallel. When data arrives late, we report what is firm and what is directional rather than stretching the timeline.
It usually is. Missing rebate data, inconsistent product codes after an acquisition, and discounts recorded as credits are all common. We reconcile what exists, state what is missing, and treat material gaps as findings in their own right: a target that cannot say what its customers pay has a pricing risk.
Most often the buy side, during exclusivity. On the sell side, the same tests help a company prepare the evidence a buyer will look for, such as realized increases and a documented governance model, before a process begins. A buyer who finds discounts nobody can explain tends to price that doubt into the offer.
No. The tests apply wherever a company sets prices and grants discounts: distribution, manufacturing, business services, and software. For subscription targets, tests five and six shift toward net revenue retention, price uplifts at renewal, and contract term structure. In every sector, the evidence starts from the invoice line, not the list price.
By showing the evidence for each part of it: dispersion that can be closed to the price band the best-priced comparable customers already accept, increases the target’s own customers have already absorbed, and volume assumptions tested against past behavior. The estimate is given as a range with its assumptions, never as a single number.
A sell-side analysis is a useful starting point, usually built from the same ledgers. It is prepared to present the revenue well, so it rarely separates price from volume and mix, or tests realization of past increases and dispersion among comparable customers. We use it to speed up data mapping, then run the tests independently.
The data model, the findings, and the priority actions carry straight into the first 100 days, which means post-close pricing work can begin with decisions rather than with a new diagnostic. Because the ledgers are already mapped, the first month goes to repricing the segments diligence flagged.
