You are not buying last year's revenue. You are buying the engine that produced it.
Chad Brown, Founder, CommEx Advisors — The Value Multiplier anchor essay, September 2026.
The fourth instrument
Anyone who has spent time in this sector knows the scene. A lab with four identical instruments. Three of them running, and the fourth sitting idle with a stack of paper on top of it, having not consumed a reagent in eleven months.
Nobody was misled. The instrument was bought at the end of a fiscal year by a director who had budget to spend, and the workflow it was supposed to support never got built. On the vendor's side it was a closed deal, a placement, revenue recognized. In the lab it was furniture.
That gap, between what was sold and what is actually being used, tells you more about a commercial organization than any pipeline review. A data room tells you what got sold. It does not tell you whether it will get sold again, or whether what was sold is being used.
Two kinds of diligence get done well on almost every deal. Market diligence asks whether the demand is real. Quality of earnings asks whether the numbers are what they claim to be. Both matter, however neither one looks at the engine that produced the revenue, and the engine is what you are actually buying. You are not buying last year's number, you are buying the next eight quarters and whatever has to happen for them to arrive.
I want to be careful about the claim here. A good commercial organization will not win you a higher multiple by itself. What it does is keep you from paying full price for a problem you could have seen at entry. Weak commercial execution turns into a discount later, and it is visible now, if you ask for eight things.
None of the eight are in a standard data room. Six of them are sitting in the CRM.
Group one: can they hit the number?
1. Eight quarters of forecasting accuracy
Ask for the quarterly committed forecast and the quarterly actual, side by side, going back two years. It has to be quarterly because the annual number hides everything, since a bad quarter and a good one cancel each other out.
A company that lands within five percent every quarter has a high performing system. A company that swings fifteen percent in both directions and still finishes the year close is likely being carried by one or two people, and if those people leave, all bets are off.
This is not a small thing. Two companies with identical trailing revenue can be underwritten tens of millions apart on forecast credibility alone, because the buyer who does not believe the plan simply applies a haircut to it and moves on.
2. Past due close dates
What percentage of open opportunities have a close date that has already passed? This is easy to pull, and it is the fastest read available on whether the pipeline is a forecast or a wish list. Above ten percent means the number in the data room is carrying deals that already said no or slipped to the next quarter.
3. Discount rate by week of quarter
Pull the average discount by week. A spike in the last two weeks, every quarter, means the company is buying its number rather than earning it. The habit does not stop at close, and it takes several quarters to reset what customers think the price is. It also trains the customer to wait until the final weeks of the quarter, knowing a discount is coming. Average selling price is the same signal viewed from another angle, and it declines over time wherever pricing discipline is loose.
4. Ramp time and the spread
How long does a new salesperson take to reach full production, and how wide is the gap between the best and the worst? A long ramp with a wide spread means nobody understands what the good reps are doing. Growth then depends on hiring well and getting lucky, and the hiring plan is usually where the model's growth comes from.
Group two: is the revenue worth what they say?
5. Pull through and attach rate per placement
In tools and diagnostics the instrument is not the business, because the consumable stream behind it is. Ask for revenue per installed instrument, the attach rate on new placements, and what share of the installed base has gone dormant.
This is the one that decides which group of companies the buyer prices this one against, and that is worth more than anything else on the list. Segments where most revenue is recurring trade around twenty to twenty five times EBITDA. Instrument heavy ones trade closer to fourteen to eighteen. On eight million dollars of EBITDA that is roughly a forty million dollar difference, and no amount of growth in the placement number closes it.
A dormant installed base is the clearest sign, and it is the fourth instrument in that lab. There are three reasons it happens, and they need different answers. The sale was made to a budget cycle rather than into a workflow, which is a targeting problem. Nobody owns utilization after the instrument lands, which is a coverage and strategy problem. Or the product does not fit how the lab actually works, which is neither, and no amount of commercial rigor will fix it. Ask which accounts the idle instruments sit in. If they match the profile and were properly onboarded, the problem is the product. If they never should have received a placement, the problem is commercial.
6. Net revenue retention, by cohort
Gross retention tells you who left, while net revenue retention tells you whether the customers who stayed grew. For a company selling into labs it is the closest thing available to a read on whether the product is genuinely embedded in how the customer works.
Ask for it by cohort year, meaning customers grouped by the year they arrived rather than blended together. A blended number hides a weak recent cohort behind a strong older one, and the recent cohort is the one that reflects the model running today. It also shows you something a single number cannot, which is whether the company is getting better: if each newer cohort sits above the older ones at the same age, the commercial system is genuinely improving.
7. Concentration, three ways
Customer concentration is usually in the file. Rep and territory concentration rarely are, and they matter for a different reason.
If two salespeople produce sixty percent of the revenue, the relationships that generate that revenue belong to them rather than to the company. A buyer cannot verify that those relationships transfer, because they mostly do not. So, the buyer protects itself: retention packages for the two people, a longer transition period, more of the price held back in an earnout tied to revenue that depends on them staying.
None of that shows up as a lower multiple. It shows up as less cash at close and more of the price at risk, which is the same money arriving later or not at all.
The same logic applies to territory. If one region produces most of the growth and the model assumes the others will follow, a buyer will want evidence that the motion travels rather than the person.
8. The ICP against what actually closed
Every company has an ideal customer profile on a slide. Far fewer have checked it against the last hundred deals won and lost. When the deals that close look nothing like the target segment, the growth plan in the model is describing a company that does not exist, which matters most when that plan depends on expanding into the named segment.
The consequence arrives slowly and then all at once. Coverage gets designed against the profile, so territories, headcount and quotas are built around a segment the company does not actually win in. Marketing spends against it, so the pipeline fills with opportunities that convert poorly. Reps quietly work the accounts that do close, which means the real motion diverges from the documented one and nobody writes the real one down.
By year three the company has hired against a plan it is not executing, the forecast misses for reasons nobody can name, and the growth in the model has to come from somewhere that was never tested. That is also when it becomes expensive to fix, because correcting the profile means redrawing coverage and compensation rather than editing a slide.
The thing that sits underneath all eight
Data discipline is not a ninth signal. It sits underneath the other eight, and it is why they are worth asking for.
Six of the eight come out of the CRM. If the CRM is not trustworthy, none of the eight are trustworthy either and the honest version of that finding is that the company does not know its own numbers. That is not a reporting problem. It is the reason the forecast is a guess.
So, it is worth watching how the answers arrive as closely as what they say, because numbers assembled by hand from three spreadsheets over two weeks tell you something the numbers themselves do not.
Why none of this is in the data room
None of it is hidden, it is simply never assembled, because nobody asks for it.
Management reports what the board asks for and the board asks for bookings and revenue. So, the machine underneath both goes uninspected until something breaks, and by then the conversation is about a missed quarter rather than about a system.
Forecast variance is a symptom, and the cause is what matters
Go back to the first signal for a moment. If those eight quarters of committed forecast against actual come back with a fifteen percent swing, the number itself is only the start of the conversation. The useful question is what produced it, and in this sector the answer is usually one of three things. Each has a different fix and a different timeline.
- The ideal customer profile is wrong, or nobody enforces it. The team is working accounts that were never going to buy on the timeline the forecast assumed, so deals do not die, they simply never arrive.
- The procurement path was never mapped. A technical win is not a purchase. Between the two sit a capital committee, legal, compliance, and often a clinical or biosafety review.
- There is no funnel discipline. Stages are loosely defined, exit criteria are not enforced, and commit, best case and pipeline mean different things to different people.
Ask which of the three it is. An honest answer tells you far more than the variance number does, and it tells you whether you are looking at ninety days of work or eighteen months of it.
Why eighteen months is the number
There is a version of the objection I hear often, which is that the work can be done later, when exit preparation begins. It is the most expensive answer available, and it is worth taking seriously rather than brushing off.
The first reason is that a buyer reads a trailing record. Eight quarters of forecast accuracy, several quarters of retention trend, a pull through curve that has had time to bend. Work started six months before a process has produced no trailing record, so there is nothing yet for a buyer to price. Worse, the work itself is visible. A sudden CRM cleanup, a new forecast cadence, and outside help arriving in month forty two read as remediation, and remediation gets discounted rather than credited.
The second is that the cost of intervention rises as the window closes. In year two, coverage design or compensation can be changed and the effect measured. In year four the same change can only be described. What could have been evidence becomes an assertion.
The third is that the facts surface either way, because commercial diligence will find them. The only real choice is whether the fund finds them first, while there is still time to act, or whether the buyer finds them and prices them.
So the same work, done at two points in the hold, produces opposite effects on price. In months twelve to twenty four it is operating improvement and it compounds. In the six months before a process it is a cleanup, and it is visible in the data room.
There is a fourth point that is easy to miss. Without a baseline taken early, improvement cannot be evidenced later even if it genuinely happened. The measurement at entry is what makes the record possible at exit.
But we are too busy right now
This is the objection actually heard most often, and it deserves to be taken at face value rather than treated as a stall, because it is usually true. A growth stage commercial team in the middle of a quarter does not have spare capacity.
The answer is to argue about effort rather than about principle. A baseline is a defined number of interviews, a data pull from systems that already exist, and a readout. It does not require a project team, a workstream, or a pause in selling. The load falls on a handful of people for a small number of hours, mostly in conversations they can have from a car.
It is also a measurement rather than a change program. Nothing has to change while it runs. What to take on afterwards is scoped separately, and the company chooses. The commitment being asked for is to find out, not to act.
Then use the busyness as the reason rather than the obstacle. Busy almost always means capacity is the binding constraint, and how capacity is allocated across segments, accounts and stages is precisely what the measurement examines. The quarter in which the team is most stretched is the quarter in which the finding is worth the most.
And compare it to the alternative, which is not nothing. The alternative to a few days now is several weeks of diligence later, at a moment the company does not control, run by someone whose job is to find reasons to pay less. The work happens either way. Only the timing, the cost, and who owns the findings are in question.
What to do with this
Ask for the eight at confirmatory diligence, or run them in the first ninety days if you already own the company.
If you only ask for one, ask for the eight quarters of commit against actual. It is the earliest signal, it takes an afternoon, and the answer usually tells you which of the other seven to ask for next. If you can ask for two, add pull through per placement, because that one is worth the most money.
The cost of asking is a few hours of someone's time, while the cost of not asking shows up about eighteen months before you go to market, when the fix takes longer than the runway you have left.
How we help
There are three things we do, and they run in order.
We run the eight as a scored baseline. That is the Commercial Readiness Assessment: four weeks, six cross functional leaders scoring the same 42 statements independently with evidence required on the high marks, against nine weighted pillars. It produces a number out of 100, a heatmap, and a 90 day plan ranked by what moves the score most. Where the CEO and the team disagree matters as much as the score, because that gap is usually where the commercial reality lives in one person's head.
We fix the ones that pay. Usually forecast integrity first, then attach rate, pull through, and cohort retention. Those three move which comparable set the company gets priced against, which is worth more than an improvement in the growth rate. The work is installed as operating playbooks with a named owner and a defined cadence, not delivered as a recommendation deck.
We re-measure on the same yardstick. The same 42 statements at six or twelve months, which is what makes an improvement underwritable rather than asserted.
For an investor the entry points are diligence, the first hundred days, or a common baseline across the portfolio. For an operator it is the year before a raise, while the fix still looks like ordinary operating improvement rather than a cleanup.
And if the eight tell you nothing you did not already know, that is a real outcome and a cheap one.
Ask your team this month
Of the eight, which could we produce this week without building anything new? The ones we cannot produce are the finding, and how long the answers take to arrive tells you as much as the answers do.