Commercial Due Diligence for Acquisitions

Every deal comes with a story. A founder pitches a growing market, a defensible product, loyal customers, and a revenue line that only bends upward. The numbers in the data room usually back it up, at least on the surface. Commercial due diligence exists to answer one blunt question about that story: is it real, and will it last long enough to justify the price?

I have spent a good part of my career doing the market and customer research that sits underneath investment and leadership decisions. What I have learned is that the commercial case is where most deals quietly succeed or fail, and it is also the part most often taken on trust. Financial due diligence confirms the money moved the way the accounts say it did. Legal due diligence confirms the company owns what it claims and is not carrying a lawsuit that will sink it. Commercial due diligence is different. It asks whether the business can keep making money the way it has, in a market that will still be there, against competitors who are not standing still.

This post is a practitioner’s walk through how I approach it. Not the banker’s version, heavy on models and comparable transactions, but the growth operator’s version: funnel, unit economics, product reality, and the customers who actually pay. The two views are complementary, and the best diligence I have seen puts them side by side.

What commercial due diligence actually answers

The clean way to say it is that commercial due diligence tests the durability of the commercial story. Financial diligence looks backward at what happened. Commercial diligence looks forward and asks whether it can happen again.

Concretely, you are trying to build an independent view of four things. First, the market: how big it really is, how fast it is growing, and whether it is structurally healthy or quietly rolling over. Second, the competitive position: whether the target has a real advantage or is simply the incumbent nobody has bothered to displace yet. Third, the customers: who they are, why they buy, why they stay, and how concentrated the revenue is among them. Fourth, the quality of the revenue itself: how much is recurring versus one-off, how cohorts behave over time, and whether the pipeline the seller is pointing to is real.

Everything else is detail hanging off those four questions. If you can answer them honestly, you can tell an acquirer whether the price reflects the business as it is or the business as the seller hopes it will become.

Building an independent market view

The market section of a seller’s deck is almost always the most optimistic part of the whole package. A large addressable market makes any growth rate look plausible, so sellers reach for the biggest defensible number. My job in diligence is to rebuild that number from the bottom up and see if it survives.

I do not take the top-down figure at face value. I size the market from the customers who could realistically buy, the price they would realistically pay, and the share the target could realistically win. That bottom-up discipline is the same one I lay out in market sizing for expansion, and it matters even more in a deal because the number is load-bearing for the valuation.

Size is only half of it. The harder question is health. A market can be large and still be the wrong place to be if the underlying demand is shifting. I look for the direction of travel: are budgets in this category growing or being cut, is the buying decision moving to a different function, is a substitute technology quietly eating the edges. A market that grew nicely for five years can start to roll over well before the revenue shows it, because existing contracts mask the decline for a while. Catching that early is one of the highest-value things commercial diligence does.

Testing whether the competitive advantage is real

Sellers love the word moat. My task is to work out whether the moat is a real structural advantage or just incumbency wearing a nicer jacket.

There is a genuine difference. A real advantage shows up as something a competitor cannot easily copy: switching costs that are painful for the customer, a network that gets more valuable as it grows, proprietary data, a cost position nobody else can match, a brand customers actively ask for. Incumbency looks similar from a distance. The company has the customers, the revenue is steady, nobody has taken share recently. But incumbency erodes the moment a credible challenger shows up with a better product or a lower price, because nothing is actually holding the customers in place except habit and the absence of a reason to move.

To tell them apart, I go to the market rather than the deck. Structured competitive intelligence that moves decisions is the backbone here: mapping the real alternatives a buyer considers, understanding how the target wins and loses against each, and watching where challengers are gaining ground. If the advantage is real, it holds up when you pressure-test it against what competitors are actually shipping. If it is incumbency, you will find customers who stay only because switching is annoying, not because the product is better. That distinction changes the whole thesis, because incumbency has a shelf life and a real moat does not.

The customer view: concentration, retention, and why they stay

Customers are where the commercial story becomes concrete, and where I spend a large share of my diligence time. The financials tell you revenue exists. The customers tell you whether it will still exist next year.

I start with concentration. If a handful of accounts drive most of the revenue, the business is far more fragile than a headline growth rate suggests, because the loss of one or two relationships changes everything. Concentration is not automatically a dealbreaker, but it demands a much closer look at how secure those relationships are, who owns them internally, and what happens if a key champion leaves.

Then retention. I want the real churn number, not the flattering one. Sellers can present retention in ways that hide the truth: netting expansion against losses so gross churn disappears, excluding customers who technically renewed but cut their spend, or reporting logo retention while revenue quietly leaks. I rebuild retention by cohort so I can see how each intake of customers behaves over time, which is far harder to dress up.

The most valuable part of the customer view is qualitative, and it comes from talking to customers directly. Reference calls, done well, tell you why customers bought, why they stay, and what would make them leave. The discipline is the same one behind good win-loss analysis and solid consumer research methods: ask open questions, listen for the real reason rather than the polite one, and pay attention to what customers do not say. A customer who describes the product as good enough and not worth the hassle of switching is telling you the moat is incumbency. A customer who says they would fight to keep it is telling you something the spreadsheet cannot.

Judging the quality of revenue

Not all revenue is worth the same multiple, and separating high-quality revenue from low-quality revenue is central to commercial diligence.

Recurring revenue that renews predictably is worth far more than one-off project revenue that has to be won again every year, even when the two look identical on an annual total. I break the revenue apart to see what is genuinely recurring, what is recurring in name but actually re-sold each cycle, and what is one-off dressed up as recurring. This is where the growth operator’s lens earns its keep, because I am reading revenue the way I would read a product’s usage, not the way an accountant reconciles a ledger.

Cohort retention is the sharpest tool I have here. When I track each cohort of customers by the revenue they generate over time, the shape of those curves tells me almost everything about the durability of the business. Cohorts that hold flat or expand are a sign of a product people keep valuing. Cohorts that decay steadily are a sign that the business has to run harder every year just to stand still, which means the growth is far more expensive than it looks.

Then the pipeline. Sellers point to a pipeline to justify the forward projections, and pipelines are easy to inflate. I test realism the way a growth PM tests a funnel: what are the real conversion rates stage to stage, how long does a deal actually take to close, how much of the pipeline is genuinely qualified versus wishful. A pipeline that assumes conversion rates the business has never actually achieved is not a pipeline, it is a hope.

Testing management’s projections against evidence

Every deal has a plan, and the plan almost always shows the business accelerating. My job is not to declare the plan wrong. My job is to find the evidence that would have to be true for it to work, and then check whether that evidence exists.

I take the projection apart into its drivers. If revenue is meant to grow at a certain rate, that growth has to come from somewhere specific: more customers, higher prices, better retention, expansion into a new segment. For each driver I ask what has to happen and whether the business has ever done it before. A plan built on a retention improvement the company has never demonstrated is a very different risk from a plan built on continuing a trend already visible in the cohorts.

I also look for the quiet contradictions. A projection that assumes accelerating growth while the most recent cohorts are decaying faster than older ones is telling two stories at once, and only one of them can be true. Surfacing that tension is often more useful to a decision-maker than any single number, because it forces the conversation onto the real question: what do we actually believe about this business.

The growth thesis: can it grow the way they claim

Beyond defending the current revenue, most deals are priced on the assumption that the business will grow, sometimes substantially. Testing that growth thesis is where a growth operator adds a lens that a purely financial team can miss.

Bankers tend to model growth as a rate applied to a base. I want to know the mechanism. Where does the next customer come from, what does it cost to acquire them, and does the unit economics still work at the scale the plan assumes. A business that grows profitably at its current size can become unprofitable at scale if acquisition costs rise faster than the value of each customer, and that failure mode is invisible in a top-line projection. It only shows up when you look at the economics of one customer at a time.

I also test whether the growth channels are durable. Growth that depends on a single channel, or on a channel that is getting more expensive for everyone, is far riskier than growth spread across several channels the business controls. And I look at whether the product can actually support the growth being promised, because a plan to move upmarket into larger customers often assumes a product that does not exist yet. The commercial case has to hang together with the product reality, and connecting those two is exactly the work a growth PM does day to day.

Red flags I look for

Some patterns show up often enough that I treat them as warnings until proven otherwise.

Customer concentration is the first. Heavy dependence on a few accounts means the business is one relationship away from a very different valuation, and it deserves disproportionate attention.

Hidden churn is the second. When retention is presented only on a net basis, or only by logo, I assume something is being obscured until the cohort data proves otherwise. Healthy businesses tend to show their retention plainly.

A market rolling over is the third, and the most dangerous because it is the slowest to appear in the numbers. Existing contracts keep revenue looking stable well after demand has started to fade, so I weight the forward signals in the market view heavily.

A moat that is really just incumbency is the fourth. If customers stay because switching is a hassle rather than because the product is genuinely better, the advantage will not survive a serious challenger, and the durability the whole case rests on is an illusion.

The last one is subtle: a story that is internally inconsistent. When the market view, the customer view, and the projections do not agree with each other, one of them is wrong, and the disagreement itself is the finding.

Turning findings into a recommendation

Diligence that ends in a pile of observations has failed. The point is to give a decision-maker something they can act on, which means a clear recommendation with an honest confidence level attached.

I try to separate what I know from what I believe. Some findings are well-evidenced: the retention curves are what they are, the customer concentration is a fact, the competitive picture is visible in the market. Others are judgments built on incomplete information, and I say so. A recommendation that hides its uncertainty is worse than useless, because it invites a confident decision on a shaky base. I would rather tell an acquirer that I am highly confident the current revenue is durable but only moderately confident in the growth thesis, and explain exactly why, than hand them a single verdict that pretends to a certainty I do not have.

The most useful output is usually a short, direct answer to the original question with the confidence levels made explicit, followed by the two or three things that would most change the picture if they turned out differently. That gives the decision-maker a way to price the risk rather than just accept or reject the deal. It also tells them where to keep looking after the deal closes, because commercial diligence does not end at signing. The questions you could not fully answer become the things you watch in the first hundred days.

The short version

• Commercial due diligence answers one question: is the commercial story real and durable enough to justify the price. Financial and legal diligence look at the past; commercial looks forward.
• Rebuild the market from the bottom up and judge its health, not just its size. Existing contracts can hide a market that is already rolling over.
• Separate a real moat from incumbency. If customers stay only because switching is a hassle, the advantage has a shelf life.
• Get the real retention by cohort, not the flattering net number, and treat customer concentration as a warning until proven otherwise.
• Split high-quality recurring revenue from one-off revenue dressed up as recurring, and test the pipeline the way you would test a funnel.
• Take management’s projections apart into drivers and check each against what the business has actually done before.
• The growth operator’s edge is the mechanism: where the next customer comes from, what they cost, and whether the unit economics still work at scale.
• End with a clear recommendation and explicit confidence levels, plus the few things that would most change the picture.

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I am Deepanshu Grover, a Growth Product Manager in Paris. If you are pressure-testing the commercial case behind a deal, connect on LinkedIn or get in touch.