Commercial Due Diligence: How to Test a Target's Go-to-Market Engine

Dealmaking returned in 2025, but on harder terms. Bain's Global Private Equity Report 2026 states that global buyout deal value rose 44% to $904 billion, while the number of buyout deals fell 6% to 3,018 transactions, so capital concentrated in fewer, larger transactions. Bain's M&A report records global M&A rising 40% to $4.9 trillion in 2025, yet notes that the proportion of capital allocated to M&A hit a 30-year low. Activity is high; selectivity is higher.

The arithmetic of returns explains the pressure. Bain reports that a typical PE investment in the 2010s needed only 5% annual growth in EBITDA to deliver a 20% internal rate of return, while with borrowing costs in the 8% to 9% range and leverage of 30% to 40%, typical deals now require around 10% to 12% average annual EBITDA growth. Bain calls this the shift to a world where 12 is the new 5. Cheap debt and multiple expansion no longer carry the case.

For an operating partner the implication is direct. Most of the required growth must come from revenue, and revenue comes from the go-to-market engine. A model that assumes a double-digit growth rate on a sales process that depends on two rainmakers, or on a market tailwind that is fading, is a model that does not meet the return hurdle once borrowing costs are included. Diligence on the engine is therefore the main test of the investment case, not a supporting workstream.

It is worth being precise about what the engine has to prove. A growth plan implies a chain of commercial events: enough qualified demand, converted at a given rate, at a given price, retained for a given period and expanded at a given pace. Each link can be tested against history, and the weakest link usually explains most of the risk in the plan. Operating partners who ask for that chain, link by link, tend to find the one assumption on which the whole case turns, and can then price or structure around it.

What to do: before the first management presentation, convert the required growth rate into the operating conditions it implies, such as new-logo count, average deal size, win rate, expansion rate and sales capacity. Then use diligence to test each condition against evidence. A required growth rate with no identifiable engine behind it is a reason to lower the price or walk away.

Holding periods are stretching. Bain reports that buyout holding periods at exit now hover around seven years, up from five to six years between 2010 and 2021, and that almost 40% of all companies are held for more than five years, up from 29% in 2019. The industry sits on a stock of 32,000 unsold companies worth $3.8 trillion, and distributions to limited partners were flat at 14% of net asset value in 2025.

Exits are recovering unevenly. Bain reports that global buyout-backed exit value jumped 47% to $717 billion, IPO exits increased 36% from a very low base, sponsor-to-sponsor exits grew 21% globally and the transaction volume of GP- and LP-led secondary vehicles rose 41%. In Europe, sales to sponsors rose 56%. Buyers are available, but they arrive with their own diligence teams and a sceptical view of seller-prepared growth stories.

This creates a double obligation for any acquirer. The commercial engine inherited today must be built into something a future buyer will accept as repeatable. The evidence a future buyer will request, such as cohort data, win-rate history, pipeline conversion by source and rep productivity, needs to be captured from day one of ownership. Companies that cannot produce it at exit tend to sell at lower multiples, to different buyers, or not at all.

Secondary and continuation routes add a further reason for rigour. When a growing share of exits is to other sponsors or through vehicles that re-underwrite the asset, the buyer on the other side is a professional who has seen many growth stories and will price the engine's weaknesses without sentiment. The window for selling on narrative alone has narrowed, and acquirers who plan to sell within the next hold should assume that commercial evidence, not projections, will determine the outcome.

What to do: treat the diligence file as the first page of the exit data room. Agree the definitions of bookings, annual recurring revenue, pipeline stages, churn and net retention at acquisition, instrument the CRM to report them, and review them quarterly. Doing so reduces integration risk and also produces the audit trail that supports a premium valuation years later.

Forecasts are the part of a go-to-market plan that sellers know least how to defend. Gartner's State of Sales Operations survey found that only 45% of sales leaders and sellers have high confidence in their organisation's forecasting accuracy, and that only 47% believe their organisation has high-quality data. A separate Gong survey of 928 revenue professionals in 2022 found that only 24% of sales leaders were confident in their team's forecast. Both are older and the second comes from a software vendor, but they point the same way.

Experience of integration shows how optimistic revenue plans tend to be. In a McKinsey survey of 200 seasoned M&A executives, the majority reported that their company had fallen short of its aspiration for revenue synergies, with an average gap of 23% between goal and attainment, and the process usually took on the order of five years rather than the two typical for cost synergies. Revenue takes longer and delivers less than the plan says.

The diligence consequence is to start with what has actually happened. Rebuild the pipeline from stage-level history: how many opportunities entered each stage, how long they stayed, what fraction progressed, and how those rates vary by source, segment, deal size and seller. Compare the pipeline coverage that management claims with the coverage that historically produced the quarter. Then ask what changes in conversion, cycle length or capacity the plan requires, and what evidence shows those changes are plausible.

A useful discipline is to separate three forecast layers. The contracted layer is revenue already secured by renewals and committed contracts. The weighted pipeline layer is revenue expected from qualified opportunities at historical conversion. The speculative layer is everything that depends on opportunities not yet created. Management cases routinely blur these layers. Presenting them separately lets the investment committee see how much of the plan is protected, how much is probable and how much is hope, and set price and leverage accordingly.

Win-loss analysis is the fastest route to the truth about competitiveness. Ask for the last forty or so decided opportunities, interview buyers on both sides, and compare the reasons given with the reasons recorded in the CRM. Gaps between the two are common and instructive. If buyers describe a price-led choice while the CRM records a product-led win, the target's pricing power is weaker than reported, and the growth plan should be tested against a more competitive environment.

What to do: request two years of opportunity-level data with creation date, stage history, source, amount, outcome and owner. Reconcile it to recognised revenue. If the target cannot supply it, treat that as a finding about control and discipline, and price the uncertainty. Exhibit 5 sets out the engine components, the questions to ask and the red flags to look for.

Not all revenue is equal, and headline growth can mask weak foundations. Net revenue retention is the sharpest single indicator for recurring businesses. SaaS Capital's 2025 retention benchmarks for private B2B companies put median net revenue retention at 102% for companies with annual contract values of $25,000 to $50,000, with the top quartile at 111% and the lowest quartile at 97%. A target claiming figures well above the top quartile needs corroboration; one below parity is growing only by acquiring customers faster than it loses them.

Concentration and cohort analysis add the second layer. Examine the share of revenue from the top ten customers, the dependence on any single channel or contract renewal date, and the retention curve of each annual cohort rather than the blended average. Newer cohorts that retain less well than older ones indicate that product-market fit or customer quality is deteriorating even if total revenue is rising. Price increases, one-off projects and expansions in a few accounts can all flatter growth.

Buyer experience provides an outside-in check. Gartner found that 73% of B2B buyers actively avoid suppliers who send irrelevant outreach and that 69% report inconsistencies between a seller's website and what sellers tell them. A target whose growth depends on high-volume, low-relevance outbound may be consuming a finite market and eroding its own reputation. Customer calls, win-loss interviews and a review of the target's public positioning reveal this faster than internal reports.

Pricing deserves its own review because it is the lever most often overstated. Check whether list prices are realised, how discounts vary by seller and deal size, whether recent price rises have produced churn that has not yet appeared in reported retention, and how the target compares with competitors on value delivered. A business that has grown mainly by raising prices into a captive base has a different risk profile from one that has grown by winning new customers at stable prices.

Contract terms complete the picture. Review renewal dates, notice periods, termination rights, change-of-control clauses and any most-favoured-customer provisions, since these determine how much of the revenue base is secure after the deal closes. A customer base concentrated in contracts that expire within twelve months of completion is a different asset from one locked in for several years, and the price should reflect the difference even when historical retention looks strong.

What to do: build a revenue-quality scorecard covering net and gross retention by cohort, concentration, contract length, discounting, price versus volume contribution and the share of growth from existing customers. Interview a sample of lost customers, not only referenced ones, and use the results to adjust the base-case retention assumption before applying growth.

Behind every forecast sits a capacity model: how many sellers, at what ramp, producing what each. Many targets cannot show one. Diligence should rebuild it from rep-level data, including hire dates, quota, attainment, tenure, pipeline generated and attrition. Look for dependence on a small group of high performers, long ramp times that make plans unrealistic, or recent hiring that has not yet produced evidence of productivity.

Seller wellbeing and tooling affect output more than many models assume. Gartner's 2024 survey of 1,026 B2B sellers found that 72% feel overwhelmed by the number of skills required for their job, and that overwhelmed sellers are 45% less likely to attain quota. Salesforce reports that 51% of sales leaders with AI say disconnected systems are slowing their AI initiatives. A fragmented tool stack and a culture of overload both limit the productivity gains a new owner can expect.

The same survey shows that sellers who effectively partner with AI tools are 3.7 times more likely to meet quota, a correlation rather than proof, but one that suggests a tooling upside. It should not be capitalised into the price without evidence. A buyer can reasonably identify AI-enabled productivity as an upside lever while insisting that the base case rests on the target's demonstrated performance.

Management quality is part of capacity. A sales leader who built the process and can describe, with data, how the team wins and loses is a strong signal; one who relies on anecdote is a warning. Observe how first-line managers coach, how often pipeline is inspected and whether forecasts are reconciled to outcomes. These disciplines are visible in a few well-chosen conversations and explain much of the difference between targets with similar headline metrics.

Marketing deserves the same scrutiny as sales. Establish what share of pipeline is marketing-sourced, what each channel costs per qualified opportunity and how quickly the cost is recovered, and compare the brand's search visibility and reputation with competitors. Reliance on a single paid channel, an agency the target does not control, or a small number of high-performing content assets creates fragility that does not appear in the revenue line until performance changes.

What to do: produce a productivity bridge from last year's revenue to next year's plan, split between existing seller productivity, new-hire ramp, attrition, price, expansion and any assumed tooling gains. Challenge every step that relies on a change in behaviour. Include a call with the head of sales and one with frontline managers to compare the stories they tell about pipeline health.

Dealmakers are adopting AI quickly. Bain's survey found that 45% of executives used AI tools in M&A in 2025, more than double the prior year. Deloitte's 2025 survey of 1,000 senior US corporate and private equity leaders found that 86% of responding organisations had integrated GenAI into their M&A workflows. Among adopters, 40% apply it to M&A strategy and market assessment, and 35% each to target identification and screening and to due diligence.

The same Deloitte survey records the caveats: 67% highlight data security as a leading concern and 65% data quality and availability. These concerns are particularly relevant to commercial diligence, where data rooms often contain customer names, contract terms and pricing. Using external AI tools on that material requires confidentiality controls agreed with the seller, and results should be traceable to source documents so that the investment committee can rely on them.

AI is strongest in the first half of the workstream: reading contracts at volume, clustering customer feedback, mapping competitors, extracting pricing patterns and generating hypotheses. It is weaker at what matters most in a go-to-market review, which is whether a motion is repeatable by people other than the founders. That requires conversations with customers, sellers and channel partners, and a view of context that is not in the data room.

There is also a defensive reason to understand AI in the target's own market. Generative tools lower the cost of producing content, outreach and basic software, which can erode advantages built on information asymmetry or manual services. Diligence should ask whether the target's differentiation survives a world in which customers and competitors have the same tools, and whether the target is positioned to use them. The answer affects both the growth rate and the terminal value.

What to do: use AI to widen coverage and shorten the time to a first view, and spend the time saved on primary research. Ask the target how it uses AI in its own sales and marketing, and test whether claimed productivity gains appear in the cohort and capacity data, rather than in a management presentation.

Geography changes what to test. UK Office for National Statistics data show that the value of inward M&A, where foreign companies acquire UK companies, was £27.4 billion in the fourth quarter of 2025, against £1.8 billion of domestic M&A and £1.7 billion of outward M&A. A large share of UK deals therefore involves foreign buyers assessing a UK engine, often from a distance, which raises the importance of independent evidence on customers, channels and brand strength.

The Gulf market is expanding and increasingly intra-regional. PwC Middle East reported 635 completed M&A deals in 2025, up 33% year on year, with inbound deals rising from 182 to 238 and private equity completing 252 deals. Among intra-regional deals, the UAE recorded 207, Egypt 172 and Saudi Arabia 169. Commercial diligence in this region must consider relationship-led selling, dependence on a few anchor customers or government-linked buyers, and the role of local partners and distributors.

Cross-border targets raise additional questions. Is revenue contracted in local currency? Does the engine depend on an agent or distributor whose terms may change on ownership? Do licence, localisation or ownership rules limit how the business can sell? Are sales practices consistent with the buyer's compliance standards, including anti-bribery rules? These issues are largely qualitative, and the answers often determine whether a growth plan can be transplanted.

Regulatory and transaction-structure points should be considered alongside commercial findings. Depending on the sector and jurisdiction, foreign ownership rules, licensing conditions and merger control can affect how fast a buyer can integrate sales teams and change commercial terms. Commercial advisers should work alongside legal and regulated professionals so that conclusions about growth do not assume freedoms the deal structure does not allow.

What to do: adapt the engine review to the market. For UK targets, emphasise channel concentration, public-sector exposure and renewal cycles. For Gulf targets, test the durability of relationship-led revenue and the depth of the local team beyond the founder. For all, include a compliance review of sales practices and intermediaries alongside the commercial work. We provide information here, not investment or legal advice.

Karter McKinley supports buy-side teams through its M&A & Corporate Development service, working alongside regulated advisers where required. We help structure the commercial engine review, rebuild pipeline and capacity models from source data, run customer and channel interviews, and prepare the post-acquisition measures that will support the next exit.