For Deal Teams · Resource

The GTM due diligence checklist.

The financials say what happened. The go-to-market says what happens next. These are the 47 questions we put to a target's revenue engine before the wire - organized into ten dimensions, each with the data-room artifacts that answer it and the red flag that ends the benefit of the doubt.

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How to score it. For every question, demand the artifact - not the narrative. Then grade the answer the way we do: management's claim is verified by the data, overstated against it, or unsupported because the data does not exist. Three unsupported answers inside a single dimension is not a follow-up question. It is a price conversation.
01

Revenue quality and concentration

Not all ARR is equal. The first job is to find out how much of the revenue is durable, contracted, and diversified - and how much is a handful of accounts on legacy terms.

What share of revenue sits in the top five accounts, and when do their renewals land relative to close?
How much revenue is contracted, month to month, or invoiced without a current contract?
Which accounts sit on legacy pricing that any repricing attempt would put at risk?
Can the split between new business and expansion revenue be rebuilt from invoices alone?
What share of revenue depends on a single product, channel, or partner?

FROM THE DATA ROOM · revenue by account by month (three years), the contract register with renewal dates, price book history.

RED FLAG · top accounts renewing within months of close, on terms the deck itself calls legacy.

02

Retention and churn truth

Retention metrics are the most massaged numbers in any deck. The test is whether they can be reproduced from raw billing data, and whether churn has been studied or just counted.

What is gross retention when computed from invoices rather than CRM fields?
What are the actual reasons behind the last two years of churn, bucketed and valued?
At what tenure do accounts churn, and what does that say about onboarding versus product?
How much apparent retention is contraction hiding inside renewals?
Did any health signal predict past churn in advance, or does the company only write post mortems?

FROM THE DATA ROOM · the churned-account register (reason, tenure, last billed value), cohort retention by start quarter, the stated NRR and GRR methodology.

RED FLAG · retention numbers that nobody currently employed can regenerate from the raw data.

03

The new-logo engine

Expansion revenue flatters growth. The thesis usually depends on winning strangers - so the diligence question is whether the machine that wins strangers actually exists.

How many genuinely new logos landed in each of the last three years, and at what value?
Which specific sources produced them - not a department, the actual channel and motion?
What is the win rate by deal type, with new business separated from expansion paper?
Is new-logo output concentrated in one rep, one geography, or one use case?
What did the last five new-logo wins have in common, and can it be repeated on purpose?

FROM THE DATA ROOM · new logos by year with attribution evidence, the win and loss log, bookings by rep.

RED FLAG · a growth story carried by expansion while the new-logo count is flat or falling.

04

Pipeline integrity

Pipeline is the number most likely to be quoted and least likely to be scrubbed. Businesses are routinely healthier or sicker than their own CRM says - the point is finding out which.

What share of open pipeline has aged past the median sales cycle?
How much of the pipeline is executed or dead deals that were never closed out in the system?
What does coverage look like counting committed stages only, not everything with a dollar value?
Are qualification thresholds enforced differently for new versus expansion deals - by design, or not at all?
Is the forecast a discipline with a track record, or one person's judgement restated monthly?

FROM THE DATA ROOM · a full opportunity extract with stage history and timestamps, forecast versus actual for the last eight quarters.

RED FLAG · a pipeline that has never been scrubbed - the truth could be good or bad, but the forecast inherits the mess either way.

05

Win and loss reality

Deal narratives are written by the winners of internal arguments. The raw record - who was beaten, by whom, and why - is where the actual competitive position lives.

What do the last ten closed-lost records actually say, and who wrote them?
Which competitor takes deals, at what stage, and on what argument?
Do won deals share a repeatable path, or was each one hand-crafted by a founder?
Would recent wins take a reference call, and what would they say unprompted?

FROM THE DATA ROOM · raw win and loss records, deal reviews for the largest recent wins and losses.

RED FLAG · every loss explained as price - the explanation that means nobody asked.

06

Market and demand

A market slide is a claim about strangers. Diligence replaces it with named accounts, observable buying signals, and whitespace that is measurable inside the existing base.

What is the market sizing actually built on - a bottoms-up account model, or an analyst chart?
Which live signals - hiring, expansion, regulation, technology shifts - map to named target accounts?
How much expansion whitespace exists inside the current customer base before any new market is needed?
Is demand for the roadmap validated by paying customer behavior, or by survey enthusiasm?

FROM THE DATA ROOM · the TAM build with its assumptions, a target-account list with signal evidence, product usage by module across the base.

RED FLAG · a market model that never names an account.

07

Competitive position

The question is not who appears on the landscape slide. It is who takes deals, who loses them, and whether the threat is a feature gap or a structural squeeze.

Who actually displaces the company - and whom does it displace, with examples in the deal log?
Which competitive threat is structural - platform bundling, pricing model, distribution - rather than feature level?
What is the honest reason customers choose them: capability, incumbency, or switching cost?
Where are they losing without ever seeing the deal?

FROM THE DATA ROOM · the deal log with competitor fields populated, churn destinations, pricing versus alternatives.

RED FLAG · a competitor matrix in which the company wins every row.

08

The marketing engine

Marketing is where deck and reality diverge fastest: tools quietly discontinued, campaigns paused, attribution fields at war with each other. The engine must be inspected running.

Which channels produce pipeline that closes - proven with attribution you can rebuild, not dashboard totals?
What is actually live today versus what the deck lists - which tools, campaigns, and agencies are running, paused, or quietly discontinued?
What would organic visibility look like if paid spend stopped tomorrow?
Does the site convert - measured, not assumed - and does the funnel survive a mobile visit?
Is marketing capacity a team, an agency, or one person's spare time?

FROM THE DATA ROOM · channel spend and sourced pipeline by month, working analytics access rather than screenshots, a martech inventory with live status.

RED FLAG · attribution fields that are empty or contradict each other across systems - nobody knows what works.

09

Sales organization and incentives

Strategy is what the compensation plan pays for - everything else is a slide. The org chart question is whether the team described in the deck exists, ramped, and aligned.

Does the compensation plan actually pay for the motion the thesis depends on?
Are roles fragmented - people carrying fractions of several jobs and owning none of them?
What is true capacity: quota-carrying heads, fully ramped, with real territory?
Where does founder gravity still close the deals, and what happens without it?
Who is likely to leave after the deal, and what walks out with them?

FROM THE DATA ROOM · compensation plans, the org roster with roles and tenure, bookings by rep, key-person dependency map.

RED FLAG · a comp plan that rewards renewals while the investment case depends on new logos.

10

RevOps and data hygiene

Every number in this checklist flows through the systems. If the systems are untrusted, every answer above is provisional - which is why this dimension gets tested first in practice.

Can the CRM revenue picture be reconciled to billing - and when did anyone last try?
Are lead-source fields governed, or overwritten by every system that touches them?
How long does a closed deal take to show up correctly in reporting?
Which reports does leadership actually run the business on, and do they agree with each other?
Is the business better or worse than its own systems say - and by how much?

FROM THE DATA ROOM · CRM admin access or a full extract, field history on source and stage, the exact reports leadership uses.

RED FLAG · metrics in the deck that cannot be regenerated by anyone still at the company.

After the checklist

A checklist finds the questions. A diligence engagement finds the answers - in the data room, in the systems, and in the conversations management does not script. That is the work behind our GTM due diligence report: every dimension above, tested against raw data, delivered at deal speed with a findings readout your deal team can act on.
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