Blog · gtm-audit
What should a GTM audit check at €25k+ ACV? The 8 areas
A GTM audit for €25k+ ACV B2B checks 8 areas, from ICP fit to handoffs. For each: the question to ask, the CRM evidence to pull and the baseline to record.
Why is a high-ACV GTM audit different from a marketing audit?
A marketing audit checks channels and pages; a high-ACV GTM audit checks whether the right accounts move from first signal to closed deal. At €25k+ ACV you sell to a committee that has mostly decided before it calls you. The average B2B buying decision involves 13 people↗. Buyers first contact a seller about 61% of the way through their journey↗. By then the shortlist exists: 95% of buyers purchase from their Day One shortlist, and 77% buy from the vendor that led the selection phase↗.
Two more findings change what an audit should look at. About 95% of potential buyers are out of market at any given time, according to the LinkedIn B2B Institute's 95-5 rule, which draws on its research with the Ehrenberg-Bass Institute↗. Gartner says 99% of B2B purchases are driven by organisational change inside the buyer↗. So the useful questions are practical. Can you see the few accounts that are changing? Do you reach the whole buying group early? Does someone act on it the same day?
Signals worth logging include a new executive in the buying function, a restructure or merger, a funding round or new budget, a hiring spree in a relevant team, a regulatory deadline and a champion who moves to a new company. A first action doesn't have to be a cold email. It can be account-based ads, a warm introduction, a relevant piece of content or a call where the law allows it. In Germany and several other EU markets, cold email needs prior consent even in B2B, so check before you automate.
Most published GTM audit templates start with the website, SEO, paid media, messaging and the tool stack. They assume a steady flow of inbound leads. In a finite market of a few thousand accounts, that assumption hides the leaks that cost the most: unclear definitions, slow replies, unworked leads and untrusted data.
What are the 8 areas a GTM audit should check?
A complete audit checks 8 areas: ICP and ACV fit, targeting, signals, speed-to-lead, qualification, CRM data, forecasting and handoffs. Each area gets one question, one evidence pull from your own systems and at least one baseline metric. The table below is the whole checklist. Its metrics are method choices. None of them is a benchmark.
| Area | Question to ask | Evidence to pull | Baseline metric |
|---|---|---|---|
| ICP and ACV fit | Which segments win at the deal size you need? | Closed-won and closed-lost deals, last 12 months (24 if you closed fewer than about 40 deals a year) | Win rate and median ACV by segment, with the deal count |
| Targeting | Is pipeline coming from the accounts you chose, and do you reach their buying groups? | Named target list with its size and the date it was set, matched to open pipeline and engaged contacts | Share of pipeline from target accounts; share of target accounts with an open opportunity or engaged contact in the last 90 days; share of open opportunities over €50k with 3 or more engaged contacts |
| Signals | Do you see organisational change before the buyer calls you? | Signal sources, alerts and the actions logged on them | Median days from signal to first action ("not captured" if no signal is logged today) |
| Speed-to-lead | How fast does a hand-raiser reach a person? | Form timestamps against first activity; your own test submission | Median time to first human touch; share of leads and target accounts untouched after 7 days |
| Qualification | Does sales accept the leads and accounts marketing qualifies? | Written definitions, lead status history, rejected and recycled leads | Sales acceptance rate: share of marketing-qualified leads and accounts that sales accepts (not rejected or recycled) within 5 working days |
| CRM data | Can you trust the fields that route and forecast? | Completeness of required fields on accounts, contacts and open deals | Completeness of the fields that route leads and feed the forecast |
| Forecasting | How far did last quarter's forecast miss, and how often do close dates slip? | Forecast snapshots against actual closed-won; stage history with conversion and median days per stage by ACV band | Commit forecast at week 2 of the quarter vs actual closed-won; share of deals whose close date was pushed at least once |
| Handoffs | Does anything drop between SDR, AE and customer success? | Handoff SLAs, reassignment logs, stalled deals | Share of handoffs inside the agreed SLA. With no SLA, record "no SLA" and use: median days from SDR-booked meeting to first AE meeting, share of booked meetings held, and median days from closed-won to customer kickoff |
Three rows need a fallback. A target list your reps can't cover in a quarter isn't a target list, so record its size and the date it was set. If no signal is logged against accounts today, record "not captured" as the signals baseline. If there is no written handoff SLA, record "no SLA" and use the timings in the handoffs row.
The order matters. ICP and ACV fit come first because every later metric is split by segment. A €30k deal and a €300k deal have different cycles, buying groups and win rates, so a blended average describes neither.
Four rules keep the audit honest:
- Start from records. Export closed-won and closed-lost deals, lead timestamps, activities and stage history for the last 12 months, or 24 if you closed fewer than about 40 deals a year. Interviews explain the numbers afterwards.
- Split by ACV band. Report every metric per segment. If a cell has fewer than about 20 closed deals, merge bands or extend the window to 24 months, and report the count next to every rate so nobody reads 2 of 3 as a 67% win rate. If the CRM can't split it, that is itself a finding for the CRM data area.
- Write the definition before the number. "Qualified", "engaged" and "in market" mean different things to different teams. Agree each definition in writing first, or the baseline measures an argument.
- Keep personal data out of the working file. Export IDs, timestamps and stages, and leave out names and emails. If an outside party runs the audit, sign a data processing agreement first. Report activity metrics by team. Per-rep reporting needs your works council's agreement where one exists.
Where do high-ACV GTM engines leak most?
The research points to 4 leaks: lead definitions, response time, unworked leads and untrusted data. Each is backed by published research, some of it academic and some from vendor surveys, so the audit gives them the most time once ICP and ACV fit are set.
Definitions. In a Gartner survey of over 200 sales leaders, run in late 2022 and published in 2023, 62% said their sales and marketing functions define qualified leads differently↗. Marketing and sales collaborate on only 3 of 15 commercial activities, and 90% of their executives say their priorities conflict↗. When the two teams count different things, every conversion rate between them is noise.
Response time. In the classic HBR study, researchers sent a test web lead to 2,241 US firms↗. Only 37% replied within an hour, and 23% never replied at all↗. Among firms that replied within 30 days, the average response took 42 hours↗.
Of 2,241 US firms tested with a web lead, only 37% replied within an hour and 23% never replied.
Data behind this chart
| Item | Value |
|---|---|
| Within 1 hour | 37% |
| 1–24 hours | 16% |
| More than 24 hours | 24% |
| Never | 23% |
Speed pays. In a separate HBR dataset of 1.25 million leads at 42 US companies (29 of them B2C), firms that tried to reach a lead within an hour were nearly 7× as likely to qualify it as firms that tried even an hour later↗. Qualifying meant a meaningful conversation with a key decision maker. Both datasets were published in 2011 or earlier and cover inbound web leads, and the second is mostly B2C. At high ACV those hand-raisers are rarer, so each slow reply costs more. Test it yourself: submit your own demo form on a Tuesday afternoon and time the reply.
Unworked leads. A 2013 Journal of Marketing study describes a "sales lead black hole": the roughly 70% of marketing-generated leads that sales reps never pursue↗. The abstract states that figure as the study's premise, and the authors suggest it comes from competing demands on reps' time, so check rep capacity as well as routing. A Forrester analyst writes that fewer than 1% of leads convert to closed deals↗. The audit counts how many inbound leads and target accounts have no logged activity 7 days after they arrive. That number is usually more useful than any funnel chart.
Data. 76% of CRM users say less than half of their CRM data is accurate and complete, and they report losing 16 deals a quarter to poor data on average↗. Only 35% of sales professionals completely trust the accuracy of their organisation's data↗. In a 2020 Gartner survey, 45% of sales leaders and sellers had high confidence in their organisation's forecast accuracy↗. A forecast built on fields nobody trusts inherits the problem, so the audit checks field completeness on the records that route leads and feed the forecast. The rest of the database can wait. If you don't snapshot the forecast today, start a weekly export now, so your baseline begins the week of the audit.
What baseline metrics should you leave the audit with?
Leave with 10 dated baselines, split by ACV band: win rate and ACV by segment, target-account pipeline and coverage, contact coverage, signal-to-action time, time to first touch, untouched leads, sales acceptance, field completeness, forecast miss and handoffs inside SLA. Without a baseline, nobody can show later that a fix worked. Store them where next quarter's review can find them. This is the scorecard to fill in:
| Baseline metric | Definition used | Value by ACV band | Date measured |
|---|---|---|---|
| Win rate and median ACV by segment, last 12 months (24 if fewer than about 40 deals a year) | |||
| Share of pipeline from target accounts, and share of target accounts active in the last 90 days | |||
| Share of open opportunities over €50k with 3 or more engaged contacts | |||
| Median days from buying signal to first action | |||
| Median time from form fill to first human touch | |||
| Share of leads and target accounts with no activity after 7 days | |||
| Sales acceptance rate within 5 working days | |||
| Completeness of the fields that route leads and feed the forecast | |||
| Week-2 commit forecast vs actual closed-won, and share of deals with a pushed close date | |||
| Share of handoffs inside the agreed SLA (or the fallback timings) |
Two metrics need extra care at high ACV. The first is contact coverage. In Gong's analysis of 1.8 million new business deals closed on its platform in 2024, win rates in deals over $50k were on average 130% higher when multi-threaded, and large strategic wins averaged 17 buyer contacts↗. That is vendor data and a correlation, since winning deals pull more people in, but a single-threaded €100k deal is still a red flag worth counting. Count engaged contacts per open opportunity, from logged meetings and replies. A contact who was only added to the CRM doesn't count.
The second is the shared definition behind every rate. Gartner's Billy Luckey says sales organisations that align cross-functional KPIs are nearly 3× more likely to exceed new-customer acquisition targets↗. Put the written definitions of "qualified" and "in market" on the same page as the scorecard, so a new hire reads them together.
A few red flags are worth fixing now, whatever your history says. They are rules of thumb drawn from the sources above:
- An inbound demo request with no human touch the same business day, given how fast the HBR data shows leads go cold↗.
- An open opportunity over €50k with only one engaged contact↗.
- No written, signed definition of "qualified"↗.
- No forecast snapshot to compare the quarter against.
For everything else, set what "bad" looks like from your own history. Public benchmarks for high-ACV motions are thin and rarely match your segment, deal size or cycle length. Your last 4 quarters are the fairer comparison. Mark any threshold you set as your own, and revisit it after two quarters of data.
How do you run a GTM audit in 3 weeks without buying a tool?
You can run it in 2–3 weeks with a CRM export, a spreadsheet and 6–8 interviews: sales, marketing, RevOps and customer success internally, plus 2–3 recent won or lost buyers. New software bought before the diagnosis tends to get configured around the old definitions, which repeats the leak in a new tool.
- 01Step 1
Agree definitions and pull the data
Write down ICP, qualified and in market, and get sales and marketing to sign them. Export 12 months of deals, leads, activities and stage history (24 if you closed fewer than about 40 deals a year), using IDs rather than names and emails.
Week 1 - 02Step 2
Test the front door
Submit your own demo form and a contact request. Time the reply and note who picks it up.
Week 1 - 03Step 3
Score the 8 areas
Calculate the baselines for each area, split by ACV band, with the deal count next to every rate. Use 6–8 interviews to explain the outliers: sales, marketing, RevOps and customer success, plus 2–3 recent won or lost buyers.
Week 2 - 04Step 4
Map the leaks
Rank each leak by the pipeline at risk and the effort to fix it.
Weeks 2–3 - 05Step 5
Read out and plan
Present the scorecard, the top 3 fixes and a 90-day plan with named owners. Schedule fixes that touch territories, capacity or comp for the next planning cycle. Re-run the scorecard each quarter.
Week 3
Keep the scope fixed, with one owner and one data pull. The readout ranks each leak by the pipeline at risk and the effort to fix it, then names the 3 fixes for the next 90 days. Many of those fixes are routing rules, field requirements and a written handoff SLA, which take days to a few weeks once definitions are agreed. Fixes that touch territories, rep capacity or comp plans take a planning cycle, so name them now and schedule them.
After the readout, re-run the scorecard every quarter against the baseline. Leading metrics (time to first touch, untouched leads, contact coverage, field completeness, handoffs inside SLA) should move within one quarter of a fix. If they don't, the fix was wrong or never shipped. Lagging metrics (win rate, ACV, forecast variance) need a rolling 4-quarter view before you judge them.
In practice
How we do it at panelhop
We run this audit as Panel Check (GTM audit · 2–3 weeks): 32 checks across the same 8 areas, scored from your own CRM records. You leave with a leak map, a baseline scorecard split by ACV band and a 90-day plan. The price is fixed before we start. Read the method.
If the audit finds leaks, Leak Fix (we build the fixes) closes them in your HubSpot or Salesforce, and everything we build stays in your accounts.
Questions buyers ask about this
How long should a GTM audit take?
2–3 weeks is enough for most companies selling €25k+ deals. The first week goes on definitions and the data pull, the second on scoring the 8 areas, the third on the leak map and the readout. Longer audits tend to drift into strategy work that nobody acts on.
What if our CRM data is too messy to audit?
Audit it anyway. Missing or untrusted fields are findings for the CRM data area, and they show which numbers to rebuild from email, calendar and billing records. Start with the fields that route leads and feed the forecast.
Should we run the audit ourselves or bring in an outside team?
Either works if one owner has CRM access and both sales and marketing sign off the definitions. An internal team knows the context; an outside team finds it easier to ask why a rule exists. The table and scorecard in this post work either way.
Who should own the GTM audit internally?
One person with access to the CRM and the trust of both sales and marketing, usually the head of RevOps. Sales and marketing leaders sign off the definitions in week 1 and the readout in week 3.
How often should we repeat the audit?
Re-run the baseline scorecard every quarter and the full audit once a year, or after a big change such as a new CRM, a new sales leader or a new segment. Leading metrics such as time to first touch should move within a quarter of a fix. Judge win rate, ACV and forecast variance on a rolling 4-quarter view.
