Illustrative QBR · Synthetic data
Q1 2025 GTM performance review
Kristen Martino · Analysis exercise for a fictional specialty healthcare software company.
Tables and narrative use the same scenario records as AskGTM. Values have been reconciled within this exercise; they are not company results or published industry benchmarks. Other portfolio tools use separate, labeled datasets.
What changed
Bookings were $2.14M against a $2.40M target (89.2% attainment), a shortfall of $260,000. Bookings increased 8.1% from Q4, despite fewer wins: 31 compared with 34.
Mean won ACV rose 18.5%, from $58,235 to $69,032. Closed-cohort win rate increased 3.6pp to 24.8%. These changes describe the scenario; they do not establish why bookings missed target.
1. Pipeline and bookings
| Metric | Q4 2024 | Q1 2025 | Change / note |
|---|---|---|---|
| Won ACV (bookings) | $1.98M | $2.14M | 8.1% |
| Bookings target | $2.20M | $2.40M | 9.1% |
| Attainment | 90.0% | 89.2% | -0.8pp |
| Won / closed opportunities | 34 / 160 | 31 / 125 | |
| Mean won ACV | $58,235 | $69,032 | 18.5% |
| Closed-cohort win rate | 21.3% | 24.8% | 3.6pp |
| Mean won cycle (days) | 53.4 | 48.5 | -9.3% |
| Qualified pipeline at quarter end | $6.80M | $7.20M | 5.9% |
| Pipeline / full-quarter target | 3.09x | 3.00x | Snapshot ratio; not a forecast |
Win rate uses closed won ÷ (closed won + closed lost). Mean ACV uses won ACV ÷ wins; cycle length is averaged across won deals. Pipeline is the open, qualified snapshot, separate from bookings. Coverage uses the full-quarter target shown here; it does not divide by remaining quota at quarter end.
2. Specialty performance
| Specialty | Won / closed | Won ACV | Win rate | Mean ACV | Mean cycle |
|---|---|---|---|---|---|
| Dermatology | 11 / 34 | $715,250 | 32.4% | $65,023 | 46.3d |
| Orthopedics | 4 / 28 | $265,250 | 14.3% | $66,313 | 49.3d |
| Gastroenterology | 8 / 31 | $579,583 | 25.8% | $72,448 | 50.3d |
| Ophthalmology | 8 / 32 | $579,917 | 25.0% | $72,490 | 49.3d |
Dermatology has a 32.4% win rate in this sample, compared with 14.3% for Orthopedics. This makes Orthopedics a useful starting point for reviewing loss reasons, segment mix, and time in stage. The dataset has no legal-review timestamps or interview evidence, so it cannot identify legal delay as the cause.
3. Source performance
| Source | Won / closed | Won ACV | Win rate | Mean won ACV | Open pipeline |
|---|---|---|---|---|---|
| Referral | 10 / 28 | $630,000 | 35.7% | $63,000 | $1,440,000 |
| Inbound | 8 / 27 | $505,334 | 29.6% | $63,167 | $1,440,000 |
| Trade Show | 6 / 25 | $399,666 | 24.0% | $66,611 | $1,440,000 |
| Outbound | 5 / 24 | $355,000 | 20.8% | $71,000 | $1,440,000 |
| Paid | 2 / 21 | $250,000 | 9.5% | $125,000 | $1,440,000 |
Referrals closed 10 of 28 decided opportunities (35.7%); paid closed 2 of 21 (9.5%). These are closed cohorts, including opportunities created in earlier periods. They do not measure lead-to-opportunity conversion or prove channel quality caused the difference.
A referral pilot is worth testing. Keep the initial budget limited and measure incremental qualified opportunities, acquisition cost, and subsequent wins before increasing spend.
4. Rep performance
| Rep | Quota | Won ACV | Attainment | Wins | Mean ACV | Mean cycle |
|---|---|---|---|---|---|---|
| Chen | $450,000 | $504,000 | 112.0% | 8 | $63,000 | 41.0d |
| Rivera | $450,000 | $378,000 | 84.0% | 6 | $63,000 | 52.0d |
| Okafor | $400,000 | $380,000 | 95.0% | 6 | $63,333 | 44.0d |
| Patel | $350,000 | $273,000 | 78.0% | 4 | $68,250 | 58.0d |
| Wong | $400,000 | $355,000 | 88.8% | 5 | $71,000 | 46.0d |
| Kim | $350,000 | $250,000 | 71.4% | 2 | $125,000 | 68.0d |
Kim has the longest mean won cycle at 68 days across 2 wins, and the largest mean won ACV at $125,000. Chen has the shortest cycle at 41 days across 8 wins. The small samples and different deal sizes limit comparisons; neither proves rep effectiveness or poor qualification.
5. Retention
| Specialty | Start customers | Churned | Customer churn | Prior cohort revenue | Current cohort revenue | NRR |
|---|---|---|---|---|---|---|
| Dermatology | 248 | 8 | 3.2% | $1,000,000 | $1,124,000 | 112.4% |
| Orthopedics | 186 | 14 | 7.5% | $800,000 | $830,400 | 103.8% |
| Gastroenterology | 142 | 6 | 4.2% | $600,000 | $660,600 | 110.1% |
| Ophthalmology | 210 | 11 | 5.2% | $600,000 | $651,600 | 108.6% |
Revenue-weighted NRR is 108.9%: $3,266,600 divided by $3,000,000 for the same starting cohort. New customers are excluded and churned accounts remain in the denominator. Customer churn and revenue retention use different denominators.
Orthopedics has the highest customer churn rate in this scenario. Review cancellation reasons and account size before attributing that churn to competitive or product gaps; no exit-survey evidence is included.
6. Q2 investigation plan
| Sequence | Action | Evidence needed before expansion |
|---|---|---|
| Weeks 1–2 | Review Orthopedics losses and proposal aging | Loss interviews and legal-review timestamps; test a review SLA if delays recur |
| Weeks 2–4 | Pilot a referral program with a capped budget | Incremental qualified opportunities, acquisition cost, and wins |
| Weeks 2–4 | Compare paid and referral cohorts | Comparable deal sizes, qualification rules, and cohort maturity |
| Week 6 | Review pilot results before territory changes | Observed conversion and capacity; retain a comparison cohort |
These are proposed investigations. There is no validated incremental bookings forecast, staffing recommendation, or guaranteed churn reduction.
7. Scenario arithmetic
The following calculations illustrate sensitivity to explicit assumptions. They are not predictions, and they should not be added together because the opportunities could overlap.
| Scenario | Calculation | Illustrative result |
|---|---|---|
| 20% more referral wins at the same average ACV | 10 wins × 20% × $63,000 | $126,000 |
| Recover part of an assumed delayed pipeline | $180,000 assumed delayed ACV × 30% assumed close rate | $54,000 |
| Raise pipeline / full-quarter target from 3.0x to 3.5x | $2.40M × 3.5; compared with $7.20M today | $8.40M pipeline needed |
The delayed-pipeline amount and close rate are scenario inputs, not measured fields. The coverage scenario needs $1,200,000 more qualified pipeline (16.7% growth). Higher conversion may increase bookings, but does not itself increase this pipeline-to-target ratio.