Measure Forecast Accuracy by Rep and Manager, and Adjust the Commit
The problem
The quarterly forecast is the sum of rep and manager commit calls, but nobody scores those calls afterwards, so the CFO cannot tell which reps sandbag and which over-call, whether managers improve on their reps, or how much of this quarter's commit history says will actually close.
Run it in your LLM
One prompt for Claude or ChatGPT that grades every commit call in your history against what closed: who sandbags, who over-calls, whether managers improve on their reps, and what this quarter's commit looks like once each rep's track record is applied. Type run the example after pasting it to test it on the fictional data built into the prompt.
# Forecast Accuracy by Rep
You are a RevOps and FP&A lead who owns the forecast the CFO takes to the board. From an export of commit calls and closed amounts, measure how accurate each rep's and each manager's commit has been, which way each one misses (sandbagging or over-calling), and what that history implies for this quarter's commit, with every number traceable to the export.
## Rules
- Use only the data in this conversation. Do not invent reps, amounts, accuracy benchmarks, or industry averages. If something is missing, ask for it, or use the fallback this prompt defines and label it as an assumption.
- Run the calculations in code (Python or your data analysis tool) if you can. If you cannot run code, say so at the top and show the working rep by rep.
- Show the formula and the number of quarters next to every headline number.
- Every default in this prompt (the commit snapshot, a 10% tolerance band, 3 scored quarters before a rep's own factor is used, the last four closed quarters) is a starting assumption the user can change, not a benchmark.
- Read only. Do not change anything in the CRM or the forecast tool, and do not recommend action on any individual rep's pay or job. The adjusted number is a second view of the forecast, not a replacement for the deal-by-deal call.
- Do not name individual reps in the summary. The rep table is for the user's own review.
- Format: percentages with one decimal, money to the nearest dollar, factors with three decimals.
## Step 0. Inputs
If the user says "run the example", use the EXAMPLE DATA section at the end of this prompt, do not ask any questions, and label every output "Example data (fictional)".
Otherwise ask for everything below in one message, skipping anything the user already gave:
1. A CSV export with one row per rep per quarter, plus one row per manager per quarter for the manager's own team call, with these columns (ask the user to map theirs):
- quarter (YYYY-Qn), manager_id, owner_id (the rep, or the manager on a manager row), role (rep or manager), commit, closed
- Rep rows: commit is the rep's commit call at the snapshot below; closed is what that rep closed won in the quarter. Manager rows: commit is the manager's call for the whole team at the same snapshot; leave closed blank, it is summed from the team's rep rows.
- Include rows for the current quarter with the commit and closed left blank. Those are the commits to adjust.
Where to get it: commit calls live in the CRM's forecast tool, a forecast sheet, or the notes from the weekly forecast call; closed amounts come from closed won opportunities by owner and close date. If the CRM does not keep past forecast submissions, use whatever record exists and start a snapshot each quarter from now on.
2. The snapshot: which commit counts. By default, the commit as recorded at the end of the first month of the quarter. A commit from the final week is close to what already closed, so it says little about calling the quarter.
3. The closed definition: the same bookings definition the commit was called on (for example first-year contract value or new ARR), for every row.
4. The tolerance band: by default a quarter counts as on target when closed is within 10% of the commit.
5. The minimum history: by default a rep needs 3 scored quarters before their own factor is used; below that, the team factor applies.
6. Any change to the defaults above.
Then report a data check before any analysis: rows read; rows left out and why (duplicate owner and quarter, quarter not in YYYY-Qn form, no commit recorded, no closed amount); rep-quarters scored, manager-quarters scored, and quarters covered. A past rep row with a closed amount but no commit is left out of that rep's score, but its closed amount still counts toward the team's closed total for the manager's call. Ask the user to confirm before going on, unless they said "run the example".
## Step 1. Accuracy and bias by rep
For each rep over the scored quarters, with C = sum of commits and W = sum of closed:
- Sandbagged = sum of (closed - commit) over quarters that closed above the call. Over-called = sum of (commit - closed) over quarters that closed below it.
- Accuracy = 1 - (sandbagged + over-called) / C, floored at 0%. This counts every miss, in both directions, so a rep who is far over one quarter and far under the next does not look accurate.
- Call ratio = W / C. Net error = W - C. Quarters within the band: closed within 10% of the commit.
- Bias: "Sandbags" when the call ratio is above 1 + band; "Over-calls" when it is below 1 - band; otherwise "Misses both ways" when accuracy is below 1 - band, and "Within band" when it is not.
Then the same for the whole team (all scored rep-quarters together) and by quarter. Say plainly where the net and the gross differ: sandbaggers and over-callers cancel in the team total, so a team call can land close while most individual calls miss.
## Step 2. Manager calls
For each manager and each scored quarter, the team closed total is the sum of closed on that manager's rep rows, including rows left out of rep scoring for a missing commit.
- Manager accuracy and call ratio: the manager's commit against the team closed total, with the formulas in step 1.
- Roll-up accuracy and call ratio: the sum of that team's rep commits against the same closed total.
- Say whether the manager's judgment improved on the roll-up of rep calls (manager accuracy above roll-up accuracy) or made it worse.
## Step 3. This quarter's commit, three ways
For the current quarter:
- Raw commit = sum of rep commits.
- Team factor = team W / team C. Team-factor forecast = raw commit x team factor.
- Rep factors: each rep's commit x that rep's call ratio, or x the team factor when the rep has fewer scored quarters than the minimum; label which factor each rep got. Rep-factor forecast = the sum.
- The haircut (negative) or uplift (positive) = adjusted forecast - raw commit, for both methods.
- Manager calls: each manager's current call, and the call x that manager's own ratio, next to the rep-factor forecast for the same team.
Treat the spread between these numbers as the size of the forecast risk. Do not average them into one number.
## Step 4. Output
Start with a five-line summary:
- Headline: "Team commit accuracy {accuracy} over {n} rep-quarters; calls ran {net} net ({ratio} of commit closed)."
- Bias: "{count} reps sandbag, {count} over-call, {count} miss both ways, {count} within band"; name the largest single source of over-call by amount without naming the rep.
- Managers: whether each manager's call beat the roll-up of their reps, with both accuracies.
- This quarter: raw commit, team-factor forecast, rep-factor forecast, and the managers' calls, each with the haircut or uplift.
- One line: "History is {quarters}; commit snapshot {snapshot}; band {band}; {excluded} rows were left out."
Then these tables:
1. Data check
2. By rep (rep, manager, quarters scored, commit total, closed total, sandbagged, over-called, accuracy, call ratio, bias, quarters within band, factor used, current commit, adjusted commit), for the user's review only
3. By manager (manager, quarters, manager accuracy, manager call ratio, roll-up accuracy, roll-up call ratio, current call, call adjusted by the manager's ratio, rep-factor forecast for the team)
4. By quarter (quarter, commit, closed, sandbagged, over-called, accuracy, call ratio)
5. This quarter three ways (method, total, haircut or uplift against the raw commit)
6. Assumptions, each labeled as an assumption, including: the snapshot; the closed definition; the band; the minimum quarters; four quarters of history is a small sample; history is not a forecast; a rep's factor does not account for the specific deals in this quarter's commit.
If you can create files, offer the rep table, the manager table, and the three-way forecast as CSV downloads.
## EXAMPLE DATA (fictional, for testing this prompt)
Use this only when the user says "run the example". The company, managers, and reps are made up.
- Scored quarters: 2025-Q4 to 2026-Q3; current quarter 2026-Q4
- Commit snapshot: end of the first month of each quarter; closed: new ARR closed won in the quarter
- Defaults: band 10%, 3 scored quarters for a rep's own factor, all others as written above
```csv
quarter,manager_id,owner_id,role,commit,closed
2025-Q4,M-01,R-01,rep,100000,120000
2025-Q4,M-01,R-02,rep,120000,114000
2025-Q4,M-01,R-03,rep,150000,105000
2025-Q4,M-02,R-04,rep,200000,130000
2025-Q4,M-02,R-05,rep,100000,140000
2025-Q4,M-01,M-01,manager,340000,
2025-Q4,M-02,M-02,manager,300000,
2026-Q1,M-01,R-01,rep,90000,108000
2026-Q1,M-01,R-02,rep,100000,105000
2026-Q1,M-01,R-02,rep,100000,105000
2026-Q1,M-01,R-03,rep,140000,112000
2026-Q1,M-02,R-04,rep,180000,135000
2026-Q1,M-02,R-05,rep,120000,80000
2026-Q1,M-01,M-01,manager,320000,
2026-Q1,M-02,M-02,manager,310000,
2026-Q2,M-01,R-01,rep,110000,121000
2026-Q2,M-01,R-02,rep,120000,120000
2026-Q2,M-01,R-03,rep,160000,120000
2026-Q2,M-02,R-04,rep,220000,150000
2026-Q2,M-02,R-05,rep,,30000
2026-Q2,M-02,R-06,rep,40000,30000
2026-Q2,M-01,M-01,manager,360000,
2026-Q2,M-02,M-02,manager,270000,
2026-Q3,M-01,R-01,rep,100000,115000
2026-Q3,M-01,R-02,rep,140000,135000
2026-Q3,M-01,R-03,rep,150000,113000
2026-Q3,M-02,R-04,rep,200000,145000
2026-Q3,M-02,R-05,rep,100000,125000
2026-Q3,M-02,R-06,rep,60000,53000
2026-Q3,M-01,M-01,manager,370000,
2026-Q3,M-02,M-02,manager,360000,
2026-Q4,M-01,R-01,rep,110000,
2026-Q4,M-01,R-02,rep,130000,
2026-Q4,M-01,R-03,rep,160000,
2026-Q4,M-02,R-04,rep,200000,
2026-Q4,M-02,R-05,rep,110000,
2026-Q4,M-02,R-06,rep,80000,
2026-Q4,M-01,M-01,manager,360000,
2026-Q4,M-02,M-02,manager,330000,
```
How to run it
- Open Claude or ChatGPT with code execution on (Claude's analysis tool, ChatGPT data analysis). Without it the model does arithmetic by hand, and you should check every number.
- Paste the prompt. To test it first, send "run the example" and compare with the expected results below.
- Attach a CSV with quarter, manager ID, owner ID, role, commit, and closed for the last four closed quarters plus the current quarter's commits, all taken at the same point in the quarter. The prompt lists the columns and where to find them.
- Read the data check first, then take the three-way table to the forecast call: the gap between the raw commit and the rep-factor number is the amount to question deal by deal before it goes to the CFO.
Before you paste forecast data
Commit calls and closed amounts by rep are performance data. Check your company's AI and data policy, prefer a business or enterprise plan, and replace names with rep and manager IDs: the prompt only needs IDs, quarters, and amounts. It reads the data and changes nothing in your CRM or forecast tool.
Expected results on the example data
A correct run of "run the example" should match these, give or take rounding. They are computed from the prompt's definitions by a reference implementation that is tested against hand-worked numbers. The data is fictional.
- Data check
- 39 rows; 21 rep-quarters and 8 manager-quarters scored over 2025-Q4 to 2026-Q3; left out: R-02 2026-Q1 (duplicate owner and quarter), R-05 2026-Q2 (no commit recorded, closed still counts for the team)
- Team commit accuracy
- 1 - ($134,000 sandbagged + $458,000 over-called) / $2,700,000 = 78.1%
- Team call ratio
- $2,376,000 / $2,700,000 = 0.880, over-called by $324,000 net
- Bias by rep
- 1 sandbags, 3 over-call, 1 misses both ways, 1 within the 10% band
- Managers against the roll-up
- M-01 99.0% vs 93.8% for the sum of rep calls; M-02 82.1% vs 83.4% for the sum of rep calls
- 2026-Q4 commit three ways
- raw $790,000; team factor $695,200; rep factors $704,969; managers called $690,000
| Rep | Quarters | Commit | Closed | Accuracy | Call ratio | Bias | Factor used | 2026-Q4 commit | Adjusted |
|---|---|---|---|---|---|---|---|---|---|
| R-01 | 4 | $400,000 | $464,000 | 84.0% | 1.160 | Sandbags | 1.160 (own) | $110,000 | $127,600 |
| R-02 | 4 | $480,000 | $474,000 | 96.7% | 0.988 | Within band | 0.988 (own) | $130,000 | $128,375 |
| R-03 | 4 | $600,000 | $450,000 | 75.0% | 0.750 | Over-calls | 0.750 (own) | $160,000 | $120,000 |
| R-04 | 4 | $800,000 | $560,000 | 70.0% | 0.700 | Over-calls | 0.700 (own) | $200,000 | $140,000 |
| R-05 | 3 | $320,000 | $345,000 | 67.2% | 1.078 | Misses both ways | 1.078 (own) | $110,000 | $118,594 |
| R-06 | 2 | $100,000 | $83,000 | 83.0% | 0.830 | Over-calls | 0.880 (team) | $80,000 | $70,400 |
The team call lands at 0.880 of commit, but accuracy is 78.1% because $134,000 of sandbagging offsets part of $458,000 of over-calling. R-05 closes 1.078 of commit net but scores 67.2%: $40,000 over, $40,000 under, then $25,000 over, which calls for a deal review more than a factor. M-01 called 99.0% accurately against 93.8% for the sum of their reps; M-02 did slightly worse than their reps. R-06 has 2 scored quarters, so the team factor of 0.880 applies. The spread from $790,000 raw to $695,200 is the forecast risk to work deal by deal.
Commit accuracy and this quarter's adjusted commit
Enter each rep's commit and closed amount for recent quarters, or summary totals, and this quarter's commit. Prefilled with the fictional example. Accuracy = 1 - (sandbagged + over-called) / commit; call ratio = closed / commit, applied to this quarter's commit as the haircut or uplift.
One amount per past quarter, oldest first, separated by commas; 120k means $120,000. Closed amounts go in the same order as the commits. Leave this quarter blank to score a rep without adjusting.
- Team commit accuracy
- 78.1%
- $592,000 missed on $2,700,000 called
- Call ratio (closed / commit)
- 0.880
- closed 12.0% below the call
- Bias
- Over-calls
- Net -$324,000, 5 of 21 rep-quarters within 10%
- This quarter's commit
- $790,000
- Sum of rep commits entered
- Adjusted with rep factors
- $704,969
- Haircut -$85,031
- Manager or team call
- $690,000
- $14,969 below the adjusted number
Compare the levers for this quarter
- Raw commit, no adjustment$790,000$0 vs commit
- Team factor 0.880 on every rep$695,200-$94,800 vs commit
- Each rep's own factor (3+ quarters, team factor below)$704,969-$85,031 vs commit
- Manager or team call$690,000-$100,000 vs commit
| Rep | Quarters | Accuracy | Call ratio | Bias | Factor used | Adjusted |
|---|---|---|---|---|---|---|
| R-01 | 4 | 84.0% | 1.160 | Sandbags | 1.160 own | $127,600+$17,600 |
| R-02 | 4 | 96.7% | 0.988 | Within band | 0.988 own | $128,375-$1,625 |
| R-03 | 4 | 75.0% | 0.750 | Over-calls | 0.750 own | $120,000-$40,000 |
| R-04 | 4 | 70.0% | 0.700 | Over-calls | 0.700 own | $140,000-$60,000 |
| R-05 | 3 | 67.2% | 1.078 | Misses both ways | 1.078 own | $118,594+$8,594 |
| R-06 | 2 | 83.0% | 0.830 | Over-calls | 0.880 team | $70,400-$9,600 |
Use the commit from the same point in every quarter, not the final-week number. Four quarters is a small sample, so a factor is a second view of the forecast, not a replacement for the deal-by-deal call; a rep who misses both ways needs a deal review more than a factor.
Implementation steps
- 1
Write the definitions down before pulling data: the commit is the call recorded at the same point every quarter (by default the end of the first month, an assumption you can change; the final-week number is close to what already closed); closed uses the same bookings definition the commit was called on; accuracy = 1 - (sandbagged + over-called) / commit; call ratio = closed / commit; a quarter is on target when closed is within 10% of the commit (also an assumption)
- 2
Export one row per rep per quarter for the last four closed quarters with the commit and the closed won amount, one row per manager per quarter with the manager's call for the team, and the current quarter's commits with closed left blank. Drop duplicates and list each dropped row; a rep row with closed but no commit leaves that rep's score but still counts toward the team closed total
- 3
Score each rep over the scored quarters: sandbagged = sum of closed - commit where closed came in above the call, over-called = sum of commit - closed where it came in below, accuracy, call ratio, and quarters within the band. Label the bias: sandbags when the call ratio is above 1.10, over-calls when it is below 0.90, misses both ways when the ratio is inside the band but accuracy is below 90%, otherwise within band
- 4
Score each manager two ways against the team closed total: the manager's own call, and the roll-up of that team's rep calls. A manager whose accuracy beats the roll-up is adding judgment; one whose accuracy is below it is adding noise on top of the reps
- 5
Adjust this quarter's commit three ways: raw (the sum of rep commits), the team call ratio applied to the total, and each rep's commit x that rep's call ratio (the team ratio for reps with fewer than 3 scored quarters); show the haircut or uplift of each against the raw commit, and the managers' calls next to them
- 6
Take the spread to the forecast call before the number goes to the CFO: the gap between the raw commit and the rep-factor number is the amount to work deal by deal, starting with the over-callers' committed deals and every rep who misses both ways. The factor is a second view, not a replacement for the deal-level call, and it is not a pay or performance measure
- 7
Run it with the copy-paste prompt on this page in Claude or ChatGPT, or with formulas in the template, check the scored rows against the CRM, and use the calculator on this page to try other bands, minimum quarters, and summary totals
- 8
Snapshot every rep's and manager's commit at the same point each quarter from now on and rerun after each quarter closes, so the factors use more history and you can see whether a rep's call ratio moves after coaching
Template outline
The structure to recreate in your own workspace. Field names and thresholds are starting points.
Google Sheet: Forecast accuracy FYxx (RevOps builds it, Finance owns the adjusted-forecast tab)
Tab inputs: snapshot (default end of month 1), band (default 10%), min_quarters (default 3), current_quarter
Tab calls (one row per owner per quarter): quarter, manager_id, owner_id, role (rep or manager), commit, closed
scored_rep = role = "rep" AND quarter <> current_quarter AND commit > 0 AND closed <> "" AND not a duplicate
sandbag = MAX(closed - commit, 0); overcall = MAX(commit - closed, 0)
within_band = ABS(closed - commit) <= band x commit
Tab by_rep:
C = SUMIFS(commit, owner_id, rep, scored_rep, TRUE); W = SUMIFS(closed, ...)
accuracy = MAX(0, 1 - (SUMIFS(sandbag, ...) + SUMIFS(overcall, ...)) / C); ratio = W / C
bias = IF(ratio > 1 + band, "Sandbags", IF(ratio < 1 - band, "Over-calls",
IF(accuracy < 1 - band, "Misses both ways", "Within band")))
factor = IF(COUNTIFS(owner_id, rep, scored_rep, TRUE) >= min_quarters, ratio, team_ratio)
adjusted = current commit x factor
Tab by_manager: team_closed per quarter = SUMIFS(closed, manager_id, m, role, "rep", quarter, q)
manager accuracy and ratio: manager commit against team_closed; roll-up: SUMIFS(commit, scored reps) against team_closed
Tab this_quarter: raw = SUM(current rep commits); team_factor = raw x team_ratio; rep_factors = SUM(adjusted)
Worked example (example data, fictional):
39 rows, 2025-Q4 to 2026-Q3 scored, 2026-Q4 current. Left out: R-02 2026-Q1 listed twice;
R-05 2026-Q2 has no commit (its $30,000 closed still counts for M-02's team).
Rep calls, commit -> closed ($K):
R-01 100->120, 90->108, 110->121, 100->115 C 400 W 464 sandbagged 64 ratio 1.160 accuracy 84.0% Sandbags
R-02 120->114, 100->105, 120->120, 140->135 C 480 W 474 sandbagged 5, over 11 ratio 0.988 accuracy 96.7% Within band
R-03 150->105, 140->112, 160->120, 150->113 C 600 W 450 over 150 ratio 0.750 accuracy 75.0% Over-calls
R-04 200->130, 180->135, 220->150, 200->145 C 800 W 560 over 240 ratio 0.700 accuracy 70.0% Over-calls
R-05 100->140, 120->80, 100->125 C 320 W 345 sandbagged 65, over 40 ratio 1.078 accuracy 67.2% Misses both ways
R-06 40->30, 60->53 C 100 W 83 over 17 ratio 0.830 2 quarters, team factor
Team: C $2,700,000, W $2,376,000, ratio 0.880
Accuracy = 1 - ($134,000 + $458,000) / $2,700,000 = 78.1%; net -$324,000
Managers, call vs team closed: M-01 misses 1 + 5 + 1 + 7 = $14K on $1,390K = 99.0%; roll-up of its reps 93.8%
M-02 misses 30 + 95 + 60 + 37 = $222K on $1,240K = 82.1%; roll-up of its reps 83.4%
2026-Q4 commits: 110 + 130 + 160 + 200 + 110 + 80 = $790,000
Team factor $790,000 x 0.880 = $695,200 (haircut $94,800)
Rep factors 110 x 1.160 + 130 x 0.9875 + 160 x 0.750 + 200 x 0.700 + 110 x 1.078 + 80 x 0.880
= 127.6 + 128.4 + 120.0 + 140.0 + 118.6 + 70.4 = $704,969 (haircut $85,031)
Managers M-01 $360,000 + M-02 $330,000 = $690,000
Slack to #forecast before the call goes to Finance:
"Commit check for {{quarter}}: raw commit {{raw}}; adjusted by each rep's history {{rep_factor}} ({{change}}),
team factor {{team_factor}}; managers called {{manager_total}}. Reps to review deal by deal: {{over_call_count}}
over-calling, {{mixed_count}} missing both ways. Sheet: {{sheet_url}}"
Tab history: quarter, team accuracy, team ratio, sandbagged, over-called, raw commit, adjusted, closedCost, time saved, and break-even
Tool cost here is your share of an LLM plan, which depends on your vendor, so it is an input. Every number below is an example input; replace each with your own figure.
Inputs (all assumptions)
Replace each value with your own figure.
- THours a quarter to score commit accuracy by hand (pull commit snapshots and closed amounts, clean duplicates, score reps and managers, adjust the commit, write it up)5AssumptionTime the next quarter-end forecast review.
- VHours a quarter with the prompt or template (export, review the data check and the results)1AssumptionTime the first two quarterly runs.
- RLoaded hourly cost of the RevOps or FP&A analyst$75AssumptionSalary plus benefits, divided by working hours.
- KMonthly tool cost for the LLM plan$20AssumptionYour share of the Claude or ChatGPT plan, from current vendor pricing and your invoices; the CRM, Google Sheets, and Slack are usually already paid for.
Formulas
- Time saved per quarter
T - V - 5 - 1 = 4 hours
- Time saved per week
(T - V) / 13 - 4 / 13 = 0.3 hours
- Value of time saved per quarter
(T - V) x R - 4 x $75 = $300
- Tool cost per quarter
K x 3 - $20 x 3 = $60
- Break-even hours per quarter
K x 3 / R - $60 / $75 = 0.8 hours, so the run covers its tool cost once it saves 48 minutes a quarter
- Haircut on this quarter's commit (fictional example data)
sum of each rep's commit x that rep's call ratio - sum of commits - $704,969 - $790,000 = -$85,031; with the team ratio of 0.880 instead, $695,200, a $94,800 haircut
- The math counts analyst time only. A better-calibrated commit may change hiring, spend, or what the board hears, but what that is worth depends on your business, so it is not added here.
- Call accuracy history is not a forecast: a new manager, a pricing change, or a different deal mix can move a rep's ratio, and four quarters is a small sample. Recheck the factors each quarter before relying on them.
- Rerun the time math after the first two quarters with your real review time V.
Common questions
- How do you measure forecast accuracy for each rep?
- Take the commit each rep called at the same point every quarter and what they closed. Accuracy = 1 - (amount closed above the call + amount closed below it) / total commit, so misses in both directions count. In the fictional example R-02 called $480,000 over four quarters, missed by $16,000 in total, and scores 96.7%.
- What is the difference between sandbagging and over-calling?
- A sandbagger closes more than they commit (call ratio = closed / commit above 1), an over-caller closes less (below 1). This playbook labels a rep only when the ratio is outside a 10% band, which is an assumption you can change. In the fictional example R-01 closed 1.160 of commit and R-04 closed 0.700.
- Why can the team forecast land close while individual reps are far off?
- Sandbaggers and over-callers cancel in the total. In 2025-Q4 of the fictional example the team closed 90.9% of commit, inside the band, but accuracy was 73.0% because $60,000 of sandbagging offset part of $121,000 of over-calling. Net accuracy hides the risk; gross accuracy shows it.
- Should you apply a haircut to a rep's commit?
- Use it as a second view of the forecast, not a replacement for the deal-by-deal call, and not for pay. Apply a rep's own call ratio only with enough history (3 scored quarters by default) and the team ratio otherwise. In the fictional example R-04's $200,000 commit x 0.700 = $140,000, and the whole $790,000 commit adjusts to $704,969.
- Is the manager's call more accurate than the sum of rep calls?
- Check it in your own data: score the manager's team call and the roll-up of the reps' calls against the same closed total. In the fictional example M-01 scores 99.0% against 93.8% for the roll-up, so the manager adds judgment, while M-02 scores 82.1% against 83.4% and adds nothing to the reps' numbers.
Keep going
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Quota and headcount plans usually assume every ramped rep hits 100%, and the check afterwards is one average attainment number, so nobody sees that most reps landed below quota, that a few top performers carried the average, or that the plan needed more from the typical rep than the team has delivered.
HubSpot or SalesforceGoogle SheetsClaude or ChatGPT+1Estimate: 0.4 hrs/week
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