Diagnose a Forecast Miss

You are a sales leader who runs forecast calls at digital marketing agencies. You decompose misses into the four canonical causes: not enough pipeline, conversion dropped, deals slipped, or deal size shrank.
Agency: [AGENCY_NAME] — [SERVICES]
Quota: [QUOTA] | Actual closed-won: [ACTUAL_CLOSED]
Forecast going in: [FORECAST_AT_START] | Commit: [COMMIT_AT_START]
Pipeline at quarter start: [STARTING_PIPELINE]
Deals lost or slipped this quarter (name, stage at start, outcome, $ value, reason):
[LOST_SLIPPED_DEALS]
Closed-won deals (name, $ value, days in cycle):
[CLOSED_DEALS]
Win rate / avg deal / cycle benchmarks: [HISTORICAL_WIN_RATE] | [AVG_DEAL_SIZE] | [CYCLE_DAYS]
Diagnose the miss by attributing the gap dollar-by-dollar to one of: (1) insufficient starting pipeline, (2) conversion rate decline, (3) slipped deals, (4) shrunken deal size.

- Show ALL math: forecast − actual = miss; then attribute each dollar of miss to one cause.
- Be specific: name the deals that slipped or lost, and the stage they died in.
- Compare actuals to [HISTORICAL_WIN_RATE], [AVG_DEAL_SIZE], [CYCLE_DAYS] benchmarks — flag anything > 15% off.
- Don't accept 'bad luck' as a cause; force every dollar into one of the four buckets.
- Recommend 2-3 specific changes for next quarter tied to the dominant cause.

1. Miss summary: Quota | Forecast | Actual | Miss $
2. Attribution table: Cause | $ Attributed | % of Miss | Evidence
3. The dominant cause (one paragraph)
4. 2-3 specific next-quarter changes tied to that cause

• [AGENCY_NAME] — Your agency name
• [SERVICES] — Services sold
• [QUOTA] — Quota for the missed quarter
• [ACTUAL_CLOSED] — Actual closed-won revenue
• [FORECAST_AT_START] — Forecast number set at quarter start
• [COMMIT_AT_START] — Commit number at quarter start
• [STARTING_PIPELINE] — Total pipeline $ at quarter start
• [LOST_SLIPPED_DEALS] — Deals that lost or slipped, with reason
• [CLOSED_DEALS] — Deals won, with $ and cycle days
• [HISTORICAL_WIN_RATE] — Win rate benchmark
• [AVG_DEAL_SIZE] — Avg deal size benchmark
• [CYCLE_DAYS] — Cycle days benchmark

Agency: Quill Studio — SEO + content
Quota $250k | Actual $172k | Forecast at start $235k | Commit $190k | Starting pipeline $720k
Lost/slipped: Riverbed Inc | Negotiation | slipped to Q3 | $40k | legal delays. Saltwater Co | Proposal | lost to competitor | $28k | price.
Closed: Tinder Media $32k 41d, Umbra Tech $24k 55d, Vela Agency $50k 70d, Westfield $30k 38d, Yumi Foods $36k 50d.
Benchmarks: Proposal 35% win | Avg deal $28k | Cycle 50d

Miss summary: Quota $250k | Forecast $235k | Actual $172k | Miss $78k vs quota ($63k vs forecast).

Attribution:
- Slipped deals: $40k (51% of miss) — Riverbed slipped to Q3.
- Lost deals: $28k (36%) — Saltwater lost on price at Proposal stage.
- Conversion decline: ~$10k (13%) — Proposal conversion ran ~28% vs 35% benchmark.
- Pipeline volume: $0 — starting pipeline of $720k was 2.9x quota, healthy.
- Deal size: $0 — avg closed $34k, above $28k benchmark.

Dominant cause: Late-stage slip + price-based loss. Both deals were in Proposal/Negotiation with weak control.

Next-quarter changes:
1. Require mutual close plan signed by Proposal stage.
2. Add a 'champion + economic buyer' check at Negotiation gate.
3. Price objection playbook for Proposal stage (anchor on outcome, not hours).

• Run this with reps in the room — defensible math kills the blame-game.
• If the dominant cause is 'pipeline volume,' you're really diagnosing a marketing/SDR problem, not a sales problem.
• Save the output and compare causes quarter-over-quarter — patterns appear fast.