sales-forecast-diagnostic

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---
name: sales-forecast-diagnostic
description: Diagnose why an existing sales forecast is unreliable and recommend fixes - stage inflation and happy ears, sandbagging, deals without verifiable buyer evidence, stale or mass-pushed close dates, wrong forecast-category assignment, zombie and duplicate opportunities, late-created deals, manager roll-up overrides, and comp incentives that reward bias - separating data-quality from behavioral from genuine demand problems. Use whenever the user mentions forecast accuracy, a forecast miss, reps sandbagging, deals that keep slipping, "why did we miss the number", or "our commit is never right" - even if they never say "forecast". Covers B2B deal-based and B2C/high-volume. Do NOT use for pipeline coverage modeling - use mbfinotti/sales-skills@sales-pipeline-coverage-modeling instead.
license: MIT
metadata:
  author: Maya-Beth Finotti
  version: "1.3.4"
---

# Forecast Diagnostic

Investigate a forecast that missed, or that nobody trusts, and name the root cause with evidence. A forecast call asks "what's the number?" This diagnostic asks "was the number ever real?"

Practitioners converge on the view that most misses are inspection failures, not market failures: the evidence that a committed deal was fiction usually existed in the record before the miss.

Scope boundary, stated up front: this skill consumes whatever forecasting methodology already exists - the category set, roll-up rules, commit criteria, and coverage targets are inputs, not deliverables. Designing or redesigning that methodology is out of scope. When the investigation shows the methodology itself is undefined or ambiguous ("commit means different things to different reps"), report that as a finding and stop at the boundary.

Every confirmed root cause lands in exactly one of three classes, because each class has a different owner and a different fix:

- **Data**: the record was wrong, a human knew better.
- **Behavior**: the record faithfully reflects a biased human call.
- **Demand**: honest, evidenced pipeline still fell short.

The most common diagnostic error is issuing a behavior verdict for a data problem.

## Interview

Ask before analyzing:

- One question per message; offer multiple-choice options when possible.
- Skip anything already answered.
- Ask the first three questions below before any of the rest. Their fixes diverge by two orders of magnitude in effort and in how long they take to reach a forecast, and the answers decide the order the whole deliverable is ranked in.

Questions:

- By when must the number be more reliable - this period's call, the next one, or the next planning cycle? A fix that lands after the period closes does not help this forecast: a mid-period deadline deletes every fix slower than one cycle from the plan and leaves the hour-tier CRM-admin fixes carrying the whole load.
- One-off correction of this miss, or a standing accuracy program? One-off promotes the reconstruction, the zombie purge, and the date cleanup; standing promotes evidence gates, calibration scorecards, and threshold derivation.
- Effort ceiling: how many analyst hours, whether a CRM admin can ship a field or validation change this week, and who has authority to reopen the comp plan or change a review cadence. No admin turns every hour-tier fix into a week-tier request; no comp authority deletes the comp fix outright.
- What triggered this - a specific missed period, chronic inaccuracy, or a suspicion (sandbagging, inflated pipeline)? Which period(s)?
- Which direction is the error - over-forecast (missed the call), under-forecast (beat it by a lot), or erratic both ways?
- At which level does the number go wrong - individual rep, manager roll-up, or company-wide? Where in the roll-up can a human override the sum?
- What does the process look like today - forecast categories in use, submission cadence, who calls the final number? (Recorded as-is; not redesigned.)
- What history exists - opportunity snapshots, fi