Sales Forecast Diagnostic

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, field history (stage, close date, category, amount changes), activity logs? How far back?
• What is the motion - B2B deal-based, B2C/high-volume/self-serve model-based, or mixed? Roughly how many deals per period?
• How is the team paid - quota cliffs, accelerators, quarter-end discount authority, anything rewarded or punished based on the forecast number itself?
• Any confounders in the period - CRM migration, stage redefinition, territory change, a bulk hygiene cleanup?

Workflow

1. Run the Interview; establish the period, direction, and level of the error before touching deal data.
2. Quantify the miss before explaining it: forecast vs. actual per period, per roll-up level, per forecast category. Compute both signed error (bias) and absolute error - a symmetric average hides offsetting errors, since the same miss reads very differently depending on the chosen denominator, so fix one error definition first. Both numbers always ship together, as a correctness rule rather than a menu to rank and pick from - see references/evidence-and-metrics.md.
3. Inventory the evidence base: period-start snapshot, field history, activity logs. If no snapshots or field history exist, start capturing them now, diagnose from current-state fingerprints plus interviews, and label every conclusion lower-confidence - without history, behavior verdicts are hard to defend.
4. Reconstruct the missed period from the period-start snapshot. Classify every deal that was in a forecast category by outcome (won as called, slipped, lost, removed, still open) and every deal actually won by origin (in the forecast? which category? when created?). If your harness can execute code, script this from a two-file export (period-start forecast, period-end actuals); otherwise ask the user for both lists and reconcile them by hand.
5. Run the root-cause fingerprints in references/root-cause-fingerprints.md over the reconstruction. Require two independent signals per suspected cause, at least one from recorded history.
6. Interview reps and managers on flagged deals to split data from behavior. The discriminating question: "did you know this deal was dead (or real) before the CRM said so?" Yes means the record lagged reality - a data problem. No means behavior or demand.
7. Isolate demand last: rebuild the period-start forecast with hindsight-corrected data - zombies removed, honest categories, evidence-backed close dates. Whatever gap survives the correction is a genuine demand or coverage shortfall that no forecasting-process fix will recover; say so plainly.
8. Attribute the gap: every dollar of miss maps to a named deal (or a named model assumption in high-volume motions), each with one primary root cause and one class. Offsetting errors (sandbagged wins masking inflated losses) are attributed separately, never netted silently.
9. Check the pass thresholds in Measurement below; tune the fingerprints and re-run until every threshold holds.
10. Rank the confirmed causes for delivery using Fix leverage below: attributed dollars first, then divided by fix effort, with the adjustment shown rather than the two orders silently merged. One fix per cause matched to its class, plus the metric that will prove each fix worked within one period.
11. Deliver section by section for user approval; report shape and worked examples in references/worked-diagnosis-example.md.
12. If your harness has persistent memory, memorize the confirmed causes, the thresholds derived from this team's history, the effort tiers as they turned out for this team, and rep-level calibration baselines - the next period's re-run starts from them.

Root causes in scope

The compact map: full detection fingerprints, second signals, and fixes live in references/root-cause-fingerprints.md, in this same order.

The value axis cannot be pre-ranked here and this skill does not pretend to: which cause is worth the most dollars is the output of the attribution table, and it differs with every engagement. What is stable is the fix - what it costs to ship and how long before a forecast reflects it - so the register is ordered by that, and the diagnosis then re-ranks it against the dollars it actually found (see Fix leverage).

• fix effort (most first): comp-driven bias > rep sandbagging > stage inflation == missing evidence > late-created deals > manager override == wrong category > stale dates == zombies
• time-to-effect (slowest first): comp-driven bias > rep sandbagging > manager override > late-created deals > stage inflation == missing evidence > stale dates == wrong category > zombies