--- name: forecast-accuracy-improvement-analyzer description: "Diagnoses systematic forecast error patterns in SaaS financial models — bias direction, error by line item, seasonality misses, and assumption drift — then delivers ranked modeling and assumption adjustments with a revised forecasting cadence. Use when a SaaS forecast keeps missing, when reconciling forecast vs. actuals variance, or when tightening the accuracy of revenue, bookings, or cash projections." argument-hint: "[forecast-vs-actuals] [line-items]" allowed-tools: ["Read"] --- # Forecast Accuracy Improvement Analyzer ## Purpose Diagnose where a SaaS forecast systematically goes wrong — which line items, which direction, and which assumptions are driving the error — and deliver a ranked set of modeling changes that measurably tighten future accuracy. Turns a pile of forecast-vs-actuals variance into a specific fix list instead of another round of "we'll try to be more careful." ## When to Use Invoke this skill when the user asks to: - Understand why their SaaS forecast keeps missing (ARR, bookings, churn, cash) - Reconcile forecast vs. actuals variance and find the root cause - Reduce systematic bias in revenue or expense projections - Improve the accuracy of a bottoms-up or driver-based model - Fix a forecast that's consistently too high or too low - Rebuild forecasting assumptions after several bad quarters ## Inputs Required Before starting, collect: - **Forecast vs. actuals history**: At least 4–6 periods (months or quarters) of forecasted values against actuals, ideally by line item - **Line items in scope**: Which metrics to analyze — new bookings, expansion, churn/contraction, ARR, revenue, headcount, opex, cash - **Forecast method currently used**: Driver-based, bottoms-up rep capacity, top-down growth rate, or hybrid - **Forecast cadence and horizon**: How often re-forecast happens and how far out (e.g., monthly rolling 12-month) - **Key assumptions in the model**: The main drivers (win rate, sales cycle, ramp, NRR, seasonality factors) - **Known one-time events**: Anything that distorted actuals (large deal, layoff, pricing change, outage) If variance is only provided in aggregate (no line-item breakdown), analyze at the level available and flag that line-item decomposition would sharpen the diagnosis. ## Instructions ### Step 1: Calculate and Normalize the Error For each line item across all periods provided: - **Forecast error** = Actual − Forecast (signed, so direction is preserved) - **Percent error** = (Actual − Forecast) / Actual - **MAPE** (Mean Absolute Percent Error) across periods — the accuracy score - **Bias** = mean of signed percent errors — positive means chronic under-forecasting, negative means chronic over-forecasting Present a compact table: line item, MAPE, bias direction, error range. ### Step 2: Classify the Error Pattern Assign each line item to the dominant error type: - **Systematic bias**: Errors consistently one direction (e.g., always over-forecasting new bookings) — a broken assumption, not noise - **Volatility/noise**: Errors bounce both directions with no consistent sign — the mean may be fine but the model can't call timing - **Seasonality miss**: Errors cluster in specific periods (Q4 spike, summer slowdown, January renewal wall) - **Regime break**: Accuracy good, then a step-change where errors blow out — indicates a real business shift the model hasn't caught up to - **Compounding drift**: Error small near-term, growing at the far end of the horizon — usually a growth-rate or NRR assumption compounding wrong ### Step 3: Trace SaaS-Specific Root Causes Map each error pattern to likely SaaS drivers: - **New bookings over-forecast**: Inflated win rate, sales cycle assumed shorter than reality, rep ramp assumed faster, pipeline coverage below the ratio needed for the number - **Expansion/NRR miss**: NRR assumption not segmented by cohort, usage-based revenue modeled linearly, expansion timing lumped rather than distributed - **Churn/contraction under-forecast**: Logo churn and dollar churn conflated, renewal timing off, downgrades not modeled separately from full churn - **Revenue vs. bookings confusion**: Bookings-to-revenue recognition lag misapplied, ARR modeled as revenue, ramped/prorated deals treated as full-period - **Cash/opex drift**: Headcount plan not tied to hiring reality, collections/DSO assumption stale, spend forecast top-down rather than commitment-based ### Step 4: Quantify the Impact of Each Error Source Rank error sources by dollar impact, not by percent. A 3% miss on a $10M line beats a 30% miss on a $200K line. For each source estimate the recurring dollar error per period so the fix list is prioritized by what actually moves the number. ### Step 5: Prescribe Ranked Modeling & Assumption Fixes Produce 6–10 fixes ordered by expected accuracy gain. Each fix states: 1. **What's wrong** (the specific assumption or structure) 2. **The change** (concrete: "segment NRR by cohort year" not "improve NRR modeling") 3. **How to implement it** in the model 4. **Expected effect** on bias or MAPE for that line item Favor structural fixes (adding a driver, splitting a blended assumption, adding a seasonality index) over judgment fixes ("be more conservative"), which don't survive. ### Step 6: Recommend a Revised Cadence & Feedback Loop Specify: - **Bias correction factor**: If a line item shows persistent one-way bias, apply a documented adjustment (e.g., haircut new bookings by the historical over-forecast rate) until the structural fix lands - **Re-forecast cadence**: Match cadence to volatility — high-noise lines re-forecast more often - **Accuracy tracking**: Log MAPE and bias by line item every period so improvement is measurable - **Assumption review triggers**: What variance threshold forces an assumption re-examination ## Output Format - **Structure**: Error table → pattern classification → root-cause trace → impact ranking → ranked fix list → revised cadence - **Length**: 900–1,300 words - **Format**: Table for the error/accuracy scores; structured bullets for classification and root causes; numbered ranked list for fixes - **Sections**: Error & Accuracy Summary → Error Pattern Classification → Root-Cause Analysis → Impact Ranking → Ranked Fixes (6–10) → Revised Cadence & Feedback Loop - **Tone**: FP&A specialist — quantitative, direct, unafraid to name the broken assumption; prioritizes dollar impact over precision theater ## Quality Checklist Before delivering output, verify: - [ ] Bias is reported with direction and magnitude for each line item — not just absolute error - [ ] Each line item is classified into a specific error pattern (bias / noise / seasonality / regime break / drift) - [ ] Root causes reference SaaS-specific drivers (win rate, NRR, ramp, churn timing) — not generic "assumptions were off" - [ ] Fixes are ranked by dollar impact, and each includes a concrete implementation step - [ ] The fix list favors structural model changes over "be more conservative" judgment calls - [ ] A measurable feedback loop (MAPE/bias tracking + trigger thresholds) is specified ## Notes - Chronic one-way bias is the highest-value find — it's a broken assumption that can be corrected immediately with a documented adjustment factor while the structural fix is built - Blended assumptions are the most common accuracy killer in SaaS models: one NRR, one win rate, one churn rate applied across segments that behave differently - Distinguish noise from bias before prescribing anything — a volatile-but-unbiased line item doesn't need a new assumption, it needs a tighter cadence or a range instead of a point estimate - If the history includes a distorting one-time event, exclude or annotate it before computing bias so a single large deal doesn't masquerade as a systematic pattern --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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