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forecast-variance-analysis

Decompose forecast versus actual closed/won variance into root-cause categories with confidence gating.

58|21|Updated May 15, 2026
Search Tags:#root cause analysis#pattern detection#revenue forecasting#forecast variance#revops analytics#deal classification
One-click install
npx skills add https://github.com/t0ddc3by/claude-for-customer-success --skill forecast-variance-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forecast-variance-analysis
Source: https://github.com/t0ddc3by/claude-for-customer-success/tree/main/rev-ops/skills/forecast-variance-analysis
Command: npx skills add https://github.com/t0ddc3by/claude-for-customer-success --skill forecast-variance-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Forecast Variance Analysis explains the gap between submitted forecast and actual closed/won outcomes by decomposing the miss into meaningful root-cause categories and identifying systemic patterns rather than one-off anecdotes.

Core Features & Use Cases

  • Variance decomposition: Computes variance amount and percent for a period by comparing submitted forecast vs. actual closed/won.
  • Root-cause classification: Classifies variance into rep-level, deal-size band, stage-entry, seasonal, or product/segment drivers using the provided taxonomy.
  • Pattern confidence gating: Surfaces systemic pattern memos only when evidence meets the minimum threshold (≥3 deals or ≥2 consecutive quarters).
  • Rep call accuracy scorecard: Produces an analytical submitted-vs-actual accuracy table when rep data is available.
  • Downstream-ready output: Feeds variance findings into revenue-brief generation and GTM metrics pulse.

Quick Start

Ask it: "Analyze why we missed our forecast for Q2, classify the root causes, and include any systemic pattern only if the evidence threshold is met."

Frequently Asked Questions about forecast-variance-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze forecast variance and find the root causes of missed revenue targets?▼

Forecast variance analysis decomposes the gap between submitted forecasts and actual closed/won revenue by classifying the miss into root-cause categories like rep-level, deal-size band, and stage-entry drivers.

Can I evaluate rep call accuracy as part of a post-quarter forecast review?▼

Yes, rep call accuracy assessment generates a submitted-versus-actual scorecard when rep data is available, matching individual representative projections to actual closed/won outcomes.

How do you identify systemic revenue forecasting patterns instead of one-off deal anomalies?▼

Pattern confidence gating surfaces systemic pattern memos only when evidence meets minimum thresholds, requiring at least three deals or two consecutive quarters of data.

What inputs are required for post-quarter variance decomposition?▼

Variance decomposition requires submitted forecast amounts, actual closed/won results by period, deal-level attribution signals, and data-as-of labeling to enforce accurate temporal boundaries.

Does this approach work for multi-quarter forecast accuracy reviews?▼

Yes, the variance analysis applies to both single- and multi-quarter forecast accuracy reviews, evaluating submitted forecasts against actuals across extended timeframes.

What is the best way to classify deal-level root causes for a RevOps forecast miss?▼

Root-cause classification sorts variance drivers into a provided taxonomy including seasonal, product or segment, deal-size band, stage-entry, and rep-level categories for RevOps analytics.

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