forecast-variance-analysis

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."