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variance-analysis skill

/plugins/revenue-forecasting-pipeline/skills/variance-analysis

This skill helps attribute forecast versus actual variances and map remediation actions across drivers, owners, and timelines.

npx playbooks add skill gtmagents/gtm-agents --skill variance-analysis

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: variance-analysis
description: Use to attribute forecast vs actual deltas and recommend remediation
  actions.
---

# Revenue Variance Analysis Skill

## When to Use
- Preparing forecast reviews or board updates that require variance explanations.
- Investigating misses/exceeds across segments, products, or channels.
- Prioritizing remediation plays tied to specific variance drivers.

## Framework
1. **Driver Taxonomy** – classify deltas into volume, conversion, price/mix, churn, expansion, currency.
2. **Attribution Logic** – define formulas for each driver and maintain consistent baselines.
3. **Root Cause Layer** – connect drivers to operational issues (pipeline quality, capacity, enablement, macro).
4. **Action Mapping** – translate each root cause into specific plays with owners and expected impact.
5. **Feedback Loop** – update forecasting assumptions once variance is understood.

## Templates
- Variance waterfall chart setup instructions.
- Driver worksheet (metric → delta → driver → root cause → owner → due date).
- Remediation tracker with status and forecast impact.

## Tips
- Keep a glossary so stakeholders interpret drivers consistently.
- Combine quantitative attribution with qualitative context from GTM leaders.
- Feed learnings back to `forecast-modeling` to tighten assumptions next cycle.

---

Overview

This skill helps attribute differences between forecasted and actual revenue and recommends targeted remediation actions. It provides a structured taxonomy of drivers, attribution formulas, and action mapping to turn variance insights into owned plays. Use it to root-cause misses or overperformance and to close the loop into forecasting.

How this skill works

The skill inspects forecast vs actual deltas and classifies each variance into drivers such as volume, conversion, price/mix, churn, expansion, and currency. It applies consistent attribution logic and links driver-level deltas to likely operational root causes like pipeline quality, capacity, enablement gaps, or macro factors. For each root cause it maps concrete remediation plays, assigns owners and due dates, and estimates expected forecast impact. It also provides templates for waterfalls, driver worksheets, and remediation trackers to operationalize follow-up and improve future forecasts.

When to use it

  • Preparing forecast reviews, board updates, or stakeholder variance narratives
  • Investigating why a segment, product, or channel missed or exceeded quota
  • Prioritizing remediation plays tied to specific variance drivers
  • Validating forecast model assumptions after a material deviation
  • Coordinating cross-functional response when multiple drivers overlap

Best practices

  • Maintain a clear driver taxonomy and a glossary so stakeholders interpret terms consistently
  • Use consistent baselines and formulas for attribution to avoid shifting explanations across periods
  • Combine quantitative attribution with qualitative input from GTM leaders for context
  • Map each root cause to a specific owner, timeline, and expected impact to drive accountability
  • Feed validated learnings back into the forecast model to tighten assumptions next cycle

Example use cases

  • Create a variance waterfall and driver worksheet for a monthly executive forecast review
  • Diagnose a sudden shortfall in a region by decomposing volume vs conversion vs price effects
  • Prioritize a remediation plan after discovery that pipeline quality caused the miss, assigning sales enablement and SDR plays
  • Quantify the forecast impact of churn spikes and map retention plays to revenue recovery
  • Update forecast-modeling assumptions after repeated variance patterns are observed

FAQ

How granular should driver attribution be?

Start at the driver level (volume, conversion, price/mix, churn, expansion, currency) and only break into sub-drivers where the business can reliably measure and act on them.

How do I estimate remediation impact?

Combine historical lift rates from similar plays with input from functional owners; use conservative, base, and upside scenarios and track actuals to refine estimates.