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name: sales-forecasting-model
description: "Build a structured sales forecast framework for any business or team. Use when asked to build a sales forecast, create a revenue model, project pipeline, or build a bottom-up forecast. Produces a forecast methodology, pipeline model, scenario analysis, and assumption log."

Sales Forecasting Model Skill

Produces a structured sales forecast framework — from pipeline conversion modelling to scenario analysis. Built for revenue and sales leaders who need a defensible forecast, not a spreadsheet guess.

Required Inputs

Ask the user for these if not provided:

• Business type (SaaS / Transactional / Services / Marketplace)
• Forecast period (monthly / quarterly / annual)
• Sales motion (inbound / outbound / channel / PLG / mixed)
• Current pipeline data (number of deals, stages, values — rough is fine)
• Historical conversion rates (if available — otherwise model will flag as assumption)
• Average deal size and sales cycle length

Output Structure

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Sales Forecast: [Team / Business] — [Period]

Forecast type: [Bottom-up pipeline / Top-down quota / Capacity-based / Hybrid]
Period: [Month / Quarter / Year]
Created: [Date]
Forecast owner: [Name]

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1. Forecast Methodology

Chosen approach: [Bottom-up / Top-down / Hybrid] — and why for this context.

Bottom-up (recommended when pipeline data exists):

Start from real deals in the pipeline. Apply stage-by-stage conversion rates. Sum to a revenue number.

Top-down (useful for planning, not for calling a number):

Start from market or quota. Work backwards to activity targets.

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2. Pipeline Stage Model

Define the sales stages and the expected conversion rate between each:

Stage	Description	% of deals that advance	Avg time in stage
Prospect	Identified, not contacted	—	—
Qualified	Discovery done, confirmed fit	[X%]	[N days]
Proposal	Proposal sent	[X%]	[N days]
Negotiation	Commercial terms being agreed	[X%]	[N days]
Closed Won	Contract signed	[X%]	—

Overall pipeline conversion rate: [X%] (Qualified → Closed Won)
Average sales cycle: [N days from Qualified to Close]

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3. Current Pipeline Snapshot

Stage	Number of deals	Total value	Expected close (weighted)
Qualified	[N]	£[X]	£[X × conversion %]
Proposal	[N]	£[X]	£[X × conversion %]
Negotiation	[N]	£[X]	£[X × conversion %]
Total		£[X]	£[weighted total]

Coverage ratio: [Weighted pipeline ÷ target = X×]
Rule of thumb: 3× pipeline coverage is needed for confident forecast; 2× is tight; below 1.5× is at risk.

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4. Scenario Analysis

Scenario	Assumption	Revenue	Probability
Upside	All Negotiation + top 50% of Proposal close	£[X]	[%]
Base	Weighted pipeline conversion at historical rates	£[X]	[%]
Downside	Conversion rates drop 20% from historical	£[X]	[%]

Committed forecast: £[X] — [The number the forecast owner is willing to call. Between base and downside.]

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5. Key Assumptions Log

Every forecast is a set of assumptions. Name them explicitly so they can be updated:

Assumption	Value	Confidence	Source	Last updated
Avg deal size	£[X]	High/Med/Low	[Last N deals]	[Date]
Sales cycle	[N days]
Close rate from Proposal	[X%]
Seasonal factor	[e.g. Q4 +20%]
Churn/contraction	[X% of ARR at risk]

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6. Activity-Based Sanity Check

Work backwards from the forecast to check if the required activity is achievable:

To hit £[target]:

• Deals needed to close: [N] (target ÷ avg deal size)
• Qualified pipeline needed (at current conversion): [N deals or £value]
• Discovery calls needed per week to build that pipeline: [N]
• Outreach needed per week (at [X%] meeting rate): [N]

Does the team have capacity to generate this? [Yes / No — flag if not]

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Quality Checks

• Forecast methodology is stated (not just a number)
• Stage conversion rates are based on historical data or flagged as assumptions
• Coverage ratio is calculated
• Three scenarios are modelled (not just one number)
• Assumption log is explicit and dated
• Activity sanity check confirms the forecast is achievable with current capacity

Example Trigger Phrases

• "Build a sales forecast for [period]"
• "Create a pipeline model for [team/business]"
• "Help me build a bottom-up revenue forecast"
• "What is our forecast for Q[N] based on current pipeline?"

Anti-Patterns

• Do not present a single forecast number without scenario analysis — a forecast without upside and downside cases hides risk
• Do not use 100% confidence on conversion rates that are not backed by historical data — flag them as assumptions
• Do not skip the activity sanity check — a forecast number that requires unreachable activity levels is not credible
• Do not use top-down quota as the only forecast method when pipeline data exists — bottom-up is more accurate and defensible
• Do not omit the coverage ratio — without it, stakeholders cannot assess whether the pipeline is sufficient to hit target