mohitagw15856/pm-claude-skills/sales-forecasting-model

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

Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked (assumed, confirm) and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).

• 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

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]

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.

2. Pipeline Stage Model

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

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

3. Current Pipeline Snapshot

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.

4. Scenario Analysis

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

5. Key Assumptions Log

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

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]

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