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

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:

| 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]

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.

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.]

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] | | | |

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 QN] 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