Mohit Aggarwal 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 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