Your board wants revenue forecasts, but you're guessing. Here's how to build reliable forecasts using pipeline data, conversion rates, and historical trends.

Weighted Pipeline Forecasting

Not all pipeline is equal:

What is Weighted Pipeline?

Pipeline value adjusted by probability of closing:

Qualified opportunity (20% probability): $100K × 20% = $20K
Proposal sent (50% probability): $100K × 50% = $50K
Negotiation (80% probability): $100K × 80% = $80K
Total weighted pipeline: $150K (not $300K)

How to Calculate

Weighted Pipeline = Sum of (Deal Value × Stage Probability)

Historical Conversion Rates

Use past data to predict future:

Calculate Conversion Rates

Lead → Opportunity: 20% (historical average)
Opportunity → Proposal: 60%
Proposal → Close: 40%
Overall: Lead → Close = 4.8% (0.20 × 0.60 × 0.40)

Apply to Current Pipeline

If you have 100 leads in pipeline:

100 leads × 20% = 20 opportunities
20 opportunities × 60% = 12 proposals
12 proposals × 40% = 5 closes
Forecast: 5 customers

Seasonality Adjustments

Account for seasonal patterns:

Common Seasonal Patterns

Q4 spike (budget flush)
Q1 slowdown (new budgets)
Summer slowdown (vacations)
Industry-specific patterns

How to Adjust

Look at historical data by month/quarter
Calculate seasonal multipliers
Apply to forecast
Example: Q4 is typically 30% higher, so multiply forecast by 1.3

The Role of Marketing in the Forecast

Marketing creates pipeline:

Marketing's Contribution

Pipeline created (leads → opportunities)
Pipeline velocity (how fast deals move)
Lead quality (conversion rates)
Pipeline coverage (need 3-4x pipeline to revenue goal)

Presenting the Forecast to the Board

How to present forecasts:

The Executive Summary

Forecasted revenue (next quarter/year)
Confidence level (high/medium/low)
Key assumptions
Risks and opportunities

Conclusion

Build reliable forecasts using weighted pipeline (not raw pipeline), historical conversion rates, seasonality adjustments, and marketing's pipeline contribution. Present clearly to stakeholders with confidence levels and assumptions. The result? Accurate forecasts that drive better decisions.