Forecasting

What is Sales Forecasting?

Sales forecasting is the process of estimating future revenue by analyzing historical data, current pipeline opportunities, market conditions, and predictive models. It's the science and art of predicting what your team will sell in a given time period—usually the current or upcoming quarter.

Forecasts range from simple spreadsheet projections to sophisticated AI-driven models that incorporate hundreds of variables. The goal is the same: provide leadership with reliable revenue predictions for resource allocation, hiring, and investor relations.

Why Forecasting Matters

For the Business:

• Resource planning (hiring, inventory, infrastructure)
• Cash flow management and runway prediction
• Investor confidence and board reporting
• Budget allocation and goal setting

For Sales Teams:

• Identify pipeline risks early
• Coach reps on deal health
• Understand which stages need attention
• Celebrate wins and diagnose shortfalls

In 2026, with economic uncertainty and increased pressure on efficiency, accurate forecasting is more critical than ever. Boards and investors no longer accept "we'll figure it out"—they demand data-driven predictions.

Benchmarks

Forecast Accuracy Standards:

• Elite: 90%+ accuracy (AI-powered forecasting)
• Top quartile: 85-90% accuracy
• Average: 70-80% accuracy
• Needs improvement: Below 70%

Accuracy by Method:

• Multivariable analysis: ±5-10%
• Opportunity stage weighting: ±10-15%
• Pipeline analysis: ±10-15%
• Historical baseline: ±15-20%

WAPE (Weighted Absolute Percentage Error) of 10-20% is considered acceptable for most B2B organizations.

Best Practices

1. Combine Multiple Methods: Don't rely on a single approach. Blend opportunity stage, historical performance, and rep-level judgment for best results.

2. Forecast at the Right Level: Aggregate forecasts are more accurate than deal-by-deal predictions. Account for some deals always slipping while others surprise positively.

3. Track Forecast Bias: Know if your team consistently over-forecasts (optimism bias) or under-forecasts (sandbagging). Adjust accordingly.

4. Use AI for Pattern Recognition: Modern forecasting tools identify patterns humans miss—seasonality, stage conversion variance, rep-specific tendencies.

5. Update Frequently: Static forecasts are worthless. Re-forecast weekly or bi-weekly as new information emerges.

Common Mistakes

• Letting reps override stage probabilities without justification
• Including unrealistic opportunities in the forecast
• Not tracking and learning from forecast misses
• Forecasting based on hope rather than data
• Ignoring historical performance patterns

Key Takeaways

• Forecasting predicts future revenue for planning and reporting
• Accuracy of 85%+ is achievable with good methodology
• Combine quantitative models with qualitative rep input
• Track forecast bias and update frequently
• AI-powered tools significantly improve accuracy