Sales Forecasting Software: 8 Top Tools for Predictable Revenue in 2026

Sales forecasting software helps sales teams predict future revenue by analyzing historical data, pipeline activity, and market trends. The best tools use AI to improve forecast accuracy, highlight at-risk deals, and automate reporting.

Erin Hueffner, Writer, Salesforce

November 5, 2026

Summary of the top sales forecasting software

Here's a quick look at the nine tools covered in this guide, what each one focuses on, and a key forecasting capability.

Tool Best for Key capability
Sales Cloud Teams that want forecasting built into the CRM, with AI agents Real-time forecast rollups with Einstein predictions
Gong Teams forecasting from customer conversations AI deal and revenue predictions
Aviso Enterprise and usage-based revenue teams WinScore deal scoring
HubSpot Sales Hub Growing teams already on HubSpot CRM Forecasts across multiple pipelines
Anaplan Enterprises connecting sales and finance planning PlanIQ machine learning forecasts
Pipedrive Growing, pipeline-focused sales teams Deal revenue forecast report
Microsoft Dynamics 365 Sales Teams that work mainly in Microsoft tools Premium forecasting with prediction factors
Zoho CRM Small and midsize teams on the Zoho suite Zia win-probability predictions

Sales forecasting software FAQs

Sales forecasting software gives leaders a reliable view of future revenue, which drives decisions on hiring, budgets, and sales targets. It replaces manual spreadsheet rollups with live forecasts that update as deals move. It also flags at-risk deals early, while there's still time to act.

Most sales forecasting tools integrate with major CRMs, and many CRMs, including Sales Cloud, include forecasting natively. Standalone tools such as Gong, and Aviso sync with your CRM to pull in opportunity and activity data. Check that the integration is two-way, so updates made in the forecasting tool flow back to the CRM.

Sales forecasting software improves team performance by showing managers which deals need attention and where reps tend to stall. Managers can coach to specific opportunities instead of general activity targets. Over time, forecast data also feeds a clearer sales report on what's working across the team.

AI isn't required to build a forecast, but it makes forecasts more accurate and faster to produce. AI models analyze deal history, activity, and engagement to predict outcomes and flag risks that manual reviews miss. AI agents can also keep CRM data current by logging updates from sales conversations, which improves the data every forecast depends on.

The main difference is the time frame and the unit of focus. Sales forecasting estimates the total revenue your team will close in a specific period. Pipeline management tracks individual deals and moves them forward stage by stage. Learn more in our guide to sales forecasting vs pipeline management.