Hi All,
I want to start a discussion around revenue operations intelligence tools that help teams align forecasting, pipeline visibility, and sales performance tracking. These tools are increasingly important for RevOps teams looking to streamline data and drive predictable growth.
Here are some of the top-rated platforms from the Revenue Operations Intelligence category on G2 that are worth evaluating:
GongGong delivers conversation intelligence by analyzing sales calls, emails, and meetings to surface insights around deal progression and rep performance. Its RevOps capabilities help teams understand buyer behavior and improve forecast accuracy using data captured directly from sales interactions.
ClariClari is built for revenue visibility, offering features like predictive forecasting, pipeline inspection, and real-time risk analysis. It provides end-to-end revenue tracking by connecting activity data with deal outcomes, making it a common choice for RevOps teams aiming to improve sales execution.
SalesloftSalesloft focuses on sales engagement, but also contributes to RevOps goals through deal intelligence, pipeline tracking, and activity analytics. It integrates with CRMs to centralize communications and help teams measure the effectiveness of sales cadences across the funnel.
People.aiPeople.ai automates the capture of go-to-market activity data from email, calendar, and CRM systems. It enriches this data to give RevOps teams visibility into sales performance, coverage gaps, and revenue attribution across accounts, without manual entry.
BoostUp.aiBoostUp.ai provides a unified revenue operations platform combining forecasting, pipeline health, and activity intelligence. Its AI-driven risk scoring and engagement analytics make it especially useful for teams looking to replace spreadsheet-based forecasting with real-time insights.
If you're using any of these tools or comparing them now, feel free to share feedback, especially around integration experience, forecasting reliability, and onboarding for RevOps use cases. It would be helpful to see what’s worked (or hasn’t) across different org sizes.