HexaLevers vs Clari

A different philosophy on forecasting

Clari is a well-known player in revenue operations, built primarily around forecast rollups and pipeline inspection. Its strength is giving sales leaders a structured way to collect and roll up forecasts from reps through managers to the CRO.

HexaLevers approaches the problem from the signal layer up. Instead of relying on human-submitted forecast categories, HexaLevers models deal outcomes using real-time engagement data, historical patterns, and pipeline velocity metrics. The forecast is generated from evidence, not from what a rep selected in a dropdown.

This distinction becomes critical at scale. When you have 500 deals in pipeline across four segments, the difference between a rollup of human judgments and a model built on behavioral signals is the difference between a 72% and a 94% accurate forecast.

Key differences

• Forecast methodology: Clari aggregates rep and manager inputs into a rollup hierarchy. HexaLevers generates model-driven forecasts that can be compared against rep submissions to identify gaps.
• Signal freshness: HexaLevers processes CRM, email, and calendar signals continuously. Pipeline health scores update within minutes of new activity. Clari typically reflects data on a sync cadence.
• Deal intelligence: HexaLevers scores every deal on 40+ risk and progression signals with explainable reasoning. Clari provides pipeline trending and stage progression views.
• Multi-scenario modeling: HexaLevers supports base, upside, and downside scenarios with configurable weighting. Clari focuses on single-track forecast categories.

"We switched from Clari to HexaLevers because we needed forecasts that told us what was going to happen, not just what our reps thought was going to happen." — Anita Morales, CRO, BrightLoop Analytics

When HexaLevers is the better fit

HexaLevers tends to win in evaluations where the revenue team has one or more of these characteristics:

• Complex pipeline with multiple products, segments, or geographies that need independent forecast models
• A history of forecast misses driven by over-reliance on rep judgment
• A CFO or board that demands a data-driven forecast methodology
• A RevOps team that wants to reduce manual forecast hygiene work by 60% or more

HexaLevers customers with at least two quarters of historical data see a median forecast accuracy of 94% within 5% of actuals. The platform also reduces the time RevOps teams spend on weekly forecast preparation by an average of 8 hours per week.