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B2B sales forecasting.

B2B sales are complex. Multiple stakeholders. Long cycles. Deals that look alive but are really dead. Your forecast needs to account for all of it. ORM builds custom models that predict B2B revenue against a 95% accuracy target.

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B2B Sales Forecasting

B2B forecasting is broken. Here is why.

The median B2B sales organization forecasts with 55-65% accuracy. That means missing the number by 35-45% every quarter. The problem is not the data. It is the method.

Rep-Submitted Forecasts Are Biased

When reps forecast their own deals, two things happen. Optimistic reps overcommit. Sandbaggers undercommit. The aggregate looks reasonable, but the individual deal predictions are wrong in both directions. ORM removes human bias by predicting deal outcomes based on data signals, not opinions. The model does not care if a rep "feels good" about a deal. It looks at velocity, engagement, and historical patterns.

REP FORECAST VS ACTUAL Rep A (optimist) Forecast: $820K Actual: $540K (-34%) Rep B (sandbagger) Forecast: $380K Actual: $610K (+61%)

Weighted Pipeline Is Too Simple

Multiplying pipeline value by stage probability is the most common B2B forecast method. It is also the least accurate. A $200K deal at 50% probability is not the same as two $100K deals at 50% each. Deal size, velocity, competitive dynamics, and stakeholder coverage all affect the real probability. ORM's models use 15+ signals per deal instead of a single stage-based number.

WEIGHTED PIPELINE VS ORM MODEL Weighted Pipeline 1 signal: stage probability 62% accuracy ORM Model 15+ signals per deal 90% accuracy

Pipeline Quality Is Invisible

Not all pipeline is equal. A $500K pipeline of 50 qualified mid-market deals converts very differently than a $500K pipeline of 5 large unqualified enterprise deals. Most B2B organizations cannot distinguish between these because they measure pipeline by dollar value, not quality. ORM scores every deal on quality signals and produces a quality-adjusted pipeline forecast.

SAME DOLLAR VALUE, DIFFERENT QUALITY Pipeline A: $2.4M 50 deals, qualified, active Converts $820K (34%) Pipeline B: $2.4M 5 deals, unqualified, stalled Converts $240K (10%)

Deal Velocity Matters More Than Stage

A deal that moved from discovery to proposal in 14 days is a very different animal than one that has been in discovery for 90 days. Both might be at the same CRM stage, but their close probability is wildly different. ORM's models track velocity at every stage transition. Deals that are moving fast get higher probability scores. Deals that have stalled get flagged before they die.

VELOCITY SIGNAL Fast movers (below median cycle) 2.1x close rate Stalled deals (2x median cycle) 0.3x close rate

What ORM sees across its customers.

The numbers that separate accurate B2B forecasts from guesswork.

95%

What ORM aims for on new and expansion revenue. We score it each quarter against what closed.

3-5x

The coverage range across ORM customers. Most sit near 3.5x, and a few run as low as 1.4x.

20%

Of the pipeline dated to close in the quarter on day one, this share closes inside that quarter.

Frequently asked questions

B2B Forecasting
What is B2B sales forecasting?+
B2B sales forecasting is the process of predicting future revenue from business-to-business sales based on pipeline data, historical close rates, deal velocity, and rep performance patterns. Accurate B2B forecasts account for long sales cycles, multi-stakeholder buying, and complex deal structures.
Why do most B2B sales forecasts miss?+
Most B2B forecasts rely on rep-submitted predictions and simple weighted-pipeline calculations. Both methods introduce bias. Neither accounts for deal velocity, pipeline quality, or rep-level performance patterns. The result: median B2B forecast accuracy of 55-65%.
How does ORM improve B2B sales forecasting?+
ORM builds custom mathematical models on your CRM data. The models analyze deal velocity, stage conversion rates, rep performance curves, pipeline quality signals, and historical close patterns to predict quarter-end revenue. Models update weekly and target 95% accuracy for organizations with 6+ months of data.

Fix your B2B forecast.

ORM builds custom models that go beyond weighted pipeline to predict B2B revenue against a 95% accuracy target.

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