Introduction
Sales forecasts are the lifeblood of a B2B tech company. CROs, CEOs, and RevOps teams rely on them to set growth expectations, steer investments, and build trust with the board. But there’s a problem: most forecasts are still wrong.
In fact, research shows fewer than 20% of B2B sales organizations consistently forecast within 5% of actuals. For CROs, that means pipeline reviews feel more like guesswork than guidance — and credibility takes a hit.
That’s why sales forecast accuracy has become a core metric. It tells you how close your CRM-based forecast is to what actually closes, and it gives you the foundation for improving both pipeline quality and leadership trust.
What Is Sales Forecast Accuracy?
Sales forecast accuracy measures the gap between your forecasted sales revenue and the actual revenue your team closes. In practice, many CROs talk about this in terms of improving their sales forecasting accuracy — how reliable their CRM-driven projections really are.
At a board level, it answers the simplest but most important question: “When we call a number, how close do we actually land?”
Gartner defines forecast accuracy as the degree to which sales leaders can reliably predict outcomes — a measure that directly shapes strategic decisions.
How to Calculate Forecast Accuracy (Using MAPE)
The most widely used formula for sales forecast accuracy is MAPE (Mean Absolute Percentage Error). It calculates the deviation between forecasted and actual results as a percentage.
Formula:
MAPE = (1/n) × Σ ( |Actual - Forecast| / Actual ) × 100
Example:
If your forecast was $1,000,000 but your team actually closed $900,000:
MAPE = ( |1,000,000 - 900,000| / 900,000 ) × 100
MAPE = (100,000 / 900,000) × 100
MAPE = 11.1%This means the forecast accuracy was:
Accuracy = 100% - 11.1% = 88.9%Benchmarks:
Best-in-class sales organizations target 90–95% forecast accuracy.
Hitting 85% is already considered strong in most B2B sales environments.
How Accuracy Is Measured (What CROs Care About)
Top B2B tech sales leaders use a handful of well-established methods to track sales forecast accuracy.
1. Point-in-time accuracy
Formula:
Accuracy = 1 - |Forecast - Actual| / ActualUsed for quarterly “did we land it?” reporting.
2. Revenue-weighted accuracy (WMAPE)
Formula:
Accuracy = 1 - ( Σ |Forecast_i - Actual_i| / Σ Actual_i )Preferred in sales because it avoids small deals skewing percentages — it reflects business impact more accurately.
3. Bias (MPE)
Formula:
Bias = Σ (Forecast - Actual) / Σ ActualShows whether teams are consistently sandbagging (under-calling) or being over-optimistic.
4. Horizon (“as-of”) accuracy
Tracks accuracy at different checkpoints before the period closes (e.g., 8 weeks out, 4 weeks out, 2 weeks out).
This shows whether forecasts converge toward reality or stay volatile right until the end.
5. Category accuracy
Compares Commit vs. Actual and Best Case vs. Actual.
This highlights whether forecast categories are being used consistently by the sales team.
While MAPE is the most common formula, many CROs pair it with WMAPE and Bias for a more complete view. That’s the kind of reporting Dear Lucy now automates directly inside HubSpot, Salesforce, Pipedrive, and Dynamics 365.
Many CROs will also quote a headline number like “We’re within ±5% by the last month of the quarter,” but behind the scenes they (or RevOps) track the weighted and bias-adjusted metrics.
What Sales Leaders Actually Say About Accuracy
When CROs talk to each other, they don’t mention formulas — they give a clean number and some context:
“We’re typically within ±5% in the last four weeks of the quarter.”
“Commit accuracy runs 95%+, Best Case is looser at ~80%.”
“Renewals are 97%+ accurate, but new logo forecasting is still in the 70s.”
These headline statements come directly from the accuracy metrics above — simplified for executive and board discussions.
How Accuracy Is Followed Up (The Operating Rhythm)
Measuring accuracy is only half the battle — the real impact comes from how leadership follows up on it. Leading sales organizations build forecast accuracy into their operating rhythm, reviewing it regularly and holding teams accountable for the variance between forecast and actuals.
Weekly forecast calls
Reps update deals, managers inspect categories, CRO owns the call.
Variance reviews
Track adds, slips, pull-ins, and downsizes since last call.
Publish bias trend (rolling over- or under-call).
Category QA
Inspect Commit deals: win rates, push counts, stage aging.
Hold a high bar for what qualifies as Commit.
Segmentation
Accuracy tracked by region, business unit, and revenue type (new, expansion, renewal).
Reveals systemic bias (e.g., “Enterprise always sandbags, SMB consistently overcalls”).
Rep coaching
Individual accuracy trends are inspected for coaching, but rarely shared upwards.
Introducing the Forecast Accuracy Dashboard
Until now, tracking all this meant hours in spreadsheets or heavy enterprise tools. With Dear Lucy, you can measure and follow up on sales forecasting accuracy directly inside your CRM — and finally bring transparency into how reliable your pipeline really is.
Available for HubSpot, Salesforce, Pipedrive, and Dynamics 365, the new Forecast Accuracy Dashboard uses MAPE (Mean Absolute Percentage Error) — the industry’s most common standard — to calculate deviation and display accuracy in a way that both reps and CROs can trust.
What you’ll see in one view:
Quarterly & monthly forecast accuracy % (MAPE)
Forecast vs. actual charts with clear deviation in €/$
Trendlines showing accuracy improving or slipping over time
Drilldowns by team, manager, and business unit
Bias indicators (over- vs under-calling)
Why This Matters for CROs and CEOs
Board confidence: Replace vague pipeline talk with quantified accuracy.
Revenue leadership: Spot systemic bias early (e.g., “APAC always overcalls”).
Manager accountability: Hold teams responsible for their forecast quality.
Rep coaching: Identify who sandbags vs. who over-commits.
RevOps efficiency: Automate a process that normally eats hours each week.
Conclusion
Sales forecast accuracy is more than a metric — it’s a measure of leadership credibility. By tracking it consistently, breaking it down by team and category, and automating the reporting inside your CRM, you can run a tighter revenue operation and give your board the confidence they expect.
With Dear Lucy’s new Forecast Accuracy Dashboard, CROs and RevOps leaders can finally measure and improve forecast accuracy without manual work — across HubSpot, Salesforce, Pipedrive, and Dynamics 365.
Start your trial today to see how accurate your sales forecasts really are.
FAQ: Sales Forecast Accuracy
Q: How is sales forecast accuracy calculated?
The most common formula is MAPE (Mean Absolute Percentage Error), which measures how far off your forecast is from actual closed sales. Many CROs prefer a weighted version (WMAPE) so big deals don’t get overshadowed by small ones.
Q: What is a good sales forecasting accuracy percentage?
Best-in-class sales teams aim to be within ±5% of their forecast by the last month of the quarter — roughly 90–95% accuracy. In reality, most B2B companies land closer to 80–85%.
Q: What causes poor forecast accuracy?
Dirty CRM data, deal slippage, inconsistent use of Commit vs. Best Case, and reps relying too much on gut feel are the most common issues.
Q: Should accuracy be tracked by rep, team, or company?
Externally, CROs usually share company-level accuracy with the board. Internally, accuracy is broken down by region, team, and manager roll-ups. Rep-level accuracy is used more for coaching than formal reporting.
Q: How can sales forecasting accuracy be improved?
Consistency is key. Measure it every quarter, enforce clear forecast categories, run weekly variance reviews, and coach based on bias trends. Tools like Dear Lucy automate this process and bring forecast accuracy dashboards directly into HubSpot, Salesforce, Pipedrive, or Dynamics 365.
TL;DR: Sales Forecast Accuracy
Definition: How close your CRM-based forecast is to actual closed sales.
How it’s measured: MAPE/WMAPE + bias, tracked at company, team, and manager levels.
What CROs report: A headline % (“we’re within ±5% by the end of the quarter”) with context for Commit, Best Case, or renewals vs. new business.
Common challenges: Dirty CRM data, deal slips, and inconsistent Commit categories.
How to improve: Standardize process, clean data, coach reps on bias, and automate dashboards.
Where Dear Lucy helps: A plug-and-play Forecast Accuracy Dashboard that calculates MAPE automatically, tracks accuracy trends, and shows exactly where forecasts are going off course — all inside your CRM.