This blog is authored by Jenn Harvey of Jennifer Harvey Consulting, and Jeff Saenger, VP of Customer Success at BoostUp.

The High Cost of Forecast Bias

As a Renewals Leader, you’ve likely encountered scenarios like these:

Scenario 1: Quarter-end arrives, and your confident 90% renewal forecast drops to 75%. Finance recalculates projections, the board asks difficult questions, and investments are put on hold.

Scenario 2: Your team forecasts an 80% renewal rate all quarter despite positive customer signals.  At the last minute, they “discover” extra renewals and announce 92%.  The executive team celebrates the win, but resource planning has been compromised by artificially low forecasts all quarter.

These situations are common in SaaS organizations grappling with forecast bias. Whether from over-optimism ("happy ears") or under-forecasting ("sandbagging"), these cognitive distortions cost companies millions in misallocated resources and missed opportunities.

Milestone-based frameworks provide the essential foundation for accurate forecasting. Combining these frameworks with awareness of cognitive biases will further improve forecasting precision. Let's dive into these biases and how to address them.

Two Common Forecasting Biases

1. "Happy Ears" Syndrome: When Optimism Clouds Judgment

What it looks like:

  • Interpreting ambiguous customer signals positively
  • Overvaluing relationships as renewal indicators
  • Downplaying warning signs (e.g. declining usage)
  • Assuming past renewal patterns will continue


Warning Signs:

  • Vague commitments without specific next steps
  • Postponed renewal meetings
  • Absent decision-makers
  • Declining product usage metrics


Impact: Late-stage renewals fall out of the forecast, leadership trust erodes, resource allocation suffers, and company valuation may be impacted.

Counter with these questions:

  • "What specific words did the customer use about renewal?"
  • "Who else needs to approve this renewal?"
  • "What data points contradict our optimistic view?"
  • "If this renewal fails, what would be the reason?"

2. Sandbagging: The Protective Pessimism

What it looks like:

  • Deliberately underreporting renewal likelihood
  • Creating buffers to manage unforeseen issues
  • Protecting against missed forecast consequences


Warning Signs:

  • Pattern of consistently exceeding forecast projections
  • Reluctance to raise forecasts despite positive signals 
  • Vague explanations for forecast details
  • Renewals mysteriously closing in final days of the period 


Impact: Revenue spikes artificially, resources are allocated inefficiently, and organizational trust in forecasting diminishes.

Counter with:

  • Rewarding forecast accuracy rather than just hitting targets
  • Separating coaching conversations from forecast discussions
  • Implementing a "no surprises" policy 

The Balanced Forecaster: Finding Middle Ground

The goal isn’t to eliminate optimism or prudence, but to find balance:

  • Reality-based optimism: Stay positive but grounded in observable customer actions
  • Separate goals from predictions: Maintain ambitious targets but forecast based on evidence
  • Psychological safety: Encourage early sharing of good and bad news
  • Celebrate accuracy over heroics: Reward consistent forecasting, not last-minute saves

Building a Bias-Resistant Forecasting Culture

1. Standardize Your Language

When business terms mean different things to different people, inconsistency is inevitable.  Establish clear, shared definitions for key assessment criteria, including:

  • Opportunity Stages
  • Forecast Categories
  • Deal Health Scores

2. Leverage Objective Frameworks

Objective, milestone-based frameworks reduce bias by focusing on observable behaviors rather than subjective feelings. Make sure to:

  • Focus on observable customer behaviors rather than feelings or relationship quality
  • Create clear connections between customer actions and forecast categories
  • Use your CRM to systematically track milestone achievement

3. Reframe Your Review Meetings

Promote intellectual honesty in review meetings by:

  • Starting with factual data before subjective assessments
  • Separating risk identification from performance evaluation
  • Discussing both upside and downside scenario 
  • Documenting forecast changes to identify patterns

Practical Tools for Bias Reduction

1. Structured Forecast Delivery Format

Use a 4-part framework for more objective forecasting:

  1. High-level update: State the target, current commit, and gap
  2. Committed details: Included deals, supporting milestone evidence, backup plan
  3. Path forward: Potential deals, next steps, expected movement timeline
  4. Summary: Restate commit, clarify improvement path, highlight dependencies


This framework prevents "happy ears" from ignoring risks and stops sandbaggers from underreporting opportunities.

2. Team Exercises to Build Awareness

  • Individual Forecasting Profile: Identify bias patterns by reviewing past forecasts.
  • Decision Tracking System: Record predictions, outcomes, and information that was overlooked at the time of forecasting to improve future assessments.
  • Play Devil’s Advocate: Ask: "What could go wrong?" before finalizing forecasts.
  • Red Team Challenge: Have team members constructively challenge assumptions to uncover blind spots. 

3. Regular Accuracy Reviews

Conduct quarterly reviews to:

  • Compare forecast vs. actual by team member, segment, and deal size
  • Identify patterns in missed forecasts
  • Calculate bias tendency and develop countermeasures

Conquering Bias: Building a Culture of Forecast Integrity

Eliminating forecast bias requires more than process fixes—it demands a culture where objectivity outweighs comfort and precision matters more than perception.

Companies with forecast integrity see reality clearly, avoid optimistic distortions and protective pessimism, and make better decisions—driving smarter resource allocation and strengthening investor trust.

Top-performing SaaS companies know that accurate forecasts create a virtuous cycle: better decisions lead to stronger results, reinforcing the value of honest assessments. When teams recognize and counter "happy ears" and "sandbagging," they elevate decision-making through collective intelligence.

Start implementing the practices today:

  1. Clarify and align your forecast definitions
  2. Use milestone-based frameworks to enhance objectivity
  3. Analyze historical data to uncover bias patterns
  4. Standardize pipeline review formats
  5. Foster open discussions about forecast bias

Make forecast accuracy a core value, not just a metric. Reward those who identify risks early and deliver reliable forecasts. By combining objective milestone frameworks with bias-reducing techniques, you’ll transform renewal forecasting into a precise science—delivering the unvarnished truth your business can rely on.

Want to learn more about the milestone-based approach referenced in this article? Read our companion piece Leveraging Milestones in Renewals Forecasting - Turning Guesswork into Science.