Feedback scoring is the practice of assigning numerical values to feedback based on business importance, urgency, or priority. Scores help teams quickly identify what matters most and make data-driven prioritization decisions.

Common Scoring Dimensions

Business impact (1-5):

• 5: Critical to revenue or product function
• 4: Significant impact on important customers
• 3: Meaningful improvement
• 2: Minor enhancement
• 1: Nice-to-have

Urgency (1-5):

• 5: Immediate action required
• 4: This week
• 3: This month
• 2: This quarter
• 1: Someday/maybe

User impact (1-5):

• 5: Affects all users, core workflows
• 4: Affects many users or critical workflow
• 3: Affects moderate number of users
• 2: Affects small segment
• 1: Edge case

Effort to address (1-5):

• 5: Months of work
• 4: Weeks of work
• 3: Days of work
• 2: Hours of work
• 1: Minutes to fix

Composite Scoring

Priority score = f(Impact, Urgency, User Count, Customer Value, Effort)

Different formulas weight factors differently:

Simple average: Priority = (Impact + Urgency + User Impact) / 3

Weighted formula: Priority = (Impact × 0.4) + (Urgency × 0.3) + (User Impact × 0.2) + (Customer Value × 0.1)

Impact-Effort ratio: Priority = Impact / Effort (Optimize for high impact, low effort)

Custom formula based on strategy: Early-stage might weight customer value highly. Growth-stage might weight user count.

Manual vs. Automated Scoring

Manual scoring:

• Human reads feedback
• Considers context
• Assigns scores based on judgment
• Time-consuming but captures nuance

Automated scoring:

• AI analyzes feedback and context
• Applies consistent criteria
• Instant results at scale
• Might miss subtle context

Hybrid (best):

• AI provides initial scores
• Humans review and adjust
• System learns from adjustments
• Balance of speed and judgment

What Influences Feedback Scores

Content factors:

• Language used ("broken," "blocked," "critical")
• Specificity (vague vs. detailed)
• Problem description (severity)

User context:

• Account value (MRR, contract size)
• User role (decision maker, influencer, user)
• Account health (at-risk, healthy, champion)
• Engagement level (active, casual, dormant)

Historical patterns:

• Similar requests from other users
• Frequency of this issue
• Past resolution outcomes
• Trending vs. isolated

Strategic fit:

• Aligns with roadmap
• Target customer profile
• Product vision
• Competitive positioning

Scoring Frameworks

RICE Score (for features/requests):

• Reach: How many users affected
• Impact: How much does it help per user
• Confidence: How sure are you
• Effort: How hard to build
• Score = (Reach × Impact × Confidence) / Effort

ICE Score:

• Impact: Business value
• Confidence: Certainty
• Ease: How easy to implement
• Score = (Impact + Confidence + Ease) / 3

Value-Effort Matrix:

• Plot on 2D grid
• Prioritize high-value, low-effort
• Score = Value / Effort

Custom scoring:

• Define factors that matter to your business
• Weight them appropriately
• Create formula that reflects strategy

Score Calibration

Scores are relative, not absolute. Key is consistency:

Calibration sessions:

• Team scores same feedback independently
• Discuss disagreements
• Align on criteria
• Update guidelines

Reference examples:

• "Here's a canonical 5"
• "Here's a canonical 3"
• Compare new feedback to references

Scoring drift:

• Over time, team might score too high or low
• Periodic recalibration maintains consistency

Inter-rater reliability:

• Measure agreement between scorers

• 80% agreement is good

• <60% means unclear criteria

Score Thresholds

Define action thresholds:

• 4.5-5.0: Immediate action, alert team
• 3.5-4.4: High priority, address this week/sprint
• 2.5-3.4: Medium priority, backlog for review
• 1.5-2.4: Low priority, defer unless pattern emerges
• 0-1.4: Nice-to-have, likely won't address

Thresholds create clear decision rules and reduce ambiguity.

Displaying Scores

For teams:

• Show score prominently (color-coded)
• Explain reasoning ("High score because: enterprise customer, blocking issue")
• Allow manual override with explanation
• Track score changes over time

For reporting:

• Average score over time
• Distribution of scores (are we getting more high-priority feedback?)
• Score by customer segment
• Score by product area

For users:

• Usually don't show internal scores
• Exception: voting systems where "votes" are a form of score

Common Scoring Mistakes

Score inflation: Everything gets marked 4 or 5 because it all seems important. Makes scoring useless.

Ignoring context: Scoring feedback without considering who it's from (free user vs. enterprise).

Set-and-forget: Scoring once and never revisiting. Priorities change.

Too many dimensions: 10 different scores per item is overwhelming. Keep it simple (2-3 key scores).

No validation: Never checking if high-scored items actually mattered. Learn and iterate.

Scoring without strategy: No clear connection between scores and what you'll actually build.

Score-Based Workflows

Automated routing:

• Score >4: Alert product lead immediately
• Score 3-4: Add to this week's triage
• Score <3: Weekly batch review

SLA by score:

• Score 5: Respond in 1 hour
• Score 4: Respond same day
• Score 3: Respond in 2 days
• Score 2: Respond in 1 week

Reporting thresholds:

• Only escalate score >4 to leadership
• Weekly rollup of score 3-4 items
• Monthly review of score <3

Learning from Scores

Retrospective analysis:

• Did high-scored items actually matter when we addressed them?
• Did we miss any low-scored items that turned out critical?
• Which factors were most predictive?

Score outcome correlation:

• Track: Initial score → Action taken → Business impact
• Learn: What scores lead to best outcomes?
• Adjust: Refine scoring formula based on learnings

Feedback loops:

• Team members flag incorrect scores
• System learns from corrections
• Scoring improves over time

When Feedback Scoring Makes Sense

High volume: 50+ pieces of feedback per week. Manual prioritization breaks down.

Distributed team: Multiple people handling feedback need consistent criteria.

Data-driven culture: Want objective prioritization, not gut-feel or politics.

Clear strategy: Know what factors drive value for your business.

Resource constraints: Can't address everything, must choose wisely.

When It's Not Necessary

Low volume: Reading and prioritizing 10 items per week doesn't need formal scoring.

Highly contextual: Every decision requires deep strategic consideration unique to situation.

Early exploration: Pre-product-market-fit when you're learning, not optimizing.

Founder-driven: If founder personally reviews all feedback and decides, formal scoring adds overhead.

Start simple. Add sophistication as volume and complexity grow.