A quantitative scoring system created by Intercom to evaluate and rank potential product features, initiatives, or projects based on objective metrics rather than intuition.
The Formula
The framework calculates a single score by balancing reach, impact, and confidence against the effort required:
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Core Components
| Metric | Definition | How It Is Measured |
| Reach | The number of users or events impacted within a specific time period. | Numerical estimate per timeframe (e.g., 1,500 users/month or 500 checkout transactions/quarter). |
| Impact | The degree to which the feature moves the target key result or goal. | Standardized scale: 3 (Massive), 2 (High), 1 (Medium), 0.5 (Low), 0.25 (Minimal). |
| Confidence | A percentage reflecting how solid your data and assumptions are to mitigate bias. | Tiered percentage: 100% (High confidence/hard data), 80% (Medium/some data), 50% (Low/anecdotal). |
| Effort | The total work required from all teams (product, design, engineering) over a timeframe. | “Person-months” (or weeks) of work. For example, 1 developer and 1 designer for 2 weeks = 1 person-month. |
Worked Example
Consider evaluating a new One-Click Checkout feature over a 3-month cycle:
- Reach: 10,000 users reach the checkout page per quarter ($10{,}000$).
- Impact: Expected to significantly increase conversion rates (2 = High).
- Confidence: Supported by quantitative user drop-off metrics (80% = 0.8).
- Effort: Requires 2 engineers for 1 month (2 person-months).
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When to Use RICE
- High-volume backlogs: Best when comparing dozens of competing ideas across disparate functional areas.
- Data-rich environments: Works best when historical usage metrics or customer research are readily available to support confidence scores.
- Neutralizing HIPPO bias: Effective at counteracting the “Highest Paid Person’s Opinion” by grounding conversations in quantitative inputs.