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Start free trial →The RICE Framework is a quantitative prioritisation model used by product and growth teams to rank ideas in a backlog. Developed by Intercom, RICE stands for Reach, Impact, Confidence, and Effort. Unlike ICE or PIE, RICE produces a numerical score on a natural scale (not just 1–10 averages), making it easier to distinguish between ideas that are genuinely far apart in priority and those that are close.
RICE Score = (Reach × Impact × Confidence) / Effort
Example: A test that reaches 8,000 users/month, has High impact (2), 80% confidence, and needs 0.5 person-months of effort: RICE = (8,000 × 2 × 0.80) / 0.5 = 25,600
Another test reaching 2,000 users, Massive impact (3), 100% confidence, 0.25 effort: RICE = (2,000 × 3 × 1.0) / 0.25 = 24,000
The first test edges out despite lower impact per user because of its broader reach.
RICE is particularly valuable for ecommerce teams because it explicitly factors in reach — the number of customers who will actually experience a change. A test on a checkout page used by every buyer scores much higher on Reach than a test on a niche product category page. This prevents teams from spending cycles on changes that only a small fraction of traffic will ever see. The Effort denominator also surfaces whether a high-potential idea requires disproportionate engineering investment, helping teams balance quick wins against bigger strategic bets.
The product team at Boat is prioritising three changes: a redesigned product image gallery (reaches all PDP visitors: 50,000/month, Impact: 2, Confidence: 70%, Effort: 1) → RICE: 70,000. A size guide tooltip (reaches 10,000 footwear PDP visitors, Impact: 2, Confidence: 90%, Effort: 0.25) → RICE: 72,000. A personalised homepage banner (reaches 30,000 returning visitors, Impact: 1, Confidence: 60%, Effort: 2) → RICE: 9,000. Despite the homepage banner feeling strategically exciting, its RICE score makes it clear it should wait until the higher-value tests are done.
RICE helps decide which experiments enter the sprint first. Once an experiment is selected, the Confidence score from RICE should inform how much pre-test research (user interviews, session recordings) is needed before writing the hypothesis.
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