ICE Calculator - Prioritize Features with Impact, Confidence, Ease

Impact (1-10):

• 10: Transformative - Game-changing for the product
• 8-9: Very High - Significant improvement for many users
• 6-7: High - Notable improvement for some users
• 4-5: Medium - Moderate improvement for a few users
• 2-3: Low - Minor improvement for a small number of users
• 1: Very Low - Barely noticeable improvement

• 10: < 1 week
• 9: 1-2 weeks
• 8: 2-3 weeks
• 7: 4-5 weeks
• 6: 6-7 weeks
• 5: 8-9 weeks
• 4: 10-12 weeks
• 3: 13-16 weeks
• 2: 17-25 weeks
• 1: > 26 weeks

ICE helps teams objectively prioritize features or projects by considering impact, confidence, and ease of implementation. It balances the potential value of an initiative with the effort required and the confidence in the estimates, helping teams focus on high-impact, efficient work.

A higher ICE score indicates that a feature or project should be prioritized. Compare ICE scores across different initiatives to determine their relative priority. The higher the score, the higher the priority.

While ICE is commonly used in product management, it can be applied to various fields where prioritization of initiatives is necessary, such as marketing campaigns, business strategies, or even personal projects.

It's a good practice to recalculate ICE scores periodically, especially when there are significant changes in your product, market, or team capacity. Some teams do this quarterly or during major planning sessions.

Yes, ICE can be used in conjunction with other methods. For example, you might use ICE to prioritize within a specific category of features that was identified using another method like MoSCoW or Kano.

ICE relies on estimates, which can be subjective or inaccurate. It also doesn't account for strategic alignment or dependencies between items. It's important to use ICE as a guide rather than a strict rule, and to combine it with other considerations and expert judgment.

Consistent scoring can be achieved through team discussions, creating detailed guidelines for each parameter, and periodically calibrating scores as a group. It's also helpful to use historical data to inform estimates when possible.