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RICE Score
RICE Score
RICE Score
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How do you choose which products or features to ship and which should remain in your backlog? RICE Scores are one way of making a call.

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Actionable Takeaways
  • Identify the product or feature under consideration. 

This might be part of a backlog or from a bra ...

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The elements behind RICE are still subjective and potentially influenced by biases, though the ‘confidence’ factor is a positive inclusion in this respect. 

The ‘effort’ factor does not consider different values of people hours so, for example, there is no distinction between 1 week of senior developer time which might cost twice as much as 1 week of a junior designer.

In Practice


The RICE Score was first developed by Intercom as a way to develop product priorities and their original post included this link to a spreadsheet example to establish RICE scores.

Build your latticework
This model will help you to:

RICE scores are typically used by product managers to help prioritise products and product features, similar to the impact effort matrix or kano. 

Use the following examples of connected and complementary models to weave RICE score into your broader latticework of mental models. Alternatively, discover your own connections by exploring the category list above. 

Connected models: 

  • Impact effort matrix and kano are alternative prioritisation methods. 
  • Pareto principle: in establishing the ‘20’ features to deliver the ‘80’ value. 

Complementary models: 

  • Golden circle: to ensure that new products and features are aligned with a deeper direction. 
  • Lock-in effect: as a potential consideration in relation to impact.Agile methodology: an iterative approach that works well with this form of prioritisation. 
  • Personas: as a potential tool to explore and establish reach and impact. 
  • Zawinski’s law: a warning to prioritise and avoid product bloat. 
  • Agile methodology: an iterative approach typically requiring fast and ongoing prioritisation. 
  • Minimum viable product: as an approach to cut out unnecessary features in the first instance.
Origins & Resources

The RICE Scoring model was developed by Intercom and is outlined in some detail in their original post about it here

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