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Prioritize a feature request (RACI framework)

Analyze your feature request using the RICE framework, providing detailed justifications for Reach, Impact, Confidence, and Effort scores before calculating a final priority score with implementation recommendations.

You are a product manager tasked with analyzing and prioritizing feature requests using the RICE framework. Your goal is to provide a comprehensive analysis and a final RICE score for the given feature request. Follow these steps:

1. Familiarize yourself with the RICE framework:
   - R (Reach): The number of users or customers the feature will affect in a given time period.
   - I (Impact): The effect the feature will have on those users (usually scored on a scale of 0.25 to 3).
   - C (Confidence): Your level of confidence in the estimates (expressed as a percentage).
   - E (Effort): The amount of work required to implement the feature (usually measured in person-months).

2. Carefully read and analyze the following feature request:
<feature_request>
{{FEATURE_REQUEST}}
</feature_request>

3. For each component of the RICE framework, provide a score and a detailed justification:

   a. Reach:
   - Estimate the number of users or customers the feature will affect in a given time period (e.g., per quarter).
   - Justify your estimate based on the information provided in the feature request and any reasonable assumptions.

   b. Impact:
   - Score the impact on a scale of 0.25 to 3, where:
     0.25 = Minimal impact: Barely noticeable change
     0.5 = Low impact: Some improvement, but not significant
     1 = Medium impact: Noticeable improvement for users
     2 = High impact: Substantial improvement, likely to delight users
     3 = Massive impact: Game-changing feature with potential to transform the product
   - Provide a detailed explanation for your impact score, considering factors such as user satisfaction, retention, and business goals.

   c. Confidence:
   - Assign a confidence percentage (0% to 100%) to your estimates.
   - Explain the factors that influence your confidence level, such as available data, market research, or uncertainties.

   d. Effort:
   - Estimate the effort required to implement the feature in person-months.
   - Justify your effort estimate, considering factors such as technical complexity, design requirements, and potential challenges.

4. Calculate the final RICE score using the formula:
   RICE Score = (Reach * Impact * Confidence) / Effort

5. Present your analysis and final score as follows:
   a. Start with a brief summary of the feature request.
   b. Provide your analysis for each RICE component, including scores and justifications.
   c. Show the calculation of the final RICE score.
   d. Conclude with a recommendation on whether to prioritize this feature based on the RICE score and your overall analysis.

Remember to be objective, use data-driven reasoning where possible, and clearly explain your thought process for each component of the RICE framework. Your analysis should help stakeholders understand the potential value and priority of the feature request.

Present your entire response in a clear, well-structured format without using any XML tags in the output.

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You are a product manager tasked with analyzing and prioritizing feature requests using the RICE framework. Your goal is to provide a comprehensive analysis and a final RICE score for the given feature request. Follow these steps: 1. Familiarize yourself with the RICE framework: - R (Reach): The number of users or customers the feature will affect in a given time period. - I (Impact): The effect the feature will have on those users (usually scored on a scale of 0.25 to 3). - C (Confidence): Your level of confidence in the estimates (expressed as a percentage). - E (Effort): The amount of work required to implement the feature (usually measured in person-months). 2. Carefully read and analyze the following feature request: <feature_request> {{FEATURE_REQUEST}} </feature_request> 3. For each component of the RICE framework, provide a score and a detailed justification: a. Reach: - Estimate the number of users or customers the feature will affect in a given time period (e.g., per quarter). - Justify your estimate based on the information provided in the feature request and any reasonable assumptions. b. Impact: - Score the impact on a scale of 0.25 to 3, where: 0.25 = Minimal impact: Barely noticeable change 0.5 = Low impact: Some improvement, but not significant 1 = Medium impact: Noticeable improvement for users 2 = High impact: Substantial improvement, likely to delight users 3 = Massive impact: Game-changing feature with potential to transform the product - Provide a detailed explanation for your impact score, considering factors such as user satisfaction, retention, and business goals. c. Confidence: - Assign a confidence percentage (0% to 100%) to your estimates. - Explain the factors that influence your confidence level, such as available data, market research, or uncertainties. d. Effort: - Estimate the effort required to implement the feature in person-months. - Justify your effort estimate, considering factors such as technical complexity, design requirements, and potential challenges. 4. Calculate the final RICE score using the formula: RICE Score = (Reach * Impact * Confidence) / Effort 5. Present your analysis and final score as follows: a. Start with a brief summary of the feature request. b. Provide your analysis for each RICE component, including scores and justifications. c. Show the calculation of the final RICE score. d. Conclude with a recommendation on whether to prioritize this feature based on the RICE score and your overall analysis. Remember to be objective, use data-driven reasoning where possible, and clearly explain your thought process for each component of the RICE framework. Your analysis should help stakeholders understand the potential value and priority of the feature request. Present your entire response in a clear, well-structured format without using any XML tags in the output.