RICE — Product Prioritisation Techniques

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Too many things on the plate…

You own a great product and you have so many updates to share with your customers. Hold on! We have all been at this stage and that’s the reason you might be reading this!

Problem Statement: Which feature will maximise benefits and how do you decide the timeline?

General Dilemmas:

  1. Clever ideas VS Features that directly impact
  2. New ideas VS Existing ideas which you have confidence on
  3. Your own ideas VS Ideas with broader reach

Yes, you can’t go all in with your features. In this article, we will discuss the benefits of using RICE methodology for Product Prioritisation Roadmap.

What’s RICE Methodology?

Your customers are screaming to see something new in your product. So what should you focus on is the biggest question raised. RICE scoring template here helps measuring the people it will affect, the impact it will have on them, how much effort it takes to build it, and how confident you and your teammates are about a particular feature estimation.

RICE = (Reach x Impact x Effort)/ Confidence

RICE Method helps in

  1. Compare Ideas in a consistent way
  2. Efficient Prioritization framework

USED BY : Intercom

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How to Implement RICE Framework?

Preparation — Use an excel sheet and make the tabs accordingly. Use can use pen and paper too and write down measurements accordingly.

Reach —the total number of paying customers that would be affected by this feature. You have to choose the timeframe yourself i.e, one month, two months or quarter

Example :

  • Feature 1: 300 customers reach this point in the signup funnel each month, and 20% choose this option.

Reach : 300 × 20% × 3 = 450 customers per quarter.

  • Feature 2: This update will be for a one-time effect on 500 existing customers, with no ongoing effect. The reach is 500 customers per quarter.

Impact — the benefits enjoyed by users from new features. Impact is quantitative when we measure at the end of the release what is the conversion or qualitative if we are measuring the delight of customers.

Example:

  1. Feature 1: For each customer who encounters the feature, this will have a huge impact. The impact score is 3.
  2. Feature 2: For each customer who encounters the feature, this will have a less impact. The impact score is 1.

Effort — the reality! The amount of work that is required from the stakeholder and team to build a feature. The goal is to move quickly and have impact with the least amount of effort.

Example

  1. Feature 1: This is in the sprint planning will take 1–2 weeks designing and 2 weeks tech time resulting to effort score of 2 persons months.
  2. Feature 2: This requires a week of planning, design template exists, and a few days of tech time. So effort score of 1 person-month.

Confidence — the hero of the RICE framework is confidence. We have all been in situations when we dont have data for a feature but deep insight we have our gut feeling saying this could have a great impact. In these cases confidence lets you control that. Confidence is calculated as a percentage, and take the standard metric as 100% is “high confidence”, 80% is “medium”, 50% is “low”

Example

  1. Feature 1: We have quantitative data for reach, user research for impact, and tech team estimate for effort. This feature release gets a 100% confidence score.
  2. Feature 2: You have data to support the reach and effort, but unsure about the impact. This feature release gets an 80% confidence score.
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This is an Intercom template.

DOWNLOAD EXCEL: Feel free to duplicate the spreadsheet for your own use. Or download an .xls version.

END NOTES

With a RICE scoring system in place, you can clearly prioritise when you’re making product roadmaps. A prioritisation framework such as RICE will help you make better-informed decisions about what to work on first and defend those decisions to others. Give RICE a try in your own prioritisation process and let me know how it works for you.Anusmita Mukherjee

Discovering the scope of Digital Marketing for B2B tech — Scrapinghub| Digital Native driven by data and powerful content

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