Methods of Prioritization in Agile (II): Scoring Formulas and RICE

In the previous article of this series, I explained several methods to prioritize your backlog, your project portfolio or just a list of tasks. Now I’m going to give you some methods to do that at scale, i.e. to prioritize a big number of items involving more complex criteria to satisfy your company goals.

Scoring formulas: from subjective to strategy

At the heart of the Impact-Effort matrix lies a scoring formula. While the matrix visually represents tasks or projects based on their perceived impact and the effort required to achieve them, by assigning values to impact and effort, we have a consistent metric for decision-making.

For instance, when deciding between developing a new dashboard or improving a search function, the matrix can guide choices by dividing impact by effort:

  • Develop new dashboard: High impact (9/10) but significant effort (8/10). Score: 9/8 = 1.125
  • Improve search function: Moderate impact (6/10) with low effort (3/10). Score: 6/3 = 2

Although the dashboard seems more impactful, the search function yields a better impact-to-effort ratio. Hence, the matrix points towards prioritizing the search function. You can also see them positioned in the impact-effort matrix below.

Scoring formulas, like this in the impact-effort matrix, turn subjective debates into quantitative decisions, unifying individual perceptions (across departments and stakeholders) to collective, strategic decisions.

We can also use these formulas to accommodate unplanned tasks that arise after initial plans are set, doing a quick re-evaluation and prioritization of new tasks. In today’s ever-changing environments, these formulas support agility and complement a continuous discovery mindset, helping teams to adapt without deviating from their primary objectives.

A more granular approach: RICE

Above, we translated the Impact-Effort Matrix into a scoring formula like this:

Priority = Impact / Effort

where a high “Impact” boosts the priority score, whereas a large “Effort” decreases it. Features with the highest priority are often “quick wins”, i.e. those with high value and little effort.

Expanding on this, the RICE scoring formula follows like this:

Priority = Reach * Impact * Confidence / Effort

It introduces two new components, Reach and Confidence. But let’s explore the 4 components of the formula.

  • Reach: How many people will be impacted by that feature in a defined timeframe, such as customers per quarter, or transactions per month. Look at your funnel metrics. For example, if you estimate that 30% of your monthly leads are going to be converted, use that number.
  • Impact: For example, you can ask “How much will this feature increase conversion rate when a customer runs into it?”. A typical range goes from Massive = 3 to Minimal = 0.25, but you can use your own values. It’s an early estimate, so you don’t need accuracy.

Note that Reach * Impact represents the total impact in your business, i.e. the possible outcomes that can bring that feature. The other two variables help to understand the feasibility of the feature.

  • Confidence: Reflects certainty in your estimates, or the inverse of the risk you are assuming. Highest = 100% to Low = 50% (like throwing a coin).
  • Effort: Use person-months, but again, please avoid going into the trap of detailed estimates. A high-level number or a t-shirt size estimation (from XS for extremely easy to XL for extremely hard) is more than enough.

Let’s give an example. Suppose you’re a product manager at an e-commerce platform, deciding between:

  • Launching a chatbot assistant.

    • Reach: Expected to engage 40% of daily visitors, let’s say 10,000 users.
    • Impact: Moderate enhancement to user experience, score it as 2.
    • Confidence: Pretty certain about its benefits, score it as 80%.
    • Effort: Development and integration require 2 person-months.
    • Priority score: 10,000 * 2 * 0.8 / 2 = 8,000
  • Offering a new payment gateway.

    • Reach: Only 5% of monthly users might prefer this method, translating to 1,250 users.
    • Impact: Significant as it might increase sales for that segment, score it as 3.
    • Confidence: It’s a popular payment method elsewhere, so 90% sure.
    • Effort: 1 person-month.
    • Priority score: 1,250 * 3 * 0.9 / 1 = 3,375

Given the scores, the chatbot assistant has a higher priority based on the formula, suggesting it should be tackled before the new payment gateway. This is an example of how RICE provides a structured approach to decision-making, ensuring all stakeholders are aligned on project priorities, making it very useful if you have to facilitate this kind of conversation.

Of course, you can adapt the formula using your own criteria to simplify calculations and, especially, conversations. For instance, “Confidence” can be redefined using factors like Technical Uncertainty, Number of Teams Involved, and so on. Many tools in the market can ease this, but you can always use your preferred spreadsheet. It’s very simple. You just need to be consistent with the formula and the ranges for each variable.

In the next article…

In this “Methods of Prioritization in Agile” series, I’ve just explored the power of scoring formulas as a method to prioritize work. Techniques like the RICE method help us turn opinions into numbers. This way, teams can make decisions based on clear, shared criteria rather than personal views, and help decisions adapt to changing business needs.

In the next article, I’ll dive into more advanced techniques under the concept of Lean Portfolio Management.