Practical, Data-Driven Decision Making: Using frameworks like RICE to prioritize ROI-focused activities across business dimensions.
Quantify, score, and prioritize what is most important to your organization.
The RICE framework is a flexible methodology for prioritization that considers four key business factors: Reach, Impact, Confidence, and Effort. Effectively, it quantifies these four variables into a single score, allowing for comparison across competing priorities. It asks:
How many end-users or customers will be reached?
What tangible impact will it have on the business?
How confident are we in our ability to achieve the objective — including our confidence in reach and impact?
How much effort (time, cost, resources, etc) will be required?
Standard Calculation
Collectively, reach and impact tell us the candidate’s “magnitude”, reduced appropriately by a confidence score. The product of these three values is then divided by the effort. The higher the score, the higher the priority.
Reach is simply the actual number of users or customers impacted; for example, 2,000 users.
Impact is calculated as Minimal Impact (0.25), Low Impact (0.5), Medium Impact (1), High Impact (2), and Massive Impact (3).
Confidence is calculated as Low (0.5), Medium (0.8), and High (1).
Effort is calculated as A Few Weeks (0.5), One Month (1), Two Months (2)
These variables can be tweaked to match how you already measure priorities. For example, converting effort-based “t-shirt sizes” to numerical values that can be applied to the formula.
Example
Imagine you’re planning a product roadmap that introduces a new paid feature to a subset of customers. It has the potential for a high business impact (like increased ARR), our confidence is mid-tier, and the effort is one month of engineering time.
The final priority score for this candidate looks like this:
Even though the output isn’t neatly confined to something like a percentage score, since every candidate in the decision pool shares the same context, the value merely serves as means for comparison.
Weighted Calculation
But, prioritization isn’t always this straight-forward. Often, we need to give more credence to particular factors based on more nuanced business dynamics. For example, if we have a surplus of engineering availability and are concerned with hitting a quarterly revenue goal, we may choose to give less weight to effort and more to impact. In this case, we’re saying: it’s fine if the effort is outsized as long as we can meet the expected impact.
Skipping the Math
The steps below get into the details of calculating weighted priorities. If you prefer to skip directly to the output, we’ve prepared a Google Sheet template.
Normalization
To do this, we must first normalize the values across the entirety of our roadmap candidates. This ensures that a variable like reach (an absolute number of users) can be fairly compared against a variable like confidence, which sits between 0 and 1.
We can accomplish this using a strategy like max-ratio normalization:
For example:
For each factor, divide by the maximum value in your candidate pool (or the top of your defined scale, whichever you standardize on). This process is completed across all values for all candidates until we have a neat pile of normalized values.
The values from our example might look like this:
R → 0.40
I → 0.66
C → 1.00
E → 0.50
Establish & Apply Weights
Now that all of the variables are “speaking the same language”, we can devise an alternative RICE formula that will consider the importance of these four factors independently.
We scale weights by 4 in the exponents so that equal weights (0.25 each) recover the standard RICE formula exactly.
For example, let’s imagine we assign the following weights:
WR → 0.4
WI → 0.4
WC → 0.1
WE → 0.1
Example
Of course, this is just a single candidate. Running each candidate through the same set of weights will provide us with a clear picture of our priorities against the wider lens of the organization’s needs.
Pick one weight scheme, score every candidate, and rank them. Don't compare absolute scores across different weight vectors.
In our current weight schema, we assigned outsized importance to reach and impact, with less emphasis on confidence and effort. We’re out to make a big impact; we’re risk-on and ready to use resources to achieve it.
Candidate A
Reach: 2,000 → 0.40
Impact: 2 → 0.66
Confidence: 0.8 → 1.00
Effort: 1 → 0.50
Score = 0.1567 → Sizable reach with high impact. Effort is relatively small with high confidence of achievement.
Candidate B
Reach: 5,000 → 1.00
Impact: 0.25 → 0.08
Confidence: 0.8 → 1.00
Effort: 0.5 → 0.25
Score = 0.0306 → Largest reach in the dataset, but impact is very low. But, effort is also low with high confidence.
Candidate C
Reach: 2,500 → 0.50
Impact: 0.5 → 0.17
Confidence: 0.5 → 0.63
Effort: 2 → 1.00
Score = 0.0161 → Reach is reasonable, but impact and confidence is low compared to substantial effort.
Candidate D
Reach: 500 → 0.10
Impact: 3 → 1.00
Confidence: 0.5 → 0.63
Effort: 1 → 0.5
Score = 0.0276 → Reach is very low, though impact is high. Confidence and effort are relatively neutral.
As a sanity check, we can return to the importance we’ve assigned reach and impact and for a moment and ignore everything else. This is purely illustrative; it shows the influence of our weight schema:
Candidate A: Reach x Impact ≈ 0.26
Candidate B: Reach x Impact ≈ 0.08
Candidate C: Reach x Impact ≈ 0.09
Candidate D: Reach x Impact ≈ 0.10
Conclusion
Prioritization isn’t one-dimensional. It should reflect your organization’s current goals, timelines, resources, and risk tolerance. RICE, considering reach, impact, confidence, and effort is a great way to quantify these variables into a single, comparable priority metric.
Try This: The next time you’re making trade-offs, consider what prioritization dimensions you care about the most and apply a RICE-like concept to ensure well-rounded, data-driven decisions.
Attribution
The RICE framework was created and popularized at Intercom by Product Manager Sean McBride.
Author
Erik Smith
Erik Smith is an entrepreneur, investor and builder. He serves as the Chief Technology Officer at Rx Redefined, a venture-backed Series B healthcare technology company. His work centers on supporting leaders and teams across Product, Engineering, Data, AI, Business Intelligence, Security & Compliance, and IT/Infrastructure functions. He has a passion for helping to bridge the “curiosity gap” between technology, business, and operations. Erik lives in Northern California with his wife and two bunnies, Poppy and Bubbles.