How to calculate Impact for RICE Scoring
“RICE scoring (Reach, Impact, Confidence, Effort), the prioritisation framework Intercom’s Seán McBride published in 2018, asks product managers to put a number on the “Impact” of each roadmap option, and most people just guess it. This guide replaces the guess with data: run a stack ranking survey where your customers vote on the options, then convert the ranked output into RICE Impact scores. RICE is one of several prioritisation frameworks (alongside Kano, ICE and others); OpinionX doesn’t own it, it gives you the customer data that makes the Impact number real instead of a gut feeling. The eight steps below take you from defining your objective to assigning impact scores from the results.”
In January 2018, Intercom PM Seán McBride published a blog post explaining a prioritisation method that would quickly become one of the top three most popular frameworks in product management.
RICE Scoring is a simple formula that uses four criteria, Reach, Impact, Confidence and Effort, to measure the "total impact per time worked" for each option a product manager could pursue.
Formula graphic from "Management: RICE Scoring Model for Prioritization" by Lazaro Ibanez on Medium
I'm not going to waste your time explaining what each of the four letters means and how to calculate a RICE Score (that's been covered by a bunch of people already). Instead, this is a step-by-step guide for reliably calculating the impact criteria you'll assign to each roadmap option on your RICE spreadsheet.
Why does this matter? 80% of features built are rarely or never used. In public tech companies alone, this poorly executed prioritisation adds up to $29.5 billion in wasted resources every year.
For RICE Scoring, product managers are expected to know, down to two decimal places, what level of impact a feature will have on the company's objectives. No product manager actually knows this; you're just expected to guess. Intercom's original guide to RICE says it clearly: "Choosing an impact number may seem unscientific. But remember the alternative: a tangled mess of gut feeling."
For modern product teams, guessing isn't good enough. You need accurate data to make the right decision. A prioritisation framework alone won't fix this $29.5 billion problem, because a RICE Scoring spreadsheet is only as reliable as the data you put into it.
Here's how to accurately measure impact for RICE Scoring in 8 steps.
Step 1: Define your objective
To calculate impact, we first need to define the objective we're working towards.
The objective for product-led companies (which tend to focus on end-user needs for bottom-up adoption) is often simply to reduce friction for the end-users of their product. Others could include improving the activation rate of your self-service onboarding, converting more free-trial users to paying customers, or boosting your monthly retention rate.
For this guide, let's focus on "removing friction from the end-user experience" as our impact objective.
Step 2: Identify your customer segment
If your customers split into subcategories, you've almost certainly got multiple segments using your product to solve different problems and facing very different user-experience challenges.
To make progress towards your objective, you need to identify which segment will move the needle most. Looking at our objective to "remove friction from the end-user experience", do we want to improve the activation rate of new users, or the NPS score of our highest-paying customers?
Segmentation examples:
Company Size: Small (1-49 employees), Medium (50-499) and Large (500+).
Region: NAM, LATAM, EMEA, APAC.
Job Function: Product Management, Sales, Marketing, Customer Success, Design...
Seniority: Entry, Middle, Senior, Exec...
Revenue: Freemium, Mid-Tier, Enterprise...
Industry: B2B SaaS, Medtech, Consumer, Travel, IoT, Web3...
At this stage we don't necessarily exclude customers outside our target segment. We just define the characteristics of each segment so we can later split them into separate groups and analyse each independently of the rest of the customer base. For this example, we'll focus on revenue as our segmentation category, splitting the results into freemium, premium and enterprise customers.
Step 3: Stack ranking
We're going to use stack ranking to quantify the impact of each option on our list.
As a research method, stack ranking uses a combination of data science techniques (pairwise comparison voting, rating-system algorithms, and weighted option distribution) to compare a list of options under a core question and rank them from highest to lowest.
That sounds far more complicated than it is. All you need is a question, a list of options and a group of people to vote on them. A stack ranking tool like OpinionX automates all the analysis and data science for you.
Step 4: Question time
This step is easy: we turn our objective into a question for our customers.
Participants are shown pairs of statements and have to pick between them. Your question is the context for how they judge each pair. Our objective to "remove friction from the end-user experience" translates into a question like "Which issue is more frustrating when using our product?".
Example of a pair vote during an OpinionX stack ranking survey ^
Keep your question easy to understand and as close to your objective as possible.
Step 5: Import your options
Take all the options on your RICE Scoring list and import them as individual statements. Depending on how you store them on your roadmap or scoring spreadsheet, you might need to edit them first.
Some writing tips:
Turn each option into a short statement, like "Not being able to export my results".
Write each statement concisely, with only one main point.
Keep the perspective consistent. Mixing feature ideas with problem statements makes it hard for participants to compare them objectively (we prefer problem statements, so we can save solutioning for after we've identified our priority problems).
Avoid slang, jargon, or personal information in your statements.
Step 6: Engage participants
Getting your research underway is simple: import your stack ranking question and list of options into your research tool.
We like to use our own product OpinionX (which is free) for stack ranking, because it automates the data science analysis and lets us engage large groups of users without hassle. We also add multiple-choice questions to capture each participant's revenue tier and function within their company, so we can use those data points to segment the results later.
One more benefit of OpinionX: we can ask participants to submit extra statements if we're missing any important issues from our stack ranking list. We just include an open-response question in the survey setup, then add any participant-generated statements to the stack rank list in one click. Once we're ready to engage participants, we copy the survey link and share it (usually via email, in-app message, or popup banner).
Step 7: Converting results to Impact
No fancy calculations needed, because the output from a stack ranking survey is simple: all the options are automatically ranked from first to last based on what mattered most to participants.
Rather than overcomplicate things with a conversion formula, I like to take batches of the ranked statements and assign each batch an impact score. Imagine we're considering 100 statements ranked from most important (1st) to least important (100th), and we use a range from 0.5 up to 5 for our impact score:
Impact 5 = 1st to 10th (top 10%)
Impact 4 = 11th to 30th
Impact 3 = 31st to 50th
Impact 2 = 51st to 70th
Impact 1 = 71st to 90th
Impact 0.5 = 90th to 100th (bottom 10%)
If you want to preserve the detail in the stack ranking order, there's a more advanced formula you could use to get a real number (which can be rounded to two decimal places where needed).
Setting up a stack ranking survey to score your RICE Impact and want a hand structuring the question and options? Book a free 30-minute setup call and a research expert will walk through it with you, no cost, no obligation.
Step 8: Time to experiment
Prioritisation frameworks like RICE Scoring help us understand the opportunity cost of each option and the trade-offs we make in pursuing only a select few. Instead of filling your spreadsheet with your own internal assumptions, stack ranking gives you a structured way to see, in aggregate, the trade-offs each person would make if they were given the choice.
Stack ranking can help you understand people's biggest problems, the motivations behind their behaviour, the values underpinning their decisions, and the features they're desperate to get their hands on. All you have to do is ask them.
Create your own stack ranking survey for free on OpinionX today and swap those RICE Scoring assumptions for real, reliable data.
Frequently asked questions
What is RICE scoring? A prioritisation framework created by Intercom's Seán McBride in 2018. It scores each option on four criteria, Reach, Impact, Confidence and Effort, and combines them into a single number ("total impact per time worked") to rank what to build next.
How do you calculate the Impact score in RICE? Most teams guess it, which is the weak point of the whole formula. A more reliable way is to have customers vote on the options in a stack ranking survey, then convert the ranked results into an impact score by batching (for example, top 10% of ranked options = impact 5, next batch = 4, and so on).
Is RICE the same as OpinionX's methods? No. RICE is a general prioritisation framework OpinionX doesn't own; stack ranking, pairwise comparison and needs-based segmentation are the research methods OpinionX runs. They're complementary: RICE gives you the scoring structure, and a stack ranking survey gives you the real customer data to fill the Impact number instead of guessing it.
Why not just estimate Impact yourself? Because 80% of built features are rarely or never used, and estimated impact scores are a big reason why. RICE's own creators admit choosing an impact number "may seem unscientific". Voting data from actual customers turns that guess into evidence you can defend.
What survey method should I use to score Impact? Stack ranking (which uses pairwise comparison voting under the hood) is the most direct fit: it forces customers to trade options off against each other and returns a clean ranked list you can map to impact scores.
Want more like this? Over 42,000 researchers and product people get The Full-Stack Researcher in their inbox. Subscribe for the next one.