How to Prioritize Feature Requests: 4 Frameworks

Your feedback board has 300 feature requests. Your team can ship maybe 15 this quarter. How do you prioritize feature requests and decide which 15 to build?

If you're picking features based on gut feeling, whoever yells the loudest, or a simple vote count, you're leaving revenue on the table. You're also burning engineering time on the wrong things.

Prioritization frameworks give you a repeatable way to evaluate feature requests against each other. They won't make the decision for you. That still requires judgment. But they replace "I think we should build X" with "here's why X scores higher than Y."

This guide covers four proven frameworks, shows you how to combine them with real voting and revenue data, and highlights the mistakes that trip up most product teams.

For a deep dive into all 10 popular prioritization frameworks (including Kano, WSJF, Cost of Delay, and more), see our complete product prioritization framework guide. Not sure which framework to use? Try our framework selection guide or framework comparison.

Why Prioritization Matters

You can't build everything. That sounds obvious, but most teams behave as if they can. They say "yes" to too many things, spread engineering thin across 20 initiatives, and end up shipping nothing well.

Good prioritization creates three outcomes:

• Focus. Your team works on fewer things but ships them faster and at higher quality
• Alignment. Everyone from engineering to sales understands why you're building what you're building
• Accountability. When a stakeholder asks "why aren't we building my feature?", you have a data-backed answer instead of a shrug

The biggest risk isn't picking the wrong framework. It's not having one at all.

4 Frameworks for Prioritizing Feature Requests

Below is a quick overview of the four most practical frameworks for sorting through a feature request backlog. Each one answers a slightly different question.

RICE Scoring

RICE scores features on Reach, Impact, Confidence, and Effort. The formula — (Reach x Impact x Confidence) / Effort — produces a single number you can rank by.

It's the best fit when you have a large backlog and real usage data. A Slack integration request with 2,000 affected users and 2 person-months of effort will clearly outscore a dashboard widget with 500 users and 4 person-months — no debate needed.

Try the RICE calculator | Full RICE guide

ICE Scoring

ICE is a lighter alternative: Impact x Confidence x Ease, all on 1-10 scales. No need to look up exact user counts or estimate person-months. You trade precision for speed.

Use ICE when you need to score a batch of requests quickly in a single session. The main risk is inconsistency — keep the same person or small group scoring so the scales stay calibrated.

Try the ICE calculator | Full ICE guide

MoSCoW

MoSCoW is a classification system, not a scoring model. You sort requests into Must Have, Should Have, Could Have, and Won't Have. It forces a binary in-or-out decision for each release cycle.

The danger is that everything becomes a "Must Have." Be disciplined: no more than 60% of items should be Must or Should. If everything is a Must, nothing is.

Try the MoSCoW tool | Full MoSCoW guide

Impact-Effort Matrix

Plot requests on a 2x2 grid — high/low impact vs. high/low effort — and four quadrants emerge: Quick Wins, Major Projects, Fill-Ins, and Time Sinks. It's the fastest framework and works great for team workshops.

Use it as a first pass to separate quick wins from time sinks, then apply RICE or ICE to rank features within each quadrant.

Try the Impact-Effort tool

Which Framework Should You Pick?

You don't have to pick just one. Most teams layer them: score the top candidates with RICE or ICE, classify for a release with MoSCoW, and sanity-check the plan on an Impact-Effort matrix. For a comparison of all 10 frameworks and when to use each, read the full prioritization framework guide.

MRR-Weighted Voting: Connecting Feedback to Revenue

Frameworks give you a structured way to evaluate features, but they work even better when you feed them real data. One of the most powerful data sources is revenue.

Standard feature voting treats every vote equally. A free trial user's vote counts the same as your largest enterprise customer. That's a problem. The features your highest-paying customers need are often different from what the majority wants.

MRR-weighted voting solves this by connecting your feedback tool to your billing system. When a customer votes on a feature, their vote is weighted by their monthly recurring revenue.

Here's what that looks like in practice:

Without weighting, these are three equal votes. With MRR weighting, "API Access" has $627 in monthly revenue behind it. If a competing feature has 10 votes but only $290 in weighted value, you know which one moves the needle for your business.

How This Works in Practice

ProductLift automatically calculates total MRR for each feature request based on who voted for it. You can import MRR data via CSV, sync it through the API, or connect Stripe directly. Once connected, every feature request on your board shows its total MRR alongside the vote count.

For example, say you're comparing two requests on your "All Posts" page:

Dark mode has nearly 3x the votes, but API access has 3.6x the revenue behind it. Without MRR weighting you'd build dark mode first. With it, you can see that your highest-paying customers are asking for API access — and that's the feature that protects your revenue.

You can sort your entire backlog by MRR on the prioritization page, so the features with the most revenue behind them rise to the top automatically.

Customer Segmentation: Not All Feedback Is Equal

Even beyond revenue weighting, different customer segments want fundamentally different things:

• New users request onboarding improvements and basic features they expect from competitors
• Power users request advanced features, automations, and integrations
• Enterprise users request security, compliance, permissions, and audit trails
• Churned users tell you what was missing (if you ask during offboarding)

Smart prioritization considers the segment, not just the vote count. A feature requested by 5 enterprise accounts worth $50K/year each is worth investigating even if it only has 5 votes on your public board.

How Segmentation Works in Practice

In ProductLift, you create segments by saving user filters — for example, "Enterprise" could be all users with MRR above $500, or "Churned" could be users with a canceled status. You can combine criteria like MRR range, plan type, customer status, account age, and more.

Once segments are set up, you can use them to slice your feedback data in two ways:

Filter by segment. Show only feature requests submitted or voted on by a specific segment. For example, filter by "Enterprise" to see exactly what your highest-tier customers are asking for.

Compare segments side by side. Enable segment percentage columns on your posts page to see which segments care about which features:

Now the picture is clear: API access and mobile app are enterprise priorities. Dark mode is spread across segments with no strong signal from high-value customers.

You can also filter by "Churned" to spot patterns in what former customers were requesting before they left — useful for identifying retention risks before they become churn.

Combine segment data with MRR weighting and framework scoring for the most complete picture of what to build next.

Combining Frameworks with Voting Data

No single framework is enough on its own. The best approach combines structured scoring with real user data:

1. Collect feature requests and votes using a feature request tool. Let your users tell you what they want
2. Apply MRR weighting so high-value customers have proportional influence
3. Score the top candidates using RICE or ICE to add structured evaluation
4. Use MoSCoW to classify features for a specific release cycle
5. Plot on Impact-Effort to sanity-check the plan with your team

This layered approach gives you both bottom-up signal (what users are asking for) and top-down structure (how your team evaluates it).

7 Common Prioritization Mistakes

1. Building whatever gets the most votes

Vote count alone is misleading. A feature with 200 votes from free-tier users can matter less than one with 15 votes from enterprise accounts. Always look at who is voting, not just how many.

2. Ignoring small-but-vocal enterprise customers

Enterprise customers rarely flood your public voting board. They send emails to their CSM or mention it in QBRs. Make sure those requests make it into your prioritization process even if they don't have public votes.

3. Using no framework at all

Deciding by committee, HiPPO (Highest Paid Person's Opinion), or "let's just see what feels right" leads to inconsistent decisions and stakeholder frustration. Pick any framework and use it consistently.

4. Over-indexing on one framework

RICE scores are estimates, not gospel. A feature with a RICE score of 500 vs 480 is basically a tie. Use frameworks to separate the clear winners from the clear losers. Apply judgment for the close calls.

5. Never revisiting priorities

Customer needs change. Market conditions shift. Feature requests from six months ago may be irrelevant today. Review and re-score your backlog quarterly at minimum.

6. Scoring in isolation

Prioritization is a team exercise. When one PM scores everything alone, their biases dominate. Get cross-functional input. Engineering provides effort estimates, sales provides revenue impact, and support provides reach.

7. Prioritizing without saying no

If everything is high priority, nothing is. Effective prioritization means explicitly deciding what you won't build, not just ordering what you will. The "Won't Have" column in MoSCoW is just as important as the "Must Have."

FAQ

What is the difference between RICE and ICE scoring?

RICE uses concrete numbers for Reach (actual user count) and Effort (person-months), making it more precise. ICE uses 1-10 scales for Impact, Confidence, and Ease, making it faster but more subjective. Use RICE when you have data. Use ICE when you need speed.

When should I use MoSCoW prioritization?

MoSCoW works best for release planning and stakeholder alignment. It forces clear decisions about what's in scope and what's not. Use it when you need to communicate priorities to non-technical stakeholders or when defining what goes into a specific sprint.

Should I involve customers in feature prioritization?

Yes, but indirectly. Let customers vote on features and submit requests. Then use their input as one signal alongside revenue data, strategic goals, and effort estimates. Customers should inform prioritization, not control it.

How do I handle conflicting priorities between teams?

Use a shared scoring framework so everyone evaluates features with the same criteria. Cross-functional scoring sessions where engineering, sales, and support each contribute their perspective reduce bias and build alignment.

How often should I re-prioritize my feature backlog?

Review and re-score your backlog at least quarterly. Customer needs change, market conditions shift, and new data emerges. Weekly reviews of the top candidates keep your roadmap responsive without constant re-scoring of the full backlog.

Can I combine multiple prioritization frameworks?

Yes, and you should. Use MRR-weighted voting to surface demand, RICE or ICE to score the top candidates, MoSCoW to classify for a release, and Impact-Effort to sanity-check the plan. Each framework adds a different lens to the decision.

How ProductLift Automates Prioritization

Doing all of this manually (collecting votes, weighting by revenue, scoring with frameworks, updating statuses) is possible with spreadsheets, but it doesn't scale.

ProductLift combines everything in one platform:

• Feedback boards with voting so customers can submit and prioritize requests naturally
• Built-in RICE, ICE, MoSCoW, and Impact-Effort scoring so your team can evaluate requests without a separate spreadsheet
• Stripe integration for MRR-weighted voting that connects every vote to real revenue data
• The Journey Model that moves feature requests from Feedback to Roadmap to Changelog, notifying voters at each stage
• Customer segmentation to see what different user groups are asking for

Instead of building a process from scratch, you get a system that captures feedback, helps you prioritize it, and closes the loop with customers when you ship.

Wrapping Up

Prioritizing feature requests is a skill, not a formula. Frameworks like RICE, ICE, MoSCoW, and Impact-Effort give you structure. Revenue data and customer segmentation give you context. A consistent process gives you credibility with your team and your customers.

Start with one framework, apply it to your current backlog, and iterate. The goal isn't perfect prioritization. It's prioritization that's better than gut feeling, and that gets better over time.

Ready to stop guessing? Try ProductLift free and see your feature requests ranked by real customer demand.