You have a promising product idea and limited resources to build it. The pressure to ship something valuable is real, but so is the risk of building the wrong features. Every engineering hour spent on a nice-to-have feature is time stolen from must-haves. Get this wrong and your MVP either launches incomplete or bloated with features nobody asked for.
Smart MVP feature prioritization gives you a systematic way to make these tough calls. Instead of guessing which features matter most or arguing with stakeholders about priorities, you can use proven frameworks like RICE, MoSCoW, and Kano to evaluate and rank features objectively. These methods help you separate essential from optional and align your team around what to build first.
This guide walks you through a practical seven-step process for prioritizing MVP features. You'll learn how to clarify your goals, organize feature ideas, define value criteria, and apply multiple frameworks to validate your decisions. By the end, you'll know exactly which features belong in your MVP and have a lean roadmap to guide development.
MVP feature prioritization is the process of evaluating and ranking features to determine what belongs in your minimum viable product. You systematically assess each potential feature against criteria like user value, development effort, and strategic fit to decide which ones make the cut for your first release. This differs from general product prioritization because you operate under tighter constraints and focus exclusively on features that validate your core hypothesis.
Three elements drive effective mvp feature prioritization. First, you need clear criteria for what "minimum" and "viable" mean for your specific product. Minimum refers to the smallest feature set you can ship, while viable means users can accomplish their primary goal and get real value. Second, you require a framework that lets you compare features objectively rather than relying on gut feel or the loudest voice in the room. Third, you must balance speed to market with quality, which means resisting the urge to add every nice-to-have feature that crosses your mind.
The best MVPs solve one problem exceptionally well rather than attempting to solve many problems adequately.
Your prioritization decisions directly impact whether your MVP succeeds or fails. Build too much and you waste months on features users never wanted, delaying your feedback loop and burning through runway. Build too little and you ship something that fails to demonstrate value, leaving users confused about your product's purpose. The frameworks you'll learn in this guide help you navigate this balance by providing structured ways to evaluate features against multiple dimensions simultaneously.
Standard product prioritization methods often break down for MVPs because they assume you have established product-market fit and reliable user data. You don't have either yet. Methods that work well for mature products rely on usage analytics, customer feedback at scale, and predictable development velocity. Your MVP exists specifically to gather this information, so you need approaches that work with uncertainty and limited data. The frameworks in this guide acknowledge these constraints and help you make sound decisions even when you're operating with incomplete information about what users truly need.
Before you evaluate a single feature, you need crystal-clear boundaries for your MVP. This step prevents scope creep and gives you objective criteria to judge every feature request against. You'll define what success looks like, identify your hard constraints, and document the problem you're solving so precisely that anyone on your team can explain it in two sentences.
Start by writing down the one problem your MVP must solve. Not three problems, not a category of problems, but one specific pain point. For example, "Help small business owners track inventory without manual spreadsheets" is specific, while "Improve business operations" is too vague. This singular focus acts as your north star throughout mvp feature prioritization, letting you cut features that don't directly address this problem no matter how interesting they seem.
Your MVP needs measurable goals that tell you whether it worked. Choose 2-3 metrics that directly indicate if users find value in your solution. These metrics should focus on user behavior rather than vanity numbers like signups or page views. You want to measure actual usage and whether people accomplish their goal with your product.
Use this template to document your success criteria:
Primary Goal: [What users must accomplish]
Success Metrics:
1. [Metric name]: [Target] within [timeframe]
Example: Weekly active users: 100 within 60 days
2. [Metric name]: [Target] within [timeframe]
Example: Task completion rate: 70% within first use
3. [Metric name]: [Target] within [timeframe]
Example: User retention: 40% return in week 2
Validation Threshold: [Minimum result to proceed]
Example: If fewer than 50 users complete the core task in 60 days,
pivot or significantly revise approach.
Clear success metrics transform feature prioritization from opinion-based debates into objective decisions about what moves your key numbers.
List every constraint that limits what you can build. Time constraints might include a launch deadline driven by funding runway or a market opportunity window. Resource constraints cover your team size, technical skills available, and budget for tools or infrastructure. Technical constraints include your technology stack, existing systems you must integrate with, and performance requirements you cannot compromise on.
Create a constraints document using this format:
Every feature you consider must fit within these boundaries. Features that bust your timeline, require skills you don't have, or cost more than your budget don't make the cut regardless of how valuable they seem.
You need a central place to collect every feature idea before you start prioritizing. This prevents good ideas from getting lost in Slack threads or meeting notes and ensures you evaluate all options systematically. Right now, you're in collection mode, not judgment mode, so capture everything your team suggests without filtering. You'll ruthlessly cut later using the frameworks in subsequent steps.
Pull feature ideas from every conversation you've had about the product. Review notes from customer discovery interviews, competitor analysis, team brainstorming sessions, and stakeholder meetings. Look at similar products in your space and note features they include, then ask yourself which ones address your specific problem. Each idea represents someone's hypothesis about what users need, and you want all hypotheses on the table before you decide which to test.
Document the source for every feature idea because context matters during mvp feature prioritization. A feature requested by five potential customers carries different weight than one suggested by your CEO's friend. Include who suggested it, when, and why they thought it was valuable. This information helps you spot patterns and make informed decisions when you apply prioritization frameworks later.
Collecting feature ideas without judgment initially prevents you from prematurely dismissing options that might prove essential once you analyze them systematically.
Create a spreadsheet or simple database where each row represents one feature idea. You need specific columns that capture not just what the feature is, but why it matters and what information you have about it. This structure becomes the foundation for all your prioritization work in the following steps.
Use this template to organize your features:
Feature Name | Description | Source | User Problem Solved | Assumptions | Initial Effort Guess
-------------|-------------|--------|---------------------|-------------|--------------------
Example: Bulk import | Upload CSV to add 100+ items at once | Customer interview (3 users mentioned) | Users waste hours entering data manually | CSV format is familiar, users have data in spreadsheets | Medium (2-3 weeks)
Fill out what you know for each feature and leave blanks where you lack information. Those gaps tell you where you need more research before making prioritization decisions. Keep descriptions short but specific enough that anyone on your team understands exactly what the feature does and why someone requested it.
You cannot compare features objectively until you define exactly what "value" means for your product and how you'll measure effort consistently. This step forces you to articulate the dimensions you care about before you start scoring features. Without these definitions, your mvp feature prioritization becomes a contest of opinions rather than a data-informed process. Two team members might both call a feature "high value" while meaning completely different things.
Choose 2-4 specific value dimensions that align with your MVP goal from Step 1. These dimensions represent different ways a feature can contribute to your success metrics. Common dimensions include user impact (how many users benefit), problem severity (how painful is the issue), revenue potential (monetization opportunity), and strategic alignment (fits long-term vision). You'll score each feature against these dimensions, so pick ones that actually differentiate between must-haves and nice-to-haves.
Define each dimension with concrete examples so everyone scores consistently. If "user impact" is a dimension, specify whether it means percentage of your target users, absolute number of users, or frequency of use. Create a simple scale (typically 1-5 or 1-10) and describe what each level means. This eliminates ambiguity when your team debates whether a feature rates a 3 or a 4.
Use this template to document your value dimensions:
Dimension: User Impact
Definition: Percentage of target users who will use this feature weekly
Scale:
5 = 80%+ of users (core functionality everyone needs)
4 = 50-79% of users (major use case)
3 = 25-49% of users (common scenario)
2 = 10-24% of users (occasional need)
1 = <10% of users (edge case)
Dimension: Problem Severity
Definition: How much pain users experience without this feature
Scale:
5 = Blocker (cannot accomplish primary goal)
4 = Major pain (significant workaround required)
3 = Moderate pain (minor workaround exists)
2 = Mild inconvenience (slightly less efficient)
1 = Cosmetic (preference, not functional)
Clear value definitions transform feature debates from "I think this is important" to "this scores 4 on user impact because 60% of users need it weekly."
Standardize how your team estimates effort so you can compare the cost of different features. Effort typically includes development time, design time, testing requirements, and any external dependencies. Create categories that match your team's capacity and timeline, such as T-shirt sizes (S, M, L, XL) or time buckets (days, weeks, months). Smaller MVPs work better with fewer, broader categories because precise estimates matter less when you're cutting features aggressively.
Document what each effort level means in concrete terms. Specify not just duration but also how many people are involved and what skills they need. A two-week feature that requires your only senior developer has different implications than a two-week feature a junior developer can handle. This clarity helps you spot features that might bust your constraints from Step 1 before you commit to them.
Effort Level: Small (S)
Time: 2-5 days
Resources: 1 developer, minimal design
Dependencies: None or easily resolved
Risk: Low, straightforward implementation
Effort Level: Medium (M)
Time: 1-2 weeks
Resources: 1-2 developers, design input needed
Dependencies: May require coordination with 1 other system
Risk: Moderate, some unknowns exist
Effort Level: Large (L)
Time: 3-4 weeks
Resources: 2+ developers, dedicated design work
Dependencies: Multiple system integrations required
Risk: High, significant technical complexity
You have your value criteria and effort scales defined, but now you need a systematic method to score and rank features against those criteria. This is where prioritization frameworks come in. Each framework approaches mvp feature prioritization from a different angle, and the right choice depends on your team size, data availability, and how much precision you need. You don't want to spend three weeks analyzing features when you could have built half your MVP in that time.
RICE works best when you have some data or can make educated estimates about reach and impact. You multiply Reach (how many users affected) by Impact (how much it helps them), multiply by Confidence (how sure you are), then divide by Effort. This gives you a numerical score that makes comparing features straightforward. Use RICE when you need to evaluate 20+ features and want a quantitative ranking that stakeholders can't easily dispute.
MoSCoW fits smaller MVPs with tighter constraints where you need binary decisions fast. You categorize each feature as Must have, Should have, Could have, or Won't have. This framework forces ruthless prioritization because you can't hide mediocre features in the middle of a numerical ranking. If your team struggles with consensus or you have fewer than 15 features to evaluate, MoSCoW gives you clear buckets without analysis paralysis.
Kano helps when user delight matters to your differentiation strategy. It separates features into Basic (expected), Performance (more is better), and Delighters (unexpected wow factor). You typically need user research to apply Kano properly, so save this for situations where you've done customer interviews and can classify features based on actual feedback. Kano excels at identifying which features create competitive advantage versus which ones just meet baseline expectations.
The best framework for your MVP is the one your team can execute consistently without slowing down development velocity.
Smart teams use multiple frameworks to cross-check their prioritization decisions. Start with one primary framework that matches your situation, then apply a second framework to the top candidates to verify your choices. This catches biases and blind spots that any single method might miss. For example, run RICE to get initial rankings, then use MoSCoW to sanity-check whether your "Must haves" truly represent the minimum viable set.
Your combination strategy should follow this pattern: Use a quantitative framework (RICE) to rank the full feature list and identify the top 15-20 candidates. Then apply a qualitative framework (MoSCoW or Kano) to validate that your top-ranked features actually align with your MVP goal from Step 1. This two-pass approach gives you both numerical justification and gut-check validation before you commit engineering resources.
Document your framework choice using this format:
Primary Framework: RICE
Reason: Evaluating 30+ features, need objective scoring
Team Size: 4 people, all can estimate reach/impact
Validation Framework: MoSCoW
Reason: Final check that top RICE scores are truly must-haves
Applied To: Top 12 features from RICE ranking
Decision Rule: Feature must score top 40% in RICE AND
land in "Must have" or "Should have" to make MVP cut
This structured approach prevents framework choice from becoming another debate and keeps your team moving toward clear prioritization decisions.
RICE scoring gives you a numerical framework to rank features objectively by multiplying Reach by Impact by Confidence, then dividing by Effort. This method forces you to quantify assumptions about each feature instead of relying on gut feelings or whoever argues loudest in meetings. You end up with comparable scores across all features that make prioritization decisions defensible to stakeholders and clear to your development team.
The formula works because it balances opportunity against cost. Features that reach many users with high impact naturally score well, but only if you're confident in your estimates and the effort is reasonable. A feature that reaches 1000 users with massive impact still loses to a feature reaching 500 users with moderate impact if the first requires ten times the development effort. This mathematical approach keeps your mvp feature prioritization honest and prevents expensive bets on uncertain outcomes.
Reach measures how many users will interact with a feature in a specific time period, typically per month or per quarter. You want absolute numbers here, not percentages, because reach needs to multiply with other factors in the formula. Estimate reach based on your target user base size and how frequently users will encounter the feature. A login feature reaches 100% of users, while an advanced reporting feature might reach only 20% of power users.
Use real numbers even if your product doesn't exist yet. If you're targeting small business owners and expect 500 users in your first three months, a core feature might reach 400 of them monthly while a niche feature reaches 50. These estimates come from your customer discovery work and market research. Document your assumptions so you can refine them as you learn more.
Feature: Bulk CSV Import
Target User Base: 500 small business owners
Usage Frequency: Monthly task for inventory management
Reach Calculation:
- 80% of users have 100+ inventory items
- These users will import monthly
- Reach = 500 × 0.80 = 400 users per month
Feature: Custom Email Templates
Reach Calculation:
- 25% of users send regular customer updates
- These users will use templates weekly
- Reach = 500 × 0.25 = 125 users per month
Impact quantifies how much each use of the feature improves the user's situation. You typically use a scale of 0.25 (minimal) to 3 (massive) to keep scores reasonable and differentiated. A massive impact feature (3) fundamentally changes how users accomplish their goal, while a minimal impact feature (0.25) offers slight convenience. Your value dimensions from Step 3 inform these impact scores, but you need a single number for the RICE formula.
Base impact scores on actual user pain points identified during customer research. If five users mentioned spending hours on manual data entry, a feature that eliminates that work scores 3 for massive impact. If two users mentioned wanting a dark mode for aesthetics, that scores 0.25 for minimal impact. Connect each score to specific user feedback or observed behavior to keep impact ratings grounded in reality.
RICE impact scores transform vague statements like "users would love this" into concrete assessments of how much a feature actually improves their workflow.
Impact Scale for MVP:
3.0 = Massive: Eliminates a major blocker, saves hours per week
2.0 = High: Significantly improves efficiency, saves 30+ minutes
1.0 = Medium: Moderate improvement, saves 10-15 minutes
0.5 = Low: Minor convenience, saves a few minutes
0.25 = Minimal: Aesthetic or negligible functional improvement
Confidence represents how certain you are about your reach and impact estimates. Express confidence as a percentage: 100% means you have solid data supporting your numbers, 50% means you're making educated guesses with limited information. This factor prevents risky features with inflated estimates from dominating your prioritization just because someone was optimistic. Lower confidence automatically reduces a feature's RICE score, pushing it down the priority list until you gather more evidence.
Set confidence based on the quality of your data sources. Customer interviews with ten users who explicitly requested a feature warrant 80-100% confidence. A stakeholder's hunch about what users might want deserves 50% confidence. Assumptions about user behavior you haven't validated get 20% confidence. Never use confidence to express how much you like a feature; it strictly measures data quality behind your reach and impact estimates.
Confidence Guidelines:
100% = Multiple data sources confirm (usage data + interviews)
80% = Direct user feedback from 5+ customer conversations
50% = Indirect evidence (competitor analysis, analogous products)
20% = Informed speculation, no direct validation
Effort uses the same scale you defined in Step 3, whether that's person-weeks, t-shirt sizes, or story points. The key is consistency across all features so you can compare effort fairly. Break down effort into development time, design time, and testing requirements to avoid underestimating complex features. Include any external dependencies or integration work that adds to the timeline.
Involve your technical team directly in effort estimation. Developers spot implementation challenges that product managers miss, and their buy-in on estimates prevents disputes later when actual development takes longer than expected. Document assumptions behind each estimate so you can adjust if circumstances change or you discover hidden complexity.
Calculate the RICE score using this formula: (Reach × Impact × Confidence) ÷ Effort. Work through each feature in your list and plug the numbers into a spreadsheet. Sort features by their final RICE scores from highest to lowest. The top-scoring features represent your best return on investment based on the criteria you defined, making them prime candidates for your MVP.
Feature Scoring Example:
Feature: Bulk CSV Import
Reach: 400 users/month
Impact: 3.0 (massive, eliminates manual entry)
Confidence: 80% (0.8)
Effort: 2 weeks
RICE Score: (400 × 3.0 × 0.8) ÷ 2 = 480
Feature: Dark Mode
Reach: 300 users/month
Impact: 0.25 (minimal, aesthetic preference)
Confidence: 50% (0.5)
Effort: 1 week
RICE Score: (300 × 0.25 × 0.5) ÷ 1 = 37.5
Your RICE scores now give you an objective ranking to guide which features make your MVP cut. Features scoring in the top 30-40% deserve serious consideration, while bottom scorers should wait for future releases unless they're prerequisites for higher-scoring features.
Your RICE scores give you a ranked list, but numbers alone don't catch every mistake. You need qualitative validation to confirm your top-scoring features actually represent the minimum viable set and align with user expectations. This step uses MoSCoW and Kano frameworks to challenge your RICE rankings and expose features that scored well on paper but won't deliver the value you need for a successful MVP launch.
Run this validation immediately after calculating RICE scores while the context is fresh in your mind. Take your top 15-20 features from the RICE ranking and put them through both frameworks. This double-check catches situations where a feature scored high because of optimistic reach estimates but isn't truly essential, or where a low-scoring feature is actually a basic expectation users cannot live without. The validation takes an hour or two but prevents weeks of wasted development on wrong priorities.
MoSCoW forces you to make binary decisions about each feature by categorizing it as Must have, Should have, Could have, or Won't have for this release. Start with your highest RICE scores and ask whether each feature is genuinely required for users to accomplish your MVP's primary goal from Step 1. Must haves are features without which your product cannot function or solve its core problem. If users can work around a feature's absence with a manual process, it drops to Should have regardless of its RICE score.
Work through your top features systematically and place each into exactly one category. Must haves represent your absolute minimum for launch, typically 5-8 features for a focused MVP. Should haves are important improvements you'll build if time permits but won't delay launch to include. Could haves and Won't haves get pushed to future releases. This categorization reveals whether your RICE-ranked list is actually buildable within your constraints or if you need to cut more aggressively.
MoSCoW Validation Template:
Feature: Bulk CSV Import (RICE: 480)
Category: Must Have
Reasoning: Users cannot manually enter 100+ items;
this is the core time-saver that defines our value
Feature: Real-time Collaboration (RICE: 320)
Category: Should Have
Reasoning: Valuable but users can share files manually
for MVP; not a blocker for core workflow
Feature: Advanced Reporting (RICE: 290)
Category: Could Have
Reasoning: Users can export data and analyze elsewhere;
nice feature but not essential for first release
Feature: Custom Themes (RICE: 180)
Category: Won't Have
Reasoning: Aesthetic preference only, zero impact on
solving core user problem
Your Must have category should contain only features that pass this test: if you remove this feature, can users still accomplish their primary goal? If the answer is yes, the feature doesn't belong in Must have. This ruthless filtering catches inflated RICE scores and keeps your MVP truly minimal while remaining viable.
When MoSCoW contradicts your RICE scores, trust MoSCoW because it forces you to define what "minimum" actually means for your product.
Kano classification separates features into Basic, Performance, and Delighter categories based on how they affect user satisfaction. Basic features are expected by users and create dissatisfaction when missing but no extra satisfaction when present. Performance features create proportional satisfaction as you improve them. Delighters exceed expectations and create disproportionate satisfaction. Apply Kano to verify that your Must haves from MoSCoW actually cover all basic expectations users have for your product category.
Review customer interview notes and competitive analysis to classify your top features. Basic features are table stakes that users assume exist without asking because every product in your category includes them. Authentication, data persistence, and core workflow features typically fall here. If your MoSCoW Must haves are missing any basics, your MVP will feel incomplete even if RICE scores suggested those features weren't priorities. Performance features like speed optimizations and capacity increases belong in Should have or Could have unless they're severe enough to block basic functionality.
Kano Classification Check:
Basic (Must include or users reject product):
- User authentication and account management
- Core data entry and editing
- Basic search and filtering
- Data export (users expect to own their data)
Performance (More is better, prioritize by ROI):
- Speed improvements (as long as acceptable)
- Capacity increases (within reasonable limits)
- Advanced filtering and sorting options
Delighters (Unexpected features that wow):
- Intelligent auto-categorization
- Predictive data entry suggestions
- Automated workflow recommendations
Cross-reference your Kano basics against your MoSCoW Must haves to catch gaps. If you find a basic expectation missing from your Must have list, add it immediately even if it scored poorly in RICE. Users will abandon your MVP if it lacks expected functionality, regardless of how well you execute other features. Conversely, if a Delighter ranked high in RICE but isn't in your Must haves, you've correctly prioritized it for a future release when you have margin to exceed expectations rather than just meet them.
This two-framework validation gives you confidence that your mvp feature prioritization balances quantitative analysis with qualitative user needs and market expectations. You now have a defended list of Must have features that you can commit to building, knowing they represent the genuine minimum needed to test your product hypothesis.
You have validated your Must have features through both quantitative RICE scoring and qualitative MoSCoW and Kano validation. Now you need to translate those priorities into a timeline that your team can execute against. A lean MVP roadmap shows what you'll build, when you'll build it, and how you'll know when you're done. This roadmap keeps your team aligned and prevents feature creep by making your mvp feature prioritization decisions visible to everyone involved.
Your roadmap should span no more than 12 weeks for a true MVP, ideally 6-8 weeks. Longer timelines encourage scope expansion and delay the feedback loop you need to validate your product hypothesis. Structure your roadmap into clear phases based on feature dependencies and risk, not just alphabetical order or stakeholder preferences. Each phase should deliver working functionality that builds toward your complete MVP rather than isolated features that cannot be tested until everything is done.
Map out which Must have features depend on other features to function properly. You cannot build user dashboards before you build data storage, and you cannot implement sharing features before you have user authentication. Create a simple dependency map showing which features block others and which can be built in parallel. This reveals your critical path through development and identifies bottlenecks where one feature holds up multiple others.
Start development with foundational features that enable everything else. Authentication, core data models, and basic CRUD operations typically come first because most other features need them to work. Group independent features together in phases so your team can work on multiple items simultaneously without stepping on each other's code. This sequencing prevents your developers from sitting idle waiting for prerequisites while maintaining steady progress toward launch.
Phase 1 (Weeks 1-2): Foundation
- User authentication and accounts
- Core data models and storage
- Basic UI framework
Phase 2 (Weeks 3-4): Core Value
- Primary user workflow (Must have #1)
- Data entry and editing
- Basic validation and error handling
Phase 3 (Weeks 5-6): Essential Functions
- Search and filtering (Must have #2)
- Bulk operations (Must have #3)
- Data export
Phase 4 (Weeks 7-8): Polish and Launch Prep
- Performance optimization
- Bug fixes and testing
- Documentation and onboarding
Insert review points after each phase where you assess progress against your success metrics from Step 1. These checkpoints let you course-correct before you've invested weeks in the wrong direction. Schedule a demo or user testing session at the end of each phase to validate that what you built actually solves the problem you intended. Real user feedback beats assumptions every time, and getting it early prevents costly rewrites later.
Building feedback loops into your roadmap transforms your MVP from a blind bet into a series of small, testable hypotheses you can validate or pivot from quickly.
Document what good looks like for each checkpoint so your team knows when to move forward versus when to pause and fix issues. Define specific acceptance criteria tied to your success metrics, such as "80% of test users complete the core workflow in under 5 minutes" or "zero critical bugs blocking primary use case." Clear criteria prevent endless polishing and keep you moving toward launch.
Share your roadmap with every stakeholder who has opinions about features so they understand what made the cut and why. Include brief explanations of which features didn't make it and when you might revisit them. This transparency reduces mid-development pressure to add features because people see the logic behind your decisions and know their ideas haven't been forgotten, just deferred based on your prioritization frameworks.
You can accelerate your mvp feature prioritization work by starting with proven templates rather than building spreadsheets from scratch. These resources give you immediate structure while you adapt them to your specific product and team dynamics. The following templates and examples show how successful teams translate prioritization frameworks into practical tools they use daily.
Set up a master spreadsheet that combines all three frameworks in one place so your team can track scores and decisions together. This unified view prevents switching between multiple documents and ensures everyone works from the same data when discussing priorities.
| Feature Name | Description | Reach | Impact | Confidence | Effort | RICE Score | MoSCoW | Kano Category | Final Decision |
|--------------|-------------|-------|--------|------------|--------|------------|--------|---------------|----------------|
| CSV Import | Bulk upload | 400 | 3.0 | 0.8 | 2 weeks | 480 | Must | Basic | Include Phase 2 |
| Dark Mode | Theme switch | 300 | 0.25 | 0.5 | 1 week | 37.5 | Won't | Delighter | Defer to v2.0 |
Consider a project management tool targeting small creative agencies. The team identified 25 potential features but had only eight weeks to launch. They applied RICE scoring and found that real-time notifications ranked third with a score of 340, but MoSCoW analysis revealed users expected basic task assignment first. They moved notifications to Phase 2 despite the high RICE score because Kano classified task assignment as a basic expectation while notifications were performance features. This validation prevented them from building a flashy feature before covering fundamental user needs, which would have resulted in a product that felt incomplete at launch regardless of notification quality.
Real MVPs succeed by delivering on basic expectations first, then adding performance features and delighters once you have validated product-market fit.
You now have a systematic process for mvp feature prioritization that removes guesswork and politics from feature selection. The seven-step framework walks you through clarifying goals, capturing ideas, defining criteria, applying RICE scoring, validating with MoSCoW and Kano, and building your roadmap. This structured approach transforms feature debates into objective decisions backed by multiple frameworks that catch blind spots and biases.
Your success depends on executing this process consistently rather than perfectly. Start with RICE to rank your features, use MoSCoW to verify your must-haves, and check Kano to ensure you're covering basic expectations. The frameworks work together to balance quantitative analysis with qualitative validation, giving you confidence that your MVP includes the right features in the right sequence.
Once you've prioritized your MVP features, you need a way to capture ongoing feedback and track feature requests as users interact with your product. Koala Feedback helps you collect user input, organize feature requests, and maintain your roadmap so you can continue making data-informed prioritization decisions beyond your initial launch.
Start today and have your feedback portal up and running in minutes.