How to Choose MVP Features for Startup Validation: A 2025 Framework

Choosing the right features for your MVP is the difference between proving product-market fit in weeks versus burning months on features nobody wants. Most founders get this wrong by building what seems logical instead of what actually tests their riskiest assumptions.

Here’s a proven framework for selecting MVP features that drive real validation, not vanity metrics.

The Feature Selection Framework

Step 1: List Your Core Assumptions

Before choosing any features, identify the assumptions your business depends on. Most startups have 3-5 critical assumptions:

Business Model Assumptions:

• People will pay for this solution
• They’ll pay our target price point
• They’ll use it frequently enough to retain

User Behavior Assumptions:

• Target users experience this problem regularly
• Current solutions are inadequate
• Users will change their workflow to adopt our solution

Market Assumptions:

• Market size is sufficient for our goals
• We can reach our target users affordably
• Competitors won’t easily replicate our advantage

Step 2: Rank Assumptions by Risk

Use this simple scoring system (1-5 scale):

• Evidence Level: How much proof do you have? (1 = pure guess, 5 = strong data)
• Impact if Wrong: How much would being wrong hurt? (1 = minor setback, 5 = startup dies)
• Cost to Test: How expensive is validation? (1 = cheap/fast, 5 = expensive/slow)

Focus on high-impact, low-evidence assumptions that can be tested affordably.

Step 3: Map Features to Assumptions

For each feature you’re considering, ask:

• Which assumption does this test?
• What specific evidence will it provide?
• Can I get this evidence without building the feature?

The Three Types of MVP Features

1. Core Value Features (30-40% of development)

These directly deliver your main value proposition and test willingness to pay.

Example: For a project management tool, the core value feature might be task assignment and tracking—not advanced reporting or integrations.

Selection Criteria:

• Users must experience your key benefit
• Can stand alone as a complete (if basic) solution
• Tests your primary monetization assumption

2. Essential Experience Features (40-50% of development)

These make the core value accessible and usable, but don’t add new value.

Examples:

• User authentication and profiles
• Basic navigation and UI structure
• Essential data input/output flows

Selection Criteria:

• Required for users to access core value
• Industry-standard expectations
• Enable meaningful testing of usage patterns

3. Validation Signal Features (10-20% of development)

These specifically test secondary assumptions and provide clear feedback signals.

Examples:

• Upgrade/payment flows (tests price sensitivity)
• Sharing features (tests viral potential)
• Basic analytics (measures engagement patterns)

Selection Criteria:

• Generate specific data about user behavior
• Test assumptions that inform your next decisions
• Can be implemented simply without complex logic

Feature Prioritization Matrix

Plot potential features on two axes:

Y-Axis: Assumption Risk

• High: Critical to business success, little evidence
• Low: Nice to validate but not make-or-break

X-Axis: Implementation Effort

• Low: Can build quickly with existing skills/tools
• High: Requires significant time, new technology, or complex logic

Priority Order:

1. High Risk, Low Effort (build first)
2. High Risk, High Effort (break into smaller tests)
3. Low Risk, Low Effort (include if time permits)
4. Low Risk, High Effort (skip for MVP)

Common Feature Selection Mistakes

Mistake 1: Building What Users Ask For

Users often request features that sound logical but don’t address core assumptions.

Example: Beta users of a scheduling app requested calendar integrations, but the real question was whether they’d pay for scheduling at all. Building integrations first delayed testing the payment assumption by months.

Fix: Ask why users want specific features. Often they’re solving symptoms, not core problems.

Mistake 2: Feature Parity with Competitors

Matching competitor features feels safe but doesn’t test your unique value proposition.

Example: A startup building “Slack for remote teams” spent months replicating basic messaging features instead of testing whether their unique async communication approach actually improved remote work.

Fix: Focus on what makes you different, not what makes you similar.

Mistake 3: Perfect Feature Implementation

Building production-quality features before proving users want them wastes development time.

Example: A fintech startup spent weeks building secure payment processing before testing whether users would connect their bank accounts at all. A simple “connect bank” button with behind-the-scenes manual processing would have tested the assumption faster.

Fix: Use fake doors, manual processes, and basic implementations to test demand before building scalable solutions.

Validation-Driven Feature Examples

E-commerce Platform MVP

Core Assumption: Small businesses will pay for better inventory management

High Priority Features:

• Basic product listing and inventory tracking (core value)
• Simple checkout flow (tests payment willingness)
• Basic admin dashboard (essential experience)

Skip for MVP:

• Advanced analytics, multi-location support, complex pricing rules

B2B SaaS Tool MVP

Core Assumption: Marketing teams will pay for better campaign attribution

High Priority Features:

• Campaign tracking and basic attribution (core value)
• Data import/export (essential experience)
• Pricing tiers and upgrade flow (tests price sensitivity)

Skip for MVP:

• Advanced visualizations, team management, third-party integrations

Testing Your Feature Choices

Before building, validate your feature selection:

1. User Story Test: Can you write clear user stories for each feature that connect to business assumptions?

2. Removal Test: If you removed this feature, would users still get your core value?

3. Evidence Test: What specific evidence will this feature provide about your assumptions?

4. Alternative Test: Is there a simpler way to get the same validation evidence?

Implementation Strategy

Week 1-2: Core Value Features

Build the minimum features required to deliver your main value proposition.

Week 3: Essential Experience Features

Add basic infrastructure that makes the core value accessible.

Week 4: Validation Signals

Implement features that test key assumptions and provide user behavior data.

Australian Market Considerations

When selecting MVP features for the Australian market:

• Compliance Features: Factor in Privacy Act requirements and local payment methods
• Local Integrations: Consider Australian-specific tools (Xero, MYOB, local banks)
• Market Size: Prioritize features that work with smaller user bases typical of Australian markets

Measuring Feature Success

Track metrics that validate assumptions, not just usage:

• Revenue Signals: Upgrade rates, payment conversion, pricing sensitivity
• Retention Signals: Daily/weekly active users, feature adoption patterns
• Market Signals: User feedback themes, competitive responses, market size indicators

When to Add More Features

Only add features after validating your current assumptions:

• Core value is proven (users pay and retain)
• Current features are well-utilized
• You’ve identified the next highest-risk assumption to test

Ready to Build?

Choose features that test your riskiest assumptions with the least effort. Focus on proving willingness to pay, not just willingness to use. Your MVP should be a learning machine, not a feature showcase.

Start with features that deliver core value, test payment assumptions, and provide clear validation signals. Everything else can wait until you’ve proven people actually want what you’re building.

Internal Links

• Pre-Sales Validation Playbook
• Purchase Validation Playbook for MVPs
• MVP vs Prototype: Understanding the Difference
• Startup Validation Before Building