MVP Fundamentals: What Actually Matters

A Minimum Viable Product is not about building something cheap, incomplete, or embarrassing. It's about building the smallest thing that tests your riskiest assumptions with real users. The goal is validated learning, not feature completeness. Eric Ries' Lean Startup methodology defines MVP as "that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort." See our startup fundamentals guide for foundational concepts.

The term "minimum viable" confuses people. They think "minimum" means low quality or "viable" means barely functional. Wrong on both counts. Your MVP should deliver excellent experience for its narrow scope. Dropbox's first MVP was a 3minute video explaining what the product would do—no code, just validation that people wanted the concept. That's minimum viable: the smallest experiment that generates maximum learning.

The Three Characteristics of Good MVPs

1. Solves a Real Problem

Your MVP must address a genuine pain point that people currently experience. Not a hypothetical future problem. Not a nicetohave convenience. A real problem that causes real frustration right now.

How do you know if the problem is real? People are already trying to solve it with inadequate tools. They're using spreadsheets when they need a database. They're hiring virtual assistants for tasks that should be automated. They're paying for expensive solutions they barely use. These workarounds signal real problems worth solving.

2. Delivers Enough Value Despite Limitations

Your MVP will have limitations—missing features, rough edges, manual processes. That's fine. But it must deliver enough core value that people use it anyway. If your MVP doesn't solve the problem well enough for people to tolerate its limitations, it's not viable.

Airbnb's MVP was Brian Chesky and Joe Gebbia photographing hosts' apartments with their own camera and manually posting listings. Extremely limited—but it delivered enough value (finding accommodation, making extra income) that people used it despite the manual process.

3. Generates Actionable Learning

Every MVP is an experiment testing specific hypotheses. Good MVPs have clear success criteria: metrics, behaviors, or feedback that tell you whether your core assumptions are true. Bad MVPs generate ambiguous results you can't act on.

Before building, ask: "What will I learn from this? What metrics will prove or disprove my hypothesis? What would success look like? What would failure look like?" If you can't answer these questions, you're not ready to build.

Common MVP Mistakes

Mistake #1: Building for Too Long

If your MVP takes more than 46 weeks to build, it's too big. You're building features, not testing hypotheses. Scope down. What's the absolute smallest version that still tests your core assumption?

Mistake #2: Confusing MVP with Bad UX

Limited functionality doesn't mean poor user experience. Your MVP should have a small feature set executed beautifully, not a large feature set executed poorly. Do one thing extremely well rather than ten things badly.

Mistake #3: Testing Everything at Once

Good MVPs test one risky assumption at a time. If you're simultaneously testing whether customers have the problem, whether they'll pay, whether your solution works, and whether you can acquire users—you won't learn anything definitive. When it fails (and first MVPs often do), you won't know why.

Mistake #4: Skipping Customer Development

Building an MVP before talking to customers is backwards. How do you know what assumptions to test if you haven't talked to people experiencing the problem? Customer development comes first: 2030 interviews revealing the real problem, current solutions, willingness to pay. Then MVP validates what you learned.

Building Your First MVP: A StepbyStep Framework

Y Combinator's guidance on MVPs emphasizes that MVP is not a product milestone but a learning process—the goal is discovering productmarket fit through rapid experimentation. See our implementation guides for detailed execution frameworks.

Step 1: Identify Your Riskiest Assumptions

List every assumption your business depends on. Examples: "People have this problem," "Our solution is better than alternatives," "Users will pay $X/month," "We can acquire customers for $Y." Now rank by risk (likelihood you're wrong) and impact (consequence if wrong). Your MVP should test the highest riskimpact assumptions first.

Most founders skip this step and build what they're excited about rather than what's risky. That's how you spend six months building features nobody wants. Start with the scary questions, not the fun ones.

Step 2: Choose Your MVP Approach

Different hypotheses require different MVP types:

Landing Page MVP

Tests: Whether people want the solution you're proposing.

How: Create a simple landing page describing your product's value proposition. Drive traffic through ads, communities, or content. Measure signup conversion.

Success criteria: 1020% conversion from visitor to signup indicates strong interest. Under 2% suggests weak demand.

Example: Buffer started with a landing page showing the product concept and pricing plans. Only after validating demand did they build anything.

Concierge MVP

Tests: Whether people value the outcome enough to pay, before building automation.

How: Manually deliver your service to first 510 customers. If it's a tool that automates X, you do X manually. If people won't pay for the manual version, they won't pay for the automated version.

Success criteria: Customers paying, continuing to use service, referring others despite manual limitations.

Example: Food on the Table (acquired by Scripps) started with founder manually creating meal plans for customers—no software, just spreadsheets and email. Validated people would pay before building technology.

Wizard of Oz MVP

Tests: User experience and workflow before building complex backend.

How: Create the user interface, but humans power it behind the scenes. Customer thinks it's automated; you're doing it manually.

Success criteria: Users completing workflows, achieving goals, requesting to continue using it.

Example: Zappos started by photographing shoes in retail stores and manually purchasing/shipping when customers ordered online. Validated demand for online shoe shopping before building inventory systems.

FeatureLimited MVP

Tests: Whether core feature set delivers value, before building full product.

How: Build just the core workflow—one use case executed endtoend. Cut everything else mercilessly.

Success criteria: Users completing core workflow repeatedly, retention strong despite missing features.

Example: Twitter launched with just posting 140character updates and following people. No photos, no videos, no DMs, no hashtags. Added features only after validating core loop.

Step 3: Define Success Metrics

Before building anything, decide what success looks like quantitatively. Not vanity metrics like signups or page views—actionable metrics showing whether your hypothesis is true.

For demand validation: X% conversion rate, Y signups in Z days, A% expressing willingness to pay.

For product validation: X% activation rate, Y% weekly retention, Z Net Promoter Score.

For business model validation: X% trialtopaid conversion, $Y average revenue per customer, Z months to profitability.

Step 4: Build Minimum, Launch Fast

Now—and only now—do you build. Resist feature creep. Resist polish. Build the absolute minimum that tests your hypothesis, then launch to real users. Target: 24 weeks from start to first users.

Reid Hoffman's advice: "If you're not embarrassed by your first product release, you released too late." Your MVP should make you slightly uncomfortable with its limitations. That discomfort is proof you're testing fast enough.

Step 5: Learn and Iterate

Launch is not the finish line—it's the starting line. Now you learn. Watch what users actually do (not what they say they'll do). Measure your success metrics. Conduct followup interviews. Look for patterns in behavior and feedback.

Then iterate. Fix what's broken. Double down on what's working. Cut what nobody uses. Your second version should be informed by real user behavior, not founder assumptions.

Validation Methods Before Building Anything

The best MVP is the one you don't have to build. Before writing code, validate your ideas through conversations, experiments, and observations that require minimal resources. Steve Blank's "Get Out of the Building" philosophy emphasizes that no facts exist inside your office—only opinions. Real validation requires talking to customers in their environment. See our validation case studies for proven approaches.

Customer Development Interviews

Talk to 2030 people in your target customer segment. Not to pitch your idea—to understand their problems, current solutions, and willingness to pay.

The Right Questions

• "Tell me about the last time you experienced [problem]." Gets specific stories, not hypotheticals.
• "How do you currently solve that?" Reveals existing workarounds and alternatives you're competing with.
• "How much does that cost you in time/money?" Quantifies the problem's severity.
• "If this problem disappeared tomorrow, what would that enable?" Reveals the real value, not surface symptoms.
• "What have you tried that didn't work?" Shows what solutions failed and why.

Wrong Questions to Avoid

• "Would you use a product that does X?" (People lie about future behavior.)
• "Do you like my idea?" (Everyone's polite; you get false positives.)
• "How much would you pay?" (Hypothetical pricing is meaningless.)

Look for patterns across interviews. If 15 out of 20 people mention the same pain point unprompted, you've found something real. If people are politely interested but not enthusiastically describing frustrations, the problem might not be painful enough to solve.

Landing Page Tests

Build a simple page describing your solution's value proposition. No product exists yet—just description, benefits, and email signup. Drive traffic through ads, communities, or content. Measure conversion.

What to Include

• Clear headline: What problem do you solve, for whom?
• Specific benefits: Not features—outcomes. What can users accomplish?
• Social proof: If you have any (testimonials, logos, user counts).
• Call to action: Email signup, waitlist join, or preorder.

Interpreting Results

1020% conversion: Strong validation. Build it.

510% conversion: Moderate interest. More customer development needed to sharpen value prop.

Under 2% conversion: Weak demand or poor positioning. Revisit problemsolution fit.

PreSales and Commitments

The strongest validation is people paying before the product exists. Presales derisk everything: you validate demand, validate pricing, and generate runway to build.

Approach 1: Sell before building. Explain what you're building, show mockups or demos, offer early access pricing. If people won't buy before it exists, will they buy after?

Approach 2: Signed LOIs (Letters of Intent). For B2B products, get companies to commit in writing to evaluating and potentially purchasing. Not binding, but much stronger signal than "yeah, we'd be interested."

Approach 3: Crowdfunding. Kickstarter or Indiegogo campaigns validate demand publicly. If you can't get strangers to prepay, you don't have productmarket fit yet.

Competitor Analysis

Competitors validate that market demand exists—your job is validating differentiation. Lack of competitors could mean you're brilliantly innovative or solving a nonproblem. Investigate deeply before assuming the former.

Study competitors' customer reviews. What do people love? What frustrates them? Reviews reveal unmet needs your product could address. Look for patterns: if 50 reviews complain about the same missing feature, that's your wedge.

Testing Assumptions Systematically

Every startup rests on a stack of assumptions. Most are wrong. The question is which ones—and how quickly you discover and fix them. Alistair Croll and Ben Yoskovitz's "Lean Analytics" provides frameworks for identifying the One Metric That Matters at each startup stage, focusing validation efforts on highestrisk assumptions. See our validation frameworks guide for systematic testing approaches.

The Assumption Stack

Your business has assumptions at multiple levels:

Problem Assumptions

• Target customers have problem X
• Problem X causes quantifiable pain (costs time/money/frustration)
• Problem X is frequent enough to matter
• Customers aware of problem (or you can make them aware)

Solution Assumptions

• Our solution solves problem X better than alternatives
• Solution is feasible technically and operationally
• Users can understand and successfully use our solution
• Solution creates enough value to justify switching costs

Business Model Assumptions

• Customers will pay $X for solution
• We can acquire customers for $Y (CAC < LTV)
• Market size is large enough to build sustainable business
• We can deliver solution at cost that enables profitability

Prioritization Framework: Risk x Impact

You can't test everything simultaneously. Prioritize by: (1) How likely is this assumption wrong? (2) What happens if it's wrong?

High risk + high impact = test immediately. If customers don't have the problem you think they have, nothing else matters.

Low risk + high impact = test soon. You're probably right, but stakes are high enough to verify.

High risk + low impact = monitor. Could be wrong, but won't kill business if you are.

Low risk + low impact = ignore until validated. Don't waste time on assumptions that are probably right and don't matter much anyway.

Hypothesis Testing Framework

For each critical assumption, convert it into a testable hypothesis using this template:

We believe [specific assumption]
To verify, we will [specific experiment]
And measure [specific metric]
We are right if [specific success criteria]

Example: Testing Problem Existence

We believe freelancers struggle to track time across multiple projects and clients.
To verify, we will conduct 25 interviews with freelancers asking about their time tracking workflow.
And measure percentage who report frustration with current tools and describe workarounds.
We are right if -->60% describe current time tracking as painful and mention specific frustrations unprompted.

Example: Testing Pricing

We believe customers will pay $29/month for our solution.
To verify, we will create landing page with $29/month pricing and "Start Trial" CTA.
And measure percentage of visitors who click "Start Trial" and enter payment info.
We are right if -->10% of visitors start trial signup process.

BuildMeasureLearn Loops

Lean Startup methodology emphasizes rapid buildmeasurelearn cycles. The faster you complete a full loop, the faster you discover what's wrong and fix it.

Build: Create minimum experiment (MVP, prototype, landing page) testing specific hypothesis.
Measure: Collect data on success metrics. Did users behave as hypothesized?
Learn: Analyze results. Was hypothesis correct? What did we learn? What's next?

Early stage: Optimize for loop speed, not build quality. Completing 10 rough experiments in 3 months teaches more than 1 polished experiment in 3 months.

Gathering Effective Customer Feedback

Getting feedback is easy. Getting useful feedback is hard. Most founder feedback loops generate noise, not signal. Here's how to extract truth. Teresa Torres' "Continuous Discovery Habits" framework emphasizes weekly customer conversations focused on understanding opportunity space, not validating solutions. See our process implementation guides for systematic feedback collection.

The Feedback Hierarchy: What to Trust

Tier 1: Behavioral Data (Most Reliable)

What users actually do—usage patterns, feature adoption, retention cohorts, time spent, workflows completed. Behavior reveals truth; words reveal intentions and rationalizations.

If users say they love feature X but never use it, believe the behavior. If they complain about missing feature Y but retention is strong, the complaint matters less than the retention.

Tier 2: Unprompted Feedback

What users volunteer without being asked—support tickets, feature requests, bug reports, unsolicited emails. Higher signal than survey responses because effort cost filters for people who care.

Tier 3: Prompted Qualitative Feedback

What users tell you in interviews, user testing sessions, feedback calls. Valuable for understanding "why" behind behavior, but influenced by how you ask questions and user desire to be helpful.

Tier 4: Survey Responses (Least Reliable)

What users report in surveys. Lowest signaltonoise ratio. People satisfice (give quick answers to finish survey), misremember, predict future behavior poorly. Use surveys for quantifying known issues, not discovering new insights.

Interview Techniques for Truth

Ask About Past Behavior, Not Future Intentions

Bad: "Would you use feature X?"
Good: "Tell me about the last time you needed to do X. What did you use? How did it go?"

Past behavior is data. Future predictions are fiction. Base product decisions on what people have done, not what they say they'll do.

Follow Up on Generalities with Specifics

User: "The app is confusing."
Bad response: "Ok, we'll make it clearer." (You learned nothing.)
Good response: "Can you show me where you got confused? Walk me through what you were trying to do."

Vague feedback is useless. Drill down to specific moments, specific screens, specific workflows. That's where you learn what to fix.

Listen for the Problem, Not the Solution

Users are great at identifying problems. They're terrible at designing solutions. When users request feature X, ask why they need it. Often the underlying need could be solved better differently.

User: "You need to add bulk export!"
Wrong response: Add bulk export to roadmap.
Right response: "Why do you need bulk export? What are you trying to accomplish?"
Discovery: User wants to generate reports for their boss. Better solution: build reporting, not export.

Feedback Categorization System

Sort incoming feedback into buckets:

1. Feature Requests

Users asking for new capabilities. Don't add all of them—most feature requests come from outlier use cases. Look for patterns: same request from 10 users is signal; unique request from 1 user is noise.

2. Usability Issues

Users struggling to use existing features. High priority—if people can't successfully use what exists, more features won't help. Fix friction in core workflows first.

3. Bugs

Things broken. Obviously fix these, prioritized by severity and frequency.

4. Conceptual Misunderstandings

Users don't understand what problem you solve or how to use the product. Extremely high priority—suggests onboarding, positioning, or core UX problems. These kill activation and retention.

The Power User Trap

Your most engaged users give the most feedback. They're also least representative of typical users. Power users want complexity, configurability, advanced features. Most users want simplicity.

Listen to power users for identifying edge cases and optimization opportunities. Don't let them dictate product direction. Build for the mainstream use case first, then layer in power features.

MVP Validation Metrics That Actually Matter

Vanity metrics feel good but teach nothing. Actionable metrics reveal whether you're building something people want. Here's what to track and why. Amplitude's product analytics research shows that retention curves are the strongest early indicator of productmarket fit—companies with 40%+ monthly retention at 6 months are 5x more likely to reach sustainable growth. See our measurement and learning guide for datadriven validation.

Activation: Do Users Experience Value?

Activation rate: Percentage of signups completing key action indicating value realization.

For Slack: Sending 2,000 messages within a team.
For Dropbox: Putting file in one device, accessing it on another.
For Facebook (early days): Adding 7 friends in 10 days.

Identify your "aha moment"—the action that correlates with retention. Then measure what percentage of new users reach it and how long it takes. Target: -->30% activation within first session, -->60% within first week.

Why it matters: Users who don't activate churn immediately. Activation rate predicts retention rate. Fix activation before worrying about acquisition.

Retention: Do Users Come Back?

Retention by cohort: Percentage of users from each signup cohort active after 1 day, 7 days, 30 days, 90 days.

This is the single best indicator of productmarket fit. Strong retention means you've built something people need repeatedly. Poor retention means you're solving a onetime problem or not solving it well enough.

Targets:
Consumer apps: -->20% monthly retention = good, -->40% = excellent
B2B SaaS: -->40% monthly retention = good, -->70% = excellent
Social/habitforming: -->50% monthly retention = good, -->80% = excellent

How to analyze: Plot retention curves by cohort. If curves flatten (stabilize above zero), you've built something sticky. If curves decline to nearzero, people try once and leave.

Engagement: How Often Do Retained Users Engage?

DAU/MAU ratio: Daily active users divided by monthly active users. Measures stickiness.

Interpretation:
-->50% = extremely sticky (users engage most days)
2050% = moderately sticky (weekly+ usage)
<20% = low stickiness (monthly or less)

Why it matters: High engagement predicts retention and monetization. Users who engage daily are more valuable than users who engage monthly.

Customer Acquisition: Where Do Users Come From?

Acquisition by channel: Track source of every signup—organic search, paid ads, referrals, content, social, direct.

Cost per acquisition: Money spent on channel divided by customers acquired through channel.

Why it matters: You need to know which channels work before scaling acquisition. Early stage: experiment with multiple channels. Growth stage: double down on what works.

Revenue Metrics (If Charging)

Trialtopaid conversion: Percentage of trial users converting to paid. Target -->10% for B2B SaaS, -->2% for consumer.

Churn rate: Percentage of customers canceling monthly. Target <5% monthly for B2B, <10% for consumer.

Average revenue per account (ARPA): Total MRR divided by number of paying accounts.

The Sean Ellis Test

Ask users: "How would you feel if you could no longer use this product?"

• Very disappointed
• Somewhat disappointed
• Not disappointed

If -->40% answer "very disappointed," you likely have productmarket fit. Below 40%, more work needed on core value proposition.

Net Promoter Score (NPS)

Ask users: "How likely are you to recommend us to a friend?" (010 scale)

Promoters (910) minus Detractors (06) = NPS

Interpretation:
NPS -->50 = excellent (strong wordofmouth)
NPS 050 = okay (some promoters, some detractors)
NPS <0 = problem (more detractors than promoters)

Vanity Metrics to Ignore

• Total signups (without activation and retention context = meaningless)
• Page views (without engagement context = meaningless)
• Social followers (without conversion context = meaningless)
• App downloads (without retention context = meaningless)

These feel good but don't predict success. Focus on metrics showing whether users find value (activation, retention, engagement).

When to Pivot vs Persevere

The hardest startup decision: is our direction wrong (pivot) or is execution insufficient (persevere)? Get it wrong and you either quit too early or persist too long. Andrew Chen's analysis of pivots reveals that successful pivots preserve validated learnings while changing the hypothesis—you're not starting over, you're applying what you learned to a better opportunity. See our decision framework guides for systematic evaluation.

Clear Signals to Pivot

1. Retention Remains Poor After Multiple Iterations

You've built MVP, shipped updates, fixed obvious problems—but retention stays below 10% monthly (B2B) or 5% monthly (consumer) after 36 months. This suggests you're solving wrong problem or targeting wrong customer.

One iteration with poor retention doesn't mean pivot. Persistent poor retention after 510 iterations means fundamental mismatch between product and market.

2. Users Don't Activate

Most signups never experience core value. They sign up, look around, leave. Activation rate stuck below 20% despite onboarding improvements. Suggests product is confusing, doesn't solve real problem, or value prop isn't compelling.

3. Customer Development Reveals You're Solving Wrong Problem

You interview churned users and discover they don't actually have problem you're solving—or your solution doesn't address their real pain point. When reality contradicts your assumptions, believe reality.

4. Unit Economics Are Fundamentally Broken

CAC (customer acquisition cost) is 5x+ higher than LTV (lifetime value) with no clear path to improvement. You can't acquire customers profitably even at scale. Unless you discover radically cheaper acquisition channel or highervalue use case, business model doesn't work.

5. Market Too Small

You've identified all potential customers and there aren't enough to build sustainable business. Even with 100% market share, annual revenue would be <$5M. Hard to admit, but sometimes markets are just too small.

6. Team Discovers Better Opportunity

While building current product, you discover adjacent problem with bigger market, clearer pain point, stronger demand. Common pivot trigger: side feature gets more traction than core product.

Clear Signals to Persevere

1. Core Metrics Improving Steadily

Retention increasing monthovermonth. Activation improving with each iteration. Organic growth accelerating. NPS rising. Direction is right; execution needs refinement.

2. Small Group of Power Users Deeply Engaged

You have 50100 users who would be "very disappointed" without product. They use it daily, refer friends, provide detailed feedback. Start by serving this core extremely well before expanding.

3. Clear Path to PMF Visible

You understand why retention is suboptimal and have specific plans to fix it. Cohort analysis shows newer cohorts performing better than old ones—learning and iteration is working.

4. Problem Validation Strong, Solution Needs Work

Customer development confirms people have problem and current solutions are inadequate. Your solution isn't quite right yet, but problem is real and painful. Keep iterating on solution.

Types of Pivots

Customer Segment Pivot

Same product, different customer. You built for small businesses but enterprise shows stronger interest. You targeted consumers but B2B has better unit economics.

Example: Slack started as internal tool for gaming company. Pivoted to serving all teams when they realized tool had broader appeal.

Problem Pivot

Same customer, different problem. You're targeting right audience but solving wrong pain point.

Example: YouTube started as video dating site. Pivoted to general video sharing when users uploaded everything except dating videos.

Solution Pivot

Same problem, different solution. Problem is real but your approach doesn't work.

Example: Groupon started as collective action platform. Pivoted to daily deals when that use case showed traction.

Business Model Pivot

Same product, different monetization. Maybe B2C freemium doesn't work but B2B subscription does. Maybe advertising works better than subscriptions.

Platform Pivot

Change from application to platform (or vice versa). Open up to thirdparty developers or verticalize into specific use case.

The Pivot Framework

Step 1: Acknowledge hypothesis failed. Be honest about what's not working and why.

Step 2: Analyze what you learned. What assumptions were wrong? What worked? What user insights emerged?

Step 3: Form new hypothesis. Based on learnings, what should you test next?

Step 4: Design minimum experiment. How do you test new hypothesis quickly and cheaply?

Step 5: Set decision criteria. What results would validate new direction? What would invalidate it?

Common MVP Mistakes and How to Avoid Them

First Round Review's deep dive on Superhuman's PMF process reveals how disciplined experimentation and metricdriven validation prevent common MVP mistakes—they didn't scale until 40%+ users said they'd be "very disappointed" without the product. See our common pitfalls guide for avoiding startup traps.

Mistake #1: Building in Isolation

The error: Founders spend months building without talking to customers, then launch and discover nobody wants it.

Why it happens: Building feels productive. Talking to customers feels scary (they might say your idea is bad).

The fix: Customer development before building. 2030 interviews minimum. You should know your target customers' problems, current solutions, and willingness to pay before writing any code.

Mistake #2: Confusing MVP with MMP

The error: Building "Minimum Marketable Product"—polished product with all expected features—instead of Minimum Viable Product—minimum experiment testing riskiest assumption.

Why it happens: Founders worry about looking unprofessional or embarrassing themselves with rough MVPs.

The fix: Remember MVP is for learning, not launching. Your first 10100 users should be early adopters who forgive limitations if core value is strong. Polish comes after validation, not before.

Mistake #3: Ignoring Behavioral Data

The error: Making product decisions based on what users say rather than what they do.

Why it happens: User feedback feels concrete. Behavioral data requires analysis.

The fix: Instrument everything. Track activation, retention, feature usage. When user feedback conflicts with behavioral data, believe behavior. Users asked for feature X but never use it? The behavior is truth.

Mistake #4: Optimizing for Acquisition Before Retention

The error: Spending on ads, content, and growth tactics before achieving strong retention.

Why it happens: Acquisition feels like progress. Growing user count feels like success.

The fix: Retention first, acquisition second. No point acquiring users if they churn immediately. Get to -->40% monthly retention (B2B) or -->20% (consumer) before scaling acquisition. Otherwise you're pouring water into leaky bucket.

Mistake #5: Building for Everyone

The error: Trying to serve multiple customer segments, use cases, or problems simultaneously.

Why it happens: TAM (total addressable market) looks small if you focus narrowly. Investors want big markets.

The fix: Nail one narrow use case for one specific customer segment before expanding. Better to be essential to 100 people than nicetohave for 10,000. You expand from beachhead, not by launching everywhere at once.

Mistake #6: Chasing Every Feature Request

The error: Adding every feature users request, leading to bloated product that's confusing and hard to use.

Why it happens: Saying yes feels customercentric. Saying no feels like ignoring feedback.

The fix: Most feature requests are symptoms, not solutions. Dig into why users want feature X. Often the underlying need can be solved better with simpler approach. Focus on making core use case excellent before adding more capabilities.

Mistake #7: Not Defining Success Criteria

The error: Building MVP without clear metrics defining success vs failure.

Why it happens: Founders afraid to commit to success criteria because failure becomes explicit.

The fix: Before building, write down: "We believe [hypothesis]. To validate, we'll measure [metric]. Success is [specific threshold]. If we don't hit it, we'll [pivot/iterate/shut down]." This forces clear thinking and prevents goalpostmoving after ambiguous results.

Mistake #8: Scaling Prematurely

The error: Hiring team, raising capital, spending on growth before validating productmarket fit.

Why it happens: Funding available, competitors moving fast, pressure to grow.

The fix: Scale validates productmarket fit. PMF validates scale. Get sequence right. Indicators of PMF: -->40% "very disappointed" on Sean Ellis test, strong retention, organic growth, clear repeatable acquisition channels. Until then, stay lean and iterate.