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Product-Market Fit Survey Analyzer

Analyze Sean Ellis PMF survey results with NPS and engagement signals. Enter values for instant results with step-by-step formulas.

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Worked Examples

Example 1: Early-Stage SaaS

Problem: B2B SaaS with 200 active users. 30% very disappointed, 40% somewhat, 30% not. NPS +15.

Solution: PMF score 30% is approaching but not there. Focus on converting 'somewhat disappointed' users. Interview top 30% to understand what makes them love it, then replicate for others.

Result: 30% PMF (below threshold) | Focus on core users | Iterate before scaling

Example 2: Consumer App Launch

Problem: Consumer app surveyed 500 users. 50% very disappointed, 30% somewhat, 20% not. NPS +45.

Solution: Strong PMF at 50%! Combined with +45 NPS indicates ready to scale. Invest in growth, paid acquisition, and virality features.

Result: 50% PMF (strong) | NPS +45 | Ready for growth investment

Example 3: Struggling Product

Problem: 200 responses: 12% very disappointed, 25% somewhat, 63% not. NPS -10.

Solution: No PMF at 12%. Major pivot or repositioning needed. Stop growth spending. Interview the 12% who love it—why? Rebuild around their use case.

Result: 12% PMF (no fit) | Pivot needed | Focus on core 12%

Frequently Asked Questions

What is the Sean Ellis PMF survey?

The Sean Ellis test asks users: 'How would you feel if you could no longer use [product]?' The key metric is the percentage answering 'Very disappointed.' If 40%+ say very disappointed, you likely have product-market fit.

How many survey responses do I need?

Minimum 100 responses for directional insight, ideally 200-400 for statistical reliability. Survey users who have experienced your core value proposition—not just signed up but actually used the product meaningfully.

Who should I survey for PMF?

Survey active users who have experienced your product's core value. Avoid churned users, very new users, or those who never engaged. You want to know if people who 'get it' would miss it.

Why might my result differ from another tool or reference?

Differences typically arise from rounding conventions, the specific version of a formula (for example, simple vs compound interest), or unit inconsistencies between inputs. Check that both tools are using the same formula variant and the same units. The References section links to the authoritative source behind the formula used here.

What inputs do I need to use Product-Market Fit Survey Analyzer accurately?

Each field is labelled with the required unit (metric or imperial). Gather your source values before starting — for example, a weight measurement in kilograms, a distance in metres, or a dollar amount — and enter them exactly as measured. The formula section on this page lists every variable and explains what each represents.

How do I verify Product-Market Fit Survey Analyzer's result independently?

The Formula section on this page shows the equation used. You can reproduce the calculation manually or in a spreadsheet using those steps. Compare your answer against the worked examples in the Examples section, which use known reference values so you can confirm the calculator is behaving as expected.

Background & Theory

The Product-Market Fit Survey Score Analyzer applies the following established principles and formulas. Break-even analysis identifies the sales volume at which total revenue equals total costs, producing neither profit nor loss. The formula divides total fixed costs by the contribution margin per unit, where contribution margin equals selling price minus variable cost per unit. If a software product has $50,000 in monthly fixed costs and each licence generates $20 above its variable cost, break-even requires 2,500 unit sales per month. Above that threshold, each additional unit contributes directly to profit. Gross margin expresses the percentage of revenue remaining after direct cost of goods sold: gross margin equals revenue minus COGS, divided by revenue. A SaaS company with 80 percent gross margins retains $0.80 of every revenue dollar to cover operating expenses, while a manufacturer with 30 percent gross margins faces much tighter operating leverage. Customer acquisition cost (CAC) divides total sales and marketing expenditure in a period by the number of new customers acquired in that same period. Customer lifetime value (LTV) estimates the total profit attributable to a customer relationship. The standard formula multiplies average revenue per user (ARPU) by gross margin and divides by the monthly churn rate. A business with $50 ARPU, 75 percent gross margin, and 2 percent monthly churn has an LTV of $1,875. The LTV:CAC ratio benchmarks unit economics health; a ratio above 3:1 is generally considered sustainable, while ratios below 1:1 indicate the business is acquiring customers at a loss. Burn rate measures monthly cash expenditure net of revenue. Cash runway equals current cash reserves divided by net monthly burn. A company with $1.2 million in the bank burning $100,000 per month has twelve months of runway. The Rule of 40 is a benchmark for SaaS health: the sum of annual revenue growth rate (as a percentage) and profit margin (as a percentage) should equal or exceed 40. High-growth companies burning cash can still pass this rule if their growth rate compensates.

History

The history behind the Product-Market Fit Survey Score Analyzer traces back through the following developments. Early economic thought centred on mercantilism, the 16th and 17th century doctrine that national wealth derived from accumulating precious metals through export surpluses and colonial extraction. Adam Smith's "Wealth of Nations" in 1776 dismantled this framework, arguing that genuine prosperity arose from specialisation, division of labour, and freely operating markets. David Ricardo extended Smith's work with the theory of comparative advantage in 1817, demonstrating mathematically that mutually beneficial trade was possible even when one country was less productive in every industry. Alfred Marshall's "Principles of Economics" published in 1890 provided the modern framework of supply and demand curves, consumer surplus, price elasticity, and marginal analysis, establishing neoclassical economics as the dominant academic paradigm for decades. The Great Depression exposed the limits of laissez-faire assumptions, and John Maynard Keynes's "General Theory of Employment, Interest and Money" in 1936 argued that private-sector aggregate demand failures required countercyclical government fiscal intervention to restore full employment, shifting the policy consensus toward active macroeconomic management. The post-World War II decades constructed mixed-economy models combining market allocation with expanded welfare states and Keynesian demand management. Milton Friedman and the Chicago School challenged this consensus from the 1960s onward, championing monetarism and arguing that stable money supply growth was superior to discretionary fiscal policy. Their influence shaped the deregulatory and privatisation policies of the Reagan and Thatcher eras in the 1980s. Behavioural economics emerged through the work of Daniel Kahneman and Amos Tversky in the 1970s and Richard Thaler in the 1980s, using psychology to demonstrate that real human decision-making deviates systematically from rational-actor models through heuristics and biases. The rise of the internet and mobile platforms in the 2000s and 2010s created a new category of platform economics, where network effects, near-zero marginal cost of digital goods, and two-sided market dynamics generated winner-take-most competitive outcomes requiring new analytical frameworks for business valuation.

References