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Startup MRI

Startup Score Calculator — Score Your Idea in 60 Seconds

A structured 0–100 score across 7 dimensions — validation, market opportunity, competition, distribution, monetization, build difficulty, and founder fit — in under 60 seconds. Free, no signup.

Last updated · August 26, 2026

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Quick answer

What is a startup score calculator?

A startup score calculator is a tool that takes a one-sentence startup idea and a small set of structured inputs (target audience, monetization model, distribution channel, technical background, and the risks the founder already sees) and returns a numeric score on a 0–100 scale, usually broken into per-dimension sub-scores. The score is a structured way to compare ideas and to surface the assumptions the plan most depends on. A score calculator does not predict whether a startup will succeed — no tool can — and any calculator that promises a correlation with success is making a claim it cannot back up. The honest use of a startup score is as a decision-making input: it tells you which of the named assumptions to test first, which of the dimensions is the weakest, and which experiment is cheapest to run. Yibud's startup score calculator is the Startup MRI engine. It produces the score in under 60 seconds, free, with no signup. The score comes from a deterministic rule engine, so the same inputs always produce the same number.

Key takeaways

What a startup score is, in plain language

  • A startup score is a 0–100 number derived from a set of structured inputs — the idea, the audience, the monetization model, the distribution channel, the technical background, and the risks the founder already sees.
  • A score is a decision-making input, not a verdict. The same score can come from very different combinations of dimensions, and the per-dimension breakdown is usually more useful than the overall number.
  • Deterministic rule engines produce more trustworthy scores than LLM-generated numbers: the same inputs always produce the same output, and every score traces back to a specific fired rule.
  • A score cannot predict whether a startup will succeed. The honest use of a score is to identify the assumption most worth testing next and to prioritize the cheapest experiment that can produce evidence.
  • Free score calculators that pair the number with a structured report (risks, MVP scope, first-customer plan) are most useful to first-time founders — the scoring is deterministic, so the same inputs always produce the same report.

What a score is built from

Four inputs a startup score is built from

Every score is the result of four structured inputs. The clearer the inputs, the more useful the score.

  • Input 1

    The idea itself

    A one-sentence description of what the founder wants to build and who it is for. The clearer this input, the more useful the score. 'An AI tool for marketers' is too vague; 'a Slack bot that summarizes customer-call transcripts for SMB sales teams' is clear enough to score.

  • Input 2

    The target audience

    A named, reachable segment — not 'small businesses' or 'everyone'. The narrower the audience, the sharper the score. 'Independent product designers in North America who run their own studios' is the kind of input that produces a useful score.

  • Input 3

    The business model

    Subscription, one-time purchase, freemium, marketplace, ads, or 'not sure'. The business model drives the willingness-to-pay assumption, which is the single most-tested assumption in a startup score.

  • Input 4

    The distribution channel and technical background

    How the founder plans to reach the buyer (SEO, Reddit, Product Hunt, community, cold outreach, paid ads) and the founder's technical background (non-technical, beginner, intermediate, experienced). The two together determine whether the chosen distribution matches the chosen build.

Scoring factors

Seven factors that influence a startup score

The seven dimensions the score is built from. Each one is scored 0–100 and combined into the overall startup score.

  • Factor 1

    Market opportunity

    How large and how reachable is the target market? The score rewards large markets with strong, repeated pain and penalizes small markets with weak workarounds already in place.

  • Factor 2

    Competition

    How saturated is the market? The score rewards sharp angles into otherwise crowded markets and penalizes me-too products entering markets dominated by well-funded incumbents.

  • Factor 3

    Distribution

    Can the founder actually reach the buyer? The score rewards chosen distribution channels that match the named target audience and penalizes mismatches (paid ads to a sub-$10/month SaaS, cold outreach to a consumer segment).

  • Factor 4

    Monetization

    Does the business model produce revenue at the chosen price? The score rewards business models with evidence of willingness to pay and penalizes business models whose only evidence is the founder's belief.

  • Factor 5

    Build difficulty

    How long will the MVP take and what skills does it require? The score rewards MVPs that match the founder's skills and penalizes ambitious builds paired with non-technical founders or limited runway.

  • Factor 6

    Founder fit

    Does the founder's skills, time, and existing audience match the work? The score rewards domain expertise, an existing audience, and shipped history; it penalizes first-time founders entering unfamiliar markets with no audience.

  • Factor 7 · highest weight

    Validation feasibility

    Can the founder actually test this idea before building it? This dimension is weighted highest in the overall score because cheap, behavior-signal validation is what separates a fundable hypothesis from a sunk-cost build. The score rewards named waitlists, named interview counts, specific buyer numbers, and clear JTBD statements; it penalises vague audiences, generic evidence, and high-cost validation paths (hardware, regulated channels, clinical trials).

How to use it

How founders use a startup score

Four uses, framed as decision-making inputs. The score is a starting point for action, not a verdict.

  1. Compare two or three candidate ideas on the same dimensions

    Score each candidate through the calculator. The per-dimension breakdown shows you which one is strongest on the dimensions you care about (often distribution and founder fit) and which one is weakest on the dimensions you are most worried about (often monetization or build difficulty).

  2. Identify the single critical assumption most worth testing

    Read the report's top three risks. The single critical assumption is the one whose failure would invalidate the rest of the plan. Test that assumption first, before any other.

  3. Prioritize the cheapest validation experiment

    The calculator recommends the cheapest experiment for each dimension — usually a problem interview, a landing page, or a 30-day concierge. Pick the experiment that targets the weakest dimension and run it before you write any code.

  4. Track how the score changes as you learn

    Re-run the score after each validation experiment. The score should move as you replace beliefs with evidence, and the per-dimension shifts tell you which of your experiments is producing the strongest signal.

FAQ

Frequently asked questions about startup scoring

Short answers, in the same vocabulary the Startup Validation hub uses. Longer answers live in the linked articles.

What is a good startup score?
There is no single 'good' number. A score in the 70s across most dimensions with a clear critical assumption is very different from a score in the 90s that hides a single weak dimension. The per-dimension breakdown is usually more useful than the overall number, because it tells you which assumption to test first.
Can a startup score predict success?
No. A startup score is a structured input to a decision, not a prediction. The same score can come from very different combinations of dimensions, and no published research links any particular score to any particular success rate. The honest use of a score is to identify the assumption most worth testing next — not to predict outcomes.
How is a startup score calculated?
A startup score is calculated by scoring each of several dimensions (validation, market opportunity, competition, distribution, monetization, build difficulty, founder fit) on a 0–100 scale and then combining the per-dimension scores into an overall number. Yibud's score is produced by a deterministic rule engine, so the same inputs always produce the same output.
What factors influence a startup score?
Seven factors: validation, market opportunity, competition, distribution, monetization, build difficulty, and founder fit. Each factor is scored on a 0–100 scale. The factors are weighted and combined into the overall score, with the per-dimension breakdown usually more useful than the overall number.
Is a startup score calculator free?
Yes. A free Startup MRI score takes about five minutes and produces a structured 7-dimension report plus the overall score. No signup, no payment, no email required for the first report. The full report is yours to keep.
How long does a startup score take?
A free Startup MRI score takes about five minutes to produce. The full validation sequence — interviews, landing page, pricing experiment, 30-day concierge — takes two to six weeks. The score is the first five minutes; the sequence is the next two to six weeks.
Should I trust a high startup score?
Treat a high score as a starting point, not a verdict. A high score across all seven dimensions is rare; more often the score is high on four dimensions and weak on two or three, and the weak dimensions are where the work is. Read the per-dimension breakdown, not just the overall number.
What is the difference between a startup score and a startup valuation?
A startup score is a 0–100 number that describes how the idea rates across a small set of named dimensions (validation, market, competition, distribution, monetization, build, founder fit). A startup valuation is a dollar figure, derived from revenue, growth, market size, and comparable transactions. The two are different objects: a high score is not the same as a high valuation, and a low score is not the same as a low valuation. Yibud's calculator produces a score, not a valuation.

Why the score is structured, not summed

Established methodology: the scorecard is a hypothesis-testing surface, not a verdict

Steve Blank's customer-development framing — a startup is a hypothesis-testing organization, not a feature-building organization — and Eric Ries's validated-learning loop both rest on the same observation: assumptions are not facts, and the cheapest way to test them is before you build. The 7-dimension structure below exists for the same reason. Each dimension is a hypothesis your plan depends on. The score on each is the engine's structured read on how clearly your inputs address it. The overall number is a weighted sum; it is not a verdict. Rob Fitzpatrick's Mom Test rule — talk about the customer's life, not the idea — is what makes the per-dimension interpretation auditable: every score traces back to a specific input, and the inputs are the founder's own words.

Score bands

What each band means in decision terms

Four bands, derived from how Startup MRI's rule engine tends to distribute scores across the seven dimensions. None of them predict success. Each tells you what to do next.

  1. Band 1 · 0–39

    Foundations missing

    At least one critical assumption is unsupported by the inputs you provided. Read the per-dimension breakdown to find the lowest dimension, then run the cheapest experiment that produces a behavior signal in that dimension before any build work. The most common cause is a vague audience description, an 'unsure' monetization model, or a chosen distribution channel that does not match the named buyer.

  2. Band 2 · 40–59

    Mixed signals

    Some inputs are well-aligned, others are not. The work is in the gap between the strongest and the weakest dimensions — usually a gap of 20 or more points. Read the per-dimension breakdown and pick the two weakest dimensions; each one has a specific test that produces evidence in 2–4 weeks.

  3. Band 3 · 60–79

    Most inputs aligned

    The most common pattern in real Startup MRI reports. Three or four dimensions are well-aligned; one or two are still unclear. The remaining work is execution and validation, not idea-shape. Read the weakest dimension to find the single critical assumption; that is the one whose failure would invalidate the rest of the plan.

  4. Band 4 · 80–100

    Uncommon in practice

    All seven dimensions are aligned. Rare, because most inputs have at least one or two dimensions that are unclear. Treat a high score as a reason to move fast, not a reason to skip validation — the assumption most worth testing is still the weakest dimension, even when the score is high. A perfect score on inputs is not a guarantee on outcomes.

Common patterns

Four common scorecard patterns and what they usually mean

Patterns the engine sees often enough that the interpretation is worth naming. None are verdicts — each is a hypothesis about where the work is. Treat the matching pattern as a starting question, not a conclusion.

  • High market, low distribution

    A real, reachable buyer is named, but the channel chosen does not match. The most common reading is that the founder has a clear audience in mind but no credible plan for reaching them. The typical fix is to switch the named channel to one that matches the audience — SEO for high-intent searchers, warm intros for low-trust buyers, community for buyer-of-buyer dynamics, cold outreach for B2B procurement gates.

  • High distribution, low monetization

    The channel works, but the chosen business model does not produce revenue at the named price. The most common reading is reach without willingness to pay. The typical fix is either a willingness-to-pay test at the actual price (a 30-day concierge, a paid pilot, a deposit) or a different monetization model — most often subscription vs one-time, or a price-point adjustment inside the same model.

  • High founder fit, low market

    The founder has shipped history and domain expertise, but the named opportunity is too small for the founder's time. The most common reading is: the founder's best move is to widen the audience, narrow the price point to a higher tier, or choose a different idea entirely. Founder expertise does not rescue a market that cannot support the business model at the named price.

  • Strong everywhere except build

    All six other dimensions are aligned, but the build difficulty is high for the founder's skills or runway. The most common reading is: build it anyway, because the other dimensions are strong and the build is solvable — but budget honestly for the time and capital the build will take. The alternative — a no-code, concierge, or smoke-test MVP — can produce the willingness-to-pay signal without the full build, and is usually cheaper than skipping validation entirely.

Reading flow

The four-step reading order we recommend

The order in which to read the report, based on which sections contain the actual signal. Reading the report in this order is what turns the score into a next action.

  1. Read the overall number once

    Read the overall 0–100 score and then move on. The overall number is a summary; it does not contain the actionable signal. Spending more than a few seconds on it is the most common mistake founders make when reading their first report.

  2. Find the single critical assumption

    The report names a single critical assumption — the one whose failure would invalidate the rest of the plan. This is the most actionable section in the whole report. The assumption usually sits in the dimension with the lowest per-dimension score, but not always — sometimes a moderately low dimension contains the most consequential assumption.

  3. Read the per-dimension breakdown

    Seven per-dimension scores, each with a short plain-language interpretation and the positive and negative signals that fired. The signals are the auditable trail — every score traces back to a specific input and a specific rule that fired because of that input.

  4. Run the cheapest experiment on the weakest dimension

    The report recommends the cheapest experiment for the weakest dimension — usually a problem interview, a landing page, or a 30-day concierge. Run that experiment this week. Re-run the score after the experiment produces evidence; the per-dimension shifts tell you which experiment was producing the strongest signal.

Closing note

The score is the start, not the finish line

The score is a structured way to compare ideas and surface the assumption most worth testing. The decision — and the outcome — are still yours. Re-run the score after each experiment; the per-dimension shifts tell you which of your experiments is producing the strongest signal.

What you receive

What's in every Startup MRI score

A numeric score plus the structured report that goes with it. The number is a starting point; the report is the part you act on.

  • Overall 0–100 score

    A single number combining seven per-dimension sub-scores, derived from a deterministic rule engine. The same inputs always produce the same number.

  • Per-dimension breakdown

    Seven per-dimension sub-scores — validation, market, competition, distribution, monetization, build, founder fit — each with a short plain-language interpretation.

  • Three top risks

    The risks most likely to invalidate the plan, each with a named failure mode and a recommended validation experiment.

  • MVP blueprint

    A small list of features to build first, a list to skip, and a complexity estimate.

  • First-customer plan

    The recommended acquisition channel, why it was chosen, and a 4-step plan you can run in the first 6 weeks.

Example score

See a real example score before you run your own

Three worked examples — each shows the score, the per-dimension breakdown, and the report that goes with it.

Limitations

What a startup score is not

The honest list, so you do not mistake the number for something it is not.

  • It does not predict whether your startup will succeed. No tool can. The score is a list of risks and the assumption most worth testing next — not a verdict.
  • It is not a valuation. A startup score is a 0–100 number describing how the idea rates on named dimensions. A valuation is a dollar figure derived from revenue, growth, and comparable transactions.
  • It is not a market-research number. The engine has no live data feeds; the score comes from the inputs the founder typed in.
  • It is not personalised advice. The score is calibrated to the founder's inputs and a small set of named defaults — not to a founder's specific industry or geography.

Calculate your startup score

Five short questions. A structured 7-dimension score in under 60 seconds. Free, no signup, and the same inputs always produce the same report.

Start the free score →Use the checklist →Or try the startup idea validator →See the decision chain →What a score of 60 means →