Frame the decision
Name the audience, requested decision and source pack. Review imported tables and extracted text; record gaps instead of allowing a polished narrative to conceal them.
Product-market-fit assessment
"Do we have product-market fit?" is a measurable question. This assessment scores the five signal families that actually answer it — demand, retention, usage, pain, and expansion — on a deterministic 0–100 scale, and tells you which signals are real and which are still hope.
Runs inside the Gixo Business journey. ~10 questions, 5 dimensions, deterministic scoring. Same answers, same score.
A product-market-fit assessment scores whether you have PMF by measuring five signal families — demand, retention, usage, pain, and expansion — each worth 20% of a deterministic 0–100 score, not a vibe check. Fixed questions and fixed answer values mean the same answers always produce the same score, and the result places you in one of four bands: Not Ready (0–39), Emerging (40–59), Close (60–79), or Ready (80–100). The output that matters most isn't the number — it's the gap list naming exactly which weak signals pulled the score down and what to do about each one.
Business · real output
Keep the recommendation connected to its inputs.
Inspect an actual Business report. A readable decision brief needs the question, supplied evidence, options, assumptions and next decision—not just a shorter source document.
Analyst Dossier: The U.S. Utility-Scale Battery Storage Market
A dossier built from four public EIA pages: a snapshot of figures, an executive summary, market size and growth, key drivers and trends, the competitive landscape, segments and buyers, an outlook, recommendations and a sources appendix, ending with the product's own faithfulness review of the text.
Inspect the source brief or data
Research target: The U.S. utility-scale battery storage market. Focus: How fast U.S. utility-scale battery storage capacity has grown, what is planned for 2025 and 2026, and how battery storage compares with the other kinds of generating capacity being added. Describe a trend, ranking or comparison only where a source states it, and say where the sources are silent.
Read the sample review notes
Open the full export and compare its statements with the cited sources before reuse.
Inspect the analysis
Separate supplied facts, calculated values, assumptions and recommendations. For variance work, reconcile the period, unit, baseline and actual value before interpreting the direction of a difference.
Make the handoff usable
Use options, trade-offs, risks and named next actions. Review the final artifact and, when using Analyst calculations, inspect the calculation evidence separately from the narrative.
Source packs, conflicts and review decisions
Select source documents, case context, working notes, assumptions, options and decision constraints before generation. Inspect claims, metrics, exact quotations, source tables and conflicting figures. Review decisions are scoped to the source or artifact and can become stale after evidence changes; unresolved evidence should stay visible.
Calculation evidence and scenario comparison
Keep investment, recurring costs, benefits, delays, growth, discount assumptions and scenario probabilities explicit. Inspect deterministic cash flow and the available return, payback and sensitivity outputs. Compare scenarios before selection, and inspect the formula workbook or calculation receipt separately from the narrative recommendation.
Analyst, repeat periods and execution
Inspect the dossier's source tables, working rows and columns, calculation ledger and workbook handoff. Metric packs retain reviewable calculation and source context. Preview source replacements for a new period before accepting them. Execution records distinguish an action owner, baseline, target, measured value, data source and the reason for a status change.
A calculation can be replayable while the supplied inputs remain wrong. A generated report is decision support, not independent diligence, audit assurance or investment advice.
What five dimensions does a PMF assessment score?
Each dimension is worth 20% of the score. Every answer option has a fixed value — the instrument can't be charmed.
Demand
What's the strongest demand evidence you have — nothing, expressed interest, paid customers, or inbound pull? And how hard are you pushing to get each sale? Pull scores above paid, paid above interest. The gap between "people say they want it" and "people chase us for it" is the whole question.
Retention
Is retention unmeasured, anecdotal, or measured in cohorts? When customers churn, do you learn why systematically or informally? Measured retention with systematic churn learning is the highest-scoring state — it's also the first thing a serious investor asks for.
Usage
Is the activation path unknown, known, or reliable? Is your product part of a workflow the customer considers critical, partial, or optional? PMF lives in workflows, not logins — a product that's missed when it breaks scores differently from one that's merely used.
Pain
Is the problem you solve nice-to-have, important, or urgent for the buyer? Do customers show must-have behavior — strong reactions when you raise prices or threaten to remove features — or polite indifference? Urgency funds budgets; niceness funds compliments.
Expansion
Does winning one customer in a segment make the next one easier — unknown, early signs, or repeatable? Are accounts expanding in isolated cases or as a pattern? Repeatability inside a segment is what separates fit from a handful of lucky logos.
Then: the bands
Not Ready (0–39): keep discovering. Emerging (40–59): signals exist but aren't measured. Close (60–79): measure what's anecdotal and tighten the segment. Ready (80–100): the evidence would survive an investor's diligence questions. Plus a gap list naming exactly what pulled the score down.
What does a product-market-fit assessment measure?
| Dimension | Weight | What it asks | Highest-scoring state |
|---|---|---|---|
| Demand | 20% | Strongest evidence: nothing, expressed interest, paid customers, or inbound pull? | Inbound pull — customers chasing you |
| Retention | 20% | Is retention unmeasured, anecdotal, or measured in cohorts? | Measured retention + systematic churn learning |
| Usage | 20% | Is the activation path known/reliable? Is the product critical, partial, or optional to the customer's workflow? | A workflow the customer considers critical |
| Pain | 20% | Is the problem nice-to-have, important, or urgent? Do customers show must-have behavior (react strongly to price/feature changes)? | Urgent problem + must-have behavior |
| Expansion | 20% | Does winning one customer make the next easier — unknown, early signs, or repeatable? | Repeatable expansion within a segment |
A real PMF Signal result: the banded score, all five dimension scores, and the gaps that pulled it down — each with a recommended next step.
Why is a deterministic PMF score better than a vibe check?
Most PMF conversations fail the same way: the founder cites enthusiasm, the investor hears anecdotes, and nobody agrees on what would count as evidence. This assessment forces the question into measurable states. "Retention signal: none / anecdotal / measured" is not a feeling — you either have cohort data or you don't. The famous benchmarks (the Sean Ellis 40% "very disappointed" test, cohort retention curves) are things you go measure; this instrument tells you whether your evidence base is built enough for those numbers to mean anything.
Because scoring is fixed, the assessment also works between people. Have each founder run it separately: where the answers differ, you've found the actual disagreement — usually "is retention measured or anecdotal" — and that's a more useful conversation than debating the score itself.
It's a self-assessment, so it measures readiness as honestly as you answer. The output that matters isn't the number; it's the gap list — the shortest written path from where your evidence is thin to where it would hold.
Frequently Asked Questions
How does this PMF assessment fit into Gixo Business?
Scoring is deterministic (fixed questions, fixed answer values, five dimensions at 20% each), so the result is reproducible. Use the gap list to decide which source packs, business briefs, and decision materials to create next in the Business workspace.
How long does it take?
About ten minutes: roughly ten questions across demand, retention, usage, pain, and expansion, each with three or four defined answer states. The honest work is picking the answer that's true rather than the one you hope is true.
What does the result give me beyond a score?
Per-dimension scores, a band (Not Ready / Emerging / Close / Ready), and a gap list: the specific weak answers that lowered the score, each mapped to a next step — often a document the Gixo Business workspace can generate, like a retention analysis brief or segment deep dive.
Is this the same as the Sean Ellis test?
No — it's upstream of it. The Sean Ellis survey measures one demand signal from your users. This assessment audits your whole evidence base across five dimensions, including whether you're set up to run measurements like that credibly at all.
What are the PMF score bands and what do they mean?
Not Ready (0-39): keep discovering — the evidence base is too thin to act on. Emerging (40-59): signals exist but aren't measured yet. Close (60-79): measure what's still anecdotal and tighten the segment. Ready (80-100): the evidence would survive an investor's diligence questions. Every result also comes with a gap list naming exactly what pulled the score down, each mapped to a next step.
How is a PMF assessment scored?
It's deterministic: roughly ten questions spread across five dimensions (demand, retention, usage, pain, expansion), each dimension weighted 20%, and each answer option tied to a fixed point value. Because the scoring is fixed rather than judged, the same answers always produce the same score — which also means two co-founders who disagree on the score have actually found a disagreement about the underlying evidence, not about the instrument.
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