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Every factual claim in this course traces to something here, grouped by the module it serves. Where a publisher sells the thing it is measuring — a consultancy selling advisory work to private equity, a venture firm publishing the benchmarks its own portfolio is judged against, a firm describing its own operating model — that is stated in the note and again in the lesson prose. Sources that could not be opened were cut rather than cited from a summary.
The structure a design proposal lands inside, and where a UI change can enter the arithmetic at all.
WhyThe regulator’s own plain-language description of a private fund: “A fund is an entity created to pool money from multiple investors—often referred to as limited partners,” managed by an adviser with “broad discretion to make investment decisions on behalf of the fund… in accordance with the fund’s investment strategy.” Used for the legal skeleton in the fund-and-clock lesson. It is deliberately about fund formation rather than buyout operations, so it says nothing about hold periods or value creation plans — those come from Bain below.
WhySource for: holding periods at exit “now hover at around seven years – up from an average of five to six years from 2010 to 2021”; “32,000 unsold companies worth a stunning $3.8 trillion”; distributions to LPs as a share of NAV “mired below 15% for four consecutive years”; and the “12 is the new 5” framing — deals that once “required only 5% annual growth in… EBITDA” now needing “around a 10% to 12% average annual EBITDA growth.” Bain sells advisory services to private-equity firms, which is named in the lesson prose: this is an interested party describing its own market, and it is the only current source this course could open for these specific figures. The what-would-you-cut-instead lesson (writing the case) also cites it, for the same pressure behind that lesson’s displacement question rather than a new claim.
WhySource for the firm-level figures quoted in the fund-and-clock lesson: $103 billion in assets under management, 85+ portfolio companies, and the self-description “we believe our Operational Intelligence is our advantage,” all stated as of June 30, 2026.
WhySource for the five named private-equity strategies (Endeavor, Foundation, Flagship, Perennial, Evergreen Private Equity) and the 150+ investment professionals and 690+ transactions figures. The Evergreen strategy is why the lesson says a fixed clock is the common case rather than a law.
WhySource for “Vista’s Agentic Factory — In-house team of AI engineers and specialists that rapidly designs, builds, and monetizes Agentic AI products,” the “team of 100+ operators,” and the network claim “With 85+ companies and over 250 million users, Vista’s portfolio is a proving ground for agentic products at scale.” Note what is absent: nothing here describes how funding decisions are made internally, which is why the course says it is inferring the incentive from public material rather than describing a process it has seen.
WhyPrimary source for the portfolio lesson. Source for “a platform purpose-built to scale Agentic AI across our enterprise software portfolio,” the statement that it is enabled by the Value Creation team and hyperscaler partnerships, and the offer to portfolio companies of “early access to next-generation agentic tooling, engineering collaboration and go-to-market channels” — language the lesson reads as an offer rather than a mandate. It also forecasts 5–10 agents per user and billions of deployed agents; the lesson flags those explicitly as the firm’s stated ambition and refuses to treat them as evidence.
WhySource for “90+ companies serving more than 2.5 million enterprise customers and more than 750 million users worldwide,” and for the Duck Creek claims first-notice-of-loss agent as a named instance of something built through the Agentic Factory. These figures do not reconcile with the 85+ companies and 250 million users on Vista’s own value-creation page, which the fund-and-clock lesson uses as a worked example of why you quote one source rather than averaging two.
WhyPrimary source for the P&L lesson. Question 103.01 pins “earnings” in EBIT and EBITDA to net income as presented under GAAP, and requires a differently-calculated measure to be labelled Adjusted EBITDA. Questions 100.01–100.06 catalogue how a technically-true number becomes misleading — excluding normal recurring operating expenses, changing the calculation between periods, adjusting only for charges and not gains — which maps directly onto the ways a design case overclaims.
WhyUsed only for the shape and scale of a software income statement, because portfolio companies do not publish theirs: product revenue $4,472,317K against professional services of $211,629K, gross profit $3,146,141K, and operating expenses split into sales and marketing $2,062,137K, research and development $1,969,472K and general and administrative $549,697K for the twelve months ended 31 January 2026. Snowflake is public and venture-backed rather than sponsor-owned, so treat it as an illustration of statement structure, not as a benchmark for a portfolio company.
WhyThe critique the P&L lesson uses when warning against citing design-ROI indices: that the underlying report was never put through peer review and that its findings are, in the authors’ words, “both inaccurate and unsupportable” as research, since an index built on correlation cannot carry a causal claim. Named in the prose as a second interested party — Mauro’s firm sells human factors engineering and usability research, so this is a dispute about whose evidence for design’s value should count. The publisher’s own report page could not be opened while this course was written, so its headline percentages are cited nowhere in these lessons. The attribution-trap lesson (writing the case) cites it too, for the same objection applied at industry scale rather than to a single product’s quarter.
Definitions first, benchmarks second — and every benchmark here is published by a firm with an interest in how the companies it funds or lends to are measured, which the lessons name in prose rather than only in these notes.
WhyPrimary source for the ARR lesson, and cited again in the retention and CAC-payback lessons. Source for “ARR (annual recurring revenue) is a measure of revenue components that are recurring in nature. It should exclude one-time (non-recurring) fees and professional service fees,” for bookings as “the value of a contract… a contractual obligation on the part of the customer to pay the company,” and for revenue “recognized when the service is actually provided or ratably over the life of the subscription agreement” under GAAP. Also the churn-side mirror of the retention formulas — gross churn as “MRR lost in a given month / MRR at the beginning of the month” and net churn as “(MRR lost minus MRR from upsells) in a given month / MRR at the beginning of the month” — and the paid-versus-blended CAC distinction. Note what it does not give: a scope for ARR is not an annualisation formula, and the ARR lesson says so rather than inventing one.
WhyPrimary source for the CAC-payback lesson. The load-bearing admissions are “some companies adjust CAC payback for gross margins and some don’t” and “to compare performance consistently across companies, we gross margin-adjusted all our CAC paybacks” — a firm with direct visibility into many companies’ reporting saying the raw numbers were not comparable until it imposed one convention. Also cited in the ARR lesson for “Different startups use different methods to calculate and report metrics, so we standardized our metrics calculations,” which is the evidence that operating-metric definitions genuinely diverge rather than merely being explained badly.
WhyPrimary source for the retention lesson. Gives net revenue retention as arithmetic — “(Monthly Recurring Revenue in December of 2024 only from customers who were customers in December 2023) ÷ (Total MRR in December 2023)” — with gross retention as “the same calculation… excluding the upsells, cross-sells, and price increases,” which is why one is capped at 100% and the other is not. Their sample is private B2B software companies above $1M of ARR and they argue that benchmarking against public SaaS companies is of limited usefulness, a caution the lesson repeats in prose.
WhyPrimary source for the subtractive closing lesson, which uses it as a table of numbers to practise declining to claim, and cited in the retention lesson for the good/better/best net revenue retention convention of 100% / 110% / 120%+ and in the CAC-payback lesson for payback bands of 12–18 / 6–12 / 0–6 months. The payback lesson names a specific absence: the version of this page read while the lessons were written does not state which CAC denominator convention its bands assume, which is exactly the ambiguity that lesson exists to warn about. It is also where Bessemer used the Rule of 40 as a named benchmark in the era it has since reversed on.
WhyPrimary source for the Rule of 40 lesson, and the whole reason that lesson can correct the folk version. Feld credits “a late stage investor” at a board meeting rather than claiming the rule, states it as “your growth rate + your profit should add up to 40%,” scopes it to SaaS companies at scale (assume at least $50 million in revenue) while noting it correlates from around $1 million of MRR upward, and concedes that the profit term is unsettled — EBITDA, operating income, net income, free cash flow or something else — before stating only his own preference for EBITDA. The investor and their firm are unnamed in the post and the lesson does not speculate about either.
WhyThe critique the Rule of 40 lesson pairs with Feld: “assigning equal weighting to growth and profitability for late stage businesses is flawed,” and “the traditional Rule of 40 math is dead wrong as you approach breakeven and turn free cash flow positive,” replaced by (growth × a multiplier) + free cash flow margin at roughly 2x for late-stage private companies and 2–3x for public ones. They scope their own rule too, noting it is harder to apply to earlier-stage private businesses growing above 125% and burning above 75%. The lesson’s point is the provenance: this is not a critique from the investors who popularised the rule in 2015, it is a reversal by a firm that promoted the rule as a benchmark in its own later reports.
WhyThe worked contrast in the retention lesson: a filed net revenue retention rate of 126% as of 31 January 2025, computed over a trailing two-year measurement period against a cohort defined in that period’s first month, with customers who stopped using the platform kept in the denominator contributing zero. Set beside SaaS Capital’s December-to-December MRR method, it is two credible sources, two methods, one metric name. The lesson deliberately does not recompute Snowflake’s business under the other method — that would be invented numbers wearing a filed company’s name.
What a claim has to carry before it is safe to make in a room that can check it — and, in the attribution lesson, two well-known design-ROI figures this course looked for and refuses to print. Every source in this group that argues design pays also sells design services; that is named in the prose each time it is leaned on.
WhyPrimary source for the one-page-structure lesson. The regulator concedes that performance metrics “can vary significantly from company to company and industry to industry, depending on various facts and circumstances,” then says what has to travel with one: “A clear definition of the metric and how it is calculated,” “a statement indicating the reasons why the metric provides useful information to investors,” and “a statement indicating how management uses the metric in managing or monitoring the performance of the business” — plus the sentence the lesson pins above the desk, that a company “should also consider whether there are estimates or assumptions underlying the metric or its calculation, and whether disclosure of such items is necessary for the metric not to be materially misleading.” Read as a specification for an honest metric claim it is close to the four-field case, but it governs public disclosure of a metric a company already reports, not a proposal for new work. The lesson says so explicitly and does not claim the SEC requires a hypothesis or a mechanism.
WhyPrimary source for the measurement lesson, cited for the position this course half-agrees with: “We want ROI calculations to be as accurate and realistic as reasonably possible. But, ultimately, these are only estimates,” and the advice to do “only as much work on these calculations as is necessary.” The lesson accepts that an estimate is a legitimate artifact and then parts company on what happens to one as it travels — an estimate retold twice becomes a result, and the fix is to make the assumptions structurally inseparable from the number rather than adjacent to it.
This course argues against overclaiming, so it holds itself to the same rule: every number on these pages links to a source that was opened and read. Where a source sells the thing it is describing, the prose says so. If a claim looks unsupported, check the resource list and tell your teaching agent — a lesson that overclaims is a bug.