PROVEPT-PRV-9590

Case Study Metrics

Case Study Metrics proves a claim by naming a real customer and showing the exact, documented numbers their result came in at.

Definition

What it does

It names a specific subject, a company, a person, or a described study group, states the outcome as concrete figures, and supplies the context that makes the number legible: what was done, over what span, and from what starting point. Rather than asserting that the product works, it points to one identifiable case where it did and quantifies the change. The reader is handed a result that appears traceable to a named source, with enough surrounding detail to suggest it was measured rather than invented.

Why it works

A named case with a number attached reads as evidence instead of advertising, and the specificity signals the claim survived contact with a real situation. A figure tied to an identifiable subject and a method is harder to wave off as puffery than a round boast, because it invites a verification the reader almost never performs. Before-and-after context lets people map the result onto their own starting point, so the outcome feels reachable. The named party also lends borrowed credibility: if it worked for a recognizable company or a documented study group, the reader quietly extends the result to themselves.

Where it appears

Formatsprint ads, email, sales pages, sales letters, case studies, landing pages, product pages, social ads, and 1 more formats
Position in the copyproof, hooks and openers, stories, body copy, headlines, bullets, subject lines
IndustriesB2B SaaS, business coaching, advertising agencies, marketing education, fintech, and 15 more industries
In the Taxonomy79 examples from 49 brands

* Most frequent first, based on materials selected for the Persuasion Taxonomy corpus.

Examples of Case Study Metrics

2020s·Phenom

“After implementing Phenom: quadrupled applicants-to-hires. Time-to-fill reduced from 60+ days.”

A named childcare employer's recruiting results shown as before-and-after hiring metrics after adopting the platform.

Why it’s this techniqueThe copy attaches measured outcomes to a named adopter, reporting that the client 'quadrupled applicants-to-hires' and cut 'Time-to-fill reduced from 60+ days', so the product's value arrives as a client's result rather than a claim. the structural tell is the pre/post hinge 'After implementing Phenom', which frames each figure as a delta caused by adoption, separating this from a bare statistic or a feeling-based testimonial. the scale figures for employees and locations could read as reputation bragging, but the copy is built on the quantified before-and-after outcomes tied to the adopter.

Classification

Primary technique
PT-PRV-9590
Classification confidence
0.82
Source
Phenom, 2020s web page
2020s·ServiceNow

“CVS Health achieved a 66% reduction in IT service costs”

A named healthcare company's IT cost cut attributed to deploying the AI platform.

Why it’s this techniqueThe claim converts a named customer's result into a hard number, staking everything on 'CVS Health achieved a 66% reduction in IT service costs.' The percentage does the persuading; it turns an abstract promise of savings into an audited-sounding outcome a reader can weigh. the structural tell is the pairing of a specific named account with a quantified before-to-after delta, 'a 66% reduction,' rather than a vague testimonial mood or a feature list. That named-client-plus-figure shape is what marks this as evidence pulled from a documented deployment, presenting proof through measurement instead of adjective or endorsement alone.

Classification

Primary technique
PT-PRV-9590
Classification confidence
0.79
Source
ServiceNow, 2020s web page
Also an example of
2020s·AG1

“In a single-arm study that assessed the self-perceived efficacy of AG1 in a group of 35 healthy adults ages 25-48 over 90 days, 97% of participants felt more energy and noticed less gas and bloating at 1 month. At 3 months, 94% of participants felt more calm, and 97% felt digestion improved.”

A documented efficacy study on the supplement, reported with sample size, ages, and duration.

Why it’s this techniqueThe copy dresses a product claim in research clothing, citing a 'single-arm study' of '35 healthy adults ages 25-48 over 90 days' and reporting that '97% of participants felt more energy' against dated checkpoints. The move lives in the numbers bolted to a protocol: sample size, age band, duration, and paired readouts at '1 month' and 'At 3 months' give the shape of a reported trial rather than a boast. What separates this from a stack of testimonials is that the proof arrives as measured percentages keyed to a study frame, so belief rides the metrics instead of any named voice.

Classification

Primary technique
PT-PRV-9590
Classification confidence
0.78
Source
AG1, 2020s web page
2020s·Agora Financial

“I made a 6,000% gain on a social media startup agency called Buddy Media with PayPal founder Peter Theil and billionaire entrepreneur Mark Pincus. (The company was worth less than $5 million when I invested. A few years later they sold it for $900 million.) I was one of the original investors in a company called Ticketfly... I cashed out at a 3,600% gain.”

A first-person investing track record citing named companies and the multiples returned on each.

Why it’s this techniqueThe claim is built on hard numbers doing the persuasive lifting: a '6,000% gain', a company bought for 'less than $5 million' that 'sold it for $900 million', and a second exit 'at a 3,600% gain'. Each result is quantified with a specific percentage and a before-and-after dollar figure, so the outcome reads as measured fact rather than boast. The structural tell is the paired input-to-output arithmetic ('$5 million' in, '$900 million' out) that lets the reader verify the multiple, and the repetition of the pattern across two named deals, Buddy Media and Ticketfly, which frames the figures as a track record of documented returns.

Classification

Primary technique
PT-PRV-9590
Classification confidence
0.71
Source
Agora Financial, 2020s web page
2010s·Ramp

“The City of Ketchum saves 100+ hours a month”

A named municipality's monthly hours saved after automating invoice processing.

Why it’s this techniqueThe copy fastens a named customer to a counted result, pairing 'The City of Ketchum' with 'saves 100+ hours a month' so the outcome reads as measured fact rather than promise. The proper noun makes the number checkable; the number makes the customer's win legible. the structural tell is the specific quantity attached to an identified adopter, not a generic capability claim. It reports what one buyer got, in units, which is the signature of an outcome drawn from a real account rather than a feature list. The named-plus-numbered pairing is the whole load the sentence carries.

Classification

Primary technique
PT-PRV-9590
Classification confidence
0.79
Source
Ramp, 2010s web page
1910s·Barrett Manufacturing Company·Barrett Manufacturing Company

“The net roof area of these buildings is 3,100,000 square feet, or more than 70 acres. Every inch of this is roofed with Barrett materials, and, since 1897, when the first roof was covered, the cost of maintenance has been less than $10.00.”

A single landmark building's decades-long roof maintenance cost documented as one figure over a stated period.

Why it’s this techniqueThe copy proves a claim by stacking hard figures drawn from one named installation, quantifying scale as '3,100,000 square feet, or more than 70 acres' and result as maintenance that 'has been less than $10.00' across the span 'since 1897'. the structural tell is the single-account arithmetic, a specific building's dimensions, dates, and dollar figure assembled into a verifiable ratio rather than a general promise or a testimonial voice. The named site 'Bush Terminal Buildings in Brooklyn, N.Y.' anchors every number to one documented case, which is what makes this a metrics-driven case rather than an abstract performance boast.

Classification

Primary technique
PT-PRV-9590
Classification confidence
0.83
Source
Barrett Specification Roofs, 1910s print ad (attributed)

See whether your own copy uses Case Study Metrics, and what else it is doing: analyze your copy.

Boundary Conditions

When it lands

  • The subject is named and identifiable, not an anonymous 'one client we worked with'.
  • The number carries context: a timeframe, a starting point, or the method behind it.
  • The documented result maps onto a goal the reader already holds.
  • The figures are specific enough to imply they were measured, not estimated after the fact.

When it dilutes

  • The metric floats free with no named case or method, so it reads as a bare statistic.
  • The numbers are round and self-reported, inviting the suspicion they were rounded up.
  • The case is hypothetical ('suppose you invested...'), documenting nothing that actually happened.
  • So many metrics are stacked that no single result stays memorable.

Taxonomic Relationships

Provenance

Introduced in v1.0Last revised 2026-09-16MethodologyErrata