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Free B2B case study generator with computed derived metrics

By Charles Summers · Updated · Free, no signup

Short answer

This tool takes a client name, industry, before state, after state, one headline metric, and a timeframe, and returns a full situation-complication-intervention-result-lesson narrative plus real math: percentage change, monthly run rate, and annualised value, calculated directly from the numbers in your metric and timeframe. It also writes three pull-quote candidates of different lengths for different placements. If your metric has no usable number, it says so plainly instead of inventing a figure.

Use the b2b case study extractor

What does this tool actually do?

This tool takes a client name, industry, before state, after state, one headline metric, and a timeframe, and returns a full situation-complication-intervention-result-lesson narrative plus real math: percentage change, monthly run rate, and annualised value, calculated directly from the numbers in your metric and timeframe. It also writes three pull-quote candidates of different lengths for different placements.

It runs entirely in your browser. Nothing you type is sent to a server, no account is required, and there is no usage limit, because there is no cost per run to control.

What does the output look like?

This is the exact output the tool produces from the example inputs. It is generated by the same code that runs when you click the button, so what you see here is what you get.

CASE STUDY: Northwind Logistics (B2B SaaS) SITUATION Northwind Logistics, a B2B SaaS company, started here: Manually qualifying every inbound lead by hand, average 4-day response time, losing deals to faster competitors. COMPLICATION This is the kind of problem that does not stay flat under growth. More volume just meant more of the same manual work, not less. INTERVENTION Northwind Logistics worked through an activation flow rebuilt around real usage data. Concretely, that meant moving from "Manually qualifying every inbound lead by hand, average 4-day response time, losing deals to faster competitors" to "Automated lead scoring routes hot leads to reps within minutes, and reps call while intent is still high". RESULT Northwind Logistics’s headline number: MRR grew from $18,000 to $52,000, over 6 months. Automated lead scoring routes hot leads to reps within minutes, and reps call while intent is still high. TRANSFERABLE LESSON The lesson generalizes past this one account: fixing the process before adding more volume to it is what made the number possible. Scaling a broken process just gets you a bigger broken process, faster. === DERIVED METRICS === Percentage change: +188.9% (computed from $18,000 → $52,000) Monthly run rate: $5,667 / mo (the $34,000 gain spread over 6.0 months) Annualised value: $68,000 / yr (monthly run rate × 12) === PULL QUOTE CANDIDATES (draft, get client approval before publishing) === SHORT (hero / ad, ~12 words) "+188.9% in 6 months. We would not go back." - Northwind Logistics MEDIUM (testimonial card / deck, ~51 words) "We went from Manually qualifying every inbound lead by hand, average 4-day response time, losing deals… to Automated lead scoring routes hot leads to reps within minutes, and reps call while inten… in 6 months. The difference isn’t just the number, it’s that we stopped firefighting." - Northwind Logistics, B2B SaaS LONG (case study sidebar, ~87 words) "Before this, Manually qualifying every inbound lead by hand, average 4-day response time, losing deals to faster competitors. It wasn’t a resourcing problem, it was a process problem, and we couldn’t see that until it was fixed. Now Automated lead scoring routes hot leads to reps within minutes, and reps call while intent is still high, and MRR grew from $18,000 to $52,000 backs it up. The bigger win is we finally have a system that scales instead of one more thing to babysit." - Northwind Logistics

Frequently asked questions

What happens if my headline metric doesn’t have a usable number?

The derived metrics section says plainly that no calculation was possible, instead of guessing or inventing one. Something like "significantly faster onboarding" has no number to compute from. The narrative and the three pull quotes still get built either way; only the percentage-change, run-rate and annualised-value math depends on a real figure being present.

How is monthly run rate calculated?

If your metric gives two dollar figures (a before and after), the tool takes the difference and divides it by the number of months in your timeframe, so "grew from $18,000 to $52,000" over "6 months" becomes a $34,000 gain divided by 6, or roughly $5,667 added per month. If your timeframe doesn’t parse into a number of days, weeks, months, quarters or years, this step is skipped and marked as not computable.

Which pull quote should I actually use?

Use the short one in a hero banner or an ad, where a single stat has to land in under two seconds. Use the medium one on a testimonial card or in a sales deck, where there’s room for one full sentence of context. Use the long one as a sidebar pull-quote on the full case study page, where a reader is already invested and wants the fuller story.

Are these real client quotes I can publish?

No, and don’t treat them as such. These are drafted candidates built from the inputs you gave, written in a plausible client voice, meant to be sent to the actual client for approval or used as a starting point for an interview. Publishing a quote you wrote and attributing it to a client who didn’t say it is a fast way to lose that client’s trust the first time they read your case study.

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