Case studies

The leaks we find, in real numbers.

Six independent teardowns of well-known software companies, built only from public information, and three illustrative composites of the patterns a diagnostic surfaces most often.

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How to read this pageTwo kinds of proof, both honest. First, real teardowns: we ran our SaaS revenue diagnostic on three well-known SaaS companies using only public information (constructive, and not affiliated with them). Below those, illustrative composites of common patterns. Real, consented client case studies replace the composites as our early clients complete. We don't invent testimonials.

Public-data teardowns

The lens we would point at your company.

Independent analysis of named companies. Constructive, and not affiliated with any of them.

Dev tools72/100

Retool

Activation cliff behind a data-source connect + per-seat pricing capping expansion.

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NYC fintech80/100

Ramp

Free-software-via-interchange: revenue downstream of spend, growth via multi-product attach.

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DC · GovTech68/100

Appian

A federal moat + a sales-led-only motion with no public self-serve on-ramp.

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Boston · AI/ML66/100

DataRobot

Enterprise AutoML in the LLM era: the moat moves from model-building to deployment, governance + provable ROI.

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Productivity62/100

ClickUp

Enormous breadth drives growth but taxes activation; plan complexity adds checkout friction.

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PLG scheduling70/100

Calendly

A textbook viral loop on a thin wedge: free→paid tuning + expansion vs bundled-free giants.

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Illustrative composites

Three patterns, priced.

Built from frequent SaaS patterns, not paid clients. They show the format and rigour you receive.

ACTIVATIONEarly-stage B2B SaaS · ~$40K MRR · seed

Half the signups never reached first value

The leak

Time-to-first-value took 5+ manual steps with no guided path. 54% of signups stalled at the integration step and never activated, so paid conversion was capped before pricing ever mattered.

The first fix

A 3-step guided onboarding to the core "aha" action plus a one-click sample integration, so a new user sees value in minutes instead of giving up.

Health score41/100

Recoverable / yr≈ $210K

ConfidenceMedium

PRICINGGrowth-stage SaaS · ~$112K MRR · bootstrapped

One flat plan was leaving 18-28% on the table

The leak

A single flat plan, no annual option, no usage tier. Best-fit, heavy-usage accounts paid the same as light ones, capping revenue from the customers who valued the product most.

The first fix

Introduce an annual plan (with a modest discount to pull cash forward) and a usage tier above the flat plan, so price scales with value delivered.

Health score52/100

Recoverable / yr≈ $640K

ConfidenceHigh

CHURN & RISKMature SaaS · ~$300K MRR · profitable

Strong product, but a third of ARR sat in one account

The leak

The product was loved (NPS 41), but 34% of ARR depended on a single customer (a churn there would halve the runway) while early churn signals in mid-tier accounts were going unwatched.

The first fix

A named-account expansion plan plus a concentration-risk dashboard, and a lightweight health-score trigger to catch at-risk accounts before they cancel.

Health score63/100

At risk / yr≈ $430K

ConfidenceHigh

Each composite mirrors the structure of a real diagnostic: a scored health read, dollar-quantified impact, a confidence level tied to the data you provide, and a first fix. See a full sample report

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