Case Study — Healthcare Forms Platform

Analyzed 60 sales calls. Found 85 jobs nobody knew existed. Resized a $1.41B market.

A healthcare forms platform thought they knew their customers. Structured validation against real sales data revealed they were entering the wrong segment.

Sales calls validated

85+

New jobs discovered

$1.41B

Total addressable market sized

45%

Overestimation corrected on key feature

Before.

FormDR had launched across multiple healthcare verticals — small practices, multi-specialty groups, hospitals, startups. The founding team had an original JTBD framework with 27 jobs and persona hypotheses. But these were assumptions, not data.

They didn’t know which segments were viable, what providers actually needed versus what was assumed, whether pricing matched value, how large the real opportunity was, or which features to prioritize. Every strategic decision was built on gut feel.

The Situation

27 assumed jobs — no validation against real buyer conversations
Persona distribution based on gut feel — some overestimated by 2–3x
Feature priority rankings that didn’t match actual buying behavior
No market sizing grounded in verified data sources

What we did.

Systematic validation of every assumption against real sales conversations — then rebuilt the market strategy from verified data.

Step 1 — Sales Call Coding

Transcribed and coded 60 of 82 sales calls — 73% coverage. Every call scored for JTBD mentions, severity, persona match, and deal outcome.

Step 2 — Job Frequency Validation

Each of 27 original jobs tested against actual call data. Discovered 85+ new jobs — 3x the original framework. Manual data entry confirmed at 100% (vs claimed 88%). HIPAA compliance elevated from #3 to co-#1 at 97%.

Step 3 — Persona Accuracy Audit

Real distribution calculated: Sarah Chen (Office Manager) validated at 60%. Dr. Jennifer Park overestimated by 2–3x (actual 17%, claimed 38–52%). Dr. Rachel Martinez rare but real (7%, not 0% or 52%).

Step 4 — Market Sizing

Six-segment TAM/SAM/SOM analysis using NPPES database (9.4M NPI records), CMS data, Census Bureau, and AMA benchmarks. Three rounds of fact-checking. Multi-Specialty Groups identified as #1 opportunity ($360M TAM).

Step 5 — Feature Priority Correction

Integration (F15) demoted from #2 to #4 — only 43% frequency (claimed 88%), only 13% deal-breaker. HIPAA (F3) promoted to co-#1. Paper-to-digital (F44) discovered as new #3 at 57%.

After.

85+

New jobs discovered — 3x the original 27-job framework

100%

Manual data entry frequency — validated, was underestimated at 88%

45%

Integration feature overestimation — demoted from #2 to #4 priority

$1.41B

TAM across six healthcare segments, verified against NPPES + CMS

38%

Essential Plan win rate — highest converting tier identified

Calls coded — 73% coverage of total pipeline, statistically robust sample

What you can do now.

Your product team knows which features actually drive purchase decisions — not what surveys say, but what buyers do. Integration dropped from assumed #2 to actual #4. HIPAA rose from #3 to co-#1. Roadmap reprioritized around real signal.

Your sales team has segment-specific qualification criteria. Essential Plan converts at 38%. The DocuSign switcher segment (8% of calls, $6K/year current spend) is a new high-value target nobody was pursuing.

Your market strategy is grounded in verified data — NPPES database (9.4M records), CMS specialty data, Census Bureau, AMA benchmarks. Three rounds of fact-checking. Not a model. Not an estimate. Validated.

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Jake McMahon

ProductQuant

10 years building growth systems for B2B SaaS companies at $1M–$50M ARR. BSc Behavioural Psychology, MSc Data Science. This engagement required navigating ambiguous sales data, contradictory persona assumptions, and a six-segment market that looked nothing like the company expected.

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