The short answer
I'm Hugo Acurio, and I killed Iris Global Ecuador's first product launch before it drained the company, once I found that a 30% gross margin on paper went to zero after the government price cap, data fees, counter substitution, and manufacturer-paid returns. I rebuilt the market-entry method to price the whole path to the shelf before committing, then moved the company into medical devices and high-specialty oncology, where a glucometer line reached the top 4 in Ecuador and an oncology therapy reached #1 in its sub-indication (our estimate, from public government data).
The board I saw
The first launch was a three-product portfolio from a Mexican manufacturer: a cold medication, an antibiotic and an immunomodulator cream. The standard pharma market work (therapeutic category data, prescriber mapping, competitive crowding, channel mix) said the categories were live and the products had room. On paper, it was a go.
Two problems sat under that go, and the standard analysis could see neither. First, costs the category data never shows: price ceilings, fees for data needed to manage the sales force, substitution at the counter, returns the law makes you absorb. Second, a plan everyone had a reason to protect: once a launch has a partner, a name and a plan, the organization defends it, and the numbers that should stop it arrive late, one at a time, each explained away on its own.
The lead product came in at a 30% gross margin on paper. By the time the government price cap, the chains' fee for sellout data, counter substitution and manufacturer-paid expiry returns were counted, the 30% was gone. It could not make money no matter how well we sold it. The visible margin was the problem, because it was the only one anyone was looking at.
The system I invented
Step one: treat the market study as a hypothesis. Expand the therapeutic category code, then work three cuts in sequence: prescribers by specialty and volume, competitive crowding at the indication level rather than the category, and channel mix weighted by where volume moves. The output is a starting position to test, never a verdict.
Step two: overlay the hidden economics before committing. On the lead product, a 30% gross margin on paper went to zero once the government price cap, the data fee, counter substitution, and expiry returns were counted in sequence.
Step three: exit when the margin structure fails. We killed the category and moved the company into medical devices and high-specialty oncology: different margin math, different channel dynamics, different access to data. Nobody got to protect the original plan once the economics proved the analysis wrong. That was the reason to build the method.
Where the pivot landed
In fragmented markets the channel owns the customer by default. Both wins came from owning something it could not replicate.
GMate, glucometers. Razor and blade: the meter is a commodity, the strips carry the margin. One strip SKU fed four hardware entry points (Wheel + ejector for clinics, ON/SMART for app users, Voice for low vision, Origin for core retail), each built for a different buyer. A merchandising team trained pharmacy counters, a campaign with an AI bot took orders directly or routed buyers to the channel, and a QR code on every box offered a lifetime warranty on registration, which built a direct customer database instead of renting the relationship from the channel. The only continuous-use strip program in Ecuador moved the conversation from a cheaper meter to a cheaper year of testing.
High-specialty oncology. An estimated ~122 patients in the country (our estimate, from public government data and population prevalence): high specialty, sized honestly. The job was to deliver the science (trial data, new literature, real-world experience from other markets) at the level the treating oncologist works at, and leave them room to make the call. No incentives layered on top, no side conversations with the pharmacy. The switch happened when the accumulated evidence made the therapy the right answer for the patient in front of them.
In specialty care the prescriber signs their name to the patient's outcome, so restraint is a commercial asset. Doctors remember which reps pushed incentives and which ones showed up prepared and left them room to think.
The result
Top 4 glucometers in Ecuador (GMate, four meters on one strip). #1 in its sub-indication (our estimate, from public government data). ~122 patients (our estimate, from public government data and population prevalence). 1,796 monthly physician and KOL engagements, Iris Global Ecuador.
The launch we killed would have financed everyone else's P&L before selling a single unit. The method that killed it is the same one that found the categories worth entering.
What this proves
Treat the market study as a hypothesis. Size the category, then go looking for what the data cannot show you.
Price the full path before you commit. Ex-works to shelf to net, with every implicit cost: who owns the sellout data, who eats the returns, where the ceiling sits.
Own what the channel cannot replicate. A direct customer relationship, the economics of the consumable, or clinical credibility. Pick the one your category allows.
See the board, invent the system, build it.
Related case studies: Multidimensional Omron LATAM Expansion and The regulated operating system, built from zero.