There is a $500 billion global medical device industry, and most of it runs its commercial operations on spreadsheets.
Not all of it, of course. The big five — Medtronic, Johnson & Johnson, Abbott, Boston Scientific, Stryker — have invested in Salesforce, Veeva, and custom CRM platforms. But when you ask their regional managers how they actually decide which surgeons to visit this week, the honest answer is almost always the same: they export the CRM to Excel, sort by revenue, and start at the top.
This is not a technology problem. It is an intelligence problem.
The CRM Tells You What Happened. It Does Not Tell You What Will Happen.
A CRM records orders, visits, and contact information. It is a system of record. What it does not do is predict which surgeons are likely to increase adoption next quarter, which ones are quietly trialing a competitor’s product, or which Prospects in your database behave almost identically to your highest-value Power Champions.
These are the questions that determine commercial success in MedTech. And spreadsheets cannot answer them.
The Three Blind Spots
First, there is no segmentation beyond revenue tiers. Surgeons are sorted into “high, medium, low” based on last year’s order volume. But a surgeon doing $2M in cases might be declining 15% year-over-year while a surgeon doing $400K might be on a steep growth curve. Static revenue tiers miss the trajectory entirely.
Second, competitive exposure is invisible until revenue drops. By the time a rep notices a surgeon’s orders have fallen, the competitor has already completed a proctored case, secured a contract, and moved on. CMS Open Payments data, industry conference attendance, and co-authorship patterns all signal competitive activity months before it hits your P&L — but nobody is looking at that data systematically.
Third, influence networks are ignored. When a respected surgeon at a major academic center adopts a device, that decision cascades through the referral network. Fellows they trained, colleagues they publish with, and surgeons who attended their cadaver labs all become exponentially more likely to adopt. Device companies know this intuitively but have no way to map it, measure it, or act on it.
From Spreadsheets to Intelligence
The alternative is not a better CRM. It is a purpose-built intelligence layer that sits on top of whatever CRM you already use. One that ingests order history, training records, competitive signals, and network relationships — then applies machine learning to produce scored, segmented, and prioritized outputs that sales teams can act on Monday morning.
The result is a commercial operation that knows which surgeons to pursue, which to protect, and which to watch — without asking a single rep to update a spreadsheet.
If your commercial team’s Monday morning starts with an Excel sort, there is a better way.