The Names Are the Easy Part
A rep at a digital health company needs a list of practices to call this quarter. The legacy tool returns one in minutes, with names, specialties, locations, and a phone number for each. The rep ranks the list by size and specialty and picks a clinic that looks right. The rep assumes it is roughly 70% commercially insured. The visit goes ahead. Only afterward does the rep learn the real figure is closer to 20%. The account is worth a fraction of what the rep expected.
The list could not have said so. It shows who exists. It does not show three things that decide whether a call is worth making: who pays for the clinic’s patients, where those patients go for care, and who already serves them. A directory lists names. A company’s own records cover only the accounts it already works. Neither can check the assumption. One question would have saved the trip: what share of this clinic’s patients are commercially insured?
Why the Visible Numbers Mislead
Take payer mix, which shapes pricing, product fit, and which practices are worth a long sales cycle. Open claims-based data skews toward commercial volume at small practices with a light digital footprint. It reflects what passes through the channels that are easiest to see. A practice that reads as 70% commercial in visible data can be closer to 20% in the fuller picture. No one made an error: the team read a real number that described only part of the practice’s patients.
What a Wrong Number Costs
One visit is a small loss. The same data sets territories and targets, so the error repeats across accounts and shows up in every forecast. The entire revenue team inherits it. Sales leaders, commercial analytics, and revenue operations plan around whatever feed the company already pays for.
One diagnostics company ran its commercial plan on an outdated third-party feed for years. A new analytics leader questioned it. With claims-based data at the patient level, the team found 24% growth the old feed had never shown. It used that number in a competitive pitch. Its stated reason for switching: reps had to run the analysis themselves, with no analyst in the loop.
What AI Adds, and What It Needs
AI has made it fast to build and rank a list. Speed helps when the data underneath is complete, but when it is not, speed delivers the same wrong answer sooner.
Komodo Health® built Marmot™, an AI platform for healthcare analytics, on Komodo’s Healthcare Map®. The Map captures more than 330 million de-identified U.S. patient journeys. Marmot answers from that claims-based foundation, not from a directory. It is designed around five principles of AI trust: Grounded, Explainable, Reproducible, Verified, and Governed. Every output is transparent and auditable, so a seller can see how a list was built.
The Pipeline Starts With the List
For revenue leaders, Marmot’s Business Development use case shows where a company’s patients and doctors are. Before office visits, a rep sees which doctors to visit and each doctor’s patient types, volumes, payer mix, and performance. With that background, the rep knows what to say, how to position the conversation, and which referrals matter. First calls go where a conversation can become a pilot. A legacy tool tells a seller whom to call. Market visibility tells the seller which calls to make first and how to make them.
