How do I build a target account list of net-new prospects?

To build a target account list of net-new prospects, start from your best customers, not a generic filter. SignalRx models what your highest-value customers have in common, then discovers and reads net-new healthcare organizations across their own websites, social profiles, and reviews, anchors each one's identity, specialty, and parent-child structure against public registries like NPPES and CMS, scores both the account and its contacts, dedupes them against your CRM, and lands a prioritized list in Salesforce so reps work the accounts most likely to convert first. Because it resolves parent and child relationships, the list is organized by the real operating structure rather than a flat pile of look-alike records.

Most target lists are built the opposite way, by filtering a data provider on industry and title. That returns thousands of look-alike contacts that were never scored for fit. When acquisition costs are rising, spending weeks on a list where most accounts do not match your buyers is the expensive path.

Why not just filter a data provider by industry and title?

Filtering returns everyone in a category; scoring tells you which of them match the customers who actually buy. Two practices in the same specialty and size can be completely different opportunities. One matches your best customers on ownership, locations, reviews, software, and buying signals; the other never will. On a bought list they look identical. A model scored against your real closed-won accounts separates them before a rep spends a minute.

How do I build the list?

There are three ways into the same intelligence layer, depending on how much you want to source yourself:

• Upload a list you already have (an event export, a partner file, a CRM segment) and let SignalRx score and dedupe it.
• Source with Clay at scale, then add SignalRx fit scoring, segmentation, and ownership intelligence on top.
• Turnkey sourcing, where SignalRx finds net-new organizations that look like your best customers by reading the open web, social, and reviews and anchoring them to the registries.

Whichever path, the intelligence layer synthesizes many sources into one read on each account, the company website with its service lines, locations, and financing or pricing pages, Google reviews and social presence, job postings, third-party firmographics, and public registries, scores it for fit, matches it against existing records so you never create duplicates, and fills the gaps generic providers leave behind, all before it reaches a rep. Because these are net-new targets with no history with you yet, that Fit Score is built entirely from external sources triangulated into one validated signal; your own internal signals of revenue, support, and usage come into play only once an account becomes a customer.

What healthcare signals go into the score?

The score triangulates many sources rather than pulling from any one. Company websites are the primary way net-new organizations are found and read, their service lines, locations, and financing or pricing pages. Google reviews and the Business Profile add rating, volume, services, and locations, and for aesthetics and other consumer verticals Instagram and RealSelf carry more signal than any registry. LinkedIn adds leadership, headcount, and posts, and job postings flag buying and expansion, a listing for an RCM lead or a new-location GM. Third-party firmographic databases fill remaining gaps. Public registries are the identity, specialty, and structure anchor the rest is checked against, specialty and taxonomy NPI-verified against NPPES, ownership and structure such as PE-backed and MSO operators, DSOs, and rollups, number of locations and CMS-reported size and setting where a facility is CCN-identified, and parent and child relationships drawn from NPPES and CMS. Detected software and EHR and buying signals round out attributes generic providers do not carry. SignalRx reads these the same way across healthcare settings, from dental and aesthetics to behavioral health, senior living, FQHCs, and specialty pharmacy.

What does this look like in practice?

Illustrative, based on how SignalRx customers work. A company selling into dental wants net-new multi-location groups. Instead of buying a list of every dental practice, SignalRx models their best customers, reads each candidate's website and Google reviews, detects the practice-management system in use, and anchors specialty and ownership against NPPES, then finds that consolidated, PE-backed groups on a specific PM system convert best, and returns a ranked list where the top decile shares that pattern. Reps start at the top, and the reason each account is there travels with it.

How do I do this at scale?

SignalRx delivers the scored, deduped list into Salesforce as native fields, list memberships, and Revenue Plays, updated continuously, so there is no CSV to manage and no new tool to learn. What sets it apart is not owning any single dataset but synthesizing many messy sources into one validated truth about each account. The model is grounded in your own internal outcomes, your closed-won and best-performing customers, and weighs that against external public context, inferring which source to trust when sources disagree. Most teams are live in about 30 days.

Key takeaways

• Model first: define fit from your best customers, then score prospects against it.
• Scoring beats filtering, a small high-fit set converts far better than a large generic one.
• Net-new targets are found and scored by triangulating web, social, reviews, and registries; internal signals apply once they become customers.
• SignalRx dedupes against your CRM so sourcing never creates duplicate records.
• The list lands in Salesforce as native fields, ranked and ready to work.

SignalRx finds net-new organizations that look like your best customers and lands them ranked in Salesforce. Discover High-Fit Prospects with SignalRx.

Related: How do I prioritize my inbound leads by fit? and How do I score and route tradeshow and event leads?